Virtual Machine Resource Allocation Optimization in Cloud Computing Based on Multiobjective Genetic Algorithm

Abstract

Cloud computing is an important milestone in the development of distributed computing as a commercial implementation, and it has good prospects. Infrastructure as a service (IaaS) is an important service mode in cloud computing. It combines massive resources scattered in different spaces into a unified resource pool by means of virtualization technology, facilitating the unified management and use of resources. In IaaS mode, all resources are provided in the form of virtual machines (VM). To achieve efficient resource utilization, reduce users’ costs, and save users’ computing time, VM allocation must be optimized. This paper proposes a new multiobjective optimization method of dynamic resource allocation for multivirtual machine distribution stability. Combining the current state and future predicted data of each application load, the cost of virtual machine relocation and the stability of new virtual machine placement state are considered comprehensively. A multiobjective optimization genetic algorithm (MOGANS) was designed to solve the problem. The simulation results show that compared with the genetic algorithm (GA-NN) for energy saving and multivirtual machine redistribution overhead, the virtual machine distribution method obtained by MOGANS has a longer stability time. Aiming at this shortage, this paper proposes a multiobjective optimization dynamic resource allocation method (MOGA-C) based on MOEA/D for virtual machine distribution. It is illustrated by experimental simulation that moGA-D can converge faster and obtain similar multiobjective optimization results at the same calculation scale.

Key Challenges of Cloud Computing Resource Allocation in Small and Medium Enterprises

“…For example, ref. [17] discussed the advantages of resource optimization in cloud computing, such as decreased up-front capital expenses and increased resource use efficiency. Confirming this point, ref.…”

“…Moreover, ref. [17] discussed the financial advantages of cloud resource optimization, such as decreased up-front capital expenses and increased resource use efficiency. However, these studies ignored some important obstacles for RACC in SMEs.…”

LotkaVoltera and Elman Hebbian Recurrent Neural Network Cache-Based Resource Allocation in Fog Environment

“…A new multiobjective optimization method with dynamic resource allocation combining the present state and future predicted data concerning each load, virtual machine relocation cost and new VM stability were also considered comprehensively. Also, a multiobjective optimization genetic algorithm was presented to address the issues concerning time and VM allocation [16]. Yet another method for data reduction was proposed in [17] by employing a naïve bayes classifier.…”

“…The jitter is measured by identifying the average time difference between each packet sequence. 𝐽 = (𝐷𝑃𝑆𝑒𝑞 𝑇𝐷 )/𝑛 (16) 6 below lists the jitter measure results obtained using the equation from (16). Fig.…”

“…The International Journal of Intelligent Engineering and Systems, Vol. 16 results defeated round robin and least connection. IoT with FC involves hardware and software resource allocation; response time and busy time were enhanced; bandwidth and jitter rate was not.…”