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

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 app...

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Bibliographic Details
Published in:Future internet Vol. 11; no. 5; p. 109
Main Authors: Al-Rahayfeh, Amer, Atiewi, Saleh, Abuhussein, Abdullah, Almiani, Muder
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.05.2019
Subjects:
Algorithms
Bandwidths
Cloud computing
Clustering
Computer centers
DSC algorithm
Energy consumption
Information technology
Internet
Kernel functions
Load
Load balancing
Methods
MHEFT algorithm
Motivation
MSC algorithm
Optimization algorithms
ranking
Resource utilization
Response time
Schedules
Scheduling
Servers
Task scheduling
Virtual environments
VMs
WLC algorithm
ISSN:1999-5903, 1999-5903
Online Access:Get full text
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Summary: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.
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ISSN:1999-5903
1999-5903
DOI:10.3390/fi11050109