Modified Bat Algorithm for Optimal VM's in Cloud Computing

Abstract

All task scheduling applications need to ensure that resources are optimally used, performance is enhanced, and costs are minimized. The purpose of this paper is to discuss how to Fitness Calculate Values (FCVs) to provide application software with a reliable solution during the initial stages of load balancing. The cloud computing environment is the subject of this study. It consists of both physical and logical components (most notably cloud infrastructure and cloud storage) (in particular cloud services and cloud platforms). This intricate structure is interconnected to provide services to users and improve the overall system's performance. This case study is one of the most important segments of cloud computing, i.e., Load Balancing. This paper aims to introduce a new approach to balance the load among Virtual Machines (VM's) of the cloud computing environment. The proposed method led to the proposal and implementation of an algorithm inspired by the Bat Algorithm (BA). This proposed Modified Bat Algorithm (MBA) allows balancing the load among virtual machines. The proposed algorithm works in two variants: MBA with Overloaded Optimal Virtual Machine (MBA-OOVM) and Modified Bat Algorithm with Balanced Virtual Machine (MBA-BVM). MBA generates cost-effective solutions and the strengths of MBA are finally validated by comparing it with Bat Algorithm.

Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

“…where C 1 and C 2 denote constants (C) determined by the user, T indicates a variable which is based on the transfer operator (T = C 3 × TF), C 3 indicates a continuous rate, χ best represents the location of the optimal subdivision, and F indicates the flag applied to alter the element movement path. The IAOA is derived using Levy flight (LF) approach in the classical AOA [23][24][25][26][27]. It is an arbitrary walk where the steps are determined with respect to the step length that has a detailed probability distribution.…”

“…The results indicate that the IMD-EACBR model achieved the greatest PDR under all SNs. For example, with 100 SNs, the IMD-EACBR model reached a PDR of 98.83%, whereas the SFO, GWO, GA, ALO, and PSO systems obtained lower PDRs of 98.40%, 96.47%, 95.65%, 94.37%, and 93.79%, respectively [20,[23][24][25][26][27]. Moreover, with 500 SNs, the IMD-EACBR model reached an ultimate PDR of 96.56%, whereas the SFO, GWO, GA, ALO, and PSO methodologies resulted in reduced PDRs of 95.20%, 94.66%, 93.47%, 92.30%, and 91.37%, respectively.…”

A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

“…All strategies used to lower energy-specific hardware components/levels are covered in extreme detail. There is much emphasis on techniques deployed at the hardware-level (network-or server-level) that can lead to energy-efficient or ecologically friendly data centers [122][123][124][125][126][127][128][129][130][131][132].…”

“…Ref. [S30] also proved that optimization is equally significant while working on energy efficiency [130][131][132]. They proposed a method for scheduling VM workflows in hybrid and private clouds in which pre-power techniques and the least-load-first algorithms were used to make it energy efficient.…”

“…Power management is categorized into two categories: static and dynamic power management. In static power management, power is managed at the time of design circuit, architecture, and system-level, focusing on reducing the switching activity of power in circuits [128][129][130][131][132][133][134].…”