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.
Highlights
Cloud computing is an emerging technology in the field of computers
To solve the above problems and to put forward a new thinking of physical nodes on the stability of the energy-saving resource scheduling method, how to find a virtual machine distribution that activates the stable state of each physical node and how to reduce the energy consumption of virtual machines from the old state to the new state are urgent and necessary [14]
Conventional particle swarm optimization (PSO) is an intelligent bionic method, which was jointly proposed by social psychologists Kennedy and Dr Eberhart in 1995
Summary
Cloud computing is an emerging technology in the field of computers. it has been applied, its development is not perfect. As long as we grasp the rare opportunity, we can occupy a place in the future cloud computing field [1–3] In this critical period of the formation and development of new technology, whoever holds the commanding heights of technology will hold the initiative of future technology strategy [4]. E emergence of cloud computing platform virtualization technology brings new opportunities to realize energy saving in the cloud computing environment. The above resource scheduling schemes often ignore the dynamic change of application load caused by the change of user demand in the cloud environment, and they fail to consider the stability of application load distribution on each physical node. To solve the above problems and to put forward a new thinking of physical nodes on the stability of the energy-saving resource scheduling method, how to find a virtual machine distribution that activates the stable state of each physical node and how to reduce the energy consumption of virtual machines from the old state to the new state are urgent and necessary [14]
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