Cluster Computing

, Volume 22, Issue 3, pp 757–782 | Cite as

Power-network aware VM migration heuristics for multi-tier web applications

  • Amir Hossein BorhaniEmail author
  • Terence Hung
  • Bu-Sung Lee
  • Zheng Qin


Cloud computing has become an attractive and promising platform, offering on-demand resources for multi-tier web applications. However, an inappropriate and inefficient resource management practices may negatively affect the service level agreement (SLA) and the response time experienced by users, essentially for high load operating conditions. Furthermore, this may result in substantial amount of energy consumption in data centers, which consequently leads to a high operational cost. This paper proposes an effective power-network aware virtual machine (VM) migration heuristics to deal with high SLA violation (SLAV) and energy consumption. Our research consists of two parts. The first part introduces a network-aware VM migration algorithm. The algorithm considers steady-state traffic condition to minimize the negative effect of migration on other flows. The network gain (NG) is calculated for candidate VMs and the VM with the maximum NG is selected. The second part, extends the network-aware algorithm with energy-awareness capabilities. In addition to NG, power gain (PG) is calculated for each candidate VM and two lists are created for each congested link: NG list and PG list. The VM with the lowest sum of the rank is selected. An extensive simulation is done in CloudSim. The results show that the power-network aware algorithm can reduce the energy consumption without significant increase in SLAV. This research enables us to take a step further towards building low latency, energy-efficient and environment-friendly data centers running network intensive applications.


Resource management Energy efficiency and management Service level agreement Multi-tier web applications Network-aware VM migration Energy-aware VM migration 



This research was granted by Agency for Science, Technology and Research (A*Star) of Singapore, under A*STAR Thematic Programme: User and Domain Driven Data Analytics as a Service framework (SERC 1021580034) Grant. The authors would like to acknowledge Associate Professor Anis Yazidi of the Department of Computer Science, University College of Oslo and Akershus of Applied Sciences, Oslo, Norway, who kindly gave advice and help.


  1. 1.
    Temiño, V.M., Wu, P., Konig, J., Fahrenholz, J.M.: Safety of multiple aeroallergen rush immunotherapy using a modified schedule. In: Proceedings of the Allergy and Asthma Proceedings, vol. 34, pp. 255–260 . OceanSide Publications Inc. (2013)Google Scholar
  2. 2.
    Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. J. Internet Serv. Appl. 1(1), 7–18 (2010)CrossRefGoogle Scholar
  3. 3.
    Bittencourt, L.F., Madeira, E.R.M.: HCOC: a cost optimization algorithm for workflow scheduling in hybrid clouds. J. Internet Serv. Appl. 2(3), 207–227 (2011)CrossRefGoogle Scholar
  4. 4.
    Amazon Elastic Compute Cloud (EC2). (2016)
  5. 5.
    Google APP Engine. (2016)
  6. 6.
    Microsoft Azure. (2016)
  7. 7.
    Liu, H., He, B.: Vmbuddies: coordinating live migration of multi-tier applications in cloud environments. IEEE Trans. Parallel Distrib. Syst. 26(4), 1192–1205 (2015)CrossRefGoogle Scholar
  8. 8.
    Wood, T., Ramakrishnan, K., Shenoy, P., Van der Merwe, J., Hwang, J., Liu, G., Chaufournier, L.: Cloudnet: dynamic pooling of cloud resources by live wan migration of virtual machines. IEEE/ACM Trans. Netw. (TON) 23(5), 1568–1583 (2015)CrossRefGoogle Scholar
  9. 9.
    Mastroianni, C., Meo, M., Papuzzo, G.: Probabilistic consolidation of virtual machines in self-organizing cloud data centers. IEEE Trans. Cloud Comput. 1(2), 215–228 (2013)CrossRefGoogle Scholar
  10. 10.
    Iqbal, W., Dailey, M.N., Carrera, D., Janecek, P.: Adaptive resource provisioning for read intensive multi-tier applications in the cloud. Future Gener. Comput. Syst. 27(6), 871–879 (2011)CrossRefGoogle Scholar
  11. 11.
    Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28(5), 755–768 (2012)CrossRefGoogle Scholar
  12. 12.
    Guenter, B., Jain, N., Williams, C.: Managing cost, performance, and reliability tradeoffs for energy-aware server provisioning. In: Proceedings of the IEEE International Conference on Computer Communications (INFOCOM’11), Shanghai, China, pp. 1332–1340 (2011)Google Scholar
  13. 13.
    Bansal, N., Lee, KW., Nagarajan, V., Zafer, M.: Minimum congestion mapping in a cloud. In: Proceedings of the ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing (PODC’11), San Jose, CA, pp. 267–276 (2011)Google Scholar
  14. 14.
    Mann, V., Kumar, A., Dutta, P., Kalyanaraman, S.: VMFlow: leveraging VM mobility to reduce network power costs in data centers. In: Proceedings of the International IFIP TC 6 Networking Conference (NETWORKING’11), Valencia, Spain, pp. 198–211 (2011)Google Scholar
  15. 15.
    Meng, X., Pappas, V., Zhang, L.: Improving the scalability of data center networks with traffic-aware virtual machine placement. In: Proceedings of the IEEE International Conference on Computer Communications (INFOCOM’10), San Diego, CA, pp. 1–9. (2010)Google Scholar
  16. 16.
    Sonnek, J., Greensky, J., Reutiman, R., Chandra, A.: Starling: minimizing communication overhead in virtualized computing platforms using decentralized affinity-aware migration. In: Proceedings of the International Conference on Parallel Processing (ICPP’10), San Diego, CA, pp 228–237 (2010)Google Scholar
  17. 17.
    Mann, V., Gupta, A., Dutta, P., Vishnoi, A., Bhattacharya, P., Poddar, R., Iyer, A.: Remedy: network-aware steady state VM management for data centers. In: Proceedings of the International IFIP TC 6 Networking Conference (NETWORKING’12), Prague, Czech Republic, pp. 190–204 (2012)Google Scholar
  18. 18.
    Vu, H.T., Hwang, S.: A traffic and power-aware algorithm for virtual machine placement in cloud data center. Int. J. Grid Distrib. Comput. 7(1), 350–355 (2014)CrossRefGoogle Scholar
  19. 19.
    Kliazovich, D., Bouvry, P., Khan, S.U.: DENS: data center energy-efficient network-aware scheduling. Clust. Comput. 16(1), 65–75 (2013)CrossRefGoogle Scholar
  20. 20.
    Alhiyari, S., El-Mousa, A.: A network and power aware framework for data centers using virtual machines re-allocation. In: Proceedings of the 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), pp. 1–6. IEEE (2015)Google Scholar
  21. 21.
    Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr. Comput. Pract. Exp. 24(13), 1397–1420 (2012)CrossRefGoogle Scholar
  22. 22.
    Chen, M.T., Hsu, C.C., Kuo, M.S., Cheng, Y.J., Chou, C.F.: GreenGlue: power optimization for data centers through resource-guaranteed VM placement. In: Proceedings of the IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom), IEEE and Cyber, Physical and Social Computing (CPSCom), Taipei, Taiwan, pp. 510–517 (2014)Google Scholar
  23. 23.
    Rong, H., Zhang, H., Xiao, S., Li, C., Hu, C.: Optimizing energy consumption for data centers. Renew. Sustain. Energy Rev. 58, 674–691 (2016)CrossRefGoogle Scholar
  24. 24.
    Standard Performance Evaluation Corporation, 2008. (2016)
  25. 25.
    Jimenez-Peris, R., Patiño-Martinez, M., Kemme, B., Perez-Sorrosal, F., Serrano, D.: A system of architectural patterns for scalable, consistent and highly available multi-tier service-oriented infrastructures. In: Proceedings of the Architecting Dependable Systems VI, Springer, pp 1–23 (2009)Google Scholar
  26. 26.
    Huang, D., He, B., Miao, C.: A survey of resource management in multi-tier web applications. IEEE Commun. Surv. Tutor. 16(3), 1574–1590 (2014)CrossRefGoogle Scholar
  27. 27.
    Liu, X., Heo, J., Sha, L.: Modeling 3-tiered web applications. In: Proceedings of the 13th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, pp. 307–310. IEEE (2005)Google Scholar
  28. 28.
    Arlitt, M.F., Williamson, C.L.: Internet web servers: workload characterization and performance implications. IEEE/ACM Trans. Netw. 5(5), 631–645 (1997)CrossRefGoogle Scholar
  29. 29.
    Garg, S.K., Buyya, R.: NetworkCloudSim: modelling parallel applications in cloud simulations. In: Proceedings of the IEEE International Conference on Utility and Cloud Computing (UCC’11), Melbourne, Australia, pp. 105–113 (2011)Google Scholar
  30. 30.
    Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)CrossRefGoogle Scholar
  31. 31.
    UMass Trace Repository, 2009. (2016)
  32. 32.
    Repository of Availability Traces, 2010. (2016)
  33. 33.
    Park, K., Pai, V.S.: CoMon: a mostly-scalable monitoring system for PlanetLab. ACM SIGOPS Oper. Syst. Rev. 40(1), 65–74 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Amir Hossein Borhani
    • 1
    Email author
  • Terence Hung
    • 2
  • Bu-Sung Lee
    • 1
  • Zheng Qin
    • 3
  1. 1.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.Rolls-Royce Pte LtdSingaporeSingapore
  3. 3.Institute of High-Performance Computing (IHPC)SingaporeSingapore

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