Abstract
Mapping the virtual machines to the physical machines cluster is called the VM placement. Placing the VM in the appropriate host is necessary for ensuring the effective resource utilization and minimizing the datacenter cost as well as power. Here we present an efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. We developed the algorithm based on two different methods, first initial VM packing is done by checking the load of the physical host and the user constraints of the VMs. Second optimization of placed VMs is done by using a hybrid genetic algorithm based on fitness function. Our simulation results show that the proposed algorithm outperforms existing methods and enhances the rate of resource utilization through accommodating more number of virtual machines in a physical host
Highlights
Infrastructure-as-a-Service (IaaS) is the most fundamental use of cloud computing
Data center expenses can be lessened by using virtual machines (VMs) Cloud data center providers can create a huge number of virtual machines (VMs) for different types of workload and specification requirements [4]
We developed the algorithm based on two different methods, first by checking the load of the physical host, the load factor of a physical host can be measured by the way of analyzing utilization level of the individual resources like CPU, Memory and Network bandwidth
Summary
Infrastructure-as-a-Service (IaaS) is the most fundamental use of cloud computing. The virtualization technology is the base to form an IaaS platform. Placing the VM in the appropriate host is necessary for ensuring the effective resource utilization and minimizing the datacenter cost as well as power To address this problem in this paper we propose a new efficient hybrid genetic based host load aware algorithm for scheduling and optimization of virtual machines in a cluster of Physical hosts. Maintaining balanced load among server requires more number of VM migrations which leads to increase the operational cost of the service provider so VMs should be rearranged in a way such that the number of VM migrations should be minimized while satisfying resource utilization and load balance In this type of multifaceted problems, even the most prominent algorithms can’t realize all the associations between VMs, physical servers, and physical clusters to lead the most finely optimized solution. In order to achieve this goal a new grouping based genetic algorithm is proposed and we believe that our new algorithm is useful for this kind of complex optimization problem
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