An efficient reducing mechanism for energy consumption in data center using hybrid consolidation techniques

Abstract

The rise in internet user demand is a major factor in the expansion of infrastructure and the upsurge in energy use in cloud, colocation, and some business data centres. The advent of 5G has compounded the situation, as it substantially gives room for many new types of digital services, resulting in a need for richer consume a lot of energy when no scaling method is applied. Services such as mail, data storage and retrieval and other cloud services also require a lot of high energy consumption which eventually result into carbon(IV) oxide (CO2) emissions to the environment. This research therefore, focuses on lowering the energy usage of a data centre with heterogeneous power awareness either in an idle server state or high-performance state using a novel hybridized algorithm called “DyVoFesLoReMu”, comprising Dynamic Voltage Frequency Scaling (Dvfs) and a modified Local Regression Minimum Utilization (LrMu). A real dataset (workload) obtained online from PlanetLab consisting of hosts and Virtual Machines (VM) was simulated on a data center in CloudSim 3.0.3. Tool kit with preset parameters consisting of VM Allocation Policy and VM Selection Policy was used. The tool kit was utilised to create cloud infrastructure and simulate the essential features of a cloud environment. The Cloudsim was installed on Eclipse Integrated Development Environment (IDE) 2019 version on Windows 10 operating system. The hybridized algorithm was compared with other five (5) existing energy reducing algorithms and it was found to be more efficient with a range of 41-90% reduction in energy usage from the ten days workload traces and in comparison with the existing algorithms used for the simulation.