Energy Efficient Approach for Virtual Machine Placement Using Cuckoo Search

Context: Virtualization technology has facilitated the entire cloud computing scenario it has manifested the computing environment in varied ways. It enables to create small instances which are provisioned for execution of user application. These small virtual instances are employed for perceiving the potential throughput. However, there arises a need for efficaciously placing the virtual machines providing services, thereby increasing the resource utilization. This placement of virtual machines to the tangible devices or the physical machine is known as Virtual Machine Placement Problem (VMPP). In VMPP numerous VMs are consolidated on fewer physical machines so as to have energy efficient computing. Problem: The problem is to design an optimized technique for resolving the Virtual Machine Placement Problem and achieve reduction in the power consumption and number of VM migrations without violating the SLA. Objective and Focus: To achieve and propose an effective solution for dynamic Virtual Machine Placement Problem considering initial allocation and reallocation. The primary focus is by employing meta-heuristic algorithm, and thereby obtaining the robust solution and achieving the associated QoS parameters. Method: The proposed method is inspired by the peculiar behaviour of cuckoos, where cuckoos search optimal nest for laying its eggs. An algorithm integrated with machine learning has been devised and evaluated with other meta-heuristic algorithms. Result: The proposed optimization algorithm effectively optimizes virtual machine placement and migrations. For instances of 50 to 500 virtual machines, the performance has been evaluated, where for 500 virtual machines, the power consumption is 55.0660916 kW, SLA-V is 0.00208696, and the number of migrations is 36. Conclusion: To sum up, the proposed optimization algorithm, when compared with two competent and recent meta-heuristic algorithms, exhibits exceptional performance in terms of power consumption, SLA-V, and several migrations. Thus, evaluation proves the strength of the proposed algorithm.