The Journal of Supercomputing

, Volume 66, Issue 3, pp 1350–1389 | Cite as

Negotiation strategies considering market, time and behavior functions for resource allocation in computational grid

  • Sepideh Adabi
  • Ali Movaghar
  • Amir Masoud Rahmani
  • Hamid Beigy


Providing an efficient resource allocation mechanism is a challenge to computational grid due to large-scale resource sharing and the fact that Grid Resource Owners (GROs) and Grid Resource Consumers (GRCs) may have different goals, policies, and preferences. In a real world market, various economic models exist for setting the price of grid resources, based on supply-and-demand and their value to the consumers. In this paper, we discuss the use of multiagent-based negotiation model for interaction between GROs and GRCs. For realizing this approach, we designed the Market- and Behavior-driven Negotiation Agents (MBDNAs). Negotiation strategies that adopt MBDNAs take into account the following factors: Competition, Opportunity, Deadline and Negotiator’s Trading Partner’s Previous Concession Behavior. In our experiments, we compare MBDNAs with MDAs (Market-Driven Agent), NDF (Negotiation Decision Function) and Kasbah in terms of the following metrics: total tasks complementation and budget spent. The results show that by taking the proposed negotiation model into account, MBDNAs outperform MDAs, NDF and Kasbah.


Computational grid Grid resource management Grid resource allocation Negotiation model Software agent 



We want to express our gratitude to Dr. Hui Li who graciously provided us with the Standard Workload Format ( through which the CTC SP2, DAS2 fs0, DAS2 fs1, DAS2 fs2, DAS2 fs3, DAS2 fs4, HPC2N, KTH SP2, LPC EGEE, LANL CM5, LANL O2K, LCG, LLNL Atlas, LLNL T3D, LLNL Thunder, LLNL uBGL, NASA iPSC, OSC Cluster, SDSC BLUE, SDSC DS (DataStar), SDSC Par96, SDSC Par95 and SDSC SP2 traces are made publicly available.


  1. 1.
    Mazzoleni P, Crispo B, Sivasubramanian S, Bertino E (2009) Efficient integration of fine-grained access control and resource brokering in grid. J Supercomput 49(1):108–126 CrossRefGoogle Scholar
  2. 2.
    Bradley A, Curran K, Gerard P (2006) Discovering resources in computational GRID environments. J Supercomput 35(1):27–49 CrossRefGoogle Scholar
  3. 3.
    Krauter K, Buyya R, Maheswaran M (2002) A taxonomy and survey of grid resource management systems. Int J Softw Pract Exp 32(2):135–164 CrossRefzbMATHGoogle Scholar
  4. 4.
    Foster I, Kesselman C (2004) The grid 2: blueprint for a new computing infrastructure, 2nd edn. Morgan Kaufmann, San Mateo. ISBN:1-55860-933-4 Google Scholar
  5. 5.
    Xing L, Lan Z (2009) A grid resource allocation method based on iterative combinatorial auctions. In: ITCS, international conference on information technology and computer science, July 2009, vol 2, pp 322–325. Google Scholar
  6. 6.
    Weng C, Li M, Lu X (2007) Grid resource management based on economic mechanisms. J Supercomput 42(2):181–199 CrossRefGoogle Scholar
  7. 7.
    Tucker PA, Berman FD (1996) On market mechanisms as a software technique. Technical report CS96-513, Department of Computer Science and Engineering, University of California, San Diego Google Scholar
  8. 8.
    Izakian H, Abraham A, Tork Ladani B (2010) An auction method for resource allocation in computational grids. Future Gener Comput Syst J 26:228–235 CrossRefGoogle Scholar
  9. 9.
    Buyya R, Abramson D, Giddy J, Stockinger H (2002) Economic models for resource management and scheduling in grid computing. Concurr Comput Pract Exp 14:1507–1542. doi: 10.1002/cpe.690 CrossRefzbMATHGoogle Scholar
  10. 10.
    Miller M, Drexler K (1988) Markets and computation: agoric open systems. In: Huberman B (ed) The ecology of computation. North-Holland/Elsevier, Amsterdam, pp 133–176 Google Scholar
  11. 11.
    Buyya R, Abramson D, Giddy J (2000) An economy driven resource management architecture for global computational power grids. In: The 2000 international conference on parallel and distributed processing techniques and applications (PDPTA 2000), June 2000, Las Vegas, USA Google Scholar
  12. 12.
    Buyya R (2002) Economic-based distributed resource management and scheduling for grid computing. PhD dissertation, Monash Univ, Melbourne, Australia Google Scholar
  13. 13.
    Electricity trading over the internet begins in six New England states (1999, May 13) [Online]. Available:,1072,15093,00.html
  14. 14.
    Huhns M, Stephens L (2000) Multiagent systems and societies of agents. In: Weiss G (ed) Multiagent systems. MIT Press, Cambridge Google Scholar
  15. 15.
    Lazar A, Semret N (1997) Auctions for network resource sharing. Columbia Univ, TR 468-97-02 Google Scholar
  16. 16.
    Kersten G, Noronha S, Teich J (2000) Are all e-commerce negotiations auctions? In: Proc 4th int conf design cooperative syst, Sophia-Antipolis, France, pp 1–11 Google Scholar
  17. 17.
    Wolski R, Brevik J, Plank J, Bryan T (2003) Grid resource allocation and control using computational economies. In: Berman F, Hey A, Fox G (eds) Grid computing—making the global infrastructure a reality. Wiley, New York, pp 747–771 Google Scholar
  18. 18.
    Wolski R, Plank J, Brevik J (2001) G-commerce: building computational marketplaces for the computational grid [Online]. Available:
  19. 19.
    Buyya R, Vazhkudai S (2001) Compute power market: towards a market-oriented grid. In: Proc 1st IEEE/ACM int symp cluster comput grid, Brisbane, Australia, Qld, May 2001, pp 15–18 Google Scholar
  20. 20.
    Wolski R, Brevik J, Plank J, Bryan T (2001) Analyzing market-based resource allocation strategies for the computational grid. Int J High Perform Comput Appl 15(3):258–281 [Online]. Available: CrossRefGoogle Scholar
  21. 21.
    Sim KM (2010) Grid resource negotiation: survey and new directions. IEEE Trans Syst Man Cybern C Appl Rev 40(3):245–257 MathSciNetCrossRefGoogle Scholar
  22. 22.
    Chunlin L (2011) Two-level market solution for services composition optimization in mobile grid. J Netw Comput Appl 34(2):739–749 CrossRefGoogle Scholar
  23. 23.
    Johnston W (2002) The computing and data grid approach: infrastructure for distributed science applications. Comput Inform 21(4):293–319 zbMATHGoogle Scholar
  24. 24.
    Li C, Li L (2007) A distributed iterative algorithm for optimal scheduling in grid computing. Comput Inform 26(6):605–626 zbMATHGoogle Scholar
  25. 25.
    Srinivas VV, Varadhan VV (2011) Intelligent agent based resource sharing in grid computing. Inf Technol Mob Commun, Commun Comput Inf Sci 47(Part 1):106–110 CrossRefGoogle Scholar
  26. 26.
    Pastore S (2008) The service discovery methods issue: a web services UDDI specification framework integrated in a grid environment. J Netw Comput Appl 31:93–107 CrossRefGoogle Scholar
  27. 27.
    Foster I, Jennings NR, Kesselman C (2005) Brain meets brawn: why grid and agents need each other. In: Proc towards the learning grid, pp 28–40 Google Scholar
  28. 28.
    Smolinski R (2006) Fundamentals of international negotiation. In: Paluchowski WJ (ed) Negocjacje: wsrod jawnych zagrozen i ukrytych mozliwosci. Poznan, Rebis, pp 175–189 Google Scholar
  29. 29.
    Montes J, Sánchez A, Pérez MS (2011) Grid global behavior prediction. In: The 11th IEEE/ACM international symposium on cluster, cloud and grid computing (CCgrid 2011), pp 124–133 CrossRefGoogle Scholar
  30. 30.
    Mok WWH, Sundarraj RP (2005) Learning algorithms for single-instance electronic negotiations using the timedependent behavioral tactic. ACM Trans Internet Technol 5(1):195–230 CrossRefGoogle Scholar
  31. 31.
    Ren F (2010) Autonomous agent negotiation strategies in complex environments. PhD thesis, Wollongong Univ, New South Wales, Australia Google Scholar
  32. 32.
    Faratin P, Sierra C, Jennings NR (1998) Negotiation decision functions for autonomous agents. Int J Robot Auton Syst 24:159–182 CrossRefGoogle Scholar
  33. 33.
    Lang F (2005) Developing dynamic strategies for multi-issue automated contracting in the agent based commercial grid. In: International symposium on cluster computing and the grid (CCGrid 2005), Cardiff, UK. IEEE Comput Soc, Los Alamitos, pp 342–349 Google Scholar
  34. 34.
    Lawley R, Luck M, Decker K, Payne T, Moreau L (2003) Automated negotiation between publishers and consumers of grid notifications. Parallel Process Lett 13:537–548 MathSciNetCrossRefGoogle Scholar
  35. 35.
    Chavez A, Maes P (1996) Kasbah: an agent marketplace for buying and selling goods. In: Proceedings of the first international conference on the practical application of intelligent agents and multi_agent technology, London, UK, pp 159–178 Google Scholar
  36. 36.
    Guttman RH, Maes P (1998) Agent-mediated integrative negotiation for retail electronic commerce. In: Proceedings of the 2nd international workshop on cooperative information agents (CIA98), Minneapolis, Minnesota Google Scholar
  37. 37.
    Sim KM, Ng KF (2006) A relaxed-criteria bargaining protocol for grid resource management. In: Proceedings of the sixth IEEE international symposium on cluster computing and the grid workshops (CCGRIDW’06), Singapore Google Scholar
  38. 38.
    Sim KM, Ng KF (2007) Relaxed-criteria negotiation for G-commerce. Int Trans Syst Sci Appl 3:105–117. Invited Paper Google Scholar
  39. 39.
    Sim KM (2006) Grid commerce, market-driven G-negotiation, and grid resource management. IEEE Trans Syst Man Cybern, Part B 36:1381–1394 CrossRefGoogle Scholar
  40. 40.
    Sim KM (2005) Equilibria, prudent compromises, and the “waiting” game. IEEE Trans Syst Man Cybern, Part B 35:712–724 CrossRefGoogle Scholar
  41. 41.
    Zhao H, Li X (2009) Efficient grid task-bundle allocation using bargaining based self-adaptive auction. In: Proceedings of the 9th IEEE/ACM international symposium on cluster computing and the grid, CCGrid 09, Shanghai. IEEE Comput Soc, Los Alamitos, pp 4–11 Google Scholar
  42. 42.
    Czajkowski K, Foster I, Kesselman C (1999) Resource co-allocation in computational grids. In: 8th IEEE international symposium on high performance distributed computing (HPDC-8’99), Redondo Beach, California. IEEE Comput Soc, Washington, DC, pp 219–228 Google Scholar
  43. 43.
    Czajkowski K, Foster I, Kesselman C, Sander V, Tuecke S (2002) SNAP: a protocol for negotiating service level agreements and coordinating resource management in distributed systems. In: 8th workshop on job scheduling strategies for parallel processing (JSSPP). Lecture notes on computer science series, vol 2537, Edinburgh, Scotland. Springer, Berlin, pp 153–183 CrossRefGoogle Scholar
  44. 44.
    Czajkowski K, Foster I, Kesselman C (2005) Agreement-based resource management. Proc IEEE 93:631–643 CrossRefGoogle Scholar
  45. 45.
    An B (2011) Automated negotiation for complex multi-agent resource allocation. PhD thesis, University of Massachusetts–Amherst Google Scholar
  46. 46.
    Gimpel H, Ludwig H, Dan A, Kearney B (2003) PANDA: specifying policies for automated negotiations of service contracts. In: ICSOC 2003. Lecture notes in computer science, vol 2910. Springer, New York, pp 287–302 Google Scholar
  47. 47.
    Venugopal S, Chu X, Buyya R (2008) A negotiation mechanism for advance resource reservation using the alternate offers protocol. In: Proceedings of the 16th international workshop on quality of service (IWQoS 2008), Twente, The Netherlands Google Scholar
  48. 48.
    Dang Minh Q, Jorn A (2008) Bilateral bargaining game and fuzzy logic in the system handling SLA-based workflow. In: Proceedings of the 22nd international conference on advanced information networking and applications-workshops. IEEE Comput Soc, Los Alamitos Google Scholar
  49. 49.
    Wooldridge M (2002) An introduction to multiagent systems. Wiley, New York Google Scholar
  50. 50.
    Kraus S (2001) Strategic negotiation in multi-agent environments. MIT Press, Cambridge Google Scholar
  51. 51.
    Chunlin L, Layuan L (2008) A new optimal approach for multiple optimisation objectives grid resource allocation and scheduling. Int J Syst Sci 39(12):1127–1138 MathSciNetCrossRefzbMATHGoogle Scholar
  52. 52.
    Rubinstein A (1982) Perfect equilibrium in a bargaining model. Econometrica 50(1):97–109 MathSciNetCrossRefzbMATHGoogle Scholar
  53. 53.
    Allenotor D, Thulasiram R (2008) Grid resources pricing: a novel financial option based quality of service-profit quasi-static equilibrium model. In: Proceedings of the 2008 9th IEEE/ACM international conference on grid computing Google Scholar
  54. 54.
    Lang F (2005) Developing dynamic strategies for multi-issue automated contracting in the agent based commercial grid. In: Fifth IEEE international symposium on cluster computing and the grid (CCGrid’05), vol 1, pp 342–349 Google Scholar
  55. 55.
    Ito T, Klein M, Hattori H (2008) A multi-issue negotiation protocol among agents with nonlinear utility functions. Multiagent Grid Syst 4(1):1 Google Scholar
  56. 56.
    Li C, Li L, Lu Z (2005) Utility driven dynamic resource allocation using competitive markets in computational grid. Adv Eng Softw 36:425–434 CrossRefGoogle Scholar
  57. 57.
    Li Z, Cheng C, Huang F (2009) Utility-driven solution for optimal resource allocation in computational grid computer languages. Syst Struct Elsevier 35:406–421 Google Scholar
  58. 58.
    Ghosh P, Roy N, Das S, Basu K (2004) A game theory based pricing strategy for job allocation in mobile grids. In: Proc int parallel distrib process symp, Santa Fe, NM, p 8b Google Scholar
  59. 59.
    Ghosh P, Roy N, Das S, Basu K (2005) A pricing strategy for job allocation in mobile grids using a non-cooperative bargaining theory framework. J Parallel Distrib Comput 65(11):1366–1383 CrossRefzbMATHGoogle Scholar
  60. 60.
    Binmore K, Dasgupta P (1987) Nash bargaining theory: an introduction. In: Binmore K, Dasgupta P (eds) The economics of bargaining. Blackwell, London Google Scholar
  61. 61.
  62. 62.
    Osborne MJ, Rubinstein A (1990) Bargaining and markets. Academic Press, New York zbMATHGoogle Scholar
  63. 63.
    Sim KM (2006) Relaxed-criteria G-negotiation for grid resource co-allocation (position paper). ACM SIGecom, E-Commerce Exch 6(2):37–46 CrossRefGoogle Scholar
  64. 64.
    Buyya R, Murshed M (2002) GridSim: a toolkit for the modeling and simulation of distributed management and scheduling for grid computing. The journal of Concurrency and Computation: Practice and Experience (CCPE) 14(13–15) Google Scholar
  65. 65.
    Aminul H, Hashimi SA, Parthiban R (2011) A survey of economic models in grid computing. Future Gener Comput Syst J 27(8):1056–1069 CrossRefGoogle Scholar
  66. 66.
    Chacin P, Leon X, Brunner R, Freitag F, Navarro L (2008) Core services for grid markets. In: Proceedings of the CoreGrid symposium (CGSYMP), Spain. Springer, Berlin, pp 205–215 Google Scholar
  67. 67.
    Montano BR, Yoon V, Drummey K, Liebowitz J (2008) Agent learning in the multi-agent contracting system [MACS]. J Decis Support Syst 45(1):140–149 CrossRefGoogle Scholar
  68. 68.
    Shen C, Peng X, Lu Y, Liu L (2011) An adaptive many-to-many negotiation model in an open market. J Comput Inf Syst 7(4):1038–1045 Google Scholar
  69. 69.
    Sim KM (2010) Towards complex negotiation for cloud economy. In: Advances in grid and pervasive computing. Lecture notes in computer science, pp 395–406 CrossRefGoogle Scholar
  70. 70.
    Yoo D, Sim KM (2010) A multilateral negotiation model for cloud service market. Grid Distrib Comput Control Autom Commun Comput Inf Sci 121:54–63 Google Scholar
  71. 71.
    Salvatore D (1997) Microeconomics theory and applications. Addison-Wesley, Reading Google Scholar
  72. 72.
    Sim KM (2008) Evolving fuzzy rules for relaxed-criteria negotiation. IEEE Trans Syst Man Cybern, Part B 38(6):1486–1500 MathSciNetCrossRefGoogle Scholar
  73. 73.
    Nemeth Z, Gombas G, Balaton Z (2004) Performance evaluation on grids: directions, issues, and open problems. In: Proc 12th euromicro conf parallel, distrib netw-based process, pp 290–297 Google Scholar
  74. 74.
    Németh Z (2003) Grid performance, grid benchmarks, grid metrics. Cracow grid workshop. In: Proc 3rd Cracow grid workshop, Cracow, October 2003, pp 34–41 Google Scholar
  75. 75.
    Computer networks and distributed systems, retreat of the computer networks and distributed systems research group (2005) Institute of computer science and applied mathematics, University of Bern.

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Sepideh Adabi
    • 1
  • Ali Movaghar
    • 2
  • Amir Masoud Rahmani
    • 1
  • Hamid Beigy
    • 2
  1. 1.Department of computer engineering, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Sharif University of TechnologyTehranIran

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