Dynamic Multi-Objective Framework for Migrating Live Virtual Machines in the Cloud
Keywords:
Cloud Computing, Virtual Machine, VM Migration, Resource Allocation, Resource Utilization, Energy Consumption, Cloud data centers and Hybrid Heuristic Calculation.Abstract
When it comes to data storage environments, cloud computing has been a very promising area of technology. Data centres housing IT (Information Technology) servers are spread out across the world by cloud service providers in response to the high demand for storage and processing power. Efficient energy utilisation for a large number of applications running on separate physical machines is especially challenging in cloud computing environments with a scientific focus, as tasks in resource distribution are dependent on the availability of those resources. The difficult task at hand is to allocate resources more efficiently while simultaneously lowering the energy cost of maintaining data centers, which impacts the quality of service (QoS) of running scientific workflow applications. This research presents a Novel Hybrid Heuristic Framework (NHHF) that uses significant solutions for resource allocation, task scheduling, and scientific workflow optimization through optimized energy utilization. With NHHF, you may migrate virtual machines (VMs) in a multi-objective mechanism that optimizes resource wastage and other parameters, and you can use an Optimized Energy Aware Migration technique to run tasks without dominating the process and reduce energy consumption. Without interfering with or influencing the execution of workflow activities, the multi-objective combinatorial optimization issue is efficiently solved by the suggested innovative hybrid heuristic framework. Energy consumption in data centers is decreased by our proposed approach. Compare the results of the proposed technique's simulation research to those of state-of-the-art technologies, such as distributed dynamic virtual machine management (DDVM), optimized dynamic virtual machine migration approach (ODVMMA), and dynamic voltage frequency scaling (DVFS), using CloudSim
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