Skip to main content
Log in

Determining Server Locations in Server Migration Service to Minimize Monetary Penalty of Dynamic Server Migration

Journal of Network and Systems Management Aims and scope Submit manuscript

Abstract

In this paper, we propose a new class of service called server migration service (SMS) to augment the existing IaaS (Infrastructure as a Service). SMS allows servers (server-side processes of a network application) to dynamically and automatically migrate as their clients (client-side processes of a network application) change their locations in order to reduce the total monetary penalty that the SMS provider pays to its SMS subscribers when failing to provide them with the guaranteed level of QoS. In this paper, we consider the monetary impact that arises from QoS degradation due to server migration and build an integer programming model to determine when and to which location servers should migrate to minimize the total monetary penalty incurred by the SMS provider. Numerical examples show that SMS achieves up to 96% lower total monetary penalty compared to that without server migration. Numerical examples also show that the integer programming model developed in this paper requires reasonable computation time under realistic parameter settings.

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

Access this article

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

Notes

  1. The latter is a multiple provider model. In the multiple provider model, the SMS provider is a different entity from the underlying network service provider. In this paper, however, we do not consider the multiple provider model.

  2. JPN (Japan Photonic Network) [30] is a network model created by the Photonic Network Committee of the IEICEJ (Japanese counterpart of the IEEE) and closely resembles an existing nation-wide network operated by a Japanese Telecom Company. This network model includes explicit values of network parameters (e.g., node locations, physical distances of links), which are often proprietary and not disclosed.

  3. The assumption of the negligible packet transmission time is realistic, as the Internet link speed is becoming faster and faster. For instance, with a typical link speed of 10 Gbits/sec in the Internet [31], transmission time of a 12000 bit (1500 byte) packet, a typical packet length [32], becomes 0.0012 ms, much smaller than the propagation delays assumed in this section. Similarly, the assumption of negligible queuing delay is realistic, as it is often reported that a very small buffer (e.g., a buffer for 10-20 packets) is sufficient for a core router to achieve high TCP throughputs [33]. Small buffer yields negligible queuing delay at routers.

  4. See Sect. 3.2 for justification of these assumptions.

References

  1. Amazon EC2: https://aws.amazon.com/ec2. Accessed 8 Aug 2017

  2. Google Computer Engine: https://cloud.google.com/products/compute-engine. Accessed 8 Aug 2017

  3. Microsoft Azure: https://azure.microsoft.com/en-us/. Accessed 8 Aug 2017

  4. Armbrust, M., Fox, A., Griffith, R., Joseph, D.A., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., Zaharia, M.: A view of cloud computing. Commun. ACM 53, 50–58 (2010)

    Article  Google Scholar 

  5. Mell, P., Grance, T.: The NIST definition of cloud computing. http://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-145.pdf. Accessed 25 Oct 2016

  6. Lecture2: Kinds of delays, http://faculty.ycp.edu/~dhovemey/fall2005/cs375/lecture/9-7-2005.html. Accessed 8 Aug 2017

  7. How much network latency is “typical” for east–west coast USA?. https://serverfault.com/questions/137348/how-much-network-latency-is-typical-for-east-west-coast-usa. Accessed 8 Aug 2017

  8. Clincy, V., Wilgor, B.: Subjective evaluation of latency and packet loss in a cloud-based game. In: 10th International Conference on Information Technology: New Generations, pp. 473–476 (2013)

  9. Chen, K., Xu, K., Xi, K., Chao, J.H.: Intelligent virtual machine placement for cost efficiency in geo-distributed cloud systems. In: IEEE International Conference on Communications (ICC), pp. 3498–3503 (2013)

  10. Anan, M., Nasser, N.: SLA-based optimization of energy efficiency for green cloud computing. In: IEEE Global Communications Conference (GLOBECOM), pp. 1–6 (2015)

  11. Anan, M., Nasser, N., Ahmed, A., Alfuqaha, A.: Optimization of power and migration cost in virtualized data centers. In: IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–5 (2016)

  12. Harrison, G.C., Chess, M.D., Kershenbaum, A.: Mobile agents: Are they a good idea? Technical report. IBM Research Division (1995)

  13. Lange, B.D., Oshima, M.: Seven good reasons for mobile agents. Commun. ACM 42(3), 88–89 (1999)

    Article  Google Scholar 

  14. Hara, T., Tsukamoto, M., Nishio, S.: A scheduling method of database migration for WAN environments. In: Brazilian Symposium on Database (SBBD), pp. 125–136 (1999)

  15. Ranjan, S., Rolia, J., Fu, H., Knightly, E.: QoS-driven server migration for Internet data centers. In: Tenth International Workshop on Quality of Service (IWQoS), pp. 3–12 (2002)

  16. Ichihara, H., Koizumi, Y., Ohsaki, H., Hato, K., Murayama, J., Imase, M.: On the integrated control of virtual machine live migration and traffic engineering for cloud computing. In: IEEE Global Communications Conference (GLOBECOM), pp. 1629–1634 (2012)

  17. Alicherry, M., Lakshman, V. T.: Network aware resource allocation in distributed clouds. In: IEEE Conference on Computer Communications (INFOCOM), pp. 963–971 (2012)

  18. AWS global infrastructure: https://aws.amazon.com/jp/about-aws/global-infrastructure/. Accessed 8 Aug 2017

  19. Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of the MCC Workshop on Mobile Cloud Computing, pp. 13–16 (2012)

  20. Mahmud, R., Buyya, R.: Fog Computing: A Taxonomy, Survey and Future Directions. arXiv preprint arXiv: 1611, 05539 (2016)

  21. Quadrio, G., Bujari, A., Palazzi, E.C., Ronzani, D., Maggiorini, D., Ripamonti, A.L.: Network analysis of the steam in-home streaming game system. In: The 22nd Annual International Conference on Mobile Computing and Networking (MobiCom), pp. 475–476 (2016)

  22. Lee, S.C.: The revolution of StarCraft network traffic. In: The 11th Annual Workshop on Network and System Support for Games (NetGames), Article No. 18 (2012)

  23. Nathan, S., Kulkarni, P., Bellur, U.: Resource availability based performance benchmarking of virtual machine migrations. In: The 4th ACM/SPEC International Conference on Performance Engineering (ICPE), pp. 387–398 (2013)

  24. Migrating an Instance to Another Availability Zone: http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-regions-availability-zones.html#migrating-instance-availability-zone. Accessed 8 Aug 2017

  25. Amazon EC2 service level agreement: http://aws.amazon.com/ec2-sla/. Accessed 8 Aug 2017

  26. Google Compute Engine Service Level Agreement: https://cloud.google.com/compute/sla. Accessed 8 Aug 2017

  27. Lee, E.K., Gallagher, R.J., Silvern, D., Wuu, C.S., Zaider, M.: Treatment planning for brachytherapy: an integer programming model, two computational approaches and experiments with permanent prostate implant planning. Phys. Med. Biol. 44(1), 145 (1999)

    Article  Google Scholar 

  28. Meng, X., Pappas, V., Zhang, L.: Improving the scalability of data center networks with traffic-aware virtual machine placement. In: IEEE Conference on Computer Communications (INFOCOM) (2010)

  29. Ramaswami, R., Sivarajan, N.K.: Design of logical topologies for wavelength-routed optical networks. IEEE J. Sel. Areas Commun. 14(5), 840–851 (1996)

    Article  Google Scholar 

  30. Japan Photonic Network Model: http://www.ieice.org/cs/pn/jpn/jpnm.html. Accessed 8 Aug 2017

  31. Xue, L., Cui, C., Kumar, S.: Experimental evaluation of the effect of queue management schemes on the performance of high speed TCPs in 10Gbps network environment. In: International Conference on Computing, Networking, and Communications (ICNC), pp. 315–319 (2012)

  32. Vishwanath, A, Zhu, J, Hinton, K, Ayre, R, Tucker, R.: Estimating the energy consumption for packet processing, storage and switching in optical-IP routers. In: Optical Fiber Communication Conference (OFC), paper OM3A.6 (2013)

  33. Enachescu, M., Ganjali, Y., Goel, A., McKeown, N., Roughgarden, T.: Part III: Routers with very small buffers. SIGCOMM Comp Comm rev 35, 83–90 (2005)

    Article  Google Scholar 

  34. IBM ILOG CPLEX Optimizer: http://www-01.ibm.com/software/integration/optimization/cplex-optimizer/. Accessed 8 Aug 2017

  35. Woeginger, G.J.: Exact Algorithms for NP-hard Problems: A Survey. Lecture Notes in Computer Science 2570, 185–207 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  36. Arora, S.: Approximation Schemes for NP-hard Geometric Optimization Problems: A Survey. Mathematical Programming 97, 43–69 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  37. What is Docker: https://www.docker.com/what-docker. Accessed 8 Aug 2017

Download references

Acknowledgements

This research and development work was supported by the MIC/SCOPE #162108003.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yukinobu Fukushima.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fukushima, Y., Murase, T., Motoyoshi, G. et al. Determining Server Locations in Server Migration Service to Minimize Monetary Penalty of Dynamic Server Migration. J Netw Syst Manage 26, 993–1033 (2018). https://doi.org/10.1007/s10922-018-9451-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10922-018-9451-6

Keywords

Navigation