Authors:
Michael Schwind
and
Oleg Gujo
Affiliation:
Institute of Information Systems, J. W. Goethe University, Germany
Keyword(s):
Resource allocation, combinatorial auction, grid computing, agent-oriented programming.
Related
Ontology
Subjects/Areas/Topics:
Agent-Oriented Programming
;
Agents
;
Artificial Intelligence
;
Enterprise Information Systems
;
e-Procurement and Web-Based Supply Chain Management
;
Market-Spaces: Market Portals, Hubs, Auctions
;
Software Agents and Internet Computing
Abstract:
Our paper presents an agent-based simulation environment for task scheduling in a distributed computer systems (grid). The scheduler enables the simultaneous allocation of resources like CPU time, communication bandwidth, volatile, and non-volatile memory while employing a combinatorial resource allocation mechanism. The resources are allocated by an iterative combinatorial auction with proxy-bidding agents that try to acquire their desired resource allocation profiles with respect to limited monetary budget endowments. In order to achieve an efficient bidding process, the auctioneer provides information on resource prices to the bidding agents. The calculation of explicit resource prices in a combinatorial auction is computationally demanding, especially if the the bid bundles exhibit complementarities or substitutionalities. We therefore propose a new approximate pricing mechanism using shadow prices from a linear programming formulation for this purpose. The efficiency of the shad
ow price-based allocation mechanism is tested in the context of a closed loop grid system in which the agents can use monetary units rewarded for the resources they provide to the system for the acquisition of complementary capacity. Two types of proxy-bidding agents are compared in terms of efficiency (received units of resources, time until bid acceptance) within this scenario: An aggressive bidding agent with strongly rising bids and a smooth bidding agent with slowly increasing bids.
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