With the advancement of Cloud Computing over the past few years, there has been a massive shift from traditional data centers to cloud enabled data centers. The enterprises with cloud data centers are focusing their attention on energy savings through effective utilization of resources. In this work, we propose algorithms which try to minimize the energy consumption in the data center duly maintaining the SLA guarantees. The algorithms try to utilize least number of physical machines in the data center by dynamically rebalancing the physical machines based on their resource utilization. The algorithms also perform an optimal consolidation of virtual machines on a physical machine, minimizing SLA violations. In extensive simulation, our algorithms achieve savings of about 21 in terms of energy consumption and in terms of maintaining the SLAs, it performs 60 better than Single Threshold algorithm.
In @cite_16 , the authors propose Single Threshold algorithm which sorts all the VMs in decreasing order of their current utilization and allocates each VM to a physical machine that provides the least increase of power consumption due to this allocation. The algorithm does optimization of the current allocation of VMs by choosing the VMs to migrate based on CPU utilization threshold of a particular physical machine called Single Threshold'. The idea is to place VMs while keeping the total utilization of CPU of the physical machine below this threshold. The reason for limiting CPU usage below the threshold is to avoid SLA violation under a circumstance where there is a sudden increase in CPU utilization of a VM, which could be compensated with the reserve. Single Threshold algorithm works better in terms of energy conservation when compared to Dynamic Round Robin Algorithm discussed in . This algorithm is fairly improved one which takes into consideration of power consumption and CPU usage of physical machines.
@cite_16
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Due to the growing demand of cloud services, allocation of energy efficient resources (CPU, memory, storage, etc.) and resources utilization are the major challenging issues of a large cloud data center. In this paper, we propose an Euclidean distance based multi-objective resources allocation in the form of virtual machines (VMs) and designed the VM migration policy at the data center. Further the allocation of VMs to Physical Machines (PMs) is carried out by our proposed hybrid approach of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) referred to as HGAPSO. The proposed HGAPSO based resources allocation and VMs migration not only saves the energy consumption and minimizes the wastage of resources but also avoids SLA violation at the cloud data center. To check the performance of the proposed HGAPSO algorithm and VMs migration technique in the form of energy consumption, resources utilization and SLA violation, we performed the extended amount of experiment in both heterogeneous and homogeneous data center environments. To check the performance of proposed HGAPSO with VM migration, we compared our proposed work with branch-and-bound based exact algorithm. The experimental results show the superiority of HGAPSO and VMs migration technique over exact algorithm in terms of energy efficiency, optimal resources utilization, and SLA violation.