Efficient task scheduling algorithms for heterogeneous multi-cloud environment

Cloud Computing has grown exponentially in the business and research community over the last few years. It is now an emerging field and becomes more popular due to recent advances in virtualization technology. In Cloud Computing, various applications are submitted to the datacenters to obtain some s...

Full description

Saved in:
Bibliographic Details
Published in:The Journal of supercomputing Vol. 71; no. 4; pp. 1505 - 1533
Main Authors: Panda, Sanjaya K., Jana, Prasanta K.
Format: Journal Article
Language:English
Published: Boston Springer US 01.04.2015
Subjects:
Algorithms
Business
Cloud computing
Compilers
Computer Science
Heterogeneity
Interpreters
Processor Architectures
Programming Languages
Scheduling
Task scheduling
Utilization
Workload
Multi-cloud environment
Task scheduling
Cloud computing
Makespan
Cloud utilization
ISSN:0920-8542, 1573-0484
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Cloud Computing has grown exponentially in the business and research community over the last few years. It is now an emerging field and becomes more popular due to recent advances in virtualization technology. In Cloud Computing, various applications are submitted to the datacenters to obtain some services on pay-per-use basis. However, due to limited resources, some workloads are transferred to other data centers to handle peak client demands. Therefore, scheduling workloads in heterogeneous multi-cloud environment is a hot topic and very challenging due to heterogeneity of the cloud resources with varying capacities and functionalities. In this paper, we present three task scheduling algorithms, called MCC, MEMAX and CMMN for heterogeneous multi-cloud environment, which aim to minimize the makespan and maximize the average cloud utilization. The proposed MCC algorithm is a single-phase scheduling whereas rests are two-phase scheduling. We perform rigorous experiments on the proposed algorithms using various benchmark as well as synthetic datasets. Their performances are evaluated in terms of makespan and average cloud utilization and experimental results are compared with that of existing single-phase and two-phase scheduling algorithms to demonstrate the efficacy of the proposed algorithms.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-014-1376-6