Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

New approach based on group technology for the consolidation problem in cloud computing-mathematical model and genetic algorithm

  • Published:
Computational and Applied Mathematics Aims and scope Submit manuscript

Abstract

Resource management is a hotspot issue in distributed systems like cloud computing (CC). It means how to prepare the computational resources, i.e., servers and virtual machines (VMS), to execute the tasks. This paper offers a new approach based on Group Technology (GT)—known as a powerful philosophy for the resource management in cellular manufacturing systems—to deal with the resource management problem in CC. We develop a mathematical model to optimally consolidate the VMs, servers and tasks simultaneously to control several important factors such as task migrations and server load variation, as well as the number of VMs. To test the validity of our proposed model, several small problems are generated randomly and solved by LINGO 9 software. Furthermore, to cope with larger problems, which cannot be solved optimally, a genetic algorithm is proposed. We, finally, compare our methods with the most well-known algorithms in this context, round robin (RR) and first-come, first-served (FCFS) algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24

Similar content being viewed by others

References

  • Ajit M, Vidya G (2013) VM level load balancing in cloud environment. In: IEEE 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT). Tiruchengode, pp 1–5

  • Bagley JD (1976) The behavior of adaptive systems which employ genetic and correlation algorithms. Dissertation, University of Michigan

  • Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Futur Gen Comput 28:755–768

    Article  Google Scholar 

  • Bhatia J, Patel T, Trivedi H, Majmudar V (2012) HTV dynamic load balancing algorithm for virtual machine instances in cloud. In: IEEE 2012 International Symposium on Cloud and Services Computing (ISCOS. Mangalore, IN, pp 15–20

  • Burbidge J (1971) Production flow analysis. Prod Eng 50:139–152

    Article  Google Scholar 

  • Buyya R, Broberg J, Goscinski A (2011) Cloud computing principles and paradigms. Wiley, New Jersey, Hoboken

  • Chan F, Lau K, Chan L (2008) Cell formation problem with consideration of both intracellular and intercellular movements. Int J Prod Res 46:2589–2620

    Article  MATH  Google Scholar 

  • Chang H, Tang X (2011) A load-balance based resource-scheduling algorithm under cloud computing environment. In: Algorithm Under CloNew Horizons in Web-Based Learning-ICWL, (2010) Workshops. Springer, IN, pp 85–90

  • Chun HC, Goh C-H, Lee A (1996) Solving the generalized machine assignment problem in group technology. J Oper Res Soc 47:794–802

    Article  MATH  Google Scholar 

  • Dasgupta K, Mandal B, Dutta P, Mandal KJ, Dam S (2013) A genetic algorithm (GA) based load balancing strategy for cloud computing. First Int Conf Comput Intell Model Tech Appl (CIMTA) 10:340–347

    Google Scholar 

  • Dhinesh Babua L, Venkata Krishnab P (2013) Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl Soft Comput 13:2292–2303

    Article  Google Scholar 

  • Ergu D, Kou G, Peng Y, Yong S, Yu S (2013) The analytic hierarchy process: task scheduling and resource allocation in cloud computing environment. J Supercomput 64:835–848

    Article  Google Scholar 

  • Fang Y, Wang F, Junwei G (2010) A task scheduling algorithm based on load balancing in cloud computing. In: International Conference on Web Information Systems and Mining, China. Springer, pp 271–277

  • Gkatzikis L, Koutsopoulos I (2013) Migrate or not? Exploiting dynamic task migration in mobile cloud computing systems. Wirel Commun 20:24–32

    Article  Google Scholar 

  • Gkatzikis L, Koutsopoulos I (2014) Mobiles on cloud nine: efficient task migration policies for cloud computing systems. In: 2014 IEEE 3rd International Conference on Cloud Networking (CloudNet). Luxembourg, pp 204–210

  • Gutierrez-Garcia JO, Sim KM (2012) GA-based cloud resource estimation for agent-based execution of bag-of-tasks applications. Inf Syst Front 14:925–951

    Article  Google Scholar 

  • Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. MIT Press, Cambridge

  • Hu J, Gu J, Sun G, Zhao T (2010) A scheduling strategy on load balancing of virtual machine resources in cloud computing environment. In: IEEE The 3rd International Symposium on Parallel Architectures, Algorithms and Programming (PAAP). Dalian, pp 89–96

  • Kumar P, Verma A (2012) Independent task scheduling in cloud computing by improved genetic algorithm. Int J Adv Res Comput Sci Softw Eng 2:111–114

    Google Scholar 

  • Lei D, Wu Z (2005) Tabu search approach based on a similarity coefficient for cell formation in generalized group technology. Int J Prod Res 43:4035–4047

    Article  MATH  Google Scholar 

  • Li C-C, Wang K (2014) An SLA-aware load balancing scheme for cloud datacenters. In: 2014 International Conference on Information Networking (ICOIN). Phuket, pp 58–63

  • Liu Z, Wang X (2012) A PSO-based algorithm for load balancing in virtual machines of cloud computing environment. In: Third International Conference on Advances in Swarm Intelligence. Springer, Shenzhen, pp 142–147

  • Ma R, Wang C-L (2012) Lightweight application-level task migration for mobile cloud computing. In: 2012 IEEE 26th International Conference on Advanced Information Networking and Applications (AINA). Fukuoka, pp 550–557

  • Mahdavi I, Mahadevan B (2008) CLASS: an algorithm for cellular manufacturing system and layout design using sequence data. Robot Comput Integr Manuf 28:488–497

    Article  Google Scholar 

  • Mahdavi I, Shirazi B, Paydar MM (2008) A flow matrix-based heuristic algorithm for cell formation and layout design in cellular manufacturing system. Int J Adv Manuf Technol 39:943–953

    Article  Google Scholar 

  • Mahdavi I, Teymourian E, Tahami Baher N, Kayvanfar V (2013) An integrated model for solving cell formation and cell layout problem simultaneously considering new situations. J Manuf Syst 32:655–663

    Article  Google Scholar 

  • Mahdavi N, Tahami Baher N, Teymourian E (2010) A new cell formation problem with the consideration of multifunctional machines and in-route machines dissimilarity—a two phase solution approach. In: 17th International Conference on Industrial Engineering and Engineering Management (IE&EM). Xiamen, pp 475–479

  • Mahesh O, Srinivasan G (2002) Incremental cell formation considering alternative machines. Int J Prod Res 40:3291–3310

    Article  MATH  Google Scholar 

  • Mhedheb Y, Jrad F, Tao J, Zhao Kołodziej J (2013) Load and thermal-aware VM scheduling on the cloud. In: 13th International Conference on Algorithms and Architectures for Parallel Processing. Springer, Vietri sul Mare, pp 101–114

  • Mitrofanov SP (1966) The scientific principles of group technology. National Lending Library Translation, Boston Spa, York’s

  • Mondal B, Dasgupta K, Dutta P (2012) Load balancing in cloud computing using stochastic hill climbing—a soft computing approach. In: 2nd International Conference on Computer, Communication, Control and Information Technology. Elsevier, pp 783–789

  • Nair G, Narendran T (2010) CASE: a clustering algorithm for cell formation with sequence data. Int J Prod Res 36:157–180

    Article  MATH  Google Scholar 

  • Pandey S, Wu L, Guru S, Buyya R (2010) A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: IEEE 2010 24th IEEE International Conference on Advanced Information Networking and Applications (AINA). Perth, IN, pp 400–407

  • Ramakrishnan L, Gannon D (2008) A survey of distributed workflow characteristics and resource requirements. Indiana University, Department of Computer Science, Bloomington

  • Ramezani F, Lu J, Hussain FK (2013) Task-based system load balancing in cloud computing using particle swarm optimization. Int J Parallel Program 42:739–754

    Article  Google Scholar 

  • Rosenberg R (1970) Simulation of genetic population with biochemical properties. In: The University of Michigan, The Communication and Computer Sciences Department. Elsevier, Ann Arbor

  • Santhosh R, Ravichandran T (2013) Pre-emptive scheduling of on-line real time services with task migration for cloud computing. In: IEEE 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME). Salem, pp 271–276

  • Selvarani S, Sadhasivam G. S (2010) Improved cost-based algorithm for task scheduling in cloud computing. In: 2010 IEEE International Conference on Computational Intelligence and Computing Research. Coimbatore, India, pp 1–5

  • Shahdi-Pashaki S, Teymourian E, Kayvanfar V, Komaki G, Sajadi A (2015) Group technology-based model and cuckoo optimization algorithm. Int Fed Autom Control Conf Submiss Rev Manag Syst 48:1140–1145

    Google Scholar 

  • Shahvari O, Salmasi N, Logendran R, Abbasi B (2012) An efficient tabu search algorithm for flexible flow shop sequence-dependent group scheduling problems. Int J Prod Res 50(15):4237–4254

    Article  Google Scholar 

  • Shahvari O, Logendran R (2016) Hybrid flow shop batching and scheduling with a bi-criteria objective. Int J Prod Econ 179:239–258

  • Solimanpur M, Vrat P, Shankar R (2004) A multi-objective genetic algorithm approach to the design of cellular manufacturing systems. Int J Prod Res 42:1419–1441

    Article  MATH  Google Scholar 

  • Song B, Wang D, Shen D, Wang G (2003) An efficient user task handling mechanism based on dynamic load-balance for workflow systems. In: IEEE 5th Asia-Pacific Web Conference on Web Technologies and Applications. Xian, pp 483–494

  • Soni G, Kalra M (2014) A novel approach for load balancing in cloud data center. In: 2014 IEEE International Advance Computing Conference (IACC). Gurgaon, pp 807–812

  • Srinivasan R, Suma V, Nedu V (2013) An enhanced load balancing technique for efficient load distribution in cloud-based IT industries. Adv Intell Syst Comput 182:479–485

    Google Scholar 

  • Tavakkoli-Moghaddam R, Aryanezhad M, Safaei N, Azaron A (2005) Solving a dynamic cell formation problem using metaheuristics. Appl Math Comput 170:761–780

  • Tavakkoli-Moghaddam R, Safaei N, Sassani F (2007) A new solution for a dynamic cell formation problem with alternative routing and machine costs using simulated annealing. J Oper Res Soc 59:443–454

    Article  MATH  Google Scholar 

  • Wei G, Vasilakos A, Zheng Y, Xiong N (2010) A game-theoretic method of fair resource allocation for cloud computing services. J Supercomput 54:252–269

    Article  Google Scholar 

  • Wemmerlov U, Hyer N (1989) Cell manufacturing in the US industry: a survey of users. Int J Prod Res 27:1511–1530

    Article  Google Scholar 

  • Xu B, Zhao C, Hu E, Hu B (2011) Job scheduling algorithm based on Berger model in cloud environment. Adv Eng Softw 42:419–425

    Article  Google Scholar 

  • Yasuda K, Hu L, Yin Y (2005) A grouping genetic algorithm for the multi-objective cell formation problem. Int J Prod Res 38(2):829–853

    Article  Google Scholar 

  • Zhang Z, Xiao L, Tao Y, Tian J, Wang S, Liu H (2014) A model based load-balancing method in IaaS cloud. In: 2014 IEEE International on Advance Computing Conference (IACC). Gurgaon, IN, pp 807–812

  • Zhao C, Wu Z (2010) A genetic algorithm for manufacturing cell formation with multiple routes and multiple objectives. Int J Prod Res 38:385–395

    Article  MATH  Google Scholar 

  • Zhong H, Tao K, Zhang X (2010) An approach to optimized resource scheduling algorithm for open-source cloud systems . In: IEEE The Fifth Annual China Grid Conference (China Grid). Guangzhou, IN, pp 124–129

  • Zhu K, Song H, Liu L, Gao J, Cheng G (2011) Hybrid genetic algorithm for cloud computing applications. In: IEEE. Jeju Island, IN, pp 182–187

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, Shomal University, Amol, Iran

    S. Shahdi-Pashaki

  2. School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR, 97331-6001, USA

    Ehsan Teymourian

  3. School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Reza Tavakkoli-Moghaddam

Authors
  1. S. Shahdi-Pashaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ehsan Teymourian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Reza Tavakkoli-Moghaddam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ehsan Teymourian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shahdi-Pashaki, S., Teymourian, E. & Tavakkoli-Moghaddam, R. New approach based on group technology for the consolidation problem in cloud computing-mathematical model and genetic algorithm. Comp. Appl. Math. 37, 693–718 (2018). https://doi.org/10.1007/s40314-016-0362-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40314-016-0362-4

Keywords

Search

Navigation