article

A composite particle swarm optimization approach for the composite SaaS placement in cloud environment

Authors:

Mohamed Amin Hajji,

Haithem MezniAuthors Info & Claims

Soft Computing - A Fusion of Foundations, Methodologies and Applications, Volume 22, Issue 12

Pages 4025 - 4045

https://doi.org/10.1007/s00500-017-2613-8

Published: 01 June 2018 Publication History

Abstract

Cloud computing has emerged as a new powerful service delivery model to cope with resource challenges and to offer on-demand various types of services (e.g., software, storage, network). One of the most popular service models is Software as a Service (SaaS). To allow flexibility and reusability, SaaS can be offered in a composite form, where a set of interacting application and data components cooperate to form a higher-level functional SaaS. However, this approach introduces new challenges to resource management in the cloud, especially finding the optimal placement for SaaS components to have the best possible SaaS performance. SaaS Placement Problem (SPP) refers to this challenge of determining which servers in the cloud's data center can host which components without violating SaaS constraints. Most existing SPP approaches only addressed homogenous SaaS components placement and only considered one type of constraints (i.e., resource constraint). In addition, none of them has considered the objective of maintaining a good machine performance by minimizing the resource usage for the hosting machines. To allow finding the optimal placement of a composite SaaS, we adopt a new variation of PSO called 'Particle Swarm Optimization with Composite Particle (PSO-CP).' In the proposed PSO-CP-based approach, each composite particle in the swarm represents a candidate SaaS placement scheme. Composite particles adopt a collective behavior to explore and evaluate the search space (i.e., data center) and adjust their structures by collaborating with other composite or independent particles (i.e., servers). The implementation and experimental results show the feasibility and efficiency of the proposed approach.

References

[1]

Bhardwaj S (2015) Service level agreement aware SaaS placement in cloud. Master's thesis, National Institute of Technology Rourkela, India, May 2015. Supervised by: Bibhudatta Sahoo.

[2]

Bowen Y, Shaochun W (2012) An adaptive simulated annealing genetic algorithm for the data placement problem in SaaS. In: Industrial control and electronics engineering (ICICEE), 2012 international conference on. IEEE, pp 1037-1043.

[3]

Candan KS, Li W-S, Phan T, Zhou M (2011) At the frontiers of information and software as services. In: New Frontiers in information and software as services. Springer, pp 283-300.

[4]

Cisco (2008) Cisco service-oriented network architecture: support and optimize soa and web 2.0 applications. Technical report, Cisco Inc.

[5]

Cisco (2015) IDC report, the new need for speed in the datacenter network. Technical report, Cisco Inc. Accessed 24 May 2016.

[6]

Huang K-C, Shen B-J (2015) Service deployment strategies for efficient execution of composite SaaS applications on cloud platform. J Syst Softw 107:127-141.

Digital Library

[7]

Kichkaylo T, Ivan A, Karamcheti V (2003) Constrained component deployment in wide-area networks using AI planning techniques. In: Parallel and distributed processing symposium, 2003. Proceedings of the International. IEEE.

[8]

Kumar A (2014) Placement of software-as-a-service components in cloud computing environment. Master's thesis, National Institute of Technology Rourkela, India, June 2014. Supervised by: Bibhudatta Sahoo.

[9]

Kwok T, Mohindra A (2008) Resource calculations with constraints, and placement of tenants and instances formulti-tenant saas applications. In: Service-oriented computing--ICSOC 2008. Springer, pp 633-648.

[10]

Liu L, Yang S, Wang D (2010) Particle swarm optimization with composite particles in dynamic environments. IEEE Trans Syst Man Cybern B Cybern 40(6):1634-1648.

Digital Library

[11]

Liu Z, Hu Z, Jonepun LK (2014) Research on composite SaaS placement problem based on ant colony optimization algorithm with performance matching degree strategy. J Digit Inf Manag 12(4):225-234.

[12]

Lodi A, Martello S, Vigo D (2002) Heuristic algorithms for the three-dimensional bin packing problem. Eur J Oper Res 141(2):410-420.

[13]

Mell P, Timothy G (2011) The NIST definition of cloud computing. Technical report, National Institute of Standards and Technology.

[14]

Minas L, Ellison B (2015) The problem of power consumption in servers. Technical report, Intel Corporation. Accessed 08 June 2016.

[15]

Ni ZW, Pan XF, Wu ZJ (2012) An ant colony optimization for the composite saas placement problem in the cloud. In: Applied mechanics and materials, volume 130. Trans Tech Publications, Switzerland, pp 3062-3067.

[16]

RightScale I (2016) Rightscale 2016 state of the cloud report. Technical report, RightScale Inc.

[17]

Rosendo M, Pozo A (2010) Applying a discrete particle swarm optimization algorithm to combinatorial problems. In: Neural Networks (SBRN), 2010 Eleventh Brazilian Symposium on. IEEE, pp 235-240.

[18]

Statista I (2016) Software as a service (SaaS) subscription revenue from 2012 to 2016 by category (in billion u.s. dollars). http://www.statista.com/statistics/468649/saas-software-subscription-revenue-by-category/. Accessed 22 March 2016.

[19]

Talbi E-G, Guzek M, Bouvry P (2015) A survey of evolutionary computation for resource management of processing in cloud computing [review article]. IEEE Comput Intell Mag 10(2):53-67.

[20]

Tang M, Yusoh ZIM (2012) A parallel cooperative co-evolutionary genetic algorithm for the composite saas placement problem in cloud computing. In: Parallel Problem Solving from Nature-PPSN XII. Springer, pp 225-234.

[21]

Tang K, Yang J, Chen H, Gao S (2011) Improved genetic algorithm for nonlinear programming problems. J Syst Eng Electron 22(3):540- 546.

[22]

Urgaonkar B, Rosenberg A, Shenoy P (2004) Application placement on a cluster of servers. Int J Found Comput Sci 18:1023-1041.

[23]

Yang X, Yuan J, Yuan J, Mao H (2007) A modified particle swarm optimizer with dynamic adaptation. Appl Math Comput 189(2):1205- 1213.

[24]

Yusoh ZIM (2013) Composite SaaS resource management in cloud computing using evolutionary computation. PhD thesis, Science and Engineering Faculty Queensland University of Technology Brisbane, Australia.

[25]

Yusoh ZIM, Tang M (2010a) A penalty-based genetic algorithm for the composite SaaS placement problem in the cloud. In: Evolutionary Computation (CEC), 2010 IEEE Congress on. IEEE, pp 1-8.

[26]

Yusoh ZIM, Tang M (2010b) A cooperative coevolutionary algorithm for the composite saas placement problem in the cloud. In: Neural Information Processing. Theory and Algorithms. Springer, pp 618-625.

Cited By

Liao PPan GWang BHe XPeng WFang MHuang FChen YCheng Y(2023)Efficient Proactive Resource Allocation for Multi-stage Cloud-Native MicroservicesAlgorithms and Architectures for Parallel Processing10.1007/978-981-97-0801-7_24(411-432)Online publication date: 20-Oct-2023
https://dl.acm.org/doi/10.1007/978-981-97-0801-7_24
Chakravarthi KShyamala L(2022)TOPSIS inspired Budget and Deadline Aware Multi-Workflow Scheduling for Cloud computingJournal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2020.101916114:COnline publication date: 3-Jan-2022
https://dl.acm.org/doi/10.1016/j.sysarc.2020.101916
Massa J(2022)Data-Aware Application Placement and Management in the Cloud-IoT ContinuumService-Oriented Computing – ICSOC 2022 Workshops10.1007/978-3-031-26507-5_24(301-307)Online publication date: 29-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-26507-5_24
Show More Cited By

A composite particle swarm optimization approach for the composite SaaS placement in cloud environment
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies

Recommendations

A multi-swarm based approach with cooperative learning strategy for composite SaaS placement
SAC '17: Proceedings of the Symposium on Applied Computing

This paper explores one of the critical issues, SaaS placement in cloud data centers, for reducing execution time of composite SaaS applications. We adopt a multi-swarm variant of Particle Swarm Optimization (PSO) to propose a service placement method. ...
Security‐aware SaaS placement using swarm intelligence
Abstract
Cloud computing has emerged as a new powerful service delivery model to cope with resource challenges and to offer various on‐demand services (eg, software, storage, network, etc.). Software as a Service (SaaS) is one of the most popular service ...
SLA-Based Resource Allocation for Software as a Service Provider (SaaS) in Cloud Computing Environments
CCGRID '11: Proceedings of the 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

Cloud computing has been considered as a solution for solving enterprise application distribution and configuration challenges in the traditional software sales model. Migrating from traditional software to Cloud enables on-going revenue for software ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Soft Computing - A Fusion of Foundations, Methodologies and Applications

Soft Computing - A Fusion of Foundations, Methodologies and Applications Volume 22, Issue 12

June 2018

335 pages

ISSN:1432-7643

EISSN:1433-7479

Issue’s Table of Contents

Copyright © Copyright © 2018 Springer-Verlag GmbH Germany, part of Springer Nature.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 June 2018

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 24 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Liao PPan GWang BHe XPeng WFang MHuang FChen YCheng Y(2023)Efficient Proactive Resource Allocation for Multi-stage Cloud-Native MicroservicesAlgorithms and Architectures for Parallel Processing10.1007/978-981-97-0801-7_24(411-432)Online publication date: 20-Oct-2023
https://dl.acm.org/doi/10.1007/978-981-97-0801-7_24
Chakravarthi KShyamala L(2022)TOPSIS inspired Budget and Deadline Aware Multi-Workflow Scheduling for Cloud computingJournal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2020.101916114:COnline publication date: 3-Jan-2022
https://dl.acm.org/doi/10.1016/j.sysarc.2020.101916
Massa J(2022)Data-Aware Application Placement and Management in the Cloud-IoT ContinuumService-Oriented Computing – ICSOC 2022 Workshops10.1007/978-3-031-26507-5_24(301-307)Online publication date: 29-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-26507-5_24
Wen YCheng GDeng SYin J(2022)Microservice Workflow Modeling for Affinity Scheduling to Improve the QoSWeb and Big Data10.1007/978-3-031-25158-0_24(313-328)Online publication date: 11-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-25158-0_24
Jatoth CGangadharan GFiore UBuyya R(2019)SELCLOUDSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3120-223:13(4701-4715)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s00500-018-3120-2

View Options

View options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents