ChicWhale optimization algorithm for the VM migration in cloud computing platform
- PDF / 1,803,870 Bytes
- 15 Pages / 595.276 x 790.866 pts Page_size
- 90 Downloads / 197 Views
RESEARCH PAPER
ChicWhale optimization algorithm for the VM migration in cloud computing platform Srinivas Byatarayanapura Venkataswamy1 · Indrajit Mandal2 · Seetharam Keshavarao3 Received: 14 October 2019 / Revised: 30 January 2020 / Accepted: 8 March 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Nowadays, Virtual Machine (VM) migration becomes very popular in the cloud computing platform. Various VM migration based mechanisms are designed for optimal VM placement but remain a challenge due to improper energy consumption in the cloud model. This paper proposes an approach for VM migration in the cloud using an optimization algorithm, ChickenWhale optimization algorithm (ChicWhale), which is developed by integrating the Whale optimization algorithm in Chicken swarm optimization. In the developed approach, a local migration agent is utilized for monitoring the memory and resources utilization in the cloud continuously, and the VM is migrated using the service provider based on the requirement of the VMs to complete a task assigned. At first, the cloud system is designed, and then the proposed ChicWhale is employed by moving the VMs optimally, and the fitness function for best VM migration is carried out by considering several parameters, like load, migration cost, resource availability, and energy. The performance of the VM migration strategy based on ChicWhale is evaluated in terms of energy consumption, resource availability, migration cost, and load. The proposed ChicWhale method achieves the maximal resource availability of 0.989, minimal migration cost of 0.0564, the minimal energy consumption of 0.481, and the minimal load of 0.0001. Keywords Virtual machine migration · Chicken swarm optimization · Cloud computing · Whale optimization algorithm · Resource availability
1 Introduction Cloud computing is nothing, but a computing theory for allowing the utilization of computing infrastructure at more than one level of abstraction. Due to the implications for higher availability and flexibility at limited cost, cloud computing has been paid a great deal of attention [1–4]. Meanwhile, due to the uneven task scale and various computing capacities of nodes, some computing nodes present in the cloud are underutilized whereas others are overloaded that results in unbalanced load distribution [1, 5, 6]. Hence, it is imperative for spreading loads over computing nodes to * Srinivas Byatarayanapura Venkataswamy [email protected] 1
Information Science and Engineering, Atria Institute of Technology, Bangalore, Karnataka, India
2
Computer Science and Engineering, MINA Institute of Engineering and Technology, Nalgonda, Telangana, India
3
Research and Development, Anveshana, Hyderabad, Telangana, India
enhance user requirements [1, 7]. Several users and enterprises are permitted to maintain and construct data centers. The individuals of the cloud also enjoy several kinds of computing services provided by the public cloud [8]. Cloud computing with optimization algorithms is deter
Data Loading...
