Self Adaptive Particle Swarm Optimization for Efficient Virtual Machine Provisioning in Cloud

Abstract

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.

A Multi-objective Biogeography-Based Optimization for Virtual Machine Placement

“…Ant colony optimization method is used to pack VMs to the least number of physical machines necessary for the current workload [7]. SAPSO is a self-adaptive particle swarm optimization algorithm, and automatically adjusts VMP in response to the changing resource pools in a dynamic cloud environment [8]. Moreover, GABA is a genetic algorithm (GA) based algorithm that dynamically reconfigures the VM mappings according to the estimated future workload [9].…”

“…The work in [14] optimizes CPU utilization, network throughput, and disk I/O rate. Existing biology-based optimization algorithms for VMP include genetic algorithms [9], [10], particle swarm optimization [8], and ant colony optimization [7], [11].…”

A Heuristic Placement Selection of Live Virtual Machine Migration for Energy-Saving in Cloud Computing Environment

“…Jeyarani et al [8] have proposed SAPSO (self-adaptive particle swarm optimization) for efficient virtual machine provisioning in cloud aimed at that when mapping a set of VM instances onto a set of servers from a dynamic resource pool, the total incremental energy drawn upon the mapping is minimal and does not compromise the performance objectives. The advantage of the proposed solution is obvious.…”

A Location Selection Policy of Live Virtual Machine Migration for Power Saving and Load Balancing

“…Jeyarani et al [10] have proposed self-adaptive particle swarm optimization (SAPSO) for efficient virtual machine provisioning in cloud aimed at that when mapping a set of VM instances onto a set of servers from a dynamic resource pool, the total incremental energy drawn upon the mapping is minimal and does not compromise the performance objectives. The advantage of the proposed solution is obvious.…”