Decision procedures for multiple auctions
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Scheduling with Advanced Reservations
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Developing a bidding agent for multiple heterogeneous auctions
ACM Transactions on Internet Technology (TOIT)
On Agent-Mediated Electronic Commerce
IEEE Transactions on Knowledge and Data Engineering
Scheduling Co-Reservations with Priorities in Grid Computing Systems
CCGRID '02 Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid
Resource Allocation in the Grid Using Reinforcement Learning
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Resource trading using cognitive agents: A hybrid perspective and its simulation
Future Generation Computer Systems
DFCA: a flexible refundable auction for limited capacity suppliers
GECON'07 Proceedings of the 4th international conference on Grid economics and business models
rBundle: an iterative combinatorial auction-based approach to supporting advance reservation
GCC'05 Proceedings of the 4th international conference on Grid and Cooperative Computing
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Grid Computing is a newly emerging technology that enables users to share a large number of computing resources distributed over a network. Due to that the grid computational resources are not storable, the advance reservation of these resources are necessary for users to request resources from multiple scheduling systems at a specific time. In this paper, we use auction-based scheduling method for resource reservation. We propose a variant version of traditional ascending auction for automatic resource reservation and introduce two novel heuristic strategies to guide agents on participating actions. We also compare our bidding strategies with several other different bidding strategies for the computational resource reservation in different scenarios. The results of experiments show that our heuristic bidding strategies outperforms those methods in those cases.