TY - JOUR
T1 - Computational Markets to Regulate Mobile-Agent Systems
AU - Bredin, Jonathan
AU - Kotz, David
AU - Rus, Daniela
AU - Maheswaran, Rajiv T.
AU - Imer, Cagri
AU - Basar, Tamer
N1 - Funding Information:
This work was supported in part by Department of Defense contract MURI F49620-97-1-0382, DARPA contract F30602-98-2-0107, an EPRI/ARO contract, NSF Grant EIA-98-02068, ONR grant N00014-95-1-1204, and a grant from the Alfred P. Sloan Foundation.
PY - 2003/5
Y1 - 2003/5
N2 - Abstract 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.
AB - Abstract 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.
KW - Market-based control
KW - Mobile agents
KW - Resource allocation
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U2 - 10.1023/A:1022923422570
DO - 10.1023/A:1022923422570
M3 - Article
AN - SCOPUS:0347132459
SN - 1387-2532
VL - 6
SP - 235
EP - 263
JO - Autonomous Agents and Multi-Agent Systems
JF - Autonomous Agents and Multi-Agent Systems
IS - 3
ER -