A Predictive Workload Balancing Algorithm in Cloud Services

Abstract

In today’s business world, many companies and government agencies depend

on the infrastructures of cloud services to host and process their information. Load

processing of many cloud services is distributed in a static manner which can overload

the largest available systems. This paper is an exploratory study on the predictive

approach for dynamic resource distribution of cloud services.

Today, many cloud service providers are exploring the benefit of dynamic

workload-balancing for their resource management. Rather than issuing fixed

resources to each customer, a dynamic hosting alternative offers a way to allocate

resources dynamically and more efficiently to save computational power.

Efficient cloud resource management can be achieved by simulating cloud

services based on the predictions of incoming workloads, which can be more efficient

than static allocation methods (Wolke, Bichler, and Setzer, 2015). Previous

researchers in this area have focused on dynamic load balancing algorithms that are

based on a current workload demanded by a client. These approaches require high

computational power and additional time to meet the demands of dynamic cloud

services. This paper introduces a rule-based workload-balancing algorithm based on

the predictions of an end-to-end system called Cicada. A simulation of cloud services

can be achieved by a cloud service simulator called CloudSim and it will be used to

achieve an algorithm with lower computational demand and a faster workload

balancing. The final result will demonstrate the effectiveness of a predictive workload

balancing approach that can achieve faster workload balancing with a lower

computational power usage.