Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

Amer Al-Rahayfeh, Saleh Atiewi, Abdullah Abuhussein and Muder Almiani
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Amer Al-Rahayfeh: Department of Computer Science, Al-Hussein Bin Talal University, Ma’an 71111, Jordan
Saleh Atiewi: Department of Computer Science, Al-Hussein Bin Talal University, Ma’an 71111, Jordan
Abdullah Abuhussein: Department of Information Systems, St. Cloud State University, St. Cloud, MN 56301, USA
Muder Almiani: Department of Computer Information Systems, Al-Hussein Bin Talal University, Ma’an 71111, Jordan

Future Internet, 2019, vol. 11, issue 5, 1-15

Abstract: Cloud computing (CC) is fast-growing and frequently adopted in information technology (IT) environments due to the benefits it offers. Task scheduling and load balancing are amongst the hot topics in the realm of CC. To overcome the shortcomings of the existing task scheduling and load balancing approaches, we propose a novel approach that uses dominant sequence clustering (DSC) for task scheduling and a weighted least connection (WLC) algorithm for load balancing. First, users’ tasks are clustered using the DSC algorithm, which represents user tasks as graph of one or more clusters. After task clustering, each task is ranked using Modified Heterogeneous Earliest Finish Time (MHEFT) algorithm. where the highest priority task is scheduled first. Afterwards, virtual machines (VM) are clustered using a mean shift clustering (MSC) algorithm using kernel functions. Load balancing is subsequently performed using a WLC algorithm, which distributes the load based on server weight and capacity as well as client connectivity to server. A highly weighted or least connected server is selected for task allocation, which in turn increases the response time. Finally, we evaluate the proposed architecture using metrics such as response time, makespan, resource utilization, and service reliability.

Keywords: cloud computing; task scheduling; DSC algorithm; ranking; MHEFT algorithm; VMs; MSC algorithm; load balancing; WLC algorithm (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
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