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Evolutionary optimisation of market-based control systems for resource allocation in compute farms
Keyword(s): market-based control; trader agents; zip traders; auctions; genetic algorithms; BICAS
Abstract: This thesis describes the development of a market- based control (MBC) system used to allocate and balance computational tasks across a minimal simulated Utility Data Centre (UDC). Firstly, a re- implementation of the original ZIP trading-agent was developed and tested in a variety of basic markets. The re-implementation faithfully replicated the market equilibration behavior of the original system, and a evolutionary algorithm was then employed to successfully fine-tune the performance of the system. The MBC UDC simulation is then presented as the first of its kind based on ZIP trading agents and also being fully distributed and autonomous in its operation. Experiments indicate a successful proof-of-concept and an efficient computational load-balancing performance under different scenarios. Use of an evolutionary algorithm is then made to both further improve the market equilibration performance of the ZIP traders operating within the MBC simulation and to evolve the marketplaces they operate within. Thus this thesis presents the first ever (preliminary) results from using artificial evolution to automatically design the auction mechanism for an MBC system.
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