In order to enhance resource utilisation and power efficiency in cloud data centres it is important to perform Virtual Machine (VM) placement in an optimal manner. VM placement uses the method of mapping virtual machines to physical machines (PM). Cloud computing researchers have recently introduced various meta-heuristic algorithms for VM placement considering the optimised energy consumption. However, these algorithms do not meet the optimal energy consumption requirements. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to address the issues with VM placement focusing on the energy consumption. The performance of the proposed algorithm is evaluated using three different workloads in CloudSim tool. The evaluation process includes comparison of the proposed algorithm against the existing Genetic Algorithm (GA), Optimised Firefly Search (OFS) algorithm, and Ant Colony (AC) algorithm. The comparision results illustrate that the proposed ECS algorithm consumes less energy than the participant algorithms while maintaining a steady performance for SLA and VM migration. The ECS algorithm consumes around 25% less energy than GA, 27% less than OFS, and 26% less than AC.
|Number of pages||17|
|Journal||International Journal of Grid and Utility Computing|
|Publication status||Published - 28 Feb 2018|
Barlaskar, E., Singh, Y. J., & Issac, B. (2018). Enhanced Cuckoo Search Algorithm for Virtual Machine Placement in Cloud Data Centers. International Journal of Grid and Utility Computing, 9(1), 1-17. https://doi.org/10.1504/IJGUC.2018.10011385