Improved bee swarm optimization algorithm for load scheduling in cloud computing environment

Divya Chaudhary; Bijendra Kumar; Sakshi Sakshi; Rahul Khanna

doi:10.1007/978-981-10-8527-7_33

Improved bee swarm optimization algorithm for load scheduling in cloud computing environment

Divya Chaudhary, Bijendra Kumar, Sakshi Sakshi, Rahul Khanna

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

The cloud acts as a model that contains an aggregation of resources and data that needs to be shared among users. The scheduling of the load acts as a major challenge to fulfill the requests of the several users. Till now several algorithms have been proposed for fulfilling the purpose of load scheduling in cloud. The latest works are based on swarm-intelligence techniques. However, one such swarm-intelligence technique Bee Swarm Optimization (BSO) has not been exploited for serving this purpose. In this paper, an improvised version of BSO, the Improved Bee Swarm Optimization in Cloud (IBSO-C) has been proposed with the objective of efficient and cost-effective scheduling in cloud. It uses the swarm of particles as bees for scheduling and updated total cost evaluation function. The proposed algorithm is validated and tested by analysis on large set of iterations. The comparison of results with existing techniques has proven, the proposed IBSO-C to be a more cost-effective algorithm.

Original language	English
Title of host publication	Data Science and Analytics - 4th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2017, Revised Selected Papers
Editors	Brajendra Panda, Nihar Ranjan Roy, Sudeep Sharma
Publisher	Springer Verlag
Pages	400-413
Number of pages	14
ISBN (Print)	9789811085260
DOIs	https://doi.org/10.1007/978-981-10-8527-7_33
State	Published - 1 Jan 2018
Externally published	Yes
Event	4th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2017 - Gurgaon, India Duration: 13 Oct 2017 → 14 Oct 2017

Publication series

Name	Communications in Computer and Information Science
Volume	799
ISSN (Print)	1865-0929

Conference

Conference	4th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2017
Country/Territory	India
City	Gurgaon
Period	13/10/17 → 14/10/17

Keywords

BSO
Cloud computing
Load scheduling
PSO
Swarm intelligence

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-981-10-8527-7_33

Cite this

Chaudhary, D., Kumar, B., Sakshi, S., & Khanna, R. (2018). Improved bee swarm optimization algorithm for load scheduling in cloud computing environment. In B. Panda, N. R. Roy, & S. Sharma (Eds.), Data Science and Analytics - 4th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2017, Revised Selected Papers (pp. 400-413). (Communications in Computer and Information Science; Vol. 799). Springer Verlag. https://doi.org/10.1007/978-981-10-8527-7_33

Chaudhary, Divya ; Kumar, Bijendra ; Sakshi, Sakshi et al. / Improved bee swarm optimization algorithm for load scheduling in cloud computing environment. Data Science and Analytics - 4th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2017, Revised Selected Papers. editor / Brajendra Panda ; Nihar Ranjan Roy ; Sudeep Sharma. Springer Verlag, 2018. pp. 400-413 (Communications in Computer and Information Science).

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title = "Improved bee swarm optimization algorithm for load scheduling in cloud computing environment",

abstract = "The cloud acts as a model that contains an aggregation of resources and data that needs to be shared among users. The scheduling of the load acts as a major challenge to fulfill the requests of the several users. Till now several algorithms have been proposed for fulfilling the purpose of load scheduling in cloud. The latest works are based on swarm-intelligence techniques. However, one such swarm-intelligence technique Bee Swarm Optimization (BSO) has not been exploited for serving this purpose. In this paper, an improvised version of BSO, the Improved Bee Swarm Optimization in Cloud (IBSO-C) has been proposed with the objective of efficient and cost-effective scheduling in cloud. It uses the swarm of particles as bees for scheduling and updated total cost evaluation function. The proposed algorithm is validated and tested by analysis on large set of iterations. The comparison of results with existing techniques has proven, the proposed IBSO-C to be a more cost-effective algorithm.",

keywords = "BSO, Cloud computing, Load scheduling, PSO, Swarm intelligence",

author = "Divya Chaudhary and Bijendra Kumar and Sakshi Sakshi and Rahul Khanna",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2018.; 4th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2017 ; Conference date: 13-10-2017 Through 14-10-2017",

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Chaudhary, D, Kumar, B, Sakshi, S & Khanna, R 2018, Improved bee swarm optimization algorithm for load scheduling in cloud computing environment. in B Panda, NR Roy & S Sharma (eds), Data Science and Analytics - 4th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2017, Revised Selected Papers. Communications in Computer and Information Science, vol. 799, Springer Verlag, pp. 400-413, 4th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2017, Gurgaon, India, 13/10/17. https://doi.org/10.1007/978-981-10-8527-7_33

Improved bee swarm optimization algorithm for load scheduling in cloud computing environment. / Chaudhary, Divya; Kumar, Bijendra; Sakshi, Sakshi et al.
Data Science and Analytics - 4th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2017, Revised Selected Papers. ed. / Brajendra Panda; Nihar Ranjan Roy; Sudeep Sharma. Springer Verlag, 2018. p. 400-413 (Communications in Computer and Information Science; Vol. 799).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Improved bee swarm optimization algorithm for load scheduling in cloud computing environment

AU - Chaudhary, Divya

AU - Kumar, Bijendra

AU - Sakshi, Sakshi

AU - Khanna, Rahul

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2018.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - The cloud acts as a model that contains an aggregation of resources and data that needs to be shared among users. The scheduling of the load acts as a major challenge to fulfill the requests of the several users. Till now several algorithms have been proposed for fulfilling the purpose of load scheduling in cloud. The latest works are based on swarm-intelligence techniques. However, one such swarm-intelligence technique Bee Swarm Optimization (BSO) has not been exploited for serving this purpose. In this paper, an improvised version of BSO, the Improved Bee Swarm Optimization in Cloud (IBSO-C) has been proposed with the objective of efficient and cost-effective scheduling in cloud. It uses the swarm of particles as bees for scheduling and updated total cost evaluation function. The proposed algorithm is validated and tested by analysis on large set of iterations. The comparison of results with existing techniques has proven, the proposed IBSO-C to be a more cost-effective algorithm.

AB - The cloud acts as a model that contains an aggregation of resources and data that needs to be shared among users. The scheduling of the load acts as a major challenge to fulfill the requests of the several users. Till now several algorithms have been proposed for fulfilling the purpose of load scheduling in cloud. The latest works are based on swarm-intelligence techniques. However, one such swarm-intelligence technique Bee Swarm Optimization (BSO) has not been exploited for serving this purpose. In this paper, an improvised version of BSO, the Improved Bee Swarm Optimization in Cloud (IBSO-C) has been proposed with the objective of efficient and cost-effective scheduling in cloud. It uses the swarm of particles as bees for scheduling and updated total cost evaluation function. The proposed algorithm is validated and tested by analysis on large set of iterations. The comparison of results with existing techniques has proven, the proposed IBSO-C to be a more cost-effective algorithm.

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BT - Data Science and Analytics - 4th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2017, Revised Selected Papers

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A2 - Roy, Nihar Ranjan

A2 - Sharma, Sudeep

PB - Springer Verlag

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Y2 - 13 October 2017 through 14 October 2017

ER -

Chaudhary D, Kumar B, Sakshi S, Khanna R. Improved bee swarm optimization algorithm for load scheduling in cloud computing environment. In Panda B, Roy NR, Sharma S, editors, Data Science and Analytics - 4th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2017, Revised Selected Papers. Springer Verlag. 2018. p. 400-413. (Communications in Computer and Information Science). doi: 10.1007/978-981-10-8527-7_33

Improved bee swarm optimization algorithm for load scheduling in cloud computing environment

Abstract

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Conference

Keywords

ASJC Scopus subject areas

Access to Document

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