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Peer-to-Peer Cloud Provisioning: Service Discovery and Load-Balancing

  • Rajiv RanjanEmail author
  • Liang Zhao
  • Xiaomin Wu
  • Anna Liu
  • Andres Quiroz
  • Manish Parashar
Chapter
Part of the Computer Communications and Networks book series (CCN)

Abstract

Clouds have evolved as the next-generation platform that facilitates creation of wide-area on-demand renting of computing or storage services for hosting application services that experience highly variable workloads and requires high availability and performance. Interconnecting Cloud computing system components (servers, virtual machines (VMs), application services) through peer-to-peer routing and information dissemination structure are essential to avoid the problems of provisioning efficiency bottleneck and single point of failure that are predominantly associated with traditional centralized or hierarchical approaches. These limitations can be overcome by connecting Cloud system components using a structured peer-to-peer network model (such as distributed hash tables (DHTs)). DHTs offer deterministic information/query routing and discovery with close to logarithmic bounds as regards network message complexity. By maintaining a small routing state of O (log n) per VM, a DHT structure can guarantee deterministic look-ups in a completely decentralized and distributed manner. This chapter presents: (i) a layered peer-to-peer Cloud provisioning architecture; (ii) a summary of the current state-of-the-art in Cloud provisioning with particular emphasis on service discovery and load-balancing; (iii) a classification of the existing peer-to-peer network management model with focus on extending the DHTs for indexing and managing complex provisioning information; and (iv) the design and implementation of novel, extensible software fabric (Cloud peer) that combines public/private clouds, overlay networking, and structured peer-to-peer indexing techniques for supporting scalable and self-managing service discovery and load-balancing in Cloud computing environments. Finally, an experimental evaluation is presented that demonstrates the feasibility of building next-generation Cloud provisioning systems based on peer-to-peer network management and information dissemination models. The experimental test-bed has been deployed on a public cloud computing platform, Amazon EC2, which demonstrates the effectiveness of the proposed peer-to-peer Cloud provisioning software fabric.

Keywords

Cloud Computing Service Discovery Overlay Network Distribute Hash Table Public Cloud 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Armbrust M, Fox A, Griffith R, Joseph A, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2009) Above the clouds: a Berkeley view of cloud computing. University of California at Berkley, USA. Technical Rep UCB/EECS-2009-28Google Scholar
  2. 2.
    The Reservoir Seed Team (2008) Reservoir – an ICT infrastructure for reliable and effective delivery of services as utilities. IBM Res Rep H-0262Google Scholar
  3. 3.
    Buyya R, Yeo C, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gen Comput Syst 25:599–616CrossRefGoogle Scholar
  4. 4.
    Google (2009) Google App Engine. https://appengine.google.com/. Accessed 16 Dec 2009Google Scholar
  5. 5.
    Ultra Serve Internet Pty Ltd (2009) Rejila On Demand Cloud Computing Servers. http://www.rejila.com/. Accessed 14 Dec 2009Google Scholar
  6. 6.
    Rackspace US, Inc. (2009) The Rackspace Cloud. http://www.rackspacecloud.com. Accessed 12 Dec 2009Google Scholar
  7. 7.
    Microsoft (2009) Windows Azure Platform, http://www.microsoft.com/windowsazure/. Accessed 12 Dec 2009Google Scholar
  8. 8.
    Amazon Web Services LLC (2009) Amazon Elastic Compute Cloud, http://aws.amazon.com/ec2/. Accessed 16 Dec 2009Google Scholar
  9. 9.
    Salesforce.com (2009) Application Development with Force.com’s Cloud Computing Platform http://www.salesforce.com/platform/. Accessed 16 Dec 2009Google Scholar
  10. 10.
    Quiroz A, Kim H, Parashar M, Gnanasambandam N, Sharma N (2009) Towards autonomic workload provisioning for enterprise grids and clouds. In: Proceedings of the 10th IEEE/ACM international conference on grid computing, Banf, Alberta, Canada, 13–15 Oct 2009, IEEE Computer Society PressGoogle Scholar
  11. 11.
    Amazon Web Services LLC (2009) Amazon CloudWatch. http://aws.amazon.com/cloudwatch/. Accessed 22 Sept 2009Google Scholar
  12. 12.
    Amazon Web Services LLC (2009) Elastic Load Balancer http://aws.amazon.com/elasticloadbalancing/. Accessed 22 Sept 2009Google Scholar
  13. 13.
    Rochwerger B, Breitgand D, Levy E, Galis A, Nagin K, Llorente L, Montero R, Wolfsthal Y, Elmroth E, Caceres J, Ben-Yehuda M, Emmerich W, Galan F (2009) The reservoir model and architecture for open federated cloud computing. IBM Syst J 53Google Scholar
  14. 14.
    Chu X et al (2007) Aneka: next-generation enterprise grid platform for e-Science and e-Business applications. In: Proceedings of the 3rd IEEE international conference on e-Science and grid computing, Bangalore, IndiaGoogle Scholar
  15. 15.
    Ranjan R, Chan L, Harwood A, Karunasekera S, Buyya R (2007) Decentralised resource discovery service for large scale federated grids. In: Proceedings of the 3rd IEEE international conference on eScience and grid computing (eScience’07), Bangalore, India, IEEE Computer Society, Los Alamitos, CAGoogle Scholar
  16. 16.
    Eucalyptus Systems Inc (2009) Eucalyptus Systems. http://www.eucalyptus.com/. Accessed 22 Sept 2009Google Scholar
  17. 17.
    Amazon Web Services LLC (2009) Auto Scaling. http://aws.amazon.com/autoscaling/. Accessed 22 Sept 2009Google Scholar
  18. 18.
    GoGrid Cloud Hosting (2009) F5 Load Balancer. GoGrid Wiki. http://wiki.gogrid.com/wiki/index.php/(F5)_Load_Balancer. Accessed 21 Sept 2009Google Scholar
  19. 19.
    Bakhtiari S, Safavi-Naini R, Pieprzyk J (1995) Cryptographic Hash Functions: A Survey. http://citeseer.ist.psu.edu/bakhtiari95cryptographic.html. Accessed 22 Sept 2009Google Scholar
  20. 20.
    Balakrishnan H, Kaashoek MF, Karger D, Morris R, Stoica I (2003) Looking up data in peer-to-peer systems. Commun ACM 46(2):43–48CrossRefGoogle Scholar
  21. 21.
    Karger D, Lehman E, Leighton T, Panigrahy R, Levine M, Lewin D (1997) Consistent hashing and random trees: distributed caching protocols for relieving hot spots on the World Wide Web. In: Proceedings of the 29th annual ACM symposium on theory of computing (STOC ’97), New York. ACM Press, pp 654–663Google Scholar
  22. 22.
    Preneel B (1999) The state of cryptographic hash functions. In: Lectures on data security. Modern cryptology in theory and practice. Springer, London, pp 158–182Google Scholar
  23. 23.
    Lua K, Crowcroft J, Pias M, Sharma R, Lim S (2005) A survey and comparison of peer-to-peer overlay network schemes. IEEE Commun Surv Tutorials 7(2), IEEE Communications Society Press, Washington DC, USAGoogle Scholar
  24. 24.
    Li J, Stribling J, Gil TM, Morris R, Frans Kaashoek M (2004) Comparing the performance of distributed hash tables under churn. In: Proceedings of the 3rd international workshop on peer-to-peer systems (IPTPS04), San Diego, CAGoogle Scholar
  25. 25.
    Bharambe A, Agarwal M, Seshan S (2004) Mercury: supporting scalable multi-attribute range queries. In: Proceedings of SIGCOMM 2004 (SIGCOMM’04). ACM, Portland, ORGoogle Scholar
  26. 26.
    Castro M, Costa M, Rowstron A (2004) Should we build Gnutella on a structured overlay? SIGCOMM Comput Commun Rev 34(1):131–136CrossRefGoogle Scholar
  27. 27.
    Linga P, Demers A, Gupta I, Birman K, van R (2003) Kelips: building an efficient and stable peer-to-peer DHT through increased memory and background overhead. In: Proceedings of the 2nd international workshop on peer-to-peer systems (IPTPS03), Berkeley, CAGoogle Scholar
  28. 28.
    Spence D, Crowcroft J, Hand S, Harris T (2005) Location based placement of Whole Distributed Systems. In: Proceedings of the 2005 ACM conference on Emerging network experiment and technology (CoNEXT’05). ACM Press, New York, pp 124–134Google Scholar
  29. 29.
    Ganesan P, Yang B, Garcia-Molina H (2004) One torus to rule them all: multi-dimensional queries in peer-to-peer systems. In: Proceedings of the 7th International Workshop on the Web and Databases (WebDB ’04). ACM Press, New York, pp 19–24Google Scholar
  30. 30.
    Ranjan R, Harwood A, Buyya R (2008) Peer-to-peer based resource discovery in global grids: a tutorial. IEEE Commun Surv Tutorials 10(2):6–33CrossRefGoogle Scholar
  31. 31.
    Samet H (1989) The design and analysis of spatial data structures. Addison–Wesley, Reading, MAGoogle Scholar
  32. 32.
    Rowstron A, Druschel P (2001) Pastry: scalable, decentralized object location, and routing for large-scale peer-to-peer systems. In: IFIP/ACM international conference on distributed system platforms, HeidelbergGoogle Scholar
  33. 33.
    Ranjan R (2007) Coordinated resource provisioning in federated grids. Ph.D. thesis, The University of MelbourneGoogle Scholar
  34. 34.
    Tanin E, Harwood A, Samet H (2007) Using a distributed quadtree index in peer-to-peer networks. VLDB J 16(2):165–178, Springer, New YorkCrossRefGoogle Scholar
  35. 35.
    Gupta A, Sahin OD, Agrawal D, Abbadi AEl (2004) Meghdoot: content-based publish/subscribe over peer-to-peer networks. In: Proceedings of the 5th ACM/IFIP/USENIX international conference on Middleware (Middleware ’04), New York. Springer, New York, pp 254–273Google Scholar

Copyright information

© Springer London 2010

Authors and Affiliations

  • Rajiv Ranjan
    • 1
    Email author
  • Liang Zhao
  • Xiaomin Wu
  • Anna Liu
  • Andres Quiroz
  • Manish Parashar
  1. 1.SA Project, CRC Smart Services, Service Oriented Computing Research Group, School of Computer Science and EngineeringUniversity of New South WalesPaddingtonAustralia

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