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

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Abstract

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.

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Notes

  1. Grid resource negotiation market.

References

  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

    Article  Google Scholar 

  2. Bradley A, Curran K, Gerard P (2006) Discovering resources in computational GRID environments. J Supercomput 35(1):27–49

    Article  Google Scholar 

  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

    Article  MATH  Google Scholar 

  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. 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. Weng C, Li M, Lu X (2007) Grid resource management based on economic mechanisms. J Supercomput 42(2):181–199

    Article  Google Scholar 

  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

  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

    Article  Google Scholar 

  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

    Article  MATH  Google Scholar 

  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. 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. Buyya R (2002) Economic-based distributed resource management and scheduling for grid computing. PhD dissertation, Monash Univ, Melbourne, Australia

  13. Electricity trading over the internet begins in six New England states (1999, May 13) [Online]. Available: http://industry.java.sun.com/javanews/stories/story2/0,1072,15093,00.html

  14. Huhns M, Stephens L (2000) Multiagent systems and societies of agents. In: Weiss G (ed) Multiagent systems. MIT Press, Cambridge

    Google Scholar 

  15. Lazar A, Semret N (1997) Auctions for network resource sharing. Columbia Univ, TR 468-97-02

  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. 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. Wolski R, Plank J, Brevik J (2001) G-commerce: building computational marketplaces for the computational grid [Online]. Available: http://www.cs.ucsb.edu/~rich/publications

  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. 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: http://www.cs.utk.edu/~rich/publications/CS-00

    Article  Google Scholar 

  21. Sim KM (2010) Grid resource negotiation: survey and new directions. IEEE Trans Syst Man Cybern C Appl Rev 40(3):245–257

    Article  MathSciNet  Google Scholar 

  22. Chunlin L (2011) Two-level market solution for services composition optimization in mobile grid. J Netw Comput Appl 34(2):739–749

    Article  Google Scholar 

  23. Johnston W (2002) The computing and data grid approach: infrastructure for distributed science applications. Comput Inform 21(4):293–319

    MATH  Google Scholar 

  24. Li C, Li L (2007) A distributed iterative algorithm for optimal scheduling in grid computing. Comput Inform 26(6):605–626

    MATH  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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. 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. 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

    Chapter  Google Scholar 

  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

    Article  Google Scholar 

  31. Ren F (2010) Autonomous agent negotiation strategies in complex environments. PhD thesis, Wollongong Univ, New South Wales, Australia

  32. Faratin P, Sierra C, Jennings NR (1998) Negotiation decision functions for autonomous agents. Int J Robot Auton Syst 24:159–182

    Article  Google Scholar 

  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. 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

    Article  MathSciNet  Google Scholar 

  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. 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. 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. Sim KM, Ng KF (2007) Relaxed-criteria negotiation for G-commerce. Int Trans Syst Sci Appl 3:105–117. Invited Paper

    Google Scholar 

  39. Sim KM (2006) Grid commerce, market-driven G-negotiation, and grid resource management. IEEE Trans Syst Man Cybern, Part B 36:1381–1394

    Article  Google Scholar 

  40. Sim KM (2005) Equilibria, prudent compromises, and the “waiting” game. IEEE Trans Syst Man Cybern, Part B 35:712–724

    Article  Google Scholar 

  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. 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. 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

    Chapter  Google Scholar 

  44. Czajkowski K, Foster I, Kesselman C (2005) Agreement-based resource management. Proc IEEE 93:631–643

    Article  Google Scholar 

  45. An B (2011) Automated negotiation for complex multi-agent resource allocation. PhD thesis, University of Massachusetts–Amherst

  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. 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. 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. Wooldridge M (2002) An introduction to multiagent systems. Wiley, New York

    Google Scholar 

  50. Kraus S (2001) Strategic negotiation in multi-agent environments. MIT Press, Cambridge

    Google Scholar 

  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

    Article  MathSciNet  MATH  Google Scholar 

  52. Rubinstein A (1982) Perfect equilibrium in a bargaining model. Econometrica 50(1):97–109

    Article  MathSciNet  MATH  Google Scholar 

  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. 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. 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. Li C, Li L, Lu Z (2005) Utility driven dynamic resource allocation using competitive markets in computational grid. Adv Eng Softw 36:425–434

    Article  Google Scholar 

  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. 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. 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

    Article  MATH  Google Scholar 

  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. http://www.cs.huji.ac.il/labs/parallel/workload/logs.html

  62. Osborne MJ, Rubinstein A (1990) Bargaining and markets. Academic Press, New York

    MATH  Google Scholar 

  63. Sim KM (2006) Relaxed-criteria G-negotiation for grid resource co-allocation (position paper). ACM SIGecom, E-Commerce Exch 6(2):37–46

    Article  Google Scholar 

  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)

  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

    Article  Google Scholar 

  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. 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

    Article  Google Scholar 

  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. Sim KM (2010) Towards complex negotiation for cloud economy. In: Advances in grid and pervasive computing. Lecture notes in computer science, pp 395–406

    Chapter  Google Scholar 

  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. Salvatore D (1997) Microeconomics theory and applications. Addison-Wesley, Reading

    Google Scholar 

  72. Sim KM (2008) Evolving fuzzy rules for relaxed-criteria negotiation. IEEE Trans Syst Man Cybern, Part B 38(6):1486–1500

    Article  MathSciNet  Google Scholar 

  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. 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. 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. http://www.iam.unibe.ch/rvs/events/

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Acknowledgements

We want to express our gratitude to Dr. Hui Li who graciously provided us with the Standard Workload Format (http://www.cs.huji.ac.il/labs/parallel/workload/logs.html) 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.

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Authors and Affiliations

  1. Department of computer engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Sepideh Adabi & Amir Masoud Rahmani

  2. Sharif University of Technology, Tehran, Iran

    Ali Movaghar & Hamid Beigy

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  1. Sepideh Adabi
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  2. Ali Movaghar
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Correspondence to Sepideh Adabi.

Appendix

Appendix

For the benefit of readers, the authors summarize in Table 4 the key symbols and their definitions used in this paper.

Table 4 Notation and basic terms used in the paper (alphabetic sort)
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Adabi, S., Movaghar, A., Rahmani, A.M. et al. Negotiation strategies considering market, time and behavior functions for resource allocation in computational grid. J Supercomput 66, 1350–1389 (2013). https://doi.org/10.1007/s11227-012-0808-4

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