Abstract
Cloud computing is the most prominent established framework; it offers access to resources and services based on large-scale distributed processing. An intensive management system is required for the cloud environment, and it should gather information about all phases of task processing and ensuring fair resource provisioning through the levels of Quality of Service (QoS). Virtual machine allocation is a major issue in the cloud environment that contributes to energy consumption and asset utilization in distributed cloud computing. Subsequently, in this paper, a multiobjective Emperor Penguin Optimization (EPO) algorithm is proposed to allocate the virtual machines with power utilization in a heterogeneous cloud environment. The proposed method is analyzed to make it suitable for virtual machines in the data center through Binary Gravity Search Algorithm (BGSA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). To compare with other strategies, EPO is energy-efficient and there are significant differences. The results of the proposed system have been evaluated through the JAVA simulation platform. The exploratory outcome presents that the proposed EPO-based system is very effective in limiting energy consumption, SLA violation (SLAV), and enlarging QoS requirements for giving capable cloud service.
Highlights
Cloud computing (CC) has been established as one of the prevailing network technologies in the past
Algorithm. e step-by-step methods of virtual machine (VM) allocation based on the Emperor Penguin Optimization (EPO) algorithm would be described as follows: Step 1: initialization (i) We term the list of VMs as m in the data center (ii) We term the list of physical machine (PM) as n in the data center (iii) e total number of repetitions (iv) VM requests
Simulation Results e proposed EPO algorithm is analyzed and it is compared with BSGA, Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO)-based algorithms in Windows 10 operating system with 4 GB RAM and Intel i5 2.60 GHz processor
Summary
Cloud computing (CC) has been established as one of the prevailing network technologies in the past. For physical servers which are idle, the power utilization is exceeding two-thirds of the used server [12,13,14] To overcome this energy inefficiency, efficient usage of physical machine resources using virtualization is a promising solution. VMP refers to how VMs are allocated on PMs. Due to this, the usage of physical servers is enhanced and the number of underloaded servers is minimized. (i) We proposed a multiobjective Emperor Penguin Optimization (EPO) algorithm that successfully allocates the VMs to the least number of active PMs in cloud data centers. (ii) EPO has more efficiency than BSGA, ACO, and PSO algorithms in terms of energy consumption and resource utilization and the execution time and the quantity of active server is reduced. E remaining portion of the paper is structured as follows: we analyzed recent studies about VM placement in Section 2; the detailed problem formulation approach is presented in Section 3; the proposed EPO methodology is presented in Section 4; Section 5 provides the obtained results and its explanation; and Section 6 describes the conclusion
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