# 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.

## Keywords

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

### 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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