Haithem Mezni
ABSTRACT
This paper explores one of the critical issues, SaaS placement in cloud data centers, for reducing execution time of composite SaaS applications. We adopt a multi-swarm variant of Particle Swarm Optimization (PSO) to propose a service placement method. Also, a cooperative learning strategy is hybridized to the placement algorithm, which makes information of best candidate servers be used more effectively to generate better placement plan. In the proposed method, for each sub-swarm of servers, the worst placement learns from the best servers, so that worst servers can have more excellent exemplars to learn and can find the optimal placement for SaaS components more easily. Experiments show that our solution is efficient in comparison with existing SaaS placement approaches.
- Laplante, P. A., Zhang, J., and Voas, J.. What's in a Name? Distinguishing between SaaS and SOA. It Professional, 10(3), 46--50, 2008. Google Scholar
Digital Library
- Tang, M., Yusoh, Z. A parallel cooperative co-evolutionary genetic algorithm for the composite saas placement problem in cloud computing. In Parallel Problem Solving from Nature-PPSN XII (pp. 225--234), 2012. Google ScholarDigital Library
- Bhardwaj, S., Sahoo, B. A Particle swarm optimization approach for cost effective SaaS placement on cloud. Int. Conf. Computing, Communication & Automation, pp. 686--690, 2015. Google ScholarCross Ref
- Ni, Z. W., Pan, X. F., et al. An Ant Colony Optimization for the Composite SaaS Placement Problem in the Cloud. Applied Mechanics and Materials, pp. 3062--3067, 2012.Google Scholar
- Huang K. C., & Shen B. J. Service deployment strategies for efficient execution of composite SaaS applications on cloud platform. J. Systems and Software, 107, 127--141, 2015. Google ScholarDigital Library
- Tang, K., Li, Z., Luo, L., and Liu, B., Multi-Strategy Adaptive Particle Swarm Optimization for Numerical Optimization, J. Engineering Applications of Artificial Intelligence, vol. 37, pp. 9--19, 2015. Google ScholarCross Ref
- Charrada, F., Tebourski, N., Tata, S., et al. Approximate placement of service-based applications in hybrid clouds. 21st Int. Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, pp. 161--166, 2012. Google ScholarDigital Library
- Frey, S., Fittkau, F., Hasselbring, W. Search-based genetic optimization for deployment and reconfiguration of software in the cloud. Int. Conf. Software Engineering, pp. 512--521, 2013. Google ScholarDigital Library
- Liu, Z., Hu, Z., & Jonepun, L. K. (2014). Research on Composite SaaS Placement Problem Based on Ant Colony Optimization Algorithm with Performance Matching Degree Strategy. JDIM, 12(4), 225--234.Google Scholar
- Bowen, Y., Shaochun, W. An Adaptive Simulated Annealing Genetic Algorithm for the Data Placement Problem in SaaS. Int. Conf. on Industrial Control and Electronics Engineering (ICICEE), pp. 1037--1043, 2012. Google ScholarDigital Library
- Hajji, M.A. and Mezni, H. A composite particle swarm optimization approach for SaaS placement in cloud environment. Technical report, University of Tunis, 2016.Google Scholar
- Kennedy, J., Eberhart, R.C. Particle Swarm Optimization. Proc. IEEE Int. Conf. on Neural Networks, pp. 1942--1948, 1995. Google ScholarCross Ref
Index Terms
- A multi-swarm based approach with cooperative learning strategy for composite SaaS placement
Recommendations
Security‐aware SaaS placement using swarm intelligence
AbstractCloud computing has emerged as a new powerful service delivery model to cope with resource challenges and to offer various on‐demand services (eg, software, storage, network, etc.). Software as a Service (SaaS) is one of the most popular service ...
A composite particle swarm optimization approach for the composite SaaS placement in cloud environment
Cloud computing has emerged as a new powerful service delivery model to cope with resource challenges and to offer on-demand various types of services (e.g., software, storage, network). One of the most popular service models is Software as a Service (...
Dynamic Multi-swarm Particle Swarm Optimization with Center Learning Strategy
Advances in Swarm IntelligenceAbstractIn this paper, we propose a novel variant of particle swarm optimization, called dynamic multi-swarm particle swarm optimization with center learning strategy (DMPSOC). In DMPSOC, all particles are divided into several sub-swarms. Then, a center-...
Comments