Monthly Archives: October 2013

Automatic deployment of complex Cloud applications

Cloud applications rarely consists in single-server-single-component architecture; this means that when it comes to automatically deploy them on one (or even multiple Clouds) one has to consider the mutual dependencies that each component in each instance have with the other components not only inside the same instance but also across different instances.

We are developing an extensible software tool that can deploy complex applications/architectures on a target cloud, and we need your help in (mark one of the following ;) ):  implementing it, refactoring it, redesign it, but also extending it.

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Extending JOPera with Persistence Layer

JOpera is an extensible, java-based process engine, and is a central component of our tools on automation of experiments in the Cloud.

We need to extend the engine to allow a distributed and scalable persistence layer. This is one of the main requisite that we need to fulfill in order to create an elastic version of the JOpera engine that can automatically scale in clouds infrastructures.

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Testing Elasticity Part 1: Visualization of test cases and test executions

We test elastic computing systems by imposing time-varying workloads on them, and successively, checking conditions on the metrics that we extract from system executions.

At the moment, the whole process is implemented as a black-box, and sometimes is difficult to understand what is going on inside the system, which is a prerequisite to improve it functioning when a test fails.

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Metric distortion in dynamically adaptive system

Dynamically adaptive systems (DAS) may change their inner business logic, software architecture, or quality of service as a reaction to changes in the environment or user requirements. DAS can also self-manage, that is, monitor their own behavior and plan suitable adaptations to achieve high level goals that are specified by their designers.

In many cases, the variables monitored by such systems are aggregated according to some specific operator. For example, their are averaged over a sliding window.
However, if systems can adapt while monitoring this variable they may introduce distortions that eventually will result in degraded performance of the system, or even critical failures.

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Adaptive filters for optimal elasticity

Elastic applications are internally implemented by combining an adaptive application and an elasticity controller. The controller is usually build by making assumptions on the environment, and as long as these assumptions hold the behavior of the controller is predictable. In reality, situations may arise that invalidate these assumptions. For example, the incoming workload oscillates more frequently and with different intensity than the expectation.

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