Automatic Deployment of Services in the Cloud with Aeolus Blender

International audienceWe present Aeolus Blender (Blender in the following), a software product for the automatic deployment and configuration of complex service-based, distributed software systems in the " cloud ". By relying on a configuration optimiser and a deployment planner, Blender fully automates the deployment of real-life applications on OpenStack cloud deployments , by exploiting a knowledge base of software services provided by the Mandriva Armonic tool suite. The final deployment is guaranteed to satisfy not only user requirements and relevant software dependencies , but also to be optimal with respect to the number of used virtual machines

A Generic Deployment Framework for Grid Computing and Distributed Applications

Deployment of distributed applications on large systems, and especially on grid infrastructures, becomes a more and more complex task. Grid users spend a lot of time to prepare, install and configure middleware and application binaries on nodes, and eventually start their applications. The problem is that the deployment process is composed of many heterogeneous tasks that have to be orchestrated in a specific correct order. As a consequence, the automatization of the deployment process is currently very difficult to reach. To address this problem, we propose in this paper a generic deployment framework allowing to automatize the execution of heterogeneous tasks composing the whole deployment process. Our approach is based on a reification as software components of all required deployment mechanisms or existing tools. Grid users only have to describe the configuration to deploy in a simple natural language instead of programming or scripting how the deployment process is executed. As a toy example, this framework is used to deploy CORBA component-based applications and OpenCCM middleware on one thousand nodes of the French Grid5000 infrastructure.Comment: The original publication is available at http://www.springerlink.co

Generation of feasible deployment configuration alternatives for Data Distribution Service based systems

Data distribution service (DDS) has been defined by the OMG to provide a standard data-centric publish-subscribe programming model and specification for distributed systems. DDS has been applied for the development of high performance distributed systems such as in the defense, finance, automotive, and simulation domains. To support the analysis and design of a DDS-based distributed system, the OMG has proposed the DDS UML Profile. A DDS-based system usually consists of multiple participant applications each of which has different responsibilities in the system. These participants can be allocated in different ways to the available resources, which leads to different configuration alternatives. Usually, each configuration alternative will perform differently with respect to the execution and communication cost of the overall system. In general, the deployment configuration is selected manually based on expert knowledge. This approach is suitable for small to medium scale applications but for larger applications this is not tractable. In this paper, we provide a systematic approach for deriving feasible deployment alternatives based on the application design and the available physical resources. The application design includes the design for DDS topics, publishers and subscribers. For supporting the application design, we propose a DDS UML profile. Based on the application design and the physical resources, the feasible deployment alternatives can be algorithmically derived and automatically generated using the developed tools. We illustrate the approach for deriving feasible deployment alternatives of smart city parking system

Deriving feasible deployment alternatives for parallel and distributed simulation systems

Cataloged from PDF version of article.Parallel and distributed simulations (PADS) realize the distributed execution of a simulation system over multiple physical resources. To realize the execution of PADS, different simulation infrastructures such as HLA, DIS and TENA have been defined. Recently, the Distributed Simulation Engineering and Execution Process (DSEEP) that supports the mapping of the simulations on the infrastructures has been defined. An important recommended task in DSEEP is the evaluation of the performance of the simulation systems at the design phase. In general, the performance of a simulation is largely influenced by the allocation of member applications to the resources. Usually, the deployment of the applications to the resources can be done in many different ways. DSEEP does not provide a concrete approach for evaluating the deployment alternatives. Moreover, current approaches that can be used for realizing various DSEEP activities do not yet provide adequate support for this purpose. We provide a concrete approach for deriving feasible deployment alternatives based on the simulation system and the available resources. In the approach, first the simulation components and the resources are designed. The design is used to define alternative execution configurations, and based on the design and the execution configuration; a feasible deployment alternative can be algorithmically derived. Tool support is developed for the simulation design, the execution configuration definition and the automatic generation of feasible deployment alternatives. The approach has been applied within a large-scale industrial case study for simulating Electronic Warfare systems. © 2013 ACM

Dynamic Model-based Management of Service-Oriented Infrastructure.

Models are an effective tool for systems and software design. They allow software architects to abstract from the non-relevant details. Those qualities are also useful for the technical management of networks, systems and software, such as those that compose service oriented architectures. Models can provide a set of well-defined abstractions over the distributed heterogeneous service infrastructure that enable its automated management. We propose to use the managed system as a source of dynamically generated runtime models, and decompose management processes into a composition of model transformations. We have created an autonomic service deployment and configuration architecture that obtains, analyzes, and transforms system models to apply the required actions, while being oblivious to the low-level details. An instrumentation layer automatically builds these models and interprets the planned management actions to the system. We illustrate these concepts with a distributed service update operation

An Autonomous Engine for Services Configuration and Deployment.

The runtime management of the infrastructure providing service-based systems is a complex task, up to the point where manual operation struggles to be cost effective. As the functionality is provided by a set of dynamically composed distributed services, in order to achieve a management objective multiple operations have to be applied over the distributed elements of the managed infrastructure. Moreover, the manager must cope with the highly heterogeneous characteristics and management interfaces of the runtime resources. With this in mind, this paper proposes to support the configuration and deployment of services with an automated closed control loop. The automation is enabled by the definition of a generic information model, which captures all the information relevant to the management of the services with the same abstractions, describing the runtime elements, service dependencies, and business objectives. On top of that, a technique based on satisfiability is described which automatically diagnoses the state of the managed environment and obtains the required changes for correcting it (e.g., installation, service binding, update, or configuration). The results from a set of case studies extracted from the banking domain are provided to validate the feasibility of this propos

A Performance Benchmark of NetFlow Data Analysis on Distributed Stream Processing Systems

Modern distributed stream processing systems can be potentially applied to real time network flow processing. However, differences in performance make some systems more suitable than others for being applied in this domain. We propose a novel performance benchmark which is based on a common security analysis algorithms of NetFlow data, to determine the suitability of distributed stream processing system. Three of the most used distributed stream processing systems are benchmarked and results are compared with identified NetFlow data processing challenges and requirements. Benchmark results show that each system reached a sufficient data processing speed using basic deployment scenario with little to no configuration tuning. Our benchmark, unlike any other, enables to determine a performance of processing small structured messages on any stream processing system

On the Expressiveness of Synchronization in Component Deployment

International audienceThe Aeolus component problem of automatic deployment of complex distributed component systems. In the general setting, the task of checking if a distributed application can be deployed is an undecidable problem. However, the current undecidability proof in Aeolus assumes the possibility to perform in a synchronized way atomic configuration actions on a set of interdependent components: this feature is usually not supported by deployment frameworks. In this paper we prove that even without synchronized configuration actions the Aeolus component model is still Turing complete. On the contrary, we show that other Aeolus features like capacity constraints and conflicts are necessary: if we remove the former the deployment problem becomes non-primitive recursive, while in the latter it becomes poly-time