Task scheduling algorithms for multi-cloud systems: allocation-aware approach

Sanjaya K. Panda (), Indrajeet Gupta () and Prasanta K. Jana ()
Additional contact information
Sanjaya K. Panda: Veer Surendra Sai University of Technology
Indrajeet Gupta: Indian Institute of Technology (ISM)
Prasanta K. Jana: Indian Institute of Technology (ISM)

Information Systems Frontiers, 2019, vol. 21, issue 2, No 2, 259 pages

Abstract: Abstract Cloud computing has gained enormous popularity for on-demand services on a pay-per-use basis. However, a single data center may be limited in providing such services, particularly in the peak demand time as it may not have unlimited resource capacity. Therefore, multi-cloud environment has been introduced in which multiple clouds can be integrated together to provide a unified service in a collaborative fashion. However, task scheduling in such environment is much more challenging than that is used in the single cloud environment. In this paper, we propose three allocation-aware task scheduling algorithms for a multi-cloud environment. The algorithms are based on the traditional Min-Min and Max-Min algorithm and extended for multi-cloud environment. All the algorithms undergo three common phases, namely matching, allocating and scheduling to fit them in the multi-cloud environment. We perform extensive simulations on the proposed algorithms and test with various benchmark and synthetic datasets. We evaluate the performance of the proposed algorithms in terms of makespan, average cloud utilization and throughput and compare the results with the existing algorithms in such system. The comparison results clearly demonstrate the efficacy of the proposed algorithms.

Keywords: Cloud computing; Batch scheduling; Multi-cloud environment; Min-Min; Max-Min; Makespan; Average cloud utilization (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10796-017-9742-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:infosf:v:21:y:2019:i:2:d:10.1007_s10796-017-9742-6

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10796

DOI: 10.1007/s10796-017-9742-6

Access Statistics for this article

Information Systems Frontiers is currently edited by Ram Ramesh and Raghav Rao

More articles in Information Systems Frontiers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().