Hypercube Based Genetic Algorithm for Efficient VM Migration for Energy Reduction in Cloud Computing

Abstract

If we choose to compare computing technology to coral reef then cloud computing technology is its very live and growing end. Its challenges are new and demand innovative measure to bring the size of its expending data centers under calipers and bridle its energy consumptions. Reduction in the consumption of energy is to be brought about without compromising quality-of-service and efficacy. For this, we purpose a Hypercube based Genetic Algorithm (HBGA) for efficient VM migration for energy reduction in cloud computing under QoS (Quality-of-service) constraint. The proposed HBGA technique can be implemented in two phases. First, in a data center the physical machines organize themselves in such a way as to acquire a highly scalable structure called Hypercube. The hypercube imperceptibly grates itself up or dips low in sympathy with VM instances as they mount up or get depleted. Secondly on the basis of this representation model of the compute nodes, and given the hypercube topology in which they are organized we propose three algorithms: (a) Hypercube based Node Selection Algorithm to minimize energy consumption (b) Hypercube based VM Selection Algorithm which minimizes the number of VM to be migrated. (c) To solve the problem of VM Placement we propose Hypercube based Genetic algorithm. Experimental results of comparisons between the proposed HBGA method viz-a-viz the existing solutions show a marked reduction in energy consumption of cloud computing environment.

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

“…Finally, we would like to suggest some of the possible scopes, shortly for researcher and practitioner as a brainstorming concept: reducing the reaction time and maximizing VM's resource allocation considering the QoS factor; improving the load stability in WSN using RCNN learning; SVM-PSO based community Forensics and RNN techniques for Intrusion Detection. Feature selection from natural algorithm Koroniotis et al [133] Quality of service NF PSO and DL Enhance NF Al hawaitat et al [134] WS PNS PSO Jamming attack Shi et al [135] Anomaly detection P ADAID 1 Presented unsupervised clustering Usman et al [96] VM allocation VR EFPA 2 Energy-oriented allocation Singh et al [103] VM migration VR HBGA 3 Energy reduction Naik et al [130] VM allocation VR Fruit fly Reduce host migration Meng & Pan [136] Optimization VR FFOA solve MKP 4 Mosa & Paton [126] VM placement VR GA Reduce response time & maximize resources utilization Duan et al [137] Information leakage P DL Protect server Festag & Spreckelsen [138] Data leakage P DL Detection of protected health information Chari et al [125] Quality of service IA DL Generate password via cognitive information Li et al [139] Signal processing IA GA Feature extraction via EEG signal Saini & Kansal [127] WSN ACS SI Reduce energy consumption and increase network life time Chen et al [140] Biometric identification IA CNN Proposed GSLT-CNN using human brain EEG Cao & Fang [141] Multilayer defense scenario ACS SI Found proficient IPSO elucidating extensive WTA problem Aliyu et al [124] Resource allocation ACS Ant colony Illustrated faster convergence optimize makespan time Poonia [142] VAN ACS SI Found significant difference in VANET routing protocol and Swarm based protocol Verma et al [ [129] Feature extraction ID GA Reduce features to classify network packet Tan et al [148] Real time network attack intrusion ID NN Able to detect in network precisely…”

“…Also, the optimal solution is not highly scalable. Besides, Khurana and Singh (2019) [103] also uses FPA based algorithms along with GWO to improve VM efficiency. To conduct the experiment, they have considered the following parameters: the number of tasks, the number of workflows, the number of VM, the MIPS, and the number of processors.…”

Optimal Design of Transmission Shafts: a Continuous Genetic Algorithm Approach

“…To create this new group of individuals, the operators of mutation and recombination are adapted. In addition, classical selection is used to enable good solutions to stay in the group of individuals, thus allowing them to improve their position [25].…”