الفهرس 
REVIEW OF EXISTING ALGORITHMS IN VIRTUAL MACHINEALLOCATORS FOR CLOUD DATA CENTERSOnly 14 pages are availabe for public view
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Abstract
Cloud computing (CC) was emerged as a computing paradigm, which aims to
offer reliable, customized and Quality of Service (QoS) guaranteed dynamic
computing environments for end-users. The data centers in CC are growing
tremendously in order to meet the rising demands such as rapid computational
response, massive storage, etc. Virtual Machine (VM) is extensively adopted as
an enhancer of CC, which provides several benefits such as performance isolat io n,
security, flexibility and ease of management in the user customized platform. These
benefits impact the resource utilization, performance and power consumption of the
data centers and could be able to minimize the maintenance cost. In the task
allocation process, Software Defined Networking (SDN) is used to present poweraware dynamic allocators for virtual machine. The drawbacks of the prevailing
algorithms could be overcome with the optimal dynamic features of Earliest
Deadline First (EDF) algorithm. In this framework, our study introduces 10 VM
with various allocation methods and compares them with a baseline approach
containing first available fit. These allocators vary in accordance with allocation
policies, strategies and other network resources. Task scheduling is a method used
for allocating the tasks to server on the basis of workload capacity. In general, the
tasks are allocated to the corresponding server for minimizing time delay and traffic.
Particle Swarm Optimization (PSO) is one of the best algorithms utilized for task
scheduling in cloud platform with low computational cost. In this study, a Hybrid
Swarm Optimization (HSO) has been proposed, which is the integration of Salp
Swarm Optimization (SSO) and Particle Swarm and Optimization for resolving
the prevailing complexities. The predominant goal of the proposed framework is
task scheduling of the resources available for reducing the computational cost and
execution time. Multilayer Logistic Regression (MLR) is a technique for the
detection of overloaded VM, thereby task could be scheduled to the virtual
machine in accordance with the workload capacity. The proposed HSO with
multilayer regression was simulated in cloud sim toolkit. The output of the
proposed framework depicts the effectiveness of the proposed system in termsof cost, makespan, and execution time. When compared to the prevailing systems
such as Improved Efficiency Evolution (IDEA), Genetic algorithm (GA), and PSO,
the performance of the proposed system is shown to be more efficient in task
scheduling and virtual machine allocation. With the proposed framework, a novel
idea for overcoming the issues for task scheduling and job allocation for virtual
machines in CC has been accomplished with low percentage of cost and low time
duration.