Deploying loosely coupled, component-based applications into distributed environments

Heydarnoori, Abbas; Mavaddat, Farhad; Arbab, Farhad

doi:10.1109/ecbs.2006.34

Cited by 4 publications

(6 citation statements)

References 6 publications

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“…Abbas Heydarnoori et al proposed a graph-based deployment planning approach, and treated the mapping of application graph into environment graph as the problem of Multiway Cut [10] or Minimum Set Cover [11]. Tatiana Kichkaylo et al [12] described a general model for the component placement problem and presented an placement algorithm basing on AI planning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Deployment Planning of Component-Based Distributed Applications Using Mathematical Programming

Zhang

Qiu

Tian

et al. 2010

2010 International Conference on Computational Intelligence and Software Engineering

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Component deployment directly affects the performances of component-based distributed applications, such as response time, throughput, reliability, and security. It also affects resources utility of runtime environment. In order to reduce the cost of resources used by applications and improve the resources utility, this paper defined component graph to represent component-based distributed applications, and used tree network topology to describe the runtime environment of distributed applications. We defined the resource cost objective function and formulated component deployment optimization problem as mathematical programming problem. Example analysis demonstrated our approach was effective.

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Section: Introductionmentioning

confidence: 99%

Deployment Planning of Component-Based Distributed Applications Using Mathematical Programming

Zhang

Qiu

Tian

et al. 2010

2010 International Conference on Computational Intelligence and Software Engineering

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“…Additionally, we have support for a number of more complex algorithms, such as an approximation algorithm of the minimum set cover problem [14]. This algorithm can be used to approximate the most effective cost deployment scenario when every server has a fixed price, no matter how many services it will host.…”

Section: Division Phasementioning

confidence: 99%

“…The QoS policy specifications in Disnix consist of two parts, which are inspired by [14,13]. First, a candidate host selection phase is performed, in which for each service suitable candidate hosts are determined.…”

Section: Implementing a Quality Of Ser-vice Modelmentioning

confidence: 99%

“…The supportedTypes attribute is a list of strings indicating what types of services can be deployed and run. For instance, the machine test2 can run MySQL databases, Apache Tomcat web applications and generic processes 14 . Other attributes can be freely defined, such as the port number of Apache Tomcat 12 and the zone in which the machine resides 15 .…”

Section: Services Modelmentioning

confidence: 99%

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A self-adaptive deployment framework for service-oriented systems

Burg

Dolstra

2011

Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

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Deploying components of a service-oriented system in a network of machines is often a complex and labourious process. Usually the environment in which such systems are deployed is dynamic: any machine in the network may crash, network links may temporarily fail, and so on. Such events may render the system partially or completely unusable. If an event occurs, it is difficult and expensive to redeploy the system to the take the new circumstances into account. In this paper we present a self-adaptive deployment framework built on top of Disnix, a model-driven distributed deployment tool for service-oriented systems. This framework dynamically discovers machines in the network and generates a mapping of components to machines based on non-functional properties. Disnix is then invoked to automatically, reliably and efficiently redeploy the system.

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“…Then, deployment is defined as the mapping of the application graph to the target environment graph in such a way that the desired QoS parameter is optimized. This deployment problem is solved for the QoS parameters maximum reliability and minimum cost in [33] and [34] respectively.…”

Section: Qos-aware Deploymentmentioning

confidence: 99%

Deploying Component–Based Applications: Tools and Techniques

Heydarnoori

Software Engineering Research, Management and Applications

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Summary. Software deployment comprises activities for placing an already developed application into its operational environment and making it ready for use. For complex componentbased applications that constitute many heterogeneous components with various hardware and software requirements, this deployment process can become one of the most burning challenges. In this situation, it is difficult to manually identify a valid deployment configuration that satisfies all constraints. Thus, automated tools and techniques are required to do the complex process of software deployment. To address this requirement, a variety of tools and techniques that support different activities of the deployment process have been introduced in both industry and academia. This paper aims to provide an overview of these tools and techniques.

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Deploying loosely coupled, component-based applications into distributed environments

Abstract: Abstract

Cited by 4 publications

References 6 publications

Deployment Planning of Component-Based Distributed Applications Using Mathematical Programming

Deployment Planning of Component-Based Distributed Applications Using Mathematical Programming

A self-adaptive deployment framework for service-oriented systems

Deploying Component–Based Applications: Tools and Techniques

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