Lambda, Kappa, Microservice and Enterprise Architecture for Big Data

A few years after the emergence of the Lambda-Architecture several new architectures for Big Data have emerged. I will present and illustrate their use case scenarios. These architectures describe IT architectures, but I will describe towards the end of this blog the corresponding Enterprise Architecture artefacts, which are sometimes referred to as Zeta architecture.

Lambda Architecture

I have blogged before about the Lambda-Architecture. Basically this architecture consists of three layers:

  • Batch-Layer: This layer executes long-living batch-processes to do analyses on larger amounts of historical data. The scope is data from several hours to weeks up to years. Here, usually Hadoop MapReduce, Hive, Pig, Spark or Flink are used together with orchestration tools, such as Oozie or Falcon.

  • Speed-Layer/Stream Processing Layer: This layer executes (small/”mini”) batch-processes on data according to a time window (e.g. 1 minute) to do analyses on larger amounts of current data. The scope is data from several seconds up to several hours. Here one may use, for example, Flink, Spark or Storm.

  • Serving Layer: This layer combines the results from the batch and stream processing layer to enable fast interactive analyses by users. This layer leverages usually relational databases, but also NoSQL databases, such as Graph databases (e.g. TitanDB or Neo4J), Document databases (e.g. MongoDB, CouchDB), Column-Databases (e.g. Hbase), Key-Value Stores (e.g. Redis) or Search technologies (e.g. Solr). NoSQL databases provide for certain use cases more adequate and better performing data structures, such as graphs/trees, hash maps or inverse indexes.

In addition, I proposed the long-term storage layer to have an even cheaper storage for data that is hardly accessed, but may be accessed eventually. All layers are supported by a distributed file system, such as HDFS, to store and retrieve data. A core concept is that computation is brought to data (cf. here). On the analysis side, usually standard machine learning algorithms, but also on-line machine learning algorithms, are used.

As you can see, the Lambda-Architecture can be realized using many different software components and combinations thereof.

While the Lambda architecture is a viable approach to tackle Big Data challenges different other architectures have emerged especially to focus only on certain aspects, such as data stream processing, or on integrating it with cloud concepts.

Kappa Architecture

The Kappa Architecture focus solely on data stream processing or “real-time” processing of “live” discrete events. Examples are events emitted by devices from the Internet of Things (IoT), social networks, log files or transaction processing systems. The original motivation was that the Lambda Architecture is too complex if you only need to do event processing.

The following assumptions exists for this architecture:

  • You have a distributed ordered event log persisted to a distributed file system, where stream processing platforms can pick up the events

  • Stream processing platforms can (re-)request events from the event log at any position. This is needed in case of failures or upgrades to the stream processing platform.

  • The event log is potentially large (several Terabytes of data / hour)

  • Mostly online machine learning algorithms are applied due to the constant delivery of new data, which is more relevant than the old already processed data

Technically, the Kappa architecture can be realized using Apache Kafka for managing the data-streams, i.e. providing the distributed ordered event log. Apache Samza enables Kafka to store the event log on HDFS for fault-tolerance and scalability. Examples for stream processing platforms are Apache Flink, Apache Spark Streaming or Apache Storm. The serving layer can in principle use the same technologies as I described for the serving layer in the Lambda Architecture.

There are some pitfalls with the Kappa architecture that you need to be aware of:

  • End to end ordering of events: While technologies, such as Kafka can provide the events in an ordered fashion it relies on the source system that these events are indeed delivered in an ordered fashion. For instance, I had the case that a system in normal operations was sending the events in order, but in case of errors of communication this was not the case, because it stored the events it could not send and retransmitted them at a certain point later. Meanwhile if the communication was established again it send the new events. The source system had to be adapted to handle these situations correctly. Alternatively, you can only ensure a partial ordering using vector clocks or similar implemented at the event log or stream processing level.

  • Delivery paradigms on how the events are delivered (or fetched from) to the stream processing platform

    • At least once: The same event is guaranteed to be delivered once, but the same events might be delivered twice or more due to processing errors or communication/operation errors within Kafka. For instance, the stream processing platform might crash before it can marked events as processed although it has processed them before. This might have undesired side effects, e.g. the same event that “user A liked website W” is counted several times.

    • At most once: The event will be delivered at most once (this is the default Kafka behavior). However, it might also get lost and not be delivered. This could have undesired side effects, e.g. the event “user A liked website W” is not taken into account.

    • Once and only once: The event is guaranteed to be delivered once and only once. This means it will not get lost or delivered twice or more times. However, this is not simply a combination of the above scenarios. Technically you need to make sure in a multi-threaded distributed environment that an event is processed exactly once. This means the same event needs to be (1) only be processed by one sequential process in the stream processing platforms (2) all other processes related to the events need to be made aware that one of them already processes the event. Both features can be implemented using distributed system techniques, such as semaphores or monitors. They can be realized using distributed cache systems, such as Ignite, Redis or to a limited extent ZooKeeper. Another simple possibility would be a relational database, but this would quickly not scale with large volumes.
      • Needles to say: The source system must also make sure that it delivers the same event once and only once to the ordered event log.

  • Online machine learning algorithms constantly change the underlying model to adapt it to new data. This model is used by other applications to make predictions (e.g. predicting when a machine has to go into maintenance). This also means that in case of failure we may temporary have an outdated or simply a wrong model (e.g. in case of at least once or at most once delivery). Hence, the applications need to incorporate some business logic to handle this (e.g do not register a machine twice for maintenance or avoid permanently registering/unregistering a machine for maintenance)

Although technologies, such as Kafka can help you with this, it requires a lot of thinking and experienced developers as well as architects to implement such a solution. The batch-processing layer of the Lambda architecture can somehow mitigate the aforementioned pitfalls, but it can be also affected by them.

Last but not least, although the Kappa Architecture seems to be intriguing due to its simplification in comparison to the Lambda architecture, not everything can be conceptualized as events. For example, company balance sheets, end of the month reports, quarterly publications etc. should not be forced to be represented as events.

Microservice Architecture for Big Data

The Microservice Architecture did not originate in the Big Data movement, but is slowly picked up by it. It is not a precisely defined style, but several common aspects exist. Basically it is driven by the following technology advancements:

  • The implementation of applications as services instead of big monoliths
  • The emergence of software containers to deploy each of those services in isolation of each other. Isolation means that they are put in virtual environments sharing the same operating systems (i.e. they are NOT in different virtual machines), they are connected to each other via virtualized networks and virtualized storage. These containers leverage much better the available resources than virtual machines.
    • Additionally the definition of repositories for software containers, such as the Docker registry, to quickly version, deploy, upgrade dependent containers and test upgraded containers.
  • The deployment of container operating systems, such as CoreOS, Kubernetes or Apache Mesos, to efficiently manage software containers, manage their resources, schedule them to physical hosts and dynamically scale applications according to needs.
  • The development of object stores, such as OpenStack Swift, Amazon S3 or Google Cloud Storage. These object stores are needed to store data beyond the lifecycle of a software container in a highly dynamic cloud or scaling on-premise environment.
  • The DevOps paradigm – especially the implementation of continuous integration and delivery processes with automated testing and static code analysis to improve software quality. This also includes quick deliveries of individual services at any time independently of each other into production.

An example of the Microservice architecture is the Amazon Lambda platform (not to be confused with Lambda architecture) and related services provided by Amazon AWS.

Nevertheless, the Microservice Architecture poses some challenges for Big Data architectures:

  • What should be a service: For instance, you have Apache Spark or Apache Flink that form a cluster to run your application. Should you have for each application on them a dedicated cluster out of software container or should you provide a shared cluster of software containers. It can make sense to have the first solution, e.g. a dedicated cluster per application due to different scaling and performance needs of the application.
  • The usage of object stores. Object stores are needed as a large scale dynamically scalable storage that is shared among containers. However, currently there are some issues, such as performance and consistency models (“eventually consistent”). Here, the paradigm of “Bring Computation to Data” (cf. here) is violated. Nevertheless, this can be mitigated either by using HDFS as a temporal file system on the containers and fetching the data beforehand from the object store or use an in-memory caching solution, such as provided by Apache Ignite or to some extend Apache Spark or Apache Flink.

I see that in these environments the role of software defined networking (SDN) will become crucial not only in cloud data centers, but also on-premise data centers. SDN (which should NOT be confused with virtualized networks) enables centrally controlled intelligent routing of network flows as it is needed in dynamically scaling platforms as required by the Microservice architecture. The old decentralized definition of the network, e.g. in form of decentralized routing, does simply not scale here to enable optimal performance.

Conclusion

I presented here several architectures for Big Data that emerged recently. Although they are based on technologies that are already several years old, I observe that many organizations are overwhelmed with these new technologies and have some issues to adapt and fully leverage them. This has several reasons.

One tool to manage this could be a proper Enterprise Architecture Management. While there are many benefits of Enterprise Architecture Management, I want to highlight the benefit of managed of managed evolution. This paradigm enables to align business and IT, although there is a constant independent (and dependent) change of both with not necessarily aligned goals as illustrated in the following picture.

enterprise-architecture-managed-evolution

As you can see from the picture both are constantly diverging and Enterprise Architecture Management is needed to unite them again.

However, reaching managed evolution of Enteprise Architecture requires usually many years and business as well as IT commitment to it. Enterprise Architecture for Big Data is a relatively new concept, which is still subject to change. Nevertheless some common concepts can be identifed. Some people refer to Enterprise Architecture for Big Data also as Zeta Architecture and it does not only encompass Big Data processing, but in context of Microservice architecture also web servers providing the user interface for analytics (e.g. Apache Zeppelin) and further management workflows, such as backup or configuration, deployed in form of containers.

This enterprise architecture for Big Data describes some integrated patterns for Big Data and Microservices so that you can consistently document and implement your Lambda, Kappa, Microservice architecture or a mixture of them. Examples for artefacts of such an enterprise architecture are (cf. also here):

  • Global Resource Management to manage the physical and virtualized resources as well as scaling them (e.g. Apache Mesos and Software Defined Networking)

  • Container Management to deploy and isolate containers (e.g. Apache Mesos)

  • Execution engines to manage different processing engines, such as Hadoop MapReduce, Apache Spark , Apache Flink or Oozie

  • Storage Management to provide Object Storage (e.g. Openstack Swift), Cache Storage (e.g. Ignite HDFS Cache), Distributed Filesystem (e.g. HDFS) and Distributed Ordered Event Log (e.g. Kafka)

  • Solution architecture for one or more services that address one or more business problems. It should be separated from the enterprise architecture, which is focusing more on the strategic global picture. It can articulate a Lambda, Kappa, Microservice architecture or mixture of them.

  • Enterprise applications describe a set of services (including user interfaces)/containers to solve a business problem including appropriate patterns for Polyglot Persistence to provide the right data structure, such as graph, columnar or hash map, for enabling interactive and fast analytics for the users based on SQL and NoSQL databases (see above)

  • Continuous Delivery that describe how Enterprise applications are delivered to production ensuring the quality of them (e.g. Jenkins, Sonarqube, Gradle, Puppet etc).

Big Data: Bring Computation to Data

Big Data is the topic of the coming years. Even today large Internet companies store exabytes of data and their revenue model is based on selling products as well as services around this data. Consequently, they need to process data using advanced statistical methods, such as machine learning. Hence, they need to think about how to do this efficiently. Currently, especially in-memory is hyped to address this issue. However, this is only one aspect. A fundamentally more important aspect is where the data is processed in a distributed multi-node data environment.

A brief history on software architectures

In the beginning of software development, many applications have been single monolithic applications. They have been deployed on a single computer. This lead to several problems, such as that developers could hardly reuse code of monolithic applications and the approach did not scale very well since it was limited to a single computer. The first problem has been addressed by introducing different layers into the architecture. The resulting architectures are usually based on three layers (see next figure): data layer, service layer and presentation layer. The data layer handles any functionality for managing data, such as querying or storing it. The service layer implements business logic, e.g. it implements business process. The presentation layer allows the user to interact with the implemented business processes, e.g. entering of new customer data. The layers communicate with each other using well-defined interfaces implemented today in REST, OData, SOAP, Websockets or HTTP/2.0. threelayerarchitecture

With the emergence of the Internet, these layers had to be put physically on different machines to provide larger scalability. However, they have never been designed with this in mind. The network layer has only limited transport bandwidth and capacity. Indeed, for very large data it can be faster to store it on a large drive and transport it by truck to its destination than doing it by the network.

Additionally, during development scalability of data computation is of less interest, because in the Internet world it is often not known how many people will have access to an application and this may change over time. Hence, you need to be able to scale dynamically up an down. I observe that more and more of the development efforts in this area have moved to operations, who need to implement monitors, load-balancer and other technology to scale applications. This is also the reason why DevOps is a popular and emerging paradigm for developing and operating Internet-scale web applications, such as Netflix.

Towards New Software Architectures: Bring Computation to Data

The multiple layer approach does make sense and you could it even split it into more layers (“services”), but you have to evaluate carefully complexity and reusability of your service design. More important, you will have to think about new interfaces, because if components are located on different machines or different memory instances, your application will spend a lot of time for moving data between them. For instance, the application logic on the application server may request all customer transactions from the database and then correlate them to write the results back into the database. This requires a lot of data to be transferred from the database to the application server and potentially costs a lot of performance. Finally, it does not scale at all.

This problem first emerged when companies introduced the first Online Analytical Processing (OLAP) engines as part of business intelligence solutions for understanding their business. Database queries proved as too simple and would require to transfer first a lot of data to the application server. Hence, the Structured Query Language (SQL) for databases was extended to cope with these new requirements (e.g. the CUBE operator). Moreover, you can define your own custom functions (e.g. SQL Stored procedures), but they have to be implemented very vendor specific. For instance, distributed databases based on Apache Hadoop support custom functions. However, you can integrate sometimes other programming languages, such as Java. While stored procedures are already an improvement in terms of security (protection against SQL injection attacks), they have the problem that it is very difficult to write sophisticated programs to handle modern Big Data applications. For instance, many applications require machine learning, statistical correlation or other statistical methods. It is difficult to write them as stored procedures and to maintain support for different vendors. Furthermore, it leads again to monolithic applications. Finally, they are not dynamic – the application cannot decide to do any new computation on the fly without reimplementing it in the database layer (e.g. implement a new machine learning algorithm). Hence, I suggest another way to address this issue.

A Standard for Bringing Computation to Data?

As mentioned, we want to support modern Big Data applications by providing suitable language support for machine learning and statistical methods on top of any database system (e.g. MySQL, Hadoop, Hbase or IBM DB2). The next figure illustrates the new approach. The communication between the presentation and service layer works as usual. However, the services do not call functions on the data layer, but send any data-intensive computation they want to perform as an R script to the data layer, which executes it and only sends back the result.

bringcomputationtodataarchitecture

I observed that the programming language R for statistical computing has been recently integrated in various data environments, such as transactional databases, Apache Hadoop clusters or in-memory databases, such as SAP HANA. Hence, I think R could be a suitable language for describing computation that operates on data. Additionally, R has already a lot of built-in packages for machine learning or statistical data processing. Finally, depending on the openness of the underlying data environment, you can integrate R tightly into it, so you may not have to do extensive in-memory transfers.

The advantage of the approach are:

  • business logic stays in the service level and does not move to the data layer
  • You can easily add new services without modifying the data layer – so you avoid a tight coupling, which makes it easier to change the data layer or to introduce new functionality
  • You can mine R scripts generated by services to determine which computation the user is likely to do next to start executing it before the user requests it.
  • Caching and distribution of data processing can be based on a more sophisticated analysis of the R scripts using the R Profiler Rprof
  • R is already known by many business analysts or social scientists/psychologists

However, you will need to have some functionality for governing the execution of the R scripts in the data layer. This includes decisions on when to schedule computation or creating new computing/data nodes (e.g. real-time vs batch). This will require a company-wide enterprise architecture approach where you need to define which data should be real-time and which data should be batch-processed. Furthermore, you need to take into account security and separation of concerns.

In this context, Apache Hadoop might be an interesting solution from the technology perspective.

What is next

The aforementioned approach is only the beginning. By using this solution, you can think about true inter-cloud deployments of your application. Finally, you can enable inter-organizational data-processing business processes.

Enterprise Architecture Management in Business Networks

In my last blog post, I wrote about multi-cloud scenarios for enterprise applications focusing on enterprise applications of one company distributed over several different cloud providers. This blog post will be about enterprise applications connecting data, processes and the organization of different companies within business networks. Particularly complex scenarios with a high competition and margins, such as third party logistics (3PL) require a sophisticated approach ensuring and extending competitive advantages. We will see challenges when applying reference models, such as EDIFACT, ASC X12 or SCOR. Nevertheless, I see reference models – or more particularly their combination – as key success factors for business networks, since they represent best practices, common understanding and can significantly improve on-boarding as well as continuous education of new business network members. Hence, I will discuss how enterprise architecture and portfolio management can support the application and combination of different reference models in business networks. Finally, I present how the emerging concept of virtual software containers can support this approach from a technology perspective.

Types of Business Networks

One interesting question is what constitutes a business network [1]. Of course, it can be predefined and agreed upon, but there are a lot of business networks, where there are undefined and informal relations between two companies that have also (in-)dependent relations with other companies. The whole network of relations is called a business network. This is very similar to social networks where there are two related human beings that have independent relations to other human beings. However, all types of business networks have different forms of implicit or explicit governance, i.e. decision-making structures. Implicit governance refers to the fact that the chosen governance model has not been defined or agreed on by all involved parties in a business network. Explicit governance refers to an awareness and definition of governance arrangements by all parties in a business network.

The following generic modes of governance can exist in a business network (see next figure):

  • One inherits most / all types of decision-making roles and the others have merely an execution role

  • A group inherits most / all types of decision-making roles and a majority has only a execution role

  • Several large groups with decision-making roles related to different aspects and a majority has only execution role

  • Everybody has every role

businessnetworkgovernance

Additionally, business networks may expose a different degree of awareness and intensity of relations. On the one hand you may have a very structured business network, such as supply chains and on the other hand there is the free market where two parties directly interact without considering other parties in their interaction. Both extremes are unlikely and we will find companies on the whole spectrum. For instance, within a larger supply chain, one company may know only the direct predecessor and the direct successor company. It may just agree on the specification of the product to be delivered, but may not include any data or impose any processes on how the product should be manufactured. This means there is a limited degree of awareness and the intensity is less strong, because they do not really know how something is achieved by the other organizations in a business network.

contractlogistics

It can be observed that business networks become more complex, because new types of business networks emerge, such as contract logistics or third party logistics, where your business partners directly integrate dynamically in your manufacturing plant or point of sale as well as corresponding business processes. Hence, you need to work out best practices and stay ahead of the competition. An example can be seen in the previous figure, where the third party logistics provider has a packaging business process deployed at “Manufacturing Plant A”. This business process leverages applications and other resources within the sphere of “Manufacturing Plant A”. Besides delivery, the third party logistics provider integrates similarly in “Manufacturing Plant B”, where it does pre-assembly of the delivered parts from “Manufacturing Plant A”.

Applying Reference Models for Business Networks

Reference models exist since several decades in the area of business information systems, management and software engineering. Some are driven by academia and others are driven by industry. Usually both have been validated scientifically and in practice.

Reference models represent best industry practices for business processes derived from experts and organizations. They can cover the process, organization/governance, product, data and/or IT application perspective within a given business domain. Hence, they can also be viewed as standards. Examples for reference models are EDIFACT, SCOR, Prince2 or TOGAF. These are rather generic models, but there are also industry specific ones, such as the one existing for humanitarian supply chain operations [2] or retailing [3].

The main benefits of reference models with respect to business networks are:

  • Support your Enterprise Architecture Management (e.g. by reduced modeling efforts, transparency or common language)

  • Benchmark against industry

  • Evaluation of applications for enabling business networks

  • Business network integration by integrating available applications in a business network

There some issues involved when using reference models:

  • They are “just” models. Having them is like having a book on a shelf – pretty useless

  • Some of them are very generic applying to any business case/network and others are very specific

  • Some focus on business processes (e.g. SCOR), some on business data (e.g. EDIFACT, ASC X12), nearly none on organizational/governance aspects, others on material or money flows and others combine only some of the aspects (e.g. ARIS)

  • Some do provide key performance indicators for benchmarking your performance against the reference model, but most do not

  • It is unclear how different reference models can be combined and tailored to enable business networks

  • Tools supporting definition, viewing, visualizing, expertise provisioning, publishing or adaptation of these models are not standardized and a wide variety exists

  • Tools supporting monitoring the implementation of reference models in information systems consisting of technology and humans do not really exist

There exist already reference models for business networks, such as EDIFACT, and they are used successfully in practice. However, in order to gain benefit from reference models in a business network, you will need to have an integrated approach addressing the aforementioned issues as I will present in the next section.

Enterprise Architecture Management in Business Networks

Reference models are needed for superior business performance to deal with the increasing complexity of business networks. You will never have a perfect world by using only one reference model. Hence, you will need an enterprise architecture management approach for business networks to efficiently and effectively address the issues of one single reference model by combining several reference models (see next figure). Traditionally, enterprise architecture management focused only on the single enterprise and not business networks, but given the growing complexity of business networks and disrupting societal changes, it is mandatory to consider the business network dimension.

referencemodelpuzzle

Establishing an enterprise architecture management approach depends on the type of business network as I have explained before. For example, you may have one organization selecting and managing your reference model portfolio and application landscape for the whole business network. You may have also no one responsible, but you need to align and be aware of each other’s portfolio. For instance, you can create a steering board for this. Additionally, you will need to establish key performance indicators and benchmarking processes with respect to the business network’s reference model portfolio.

Once you have your enterprise architecture management approach leveraging combined and tailored reference models, you will have to address the aforementioned dynamics as well as tight integration between business partners in the business network’s information systems. Traditional ERP, CRM and SCM software packages will face difficulties, because even if all partners would use the same systems, there would be a huge variety of configurations to reflect the different internal business processes of members of a business network. Additionally, you will have to manage access and provisioning over the Internet.

Cloud-based solutions address these challenges already partially. They help you to understand how to manage access, governance and provide clearly defined interfaces via the emerging concept of API Management. However, these approaches do not reach far enough. You cannot move dynamically business processes and corresponding applications and data between organizations as a package to integrate it at your business partners’ premise. Furthermore, business processes may change quickly and you want to reuse as well as leverage the change in many different organizations using corresponding applications. This may facilitate a lot of scenarios, such as “bring your own digital business process” in third party logistics. Hence, there is still a need for further technology innovation and research.

Conclusion: Software Containers for “Bring your own Digital Business Process”

We have seen that new complex scenarios in business networks, such as third party logistics, as well as the high competition, tight network integration and dynamics impose new challenges. Instant business network adaptation as well as tight integration between business partners will be a key differentiator between business networks and ultimately decide about their success. Reference models representing industry best practice need to be combined and tailored on the business network level to achieve its future goals. However, no silver bullet exists, so you will also need to enable enterprise architecture management at the business network level. Finally, you need tools to enable dynamic movement of business processes as well as applications between different organizations in a business network. A coherent and reusable approach should be used.

Unfortunately, these tools do not exist at the moment, but there are some first approaches, which you should investigate in this context. Docker can create containers consisting of digital business process artifacts, applications, databases and many more. These containers can be sent over the business network and easily be integrated with containers existing in other organizations. Hence, the vision of instant dynamic business network adaption might not be as far-fetched as we think. The next figure illustrates this idea: The third party logistics provider sends the containers “Packaging” and “Pre-Assembly” to its business partners. These containers consists of applications supporting the corresponding business process. They are executed in the business partners’ clouds and they integrate with the existing business processes and applications there (e.g. the ERP system). Employees of the third party logistics provider use them at the side of the business partner. The containers are executed at the business partner side, because the business process takes anyway place there and thus it makes sense to let it also digitally happen there, before we send a lot of data and information to the network back and forth or having a lack of application integration.

businessnetworksoftwarecomponent

References

[1] Harland, C.M.: Supply Chain Management: Relationships, Chains and Networks, British Journal of Management, Volume 7, Issue Supplement s1, p . 63-680, 1996.

[2] Franke, Jörn; Widera, Adam; Charoy, François; Hellingrath, Bernd; Ulmer, Cédric: Reference Process Models and Systems for Inter-Organizational Ad-Hoc Coordination – Supply Chain Management in Humanitarian Operations, 8th International Conference on Information Systems for Crisis Response and Management (ISCRAM’2011), Lisbon, Portugal, 8-11 May, 2011.

[3] Becker, Jörg; Schütte, Reinhard: A Reference Model for Retail Enterprise, Reference Modeling for Business Systems Analyses, (eds.) Fettke, Peter; Loos, Peter, pp. 182-205, 2007.

[4] Verwijmeren, Martin: Software component architecture in supply chain management, Computers in Industry, 53, p. 165-178, 2004.

[5] Themistocleous, Marinos; Irani, Zahir; Love, Peter E.D.: Evaluating the integration of supply chain information systems: a case study”, European Journal of Operational Research, 159, p. 393-405, 2004.

Scenarios for Inter-Cloud Enterprise Architecture

The unstoppable cloud trend has arrived at the end users and companies. Particularly the first ones openly embrace the cloud, for instance, they use services provided by Google or Facebook. The latter one is more cautious fearing vendor lock-in or exposure of secret business data, such as customer records. Nevertheless, for many scenarios the risk can be managed and is accepted by the companies, because the benefits, such as scalability, new business models and cost savings, outweigh the risks. In this blog entry, I will investigate in more detail the opportunities and challenges of inter-cloud enterprise applications. Finally, we will have a look at technology supporting inter-cloud enterprise applications via cloudbursting, i.e. enabling them to be extended dynamically over several cloud platforms.

What is an inter-cloud enterprise application?

Cloud computing encompasses all means to produce and consume computing resources, such as processing units, networks and storage, existing in your company (on-premise) or the Internet. Particularly the latter enable dynamic scaling of your enterprise applications, e.g. you get suddenly a lot of new customers, but you do not have the necessary resources to serve them all using your own computing resources.

Cloud computing comes in different flavors and combinations of them:

  • Infrastructure-as-a-Service (IaaS): Provides hardware and basic software infrastructure on which an enterprise application can be deployed and executed. It offers computing, storage and network resources. Example: Amazon EC2 or Google Compute.
  • Platform-as-a-Service (PaaS): Provides on top of an IaaS a predefined development environment, such as Java, ABAP or PHP, with various additional services (e.g. database, analytics or authentication). Example: Google App Engine or Agito BPM PaaS.
  • Software-as-a-Service (SaaS): Provides on top of a IaaS or PaaS a specific application over the Internet, such as a CRM application. Example: SalesForce.com or Netsuite.com.

When designing and implementing/buying your enterprise application, e.g. a customer relationship management (CRM) system, you need to decide where to put in the cloud. For instance, you can put it fully on-premise or you can put it on a cloud in the Internet. However, different cloud vendors exist, such as Amazon, Microsoft, Google or Rackspace. They offer also a different flavor of cloud computing. Depending on the design of your CRM, you can put it either on a IaaS, PaaS or SaaS cloud or a mixture of them. Furthermore, you may only put selected modules of the CRM on the cloud in the Internet, e.g. a module for doing anonymized customer analytics. You will also need to think about how this CRM system is integrated with your other enterprise applications.

Inter-Cloud Scenario and Challenges

Basically, the exemplary CRM application is running partially in the private cloud and partially in different public clouds. The CRM database is stored in the private cloud (IaaS), some (anonymized) data is sent to different public clouds on Amazon EC2 (IaaS) and Microsoft Azure (IaaS) for doing some number crunching analysis. Paypal.com is used for payment processing. Besides customer data and buying history, the databases contains sensor information from different point of sales, such as how long a customer was standing in front of an advertisement. Additionally, the sensor data can be used to trigger some actuators, such as posting on the shop’s Facebook page what is currently trending, using the cloud service IFTTT. Furthermore, the graphical user interface presenting the analysis is hosted on Google App Engine (PaaS). The CRM is integrated with Facebook and Twitter to enhance the data with social network analysis. This is not an unrealistic scenario: Many (grown) startups already deploy a similar setting and established corporations experiment with it. Clearly, this scenario supports cloud-bursting, because the cloud is used heavily.

I present in the next figure the aforementioned scenario of an inter-cloud enterprise application leveraging various cloud providers.

intercloudarchitecture

There are several challenges involved when you distribute your business application over your private and several public clouds.

  • API Management: How to you describe different type of business and cloud resources, so you can make efficient and cost-effective decisions where to run the analytics at a given point in time? Furthermore, how to you represent different storage capabilities (e.g. in-memory, on-disk) in different clouds? This goes further up to the level of the business application, where you need to harmonize or standardize business concepts, such as “customer” or “product”. For instance, a customer described in “Twitter” terms is different from a customer described in “Facebook” or “Salesforce.com” terms. You should also keep in mind that semantic definitions change over time, because a cloud provider changes its capabilities, such as new computing resources, or focus. Additionally, you may dynamically change your cloud provider without disruption to the operation of the enterprise application.
  • Privacy, risk and Security: How do you articulate your privacy, risk and security concerns? How do you enforce them? While there are already technology and standards for this, the cloud setting imposes new problems. For example, once you update the encrypted data regularly the cloud provider may be able to determine from the differences parts or all of your data. Furthermore, it may maliciously change it. Finally, the market is fragmented without an integrated solution.
  • Social Network Challenge: Similarly to the semantic challenge, the problem of semantically describing social data and doing efficient analysis over several different social networks exist. Users may also change arbitrarily their privacy preferences making reliable analytics difficult. Additionally, your whole company organizational structure and the (in-)official networks within your company are already exposed in social business networks, such as LinkedIn or Xing. This blurs the borders of your enterprise further to which it has to adapt by integrating social networks into its business applications. For instance, your organizational hierarchy, informal networks or your company’s address book exist probably already partly in social networks.
  • Internet of Things: The Internet of Things consists of sensors and actuators delivering data or executing actions in the real world supported by your business applications and processes. Different platforms exist to source real world data or schedule actions in the real world using actuators. The API Management challenge exists here, but it goes even beyond: You create dynamic semantic concepts and relate your Internet of Things data to it. For example, you have attached an RFID and a temperature sensor to your parcels. Their data needs to be added to the information about your parcel in the ERP system. Besides the semantic concept “parcel” you have also that one of a “truck” transporting your “parcel” to a destination, i.e. you have additional location information. Furthermore it may be stored temporarily in a “warehouse”. Different business applications and processes may need to know where the parcel is. They do not query the sensor data (e.g. “give me data from tempsen084nl_98484”), but rather formulate a query “list all parcels in warehouses with a temperature above 0 C” or “list all parcels in transit”. Hence, Internet of Thing data needs to be dynamically linked with business concepts used in different clouds. This is particularly challenging for SaaS applications, which may have different conceptualization of the same thing.

Enterprise Architecture for Inter-Cloud Applications

You may wonder how you can integrate the above scenario at all in your application landscape and why you should do it at all. The basic promise of cloud computing is that it scales according to your needs, that you can outsource infrastructure to people who have the knowledge and capabilities to run the infrastructure. Particularly, small and medium size enterprises benefit from this and the cost advantage. It is not uncommon that modern startups start their IT using the cloud (e.g. FourSquare).

However, also large corporations can benefit from the cloud, e.g. as a “neutral” ground for a complex supply chain with a lot of partners or to ramp up new innovative business models where the outcome is uncertain.

Be aware that in order to offer some solution based on the cloud you need to first have a solid maturity of your enterprise architecture. Without it you are doomed to fail, because you cannot make proper risk and security analysis, scaling and benefit from cost reductions as well as innovation.

I propose in the following figure an updated model of the enterprise architecture with new components for managing cloud-based applications. The underlying assumption is that you have an enterprise architecture, more particularly a semantic model of business objects and concepts.

intercloudarchitecturenew

  • Public/Private Border Gateway: This gateway is responsible for managing the transition between your private cloud and different public clouds. It may also deploy agents on each cloud to enable a secure direct communication between different cloud platforms without the necessity to go through your own infrastructure. You might have more fine granular gateways, such as private, closest supplier and public. A similar idea came to me a few years ago when I was working on inter-organizational crisis response information systems. The gateway is not only working on the lower network level, but also on the business processes and objects level. It is business-driven and depending on business processes as well as rules, it decides where the borders should be set dynamically. This may also mean that different business processes have access to different things in the Internet of Things.
  • Semantic Matcher: The semantic matcher is responsible for translating business concepts from and to different technical representations of business objects in different cloud platforms. This can be simple transformations of not-matching data types, but also enrichment of business objects from different sources. This goes well beyond current technical standards, such as EDI or ebXML, which I see as a starting point. Semantic matching is done automatically – there is no need for creating time consuming manual mappings. Furthermore, the semantic matcher enhances business objects with Internet of Things information, so that business applications can query or trigger them on the business level as described before. The question here is how you can keep people in control of this (see Monitor) and leverage semantic information.
  • API Manager: Cloud API management is the topic of the coming years. Besides the semantic challenge, this component provides all necessary functionality to bill, secure and publish your APIs. It keeps track how is using your API and what impact changes on it may have. Furthermore, it supports you to compose new business software distributed over several cloud platforms using different APIs subject to continuous change. The API Manager will also have a registry of APIs with reputation and quality of service measures. We see now a huge variety of different APIs by different service providers (cf. ProgrammableWeb). However, the scientific community and companies have not picked up yet the inherent challenges, such as the aforementioned semantic matching, monitoring of APIs, API change management and alternative API compositions. While there exists some work in the web service community, it has not yet been extended to the full Internet dimension as it has been described in the scenario here. Additionally, it is unclear how they integrate the Internet of Thing paradigm.
  • Monitor: Monitoring is of key importance in this inter-cloud setting. Different cloud platforms offer different and possible very limited means for monitoring. A key challenge here will be to consolidate the monitoring data and provide an adequate visual representation to do risk analysis and selecting alternative deployment strategies on the aggregated business process level. For instance, by leveraging semantic integration we can schedule request to semantically similar cloud and business resources. Particularly, in the Internet of Thing setting, we may observe unpredictable delays, which lead to delayed execution of real-world activities, e.g. a robot is notified that a parcel flew off the shelf only after 15 minutes.

Developing and Managing Inter-Cloud Business Applications

Based on your enterprise architecture you should ideally employ a model-driven engineering approach. This approach enables you automation of the software development process. Be aware that this is not easy to do and failed often in practice – However, I have also seen successful approaches. It is important that you select the right modeling languages and you may need to implement your own translation tools.

Once you have all this infrastructure, you should think about software factories, which are ideal for developing and deploying standardized services for selected platforms. I imagine that in the future we will see small emerging software factories focusing on specific aspects of a cloud platform. For example, you will have a software factory for designing graphical user interfaces using map applications enhanced with selected Odata services (e.g. warehouse or plant locations). In fact, I expect soon a market for software factories which enhances the idea of very basic crowd sourcing platforms, such as the Amazon Mechanical Turk.

Of course, since more and more business applications shift towards the private and public clouds, you will introduce new roles in your company, such as the Chief Cloud Officer (CCO). This role is responsible for managing the cloud suppliers, integrating them in your enterprise architecture and proper controlling as well as risk management.

Technology

The cloud exists already today! More and more tools emerge to manage it. However, they do not take into account the complete picture. I described several components for which no technologies exist. However, some go in the right direction as I will briefly outline.

First of all, you need technology to manage your API to provide a single point of management towards your cloud applications. For instance, Apache Delta Cloud allows managing different IaaS provider, such as Amazon EC2, IBM SmartCloud or OpenStack.

IBM Research also provides a single point of management API for cloud storage. This goes beyond simple storage and enables fault tolerance and security.

Other providers, such as Software AG, Tibco, IBM or Oracle provide “API Management” software, which is only a special case of API Management. In fact, they provide software to publish, manage the lifecycle, monitor, secure and bill your own APIs for the public on the web. Unfortunately, they do not describe the necessary business processes to enable their technology in your company. Besides that, they do not support B2B interaction very well, but focusing on business to development aspects only. Additionally, you find registries for public web APIs, such as ProgrammableWeb or APIHub, which are first starting point to find APIs. Unfortunately, they do not feature sematic description and thus no semantic matching towards your business objects, which means a lot of laborious manual work for doing the matching towards your application.

There is not much software for managing the borders between private and public cloud or even allowing more fine-granular borders, such as private, closest partner and the public. There is software for visualizing and monitoring these borders, such as the eCloudManager by Fluid Operations. It features semantic integration of different cloud resources. However, it is unclear how you can enforce these borders, how you control them and how can you manage the different borders. Dome 9 goes into this direction, but focuses only on security policies for IaaS applications. It does only understand data and low level security, but not security and privacy over business objects. Deployment configuration software, such as Puppet or Chef, are only first steps, since they focus only on deployment, but not on operation.

On the monitoring side you will find a lot of software, such as Apache Flume or Tibco HAWK. While these operate more on the lower level of software development, IFTTT enables execution of business rules over data on several cloud providers providing public APIs. Surprisingly, it considers itself at the moment more as a end user facing company. Additionally, you find in the academic community approaches for monitoring distributed business processes.

Unfortunately, we find little ready to go software in the area “Internet of Things”. I worked myself with several R&D prototypes enabling cloud and gateways, but they are not ready for the market. Products have emerged but they are only for a special niche, e.g. Internet of Things enabled point of sale shop. They lack particularly a vision how they can be used in an enterprise-wide application landscape or within a B2B enterprise architecture.

Conclusion

I described in this blog the challenges of inter-cloud business applications. I think in the near future (3-5 years) all organizations will have some them. Technically they are already possible and exist to some extent. The risk and costs will be for many companies lower than managing everything on their own. Nevertheless key requirement is that you have a working enterprise architecture management strategy. Without it you won’t have any benefits. More particularly, from the business side you will need adequate governance strategies for different clouds and APIs.

We have seen already key technologies emerging, but there is still a lot to do. Despite decades of research on semantic technologies, there exists today no software that can perform automated semantic matching of cloud and business concepts existing in different components of an inter-cloud business application. Furthermore, there are no criteria on how to select a semantic description language for business purposes that are as broad as described here. Enterprise Architecture Management tools in this area only slowly emerge. Monitoring is still fragmented with many low level tools, but only few high-level business monitoring tools. They cannot answer simple questions, such as “what if cloud provider A goes down then how fast can I recover my operations and what are the limitations”. API Management is another evolving area, but which will have a significant impact in the coming years. However, current tools only consider low-level technical aspects and not high-level business concepts.

Finally, you see that a lot of challenges mentioned in the beginning, such as the social network challenge or Internet of Thing challenge, are simply not yet solved, but large scale research efforts are on their way. This means further investigation is needed to clarify the relationships between the aforementioned components. Unfortunately, many of the established middleware vendors lack a clear vision of cloud computing and the Internet of Things. Hence, I expect this gap will be filled by startups in this area.

Modularizing your Business and Software Component Design

In this blog, I will talk about modularizing your enterprise from a business and software perspective. We start from the business perspective, where I provide some background how today’s businesses are modularized. Afterwards, we will investigate how we can support the modularized business with software components and how they can be designed. Finally, we will see some software tools enabling component-based design for a modularized business, such as the service component architecture (SCA) or OSGi.

Business perspective

You will find a lot of different definitions of what and how a business can be modularized. Most commonly, business modules are known as business functions, such as controlling, finance, marketing, sales or production. Of course you can view this also on a more fine granular level. Furthermore, we may have several instances of the same module. This is illustrated in the following figure. On the left-hand side the business modules of a single enterprise are shown. On the right-hand side you see the business modules of decentralized organizations. There, the enterprise is split up in several enterprises, one for each region. Business modules are replicated across regions, but adapted to local needs.

businessarchitecture

A module has usually clear interfaces to other modules. For instance, in earlier times you used paper forms to order something from the production department.

One of the most interesting questions is how one should design business modules. Well there is no easy answer to this, but one goal is to reduce complexity between modules. This means there should not be many dependencies between modules, if any. There can be a lot of dependencies within one module. For instance, people work very closely together in the production department, because they share common knowledge and resources, such as machines or financial ones.

On the other side, production and sales have some very different business processes. Obviously, they are still dependent, but this should be done through a clear interface between them. For example, there can be regular feedback from a sales person to the production engineer on what the customer needs.

Clearly, it depends on the economic environment how you define business modules and the organization. However, this environment changes and this means business modules can be retired, new interfaces or completely new business modules be created.

Unfortunately, this is usually not very well documented and communicated in many businesses. Particularly, the conditions why a business has been designed out of a given set of modules and dependencies exists usually only in the head of some people. Additionally, the interfaces between business modules and their purpose are often not obvious. This can mean significant loss of competitive advantages.

Linking Business and IT Perspective: Enterprise Architecture

Business and IT have not necessarily the same goals. This means they need to be aligned, so that they are not conflicting. Hence, you need to map your business modules to your IT components. This process is called Enterprise Architecture Management. During this process the Enterprise Architecture is constantly modified and adapted to the economic environment.

Of course, you cannot map all your business and your whole IT, because this would be too costly. Nevertheless, you need to choose the important parts that you want to map. Additionally, you will need to define governance processes and structures related to this, but this is not part of this blog entry.

One popular, but simple, illustration is an enterprise architecture composed of four architectures:

  • The Business Architecture describes the business functions/modules, their relations within business processes, people, the underlying strategy, business goals and the relevant economic environment.
  • The Information Architecture is about the business data, their relationships to business functions/modules and processes, the people, its value as well as security concerns.
  • The Software Architecture depicts different kind of components according to IT goals, their relations to business data and business functions/modules.
  • The Technology Architecture is about the technology foundation for enabling the other architectures. It describes the basic infrastructure in form of hardware and software components. This includes local environments as well as cloud environments, such as OpenStack, Google Compute or Amazon EC2.

Some people advocate additionally an IT security architecture. I propose to model it not as an additional architecture, but include IT security concerns in each of the aforementioned architectures. This increases the awareness for IT security in your business. Nevertheless, appropriate tools can generate from the models a complete security view over all architectures.

There are many popular tools, such as the ARIS toolset to map your enterprise architecture.

Of course, you cannot only define top-down from business to IT how this architecture should be designed. You need to take into account the IT perspective.

IT perspective

As mentioned, IT and business goals are not necessarily the same. IT focuses on three areas: storing of information (storage), processing of information (computation) and transporting information (network). The goal is to do this in an efficient manner: Only the minimum of information should be stored, processing information should be as fast as possible and transporting information should only consume minimal resources. Clearly, there are constraints ranging from physical laws over business goals to IT Security concerns. For instance, the three key IT Security goals, namely confidentiality, integrity and availability often have negative impact on all three IT goals.

As I have explained before: business modules are mapped to software components and vice versa. One big question here is about the design of software components, i.e. what software functionality (representing business functionality) should be part of one software component and not one of the others. Software components are usually different from the business modules, because they have different design criteria.

In the past, people often used heuristics. E.g. they introduce “data components” and “functional components”. This makes sense, because you should not have 50 different databases, but only the right amount of databases for your purpose (e.g. one for NoSQL, one for SQL and/or probabilistic databases). This reduces the resource needs and avoids inconsistent data. However, there is no general approach how these heuristics should be applied by different enterprise architects. Another problem is that communication patterns (e.g. via message brokers, such as RabbitMQ) are completely left out.

Hence, I think a more scientific or general approach should be taken towards the design of components, because these heuristics do not give you good guidelines for a sustainable architecture. Based on the three IT focus areas, I propose to have software components for storage (e.g. database), computation (e.g. business logic) and network (e.g. message brokers). Once you have identified these components, you need to decide which functionality you should put in which component. One key goal should be to reduce the dependencies between components. The more communication you have, the more dependencies you have between the different functions in components. Evaluating this manually can be costly and error prone. Luckily, some approaches do this for your and they can be integrated with business modeling as well as software component management tools (cf. here an approach that derives the design of software components (managed using the service component architecture (SCA)) from the communication pattern in business processed (modeled using the business process modeling notation (BPMN)).

Another mean for coherent software component design is to have enterprise architects responsible for mapping one part of the business (e.g. controlling) reviewing the software architecture for another part of the business (e.g. marketing). I introduced such a process in an enterprise some time ago. Such an approach ensures that architecture decisions are made consistent across the enterprise architecture and this fosters also learning from other areas.

Finally, a key problem that you need to consider is the lifecycle management of a software component. Similar to the lifecycle of business modules, software components are designed, implemented, deployed and eventually retired. You need tools to appropriately manage software components.

Tools for Managing Software Components

Previously, I elaborated on the requirements for managing software components:

  • Handle interfaces with other components

  • Support the lifecycle of software components

Two known information technologies for managing software components are OSGi and the Service Component Architecture (SCA).

OSGi

OSGi is a framework for managing software components and their dependencies/interfaces to other components. It is developed by the OSGi alliance. It origins from the Java world and is mostly suitable for Java components, although it has limited support for other non-Java platforms. It considers the lifecycle of components by ensuring that all needed components are started when a component is started and by being able to stop components during runtime. Furthermore, other components and their interfaces can be discovered at runtime. However, there is no deployment method for software components part of the standard.

Since Java can run on many different devices, it is available for Android, iOS, embedded devices, personal computers and servers.

Unfortunately, tool support for linking OSGi and business or information architecture is very limited. Furthermore, an automatic generation and deployment of OSGi components from your enterprise architecture does not exist at the moment. This makes it difficult to understand software component and their relations within your enterprise architecture.

Many popular software projects are based on OSGi, such as the Eclipse project.

Service Component Architecture (SCA)

The service component architecture is a specification for describing software components, their interfaces and their dependencies. It is developed by members of the Organization for the Advancement of Structured Information Standards (OASIS). It does not depend on a specific programming platform, e.g. it supports Java and C++. It supports policies that govern components, a set of components or their communication. However, SCA does not consider the software component lifecycle or how they are deployed exactly.

It is supported by many middleware frameworks, such as TIBCO Active Matrix or Oracle Fusion Middleware.

Similarly to OSGi there is little tool support for linking SCA components and the business or information architecture. However, the SCA specification has a graphical modeling guideline and some recent work describes how they can be linked via business processes. Since OASIS is responsible for other enterprise architecture relevant modeling notations (e.g. BPMN), it can be expected that enterprise architecture tools can be adapted to provide support for linking different parts of the enterprise architecture.

Conclusion

Modularizing your business and designing software component is a difficult task. Not many people understand the whole chain from business to software components. While enterprise architecture and modeling has become a popular topic in research and practice, the whole tool chain from business to software components is not. There have been attempts to introduce model-driven-architecture (MDA), but the supported models were mostly only restricted to the Unified-Modeling-Language, which is not very suitable for business modeling and can be very complex. Additionally it does not take into account the software component lifecycle. Furthermore, the roles of the different stakeholders (e.g. business and IT) using these tools are unclear.

Nevertheless, new approaches based on the business process modeling notation and frameworks for managing software components make me confident that this will change in the near future. Growing IT complexity in terms of communication and virtualization infrastructure will require software support for managing software components. Companies should embrace these new tools to gain competitive advantages in terms of agility and flexibility.

The Next Generation HTTP Protocol (HTTP 2.0) for Enterprise Applications

I will talk in this blog about the next generation HTTP Protocol (HTTP 2.0) and put special emphasis on the implications for enterprise applications. Starting with the challenges and recent improvements to the HTTP protocol, such as WebSockets, I will describe the current state of the HTTP 2.0 specification. Finally, I will discuss implications for distributed web applications and enterprise service bus/complex event processing based enterprise applications. The WebRTC protocol is seen complementary to the HTTP 2.0 specification.

Introduction

The recent version of the HTTP protocol is 1.1 and is used by most of the web servers, proxies as well as browsers on the Internet. The main difference to HTTP 1.0 is that a connection can be reused, i.e. each request for a resource, such as image or HTML files, uses the same connection without the overhead of creating a connection for each of them. This already shows the need to reduce the number of connections to avoid overload of firewalls or the network stack.

Furthermore, new protocols have emerged based on HTTP. Their goal is to support real-time applications, such as collaborative editing (cf. Apache Wave). Other examples can be found in the area of adaptive streaming, such as apple live streaming. Clearly, these new killer application required adaptation to the existing HTTP protocol standard. I will briefly describe these applications and explain why these adaptations are still somehow flawed and require a new standard: HTTP 2.0.

Applications

Real-Time

Real-time applications require a permanent connection to a web server to push events or data to the server. Contrary to the standard request-response approach the connection is never terminated. The underlying assumption is that the application does not know exactly how much data needs to be transferred when to the server. However, data transfers occur frequently. One example is collaborative editing: There, we need to transfer text additions, changes, removals to the server and ultimately to other participants in the collaborative editing sessions. More advanced collaborative editors may also transfer other events, such as clicks or highlighting text. Given the context of real-time applications, the WebSocket standard has been developed. This standard enables a permanent connection for the aforementioned purposes. Basically, it uses HTTP 1.1, but does not transfer a lot of header information (see also example of a standard HTTP request below). Mostly JavaScript applications leverage this standard. For compatibility reasons the JavaScript libraries sock.js or socket.io/engine.io support a similar approach which works for older browsers or proxies. This is based on various techniques, such as XHR polling.

Media Streaming

Many popular live video streaming protocols are based on HTTP, such as apple live streaming or MPEG DASH. Basically they offer a list of links to chunks (short media blocks of few seconds length) of the media stream. These chunks are then downloaded via HTTP.

Challenges

Although we identified already some improvements with respect to HTTP 1.1, there are several issues with the current HTTP protocol:

  1. Communication is text-based

  2. No prioritization of data streams

Communication is Text-based

If we look at a standard request response then we see that there is a lot of over-head due to the fact that the communication is human-readable.

This can be seen from the following requests (via Google Chrome):

HTTP Example Request (Assumption: connection to server www.wikipedia.org established)

GET / HTTP/1.1
Host: http://www.wikipedia.org
Connection: keep-alive
Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8
User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.17 (KHTML, like Gecko) Chrome/24.0.1312.70 Safari/537.17
Accept-Encoding: gzip,deflate,sdch
Accept-Language: en-GB,en-US;q=0.8,en;q=0.6
Accept-Charset: ISO-8859-1,utf-8;q=0.7,*

HTTP Example Response

Age:428
Cache-Control:s-maxage=3600, must-revalidate, max-age=0
Connection:keep-alive
Content-Encoding:gzip
Content-Length:11603
Content-Type:text/html; charset=utf-8
Date:Tue, 12 Feb 2013 22:36:12 GMT
Last-Modified:Mon, 11 Feb 2013 01:58:47 GMT
Server:Apache
Vary:Accept-Encoding
X-Cache:HIT from amssq38.esams.wikimedia.org
X-Cache:HIT from knsq24.knams.wikimedia.org
X-Cache-Lookup:HIT from knsq24.knams.wikimedia.org:80
X-Cache-Lookup:HIT from amssq38.esams.wikimedia.org:3128
X-Content-Type-Options:nosniff

[..] (content of the html page)

All attributes and values are human-readable. Thus, they consume a lot of overhead for transferring them each time we make a request and receive a response. Clearly this is a problem for real-time or media streaming applications. Furthermore, there is overhead when parsing them for further processing.

No Prioritization of Data Streams

The underlying assumption of HTTP is that all data streams have the same priority. However, this is not true for all applications. Let us imagine you upload several large images for your collaborative editing application. At the same time you modify some text. This can mean that the other users in the collaborative editing session won’t see the updated text until the images are uploaded. Clearly this is an undesirable situation.

The Current State

If we imagine further applications, such as an enterprise service bus, which can be based on web services/SOAP services or REST services, then we can see a lot of room for improvement. Hence, some big vendors have developed proprietary HTTP extensions:

SPDY by Google has been used as a first draft for the new HTTP 2.0 protocol, which is in process of being standardized by the IETF. Microsoft approach seems to be based on SPDY, but makes some parts of it optional to take into account mobile devices, which have limited resources and should, for example, only deal with encrypted content if necessary. Microsoft proposed extensions have been submitted to the IETF to be taken into account for HTTP 2.0

The main improvements of SPDY compared to the HTTP 1.1 protocol are the following:

  • Reduce the overhead of HTTP by tokenizing headers, compressing data or remove unnecessary headers.
  • Single data channel for multiple request (multiplexing). This has been to some extent already part of HTTP 1.1.
  • Prioritization of data (e.g. text editing events have higher priority than image upload events).
  • Server can push data to the client or suggest data to the client to be requested.
  • Further security features.

Luckily SPDY is designed in a way that it is backward-compatible. It only changes the way how HTTP data is transmitted, but existing applications do not need to be modified. The underlying assumption is that there is a translator in the middle (e.g. a proxy or directly provided by the application/library). More advanced features/applications have to implement HTTP 2.0 natively to control and leverage all features.

At the moment the following browsers implement SPDY:

  • Google Chrome

  • Mozilla Firefox

  • Opera

  • Amazon Silk (Cloud Browser)

Some proxies, such as NGINX, support SPDY. Furthermore, first popular web pages implement SPDY.

Implications

Clearly, the design of HTTP 2.0 has many interesting implications. Firstly, we note that HTTP 2.0 is not purely an application layer protocol as it is described in the OSI network layer architecture. It is partly a session, presentation and transport protocol.

Secondly, we can see that prioritization of data streams is an extremely powerful feature. Especially, if we consider not only one application, but several applications integrated via an enterprise service bus. The enterprise service bus can be also compared with some kind of advanced HTTP 2.0 proxy. Imagine premium customers connected via a web site. The website is integrated with the CRM and production system via the enterprise service bus. Interactions with these customers via the web site are prioritized over to interactions with basic customers.

Thirdly, complex event processing application can leverage the speed improvements of HTTP 2.0 over HTTP 1.0 and analyze streams as well as correlate events in different streams over different users.

HTTP 2.0 may have the potential to replace not only HTTP 1.1, but also other protocols, in the future. Support and implementations of major software vendors demonstrate the seriousness of HTTP 2.0.

At the same time, we see further protocols emerging, such as the WebRTC protocol. The WebRTC protocol is a browser-based peer to peer protocol for voice/video chat and real-time applications. It is thus not server-based. Hence, it can be seen as complementary to the HTTP 2.0 protocol.

In the future WebRTC and HTTP 2.0 may also be combined to HTTP 3.0 to fulfill the vision of a truly decentralized Internet architecture.

OData – The Evolution of REST

Introduction

Making the data, hidden in various cloud platforms, available and understandable so that it can be processed by software services as well as human beings is one of the key challenges. Tim Berners-Lee coined this challenge “Linked Data”. However, he remained rather vague on how this data should be linked technically. An initiative of major software vendors, such as Citrix, IBM, Microsoft, Progress Software, SAP and WSO2, addressed this problem by proposing the Open Data Protocol (OData) standard to the Organization for the Advancement of Structured Information Standards (OASIS). All kind of software can leverage OData, but it is particularly interesting for business software, such as business process management systems, business rule systems and complex event processing middleware. OData can be seen as the technical foundation for open data, which comprises initiatives from various governments and organizations to make data publicly available.

Foundations

OData is inspired by the Representation State Transfer (REST) approach. Basically, REST is about clients creating, accessing, deleting or modifying resources, identified by Unique Resource Identifiers (URI), from the server. A resource can represent any concept in form of any data structure. Data structures can be described in a plethora of formats, such as the JavaScript Object Notation (JSON) or the eXtended Markup Language (XML). REST was becoming more and more popular with the emergence of complex web applications using HTML5 and Javascript.

Although not limited to any specific protocol between clients and servers, REST has been originally described and is now mostly implemented using the Hyper Text Transfer Protocol (HTTP). This allows creating, modifying, deleting or reading resources. Furthermore, we can create more sophisticated Internet based architectures for managing the data (e.g. proxies or IT security concepts).

Example (REST) We assume that a client (C) communicates with the server (S) using HTTP. The client requests the data of a book identified by the following URI: http://example.com/library/book/ISBN978-3787316502. The server answers with basic book data using the JSON format. The protocol can be described as follows:

(C) Request:

GET http://example.com/library/book/ISBN978-3787316502 HTTP/1.1

(S) Response:

HTTP/1.1 200 OK

{

“title”: “Kritik der praktischen Vernunft”,

„author“: „Immanuel Kant“,

„year“: „1788/2003“

}

Similarly, we can create, modify or delete resources.

OData

It has been shown that REST provides a lot of flexibility for managing data over the Internet. This can be seen from the plethora of web applications using it. However, it has some limitations. For example, we can only do simple data update or retrieval operation via the URI, but we cannot articulate more complex queries in our web application or for representing media streams. An example for a complex query is the following (using OData notation):

http://example.com/travelshop/Customer?$filter=Revenue gt 1000.00

This query asks for all the customers with revenue greater than 1000.00 Euro. The query can be executed similarly to the REST example presented before.

OData standardizes a wide range of different queries on the service level, which makes it very easy to reuse Odata services or do add enhanced flexibility/agility to your business processes.

Of course, we can use this query language also to update or delete resources.

 

Selected OData Concepts

OData can leverage and extend the Atom Syndication format or the Atom Publishing Protocol. You probably know these standards from web feeds (e.g. news) in your browser. They basically describe semantics for news, such as headlines, pictures or full-text. These standards can be used to represent answers to queries containing more than one entity (e.g. multiple customer records).

OData services can articulate what data they understand and produce. Clients can use this information to compose one or more OData services to fulfill their intents. For example, OData services are described using the metadata document and the service document. The first one is conceptually similar to the Web Service Description Language (WSDL). It describes operations and data provided by the OData service. The service document describes a set of entity collections that can be queried from the services. For instance, all customers that have a pay-tv subscription.

Unfortunately, it is not clear if OData is supposed to support queries requiring constructing a transitive closure. These type of queries are, for instance, useful to retrieve all the indirect flights between two airports. It may be supported by OData functions or on the client side, but not as part of the standard.

Conclusion and Outlook

OData has a lot of benefits. It leverages well proven internet concepts. HTML5 web applications can immediately start using it without changes to existing browsers. Finally, it has support by major vendors for business software.

There have been some competing proposals for managing data over the internet, such as GData by Google, but they seem to be deprecated by now.

However, some elements are missing in the OData proposal. For example, it is difficult to describe temporal queries, such as “Provide me all activities that have finished at the same time in the production process”. A lot of research has been done on representing temporal data in information systems (e.g. I used it to provide information system support for managing highly dynamic processes in the crisis response). Thus, I think this would be a beneficial feature. In fact, there have been proposals for extending the OData standard with temporal data. However, they remain rather simple and would not allow dealing with qualitative temporal queries (“finish at the same time”) as I have described before.

The same holds for geospatial queries. An example for a geospatial query is “Provide me all the warehouses in the disaster affected area”. Luckily, we have also here some proposals to extend the OData standard to support these types of queries.

What is next?

We have a lot of redundant and related data provided by different organizations. OData is focusing on single data sources and at the moment it is unclear how to relate and integrate many different sources from different information providers. This is mostly a semantic problem. For instance, a manager can be described as a director in different companies and vice versa. A solution could be to agree on OData Metadata documents to define semantics. This has to be done by domain experts as it had occurred in the area of reference models, such as the one I explained before about humanitarian supply chain management (cf. also the Humanitarian eXchange Language). Finally, we may use translation languages, such as XSLT to automate integration of semantically different information.

Further references