Software performance optimization based on constrained GSA

Software Performance Engineering (SPE) in the early life cycle of software development (software modeling) is very useful and cost-effective but does not guide the software architect through how to improve the design. Computing the least response time by controlling utilization and cost is a constrained optimization problem. This paper presents a constrained optimization method based on Gravitational Search Algorithm (GSA) for exploring the software design space automatically and proposes the best configuration in terms of performance evaluation. Presented method is compared with constrained PSO which is one of famous optimization algorithms. Obtained results confirm the efficiency of proposed method.

[1]  Heiko Koziolek,et al.  Automatic, Model-Based Software Performance Improvement for Component-based Software Designs , 2009, Electron. Notes Theor. Comput. Sci..

[2]  Connie U. Smith,et al.  Performance Engineering of Software Systems , 1990, SIGMETRICS Perform. Evaluation Rev..

[3]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[4]  Panos M. Pardalos,et al.  A Collection of Test Problems for Constrained Global Optimization Algorithms , 1990, Lecture Notes in Computer Science.

[5]  Mark Harman,et al.  The Current State and Future of Search Based Software Engineering , 2007, Future of Software Engineering (FOSE '07).

[6]  Georgios Dounias,et al.  A hybrid particle swarm optimization algorithm for the vehicle routing problem , 2010, Eng. Appl. Artif. Intell..

[7]  Ralf Reussner,et al.  Optimising multiple quality criteria of service-oriented software architectures , 2009, QUASOSS '09.

[8]  C. Murray Woodside,et al.  Performance modeling from software components , 2004, WOSP '04.

[9]  J. Kennedy,et al.  Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[10]  M. J. D. Powell,et al.  Nonlinear Programming—Sequential Unconstrained Minimization Techniques , 1969 .

[11]  Heiko Koziolek,et al.  Performance evaluation of component-based software systems: A survey , 2010, Perform. Evaluation.

[12]  Steffen Becker,et al.  The Palladio component model for model-driven performance prediction , 2009, J. Syst. Softw..

[13]  Jing Xu,et al.  Rule-based automatic software performance diagnosis and improvement , 2008, WOSP '08.

[14]  Jerome A. Rolia,et al.  A Toolset for Performance Engineering and Software Design of Client-Server Systems , 1995, Perform. Evaluation.