Computers and Operations Research
Improving dynamic voltage scaling algorithms with PACE
Proceedings of the 2001 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Energy-Efficient Real-Time Heterogeneous Server Clusters
RTAS '06 Proceedings of the 12th IEEE Real-Time and Embedded Technology and Applications Symposium
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
Optimal power allocation in server farms
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Computer Networks: The International Journal of Computer and Telecommunications Networking
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
AINA '10 Proceedings of the 2010 24th IEEE International Conference on Advanced Information Networking and Applications
Self Adaptive Particle Swarm Optimization for Efficient Virtual Machine Provisioning in Cloud
International Journal of Intelligent Information Technologies
Advances in Engineering Software
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Cloud computing aims at providing dynamic leasing of server capabilities as scalable, virtualized services to end users. Our work focuses on the Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate servers available in a data center. The cloud data center taken into consideration is heterogeneous and large scale in nature. Such a resource pool is basically characterized by high resource dynamics caused by non-linear variation in the availability of processing elements, memory size, storage capacity, bandwidth and power drawn resulting from the sporadic nature of workload. Apart from the said resource dynamics, our proposed work also considers the processor transitions to various sleep states and their corresponding wake up latencies that are inherent in contemporary enterprise servers. The primary objective of the proposed metascheduler is to map efficiently a set of VM instances onto a set of servers from a highly dynamic resource pool by fulfilling resource requirements of maximum number of workloads. As the cloud data centers are overprovisioned to meet the unexpected workload surges, huge power consumption has become one of the major issues of concern. We have proposed a novel metascheduler called Adaptive Power-Aware Virtual Machine Provisioner (APA-VMP) that schedules the workload in such a way that the total incremental power drawn by the server pool is minimum without compromising the performance objectives. The APA-VMP makes use of swarm intelligence methodology to detect and track the changing optimal target servers for VM placement very efficiently. The scenario was experimented by novel Self-adaptive Particle Swarm Optimization (SAPSO) for VM provisioning, which makes best possible use of the power saving states of idle servers and instantaneous workload on the operational servers. It is evident from the results that there is a significant reduction in the power numbers against the existing strategies.