Skip to main content

Advertisement

Placement of applications in computing clouds using Voronoi diagrams

Article metrics

Abstract

The vision of millions of users launching tens of millions of applications running on millions of globally scattered servers presents a new challenge for Cloud Providers: how to assign so many virtual applications to physical servers, while meeting latency needs, improving network utilization, and satisfying availability constraints. Today’s application placement puts too much burden on the cloud user, lacks scalability and inhibits the global reach of Public Clouds.

The size, breadth, and dynamic nature of Public Clouds present a special challenge to the task of placement. Cloud Providers able to provide rapid decisions and frequent optimizations for placement will have significant competitive advantage. Not only will they provide the best customer experience, their costs will be lower as they make more efficient use of their network resources. However, given the calculations required, data structures and the algorithms used to process location-based decisions must be as globally scalable as the Public Clouds themselves.

In our study, we define a novel data structure, the Virtual Cloud Model, for modeling global cloud resources. We adapt a well-known geometric device, Voronoi Diagrams, and combine it with near-real-time network latency information. We then solve the application placement problem and suggest an API for Cloud Providers to support both “low-latency” and “high-availability” applications. The algorithms are scalable, parallelizable and distributable.

References

  1. 1.

    Agarwal S, Dunagan J, Jain N, Saroiu S, Wolman A, Bhogan H Volley: Automated data placement for geo-distributed cloud services. In: Proceedings of 7th USENIX symposium on networked systems design and implementation (NSDI)

  2. 2.

    Aurenhammer F (1991) Voronoi diagrams—a survey of a fundamental geometric data structure. ACM Comput Surv 23(3):345–405

  3. 3.

    Bae SW, Chwa K-Y (2004) Voronoi diagrams with a transportation network on the Euclidean plane. In: Algorithms and computation: 15th international symposium, ISAAC, Hong Kong, 2004, pp 101–124

  4. 4.

    Beaumont O et al (2006) VoroNet: a scalable object network based on Voronoi tessellations. Laboratoire de l’Informatique du Parallélisme, Lyon, France, RR2006-11

  5. 5.

    Brakke KA (1985) Statistics of random plane Voronoi tessellations. Techn Report, Department of Math Sciences, Susquehanna University, Selinsgrove

  6. 6.

    Calka P (2003) An explicit expression of the distribution of the number of sides of the typical Poisson? Voronoi Cell Adv Appl Probab 35:863–870

  7. 7.

    Clark C et al. (2005) Live migration of virtual machines. In: USENIX NSDI

  8. 8.

    Demers A, Gealy M, Greene D, Hauser C, Irish W, Larson J (1989) Epidemic algorithms for replicated database maintenance. Association of Computing Machinery, New York

  9. 9.

    Devillers O, Pion S, Teillaud M (2001) Walking in a triangulation. In: 17th annual ACM symposium on computational geometry (SCG), pp 106–114

  10. 10.

    Fortune S (1992) Voronoi diagrams and Delaunay triangulations. In: Hwang F, Du D-Z (eds) Computing in Euclidean geometry. World Scientific, Singapore, pp 193–233

  11. 11.

    Hinde AL, Miles RE (1980) Monte Carlo estimates of the distributions of the random polygons of the Voronoi tessellation with respect to a Poisson process. J Stat Comput Simul 10:205–223

  12. 12.

    Hu S-Y, Liao G-M (2004) Scalable peer-to-peer networked virtual environment. In: Proc ACM SIGCOMM workshop on NetGames, pp 129–133

  13. 13.

    Icking C et al. (2003) Java applets for the dynamic visualization of Voronoi diagrams. In: Lecture notes in computer science, pp 191–205

  14. 14.

    Jin H, Liu H, Liao X, Hu L, Li P (2009) Live migration of virtual machine based on full-system trace and replay. In: HPDC ’09 proceedings of the 18th ACM international symposium on high performance distributed computing. ACM, New York

  15. 15.

    Khanban A, Edalat A, Lieutier A et al. (2002) Computability of partial Delaunay triangulation and Voronoi diagram. In: Computability and complexity in analysis, CCA 2002, July 2002

  16. 16.

    Kozuch M, Gass R, Ryan M (2009) Tashi: Location-aware cluster management. In: International conference on autonomic computing. ACM, New York, pp 43–48

  17. 17.

    Kramer L, Barbulescu L, Smith S Understanding performance tradeoffs in algorithms for solving oversubscribed scheduling. The Robotics Institute, Carnegie Mellon University

  18. 18.

    Le K, Zhang J, Meng J, Bianchini R, Nguyen T, Jaluria Y (2010) Reducing electricity cost through virtual machine placement in high performance computing clouds. Technical Report DCS-TR-680, Dept of Computer Science, Rutgers University, November 2010

  19. 19.

    Lee D, Lam SS (2008) Efficient and accurate protocols for distributed Delaunay triangulation under churn. In: Network protocols, ICNP 2008, pp 124–136

  20. 20.

    Meijering JL (1953) Interface area, edge length, and number of vertices in crystal aggregates with random nucleation. Philips Res Rep 8:270–290

  21. 21.

    Meyerhenke H (2005) Constructing higher-order Voronoi diagrams in parallel. In: EWCG, pp 123–126

  22. 22.

    Møller J (1994) Lectures on random Voronoi tessellations. Springer, New York

  23. 23.

    Na H-S, Lee C-N, Cheong O (2002) Voronoi diagrams on the sphere. In: Computational geometry: theory and applications, September 2002, pp 183–194

  24. 24.

    Okabe A, Boots B, Sugihara K (1992) Spatial tessellations: concepts and applications of Voronoi diagrams. Wiley, Chichester

  25. 25.

    Oppenheimer D, Chun B, Patterson D, Snoeren A, Vahdat A (2006) Service placement in a shared wide-area platform. In: ATEC ’06 proceedings of the annual conference on USENIX ’06 annual technical conference. USENIX Association, Berkeley

  26. 26.

    Ostrobsky-Berman Y (2005) Computing transportation Voronoi diagram in optimal time. In: EWCG, pp 159–162

  27. 27.

    Ostrovsky-Berman Y (2003) Transportation Voronoi diagrams. Leibniz Center for Research in Computer Science, Jerusalem

  28. 28.

    Sapuntzakis C, Chandra R, Pfaff B, Chow J, Lam S, Rosenblum M (2002) Optimizing the migration of virtual computers. In: Proceedings of the 5th symposium on operating systems design and implementation (OSDI’02), December 8–11, 2002, Boston, MA, USA

  29. 29.

    Sedgewick R (1990) Algorithms in C. Addison-Wesley, Reading

  30. 30.

    Setzer T, Stage A (2009) Network-aware migration control and scheduling of differentiated virtual machine workloads. In: CLOUD ’09 proceedings of the 2009 ICSE workshop on software engineering challenges of cloud computing. IEEE Computer Society, Washington

  31. 31.

    Shamos M, Hoey D (1975) Closest-point problems. In: Proc 16th annu IEEE sympos found comput sci, pp 151–162

  32. 32.

    Sriram I, Khajeh-Hosseini A (2010) Research agenda in cloud technologies. In: 1st ACM symposium on cloud computing, SOCC. ACM, New York

  33. 33.

    Tanemura M (2003) Statistical distributions of Poisson Voronoi cells in two and three dimensions. Forma 18(4):221–247

  34. 34.

    VoroGlide (2010) Interactive Voronoi diagrams. Cooperative systems [Online]. http://www.pi6.fernuni-hagen.de/GeomLab/VoroGlide/

Download references

Author information

Correspondence to Pavel Bleher.

Rights and permissions

Reprints and Permissions

About this article

Keywords

  • Cloud computing
  • Application placement
  • Virtual cloud model
  • Voronoi diagram