A commodity market algorithm for pricing substitutable Grid resources
Future Generation Computer Systems
A Novel Hybrid Evolution Algorithm Based on Agent Behavior and Paradigm Learning
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Pricing computational resources in a dynamic grid
International Journal of Grid and Utility Computing
Market based resource allocation with incomplete information
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Guarantee the victorious probability of grid resources in the competition for finite tasks
GPC'08 Proceedings of the 3rd international conference on Advances in grid and pervasive computing
GECON'07 Proceedings of the 4th international conference on Grid economics and business models
Research on a novel multi-agents dynamic cooperation method based on associated intent
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part I
Model checking agent programming languages
Automated Software Engineering
A new game theoretical resource allocation algorithm for cloud computing
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
Coordinating learning agents for multiple resource job scheduling
ALA'09 Proceedings of the Second international conference on Adaptive and Learning Agents
Future Generation Computer Systems
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Mobile-agent systems allow applications to distribute their resource consumption across the network. By prioritizing applications and publishing the cost of actions, it is possible for applications to achieve faster performance than in an environment where resources are evenly shared. We enforce the costs of actions through markets, where user applications bid for computation from host machines.We represent applications as collections of mobile agents and introduce a distributed mechanism for allocating general computational priority to mobile agents. We derive a bidding strategy for an agent that plans expenditures given a budget, and a series of tasks to complete. We also show that a unique Nash equilibrium exists between the agents under our allocation policy. We present simulation results to show that the use of our resource-allocation mechanism and expenditure-planning algorithm results in shorter mean job completion times compared to traditional mobile-agent resource allocation. We also observe that our resource-allocation policy adapts favorably to allocate overloaded resources to higher priority agents, and that agents are able to effectively plan expenditures, even when faced with network delay and job-size estimation error.