Novel Load Balancing Approach Towards Cloud Computing by Reducing Sla Degradation and Energy Consumption

Abstract

The advent development of cloud computing is an on-demand adaptive technology for many IT organizations due to its dynamic scalability and virtualization resources as a global service. There are numerous energy-conscious approaches in existence that attempt to minimize energy consumption and Service Level Agreements (SLAs) degradation of the host. In this paper, we proposed a novel load balancing approach that identifies overloaded or underloaded hosts, and selects the VMs (Virtual Machines) for allocation to the host based on the predicted load. We consider CPU utilization parameter for evaluating host loading which is far enough to compute all the characteristics of Physical Machines (PM) provided to the Linear Adam Algorithm for Overloaded detection followed by Interquartile Range (IQR) method for underloaded detection. The Power-Aware Best Fit Decreasing (PABFD) algorithm sorts the VM in descending order based on CPU utilization. Then those filtered VM is allocated to the corresponding host thereby reducing the energy consumption and SLAs degradation. We evaluate the proposed algorithm through CloudSim-3.0.3 simulation and random datasets with different workloads on a real Planet Lab. After the evaluation, the simulation result of the proposed method indicates a significant performance by reducing the metric parameters such as SLA degradation, number of VM migration, the total number of host shutdowns, and reduce energy consumption.

Pre-copy live virtual machine migration techniques in cloud computing using HDWHM algorithm

“…The VM becomes overloaded due to heavy resource usage, which prolongs the execution time of cloudlets [10]. The interquartile (IQR) span is a statistical dispersion measure that falls among the upper and lower QR and is represented as IQR=QU3-QU1 [11].…”