Peer-to-Peer Networking and Applications

, Volume 8, Issue 6, pp 938–951 | Cite as

Task allocation in volunteer computing networks under monetary budget constraints

  • Huseyin Guler
  • B. Barla Cambazoglu
  • Oznur Ozkasap
Article
  • 197 Downloads

Abstract

In volunteer computing networks, the peers contribute to the solution of a computationally intensive problem by freely providing their computational resources, i.e., without seeking any immediate financial benefit. In such networks, although the peers can set certain bounds on how much their resources can be exploited by the network, the monetary cost that the network brings to the peers is unclear. In this work, we propose a volunteer computing network where the peers can set monetary budgets, limiting the financial burden incurred on them due the usage of their computational resources. Under the assumption that the price of the electricity consumed by the peers has temporal variation, we show that our approach leads to an interesting task allocation problem, where the goal is to maximize the amount of work done by the peers without violating the monetary budget constraints set by them. We propose various heuristics as solution to the problem, which is NP-hard. Our extensive simulations using realistic data traces and real-life electricity prices demonstrate that the proposed techniques considerably increase the amount of useful work done by the peers, compared to a baseline technique.

Keywords

Volunteer computing network Electricity market Optimization Heuristics 

References

  1. 1.
    Anderson D (2011) Emulating volunteer computing scheduling policies. In: Proceedings of the IEEE international symposium on parallel and distributed processing workshops and PhD Forum, pp 1839–1846Google Scholar
  2. 2.
    Anderson DP (2007) Local scheduling for volunteer computing. In: Proceedings of the IEEE international symposium on parallel and distributed processing, pp 1–8Google Scholar
  3. 3.
    Babaioff M, Immorlica N, Kempe D, Kleinberg R (2007) A knapsack secretary problem with applications. In: Approximation, randomization, and combinatorial optimization. Algorithms and techniques, pp 16–28Google Scholar
  4. 4.
    Babaoglu O, Jelasity M, Kermarrec A, Montresor A, Van Steen M (2006) Managing clouds: a case for a fresh look at large unreliable dynamic networks. In: Operating systems review, pp 9–13Google Scholar
  5. 5.
    Babaoglu O, Marzolla M, Tamburini M (2011) Design and implementation of a P2P cloud system. Tech. Rep. UBLCS-2011-10, Department of Computer Science. University of BolognaGoogle Scholar
  6. 6.
    Barroso LA, Hölzle U (2009) The datacenter as a computer: an introduction to the design of warehouse-scale machines, 1st edn. Morgan and Claypool PublishersGoogle Scholar
  7. 7.
    Bhagwan R, Savage S, Voelker GM (2003) Understanding availability. In: Proceedings of the 2nd international workshop on peer-to-peer systems, pp 256–267Google Scholar
  8. 8.
    Böckenhauer HJ, Komm D, Královič R, Rossmanith P (2012) On the advice complexity of the knapsack problem. In: Proceedings of the 10th Latin American international conference on theoretical informatics, pp 61–72Google Scholar
  9. 9.
    Costa F, Silva L, Dahlin M (2011) Volunteer cloud computing: Map Reduce over the internet. In: IEEE international symposium on parallel and distributed processing workshops and Phd Forum, pp 1855–1862Google Scholar
  10. 10.
    Cunsolo V, Distefano S, Puliafito A, Scarpa M (2009) Cloud@home: bridging the gap between volunteer and cloud computing. In: Emerging intelligent computing technology and applications, pp 423–432Google Scholar
  11. 11.
    Cunsolo VD, Distefano S, Puliafito A, Scarpa M (2009) Volunteer computing and desktop cloud: The cloud@home paradigm. In: Proceedings of the 8th IEEE international symposium on network computing and applications, pp 134–139Google Scholar
  12. 12.
    Estrada T, Flores DA, Taufer M, Teller PJ, Kerstens A, Anderson DP (2006) The effectiveness of threshold-based scheduling policies in BOINC projects. In: Proceedings of the 2nd IEEE international conference on e-science and grid computing, p 88Google Scholar
  13. 13.
    Lombraña González D, Grey F, Blomer J, Buncic P, Harutyunyan A, Marquina M, Segal B, Skands P, Karneyeu A (2012) Virtual machines and volunteer computing: Experience from lhc@home: Test4theory project. In: The international symposium on grids and clouds, pp 36–49Google Scholar
  14. 14.
    Himyr N, Blomer J, Buncic P, Giovannozzi M, Gonzalez A, Harutyunyan A, Jones PL, Karneyeu A, Marquina MA, Mcintosh E, Segal B, Skands P, Grey F, Gonzlez DL, Zacharov I (2012) BOINC service for volunteer cloud computing. J Phys Conf Ser 396 (3)Google Scholar
  15. 15.
    Kayaaslan E, Cambazoglu BB, Blanco R, Junqueira FP, Aykanat C (2011) Energy-price-driven query processing in multi-center web search engines. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval, pp 983–992Google Scholar
  16. 16.
    Kondo D, Anderson DP, Vii JM (2007) Performance evaluation of scheduling policies for volunteer computing. In: Proceedings of the 3rd IEEE international conference on e-Science and grid computing, pp 415–422Google Scholar
  17. 17.
    Kondo D, Javadi B, Malecot P, Cappello F, Anderson D (2009) Cost-benefit analysis of cloud computing versus desktop grids. In: IEEE international symposium on parallel distributed processing, pp 1–12Google Scholar
  18. 18.
    Le K, Bianchini R, Martonosi M, Nguyen TD (2009) Cost- and energy-aware load distribution across data centers. In: Workshop on power aware computing and systemsGoogle Scholar
  19. 19.
    León X, Navarro L (2011) Limits of energy saving for the allocation of data center resources to networked applications. In: Proceedings of the 30th ieee international conference on computer communications, pp 216–220Google Scholar
  20. 20.
    Marchetti-Spaccamela A, Vercellis C (1995) Stochastic on-line knapsack problems. Math Program 68(1–3):73–104MathSciNetMATHGoogle Scholar
  21. 21.
    Panzieri F, Babaoglu O, Ghini V, Ferretti S, Marzolla M (2011) Distributed computing in the 21st century: some aspects of cloud computing. Tech. Rep. UBLCS-2011-03, Department of Computer Science. University of BolognaGoogle Scholar
  22. 22.
    Qureshi A, Weber R, Balakrishnan H, Guttag J, Maggs B (2009) Cutting the electric bill for Internet-scale systems. In: Proceedings of the ACM SIGCOMM conference on data communication, pp 123–134Google Scholar
  23. 23.
    Rao L, Liu X, Xie L, Liu W (2010) Minimizing electricity cost: optimization of distributed Internet data centers in a multi-electricity-market environment. In: Proceedings of the 29th IEEE international conference on computer communications, pp 1145–1153Google Scholar
  24. 24.
    Ren S, He Y, Xu F (2012) Provably-efficient job scheduling for energy and fairness in geographically distributed data centers. In: The 32nd international conference on distributed computing systemsGoogle Scholar
  25. 25.
    Rood B, Lewis M (2007) Multi-state grid resource availability characterization. In: 8th IEEE/ACM international conference on grid computing, pp 42–49Google Scholar
  26. 26.
    Shah AJ, Krishnan N (2008) Optimization of global data center thermal management workload for minimal environmental and economic burden. IEEE Trans Components Packag Technol39–45Google Scholar
  27. 27.
    Taufer M, Kerstens A, Estrada TP, Flores DA, Zamudio R, Teller PJ, Armen R, Brooks CL (2007) Moving volunteer computing towards knowledge-constructed, dynamically-adaptive modeling and scheduling. Int Parallel Distrib Process Symp 478Google Scholar
  28. 28.
    Valancius V, Laoutaris N, Massoulié L, Diot C, Rodriguez P (2009) Greening the Internet with nano data centers. In: Proceedings of the 5th international conference on emerging networking experiments and technologies, pp 37–48Google Scholar
  29. 29.
    Wang Z, Tolia N, Bash C (2010) Opportunities and challenges to unify workload, power, and cooling management in data centers. In: Proceedings of the 5th international workshop on feedback control implementation and design in computing systems and networks, pp 1–6Google Scholar
  30. 30.
    Zhang J, Phillips C (2011) Job scheduling via resource availability prediction for volunteer computational grids. Int J Grid Util Comput 2(1):25–32CrossRefGoogle Scholar
  31. 31.
    Zhou Y, Chakrabarty D, Lukose R (2008) Budget constrained bidding in keyword auctions and online knapsack problems. In: Proceedings of the 17th international conference on world wide web, pp 1243–1244Google Scholar
  32. 32.
    Zhou Y, Naroditskiy V (2008) Algorithm for stochastic multiple-choice knapsack problem and application to keywords bidding. In: Proceedings of the 17th international conference on world wide web, pp 1175–1176Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Huseyin Guler
    • 1
  • B. Barla Cambazoglu
    • 2
  • Oznur Ozkasap
    • 1
  1. 1.Department of Computer EngineeringKoc UniversityIstanbulTurkey
  2. 2.Yahoo LabsBarcelonaSpain

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