Computers and Operations Research
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
A metaheuristic approach to scheduling workflow jobs on a Grid
Grid resource management
Dynamic Task Scheduling using Genetic Algorithms for Heterogeneous Distributed Computing
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 6 - Volume 07
Framework for Task Scheduling in Heterogeneous Distributed Computing Using Genetic Algorithms
Artificial Intelligence Review
Simulated Annealing for Grid Scheduling Problem
JVA '06 Proceedings of the IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing
Virtual Machines: Versatile Platforms for Systems and Processes (The Morgan Kaufmann Series in Computer Architecture and Design)
Adaptive particle swarm optimization: detection and response to dynamic systems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
PSO-based algorithm for home care worker scheduling in the UK
Computers and Industrial Engineering
Multi-strategy ensemble particle swarm optimization for dynamic optimization
Information Sciences: an International Journal
pMapper: power and migration cost aware application placement in virtualized systems
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
The Eucalyptus Open-Source Cloud-Computing System
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Cloud Computing: Distributed Internet Computing for IT and Scientific Research
IEEE Internet Computing
Virtual Infrastructure Management in Private and Hybrid Clouds
IEEE Internet Computing
Design and Implementation of an Efficient Two-level Scheduler for Cloud Computing Environment
ARTCOM '09 Proceedings of the 2009 International Conference on Advances in Recent Technologies in Communication and Computing
Journal of Parallel and Distributed Computing
AINA '10 Proceedings of the 2010 24th IEEE International Conference on Advanced Information Networking and Applications
Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Design and Implementation of an Efficient Two-Level Scheduler for Cloud Computing Environment
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Future Generation Computer Systems
Power Aware Meta Scheduler for Adaptive VM Provisioning in IaaS Cloud
International Journal of Cloud Applications and Computing
An Intelligent Operator for Genetic Fuzzy Rule Based System
International Journal of Intelligent Information Technologies
International Journal of Intelligent Information Technologies
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Cloud Computing provides dynamic leasing of server capabilities as a scalable, virtualized service to end users. The discussed work focuses on Infrastructure as a Service IaaS model where custom Virtual Machines VM are launched in appropriate servers available in a data-center. The context of the environment is a large scale, heterogeneous and dynamic resource pool. Nonlinear variation in the availability of processing elements, memory size, storage capacity, and bandwidth causes resource dynamics apart from the sporadic nature of workload. The major challenge is to map a set of VM instances onto a set of servers from a dynamic resource pool so the total incremental power drawn upon the mapping is minimal and does not compromise the performance objectives. This paper proposes a novel Self Adaptive Particle Swarm Optimization SAPSO algorithm to solve the intractable nature of the above challenge. The proposed approach promptly detects and efficiently tracks the changing optimum that represents target servers for VM placement. The experimental results of SAPSO was compared with Multi-Strategy Ensemble Particle Swarm Optimization MEPSO and the results show that SAPSO outperforms the latter for power aware adaptive VM provisioning in a large scale, heterogeneous and dynamic cloud environment.