Let the Ants Deploy Your Software - An ACO Based Deployment Optimisation Strategy

Aleti, Aldeida; Grunske, Lars; Meedeniya, Indika; Moser, Irene

doi:10.1109/ase.2009.59

Cited by 27 publications

(27 citation statements)

References 22 publications

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“…We will also work on adopting different optimization techniques in the S-IDE framework. In this context, we think that adopting a constructive algorithm like P-ACO (Aleti et al, 2009a) and comparing the results with our out-of-the-box Genetic Algorithm (Mehrabi et al, 2009) will be interesting. Another interesting work would be to define our specific QoS parameters and sample scenarios in DIF (Malek et al, 2012) and use its output to generate deployment model in S-IDE.…”

Section: Resultsmentioning

confidence: 99%

“…A further analysis could be performed to identify the best performing algorithm (e.g. Aleti et al, 2009a). The approach that we have presented does not mandate a particular implementation of the CTAP algorithm, and we consider the analysis of the algorithm implementations beyond the scope of this work.…”

Section: Deployment Model Generation Performancementioning

confidence: 99%

“…Zheng et al (2007) used Mixed Integer Linear Programming (MILP) techniques and used CPLEX (IBM, 2010) as MILP solver. Aleti et al (2009a) discuss the adoption of constructive algorithms instead of iterative evolutionary algorithms for deployment optimization of embedded systems. Constructive algorithms often converge quickly and produce diverse solutions when compared to iterative algorithms (Blum and Roli, 2003).…”

Section: Defined In Sectionmentioning

confidence: 99%

“…The second, more formal approach for evaluating the generated deployment alternative is to compare the generated alternative with another deployment alternative (Aleti et al, 2009a;Malek et al, 2012). The S-IDE tool provides a quality evaluation tool that enables the comparison of two deployment models with respect to given simulation execution configurations.…”

Section: Feasibility Of the Generated Deployment Modelmentioning

confidence: 99%

“…Constructive algorithms often converge quickly and produce diverse solutions when compared to iterative algorithms (Blum and Roli, 2003). Aleti et al (2009a) adapt Pareto-Ant Colony Optimization (P-ACO) algorithm (Doerner et al, 2004) to solve a multi-objective deployment optimization problem. The performance of P-ACO is compared with a MultiObjective Genetic Algorithm (MOGA) by using the Archeopterix tool platform (Aleti et al, 2009b).…”

Section: Defined In Sectionmentioning

confidence: 99%

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S-IDE: A tool framework for optimizing deployment architecture of High Level Architecture based simulation systems

Çelik

Tekinerdoğan

2013

Journal of Systems and Software

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One of the important problems in High Level Architecture (HLA) based distributed simulation systems is the allocation of the different simulation modules to the available physical resources. Usually, the deployment of the simulation modules to the physical resources can be done in many different ways, and each deployment alternative will have a different impact on the performance. Although different algorithmic solutions have been provided to optimize the allocation with respect to the performance, the problem has not been explicitly tackled from an architecture design perspective. Moreover, for optimizing the deployment of the simulation system, tool support is largely missing. In this paper we propose a method for automatically deriving deployment alternatives for HLA based distributed simulation systems. The method extends the IEEE Recommended Practice for High Level Architecture Federation Development and Execution Process by providing an approach for optimizing the allocation at the design level. The method is realized by the tool framework, S-IDE (Simulation-IDE) that we have developed to provide an integrated development environment for deriving a feasible deployment alternative based on the simulation system and the available physical resources at the design phase. The method and the tool support have been validated using a case study for the development of a traffic simulation system.

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Section: Resultsmentioning

confidence: 99%

Section: Deployment Model Generation Performancementioning

confidence: 99%

Section: Defined In Sectionmentioning

confidence: 99%

Section: Feasibility Of the Generated Deployment Modelmentioning

confidence: 99%

Section: Defined In Sectionmentioning

confidence: 99%

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S-IDE: A tool framework for optimizing deployment architecture of High Level Architecture based simulation systems

Çelik

Tekinerdoğan

2013

Journal of Systems and Software

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Adaptive Neighbourhood Search for the Component Deployment Problem

Aleti

Drugan

2015

Search-Based Software Engineering

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Since the establishment of the area of search-based software engineering, a wide range of optimisation techniques have been applied to automate various stages of software design and development. Architecture optimisation is one of the aspects that has been automated with methods like genetic algorithms, local search, and ant colony optimisation. A key challenge with all of these approaches is to adequately set the ballance between exploration of the search space and exploitation of best candidate solutions. Different settings are required for different problem instances, and even different stages of the optimisation process. To address this issue, we investigate combinations of different search operators, which focus the search on either exploration or exploitation for an efficient variable neighbourhood search method. Three variants of the variable neighbourhood search method are investigated: the first variant has a deterministic schedule, the second variant uses fixed probabilities to select a search operator, and the third method adapts the search strategy based on feedback from the optimisation process. The adaptive strategy selects an operator based on its performance in the previous iterations. Intuitively, depending on the features of the fitness landscape, at different stages of the optimisation process different search strategies would be more suitable. Hence, the feedback from the optimisation process provides useful guidance in the choice of the best search operator, as evidenced by the experimental evaluation designed with problems of different sizes and levels of difficulty to evaluate the efficiency of varying the search strategy.

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