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...

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Vydané v:The Journal of supercomputing Ročník 71; číslo 4; s. 1505 - 1533
Hlavní autori: Panda, Sanjaya K., Jana, Prasanta K.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Boston Springer US 01.04.2015
Predmet:
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
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Shrnutí: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.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-014-1376-6