Resource allocation via competing marketplaces

This thesis proposes a novel method for allocating multi-attribute computational resources via competing marketplaces. Trading agents, working on behalf of resource consumers and providers, choose to trade in resource markets where the resources being traded best align with their preferences and constraints. Market-exchange agents, in competition with each other, attempt to provide resource markets that attract traders, with the goal of maximising their profit. Because exchanges can only partially observe global supply and demand schedules, novel strategies are required to automate their search for market niches. By applying a novel methodology, which is also used to explore, for the first time, the generalisation ability of market mechanisms, novel attribute-level selection (ALS) strategies are analysed in competitive market environments. Results from simulation studies suggest that using these ALS strategies, market-exchanges can seek out market niches under a variety of environmental conditions. In order to facilitate traders' selection between dynamic competing marketplaces, this thesis explores the application of a reputation system, and simulation results suggest reputation-based market-selection signals can lead to more efficient global resource allocations in dynamic environments. Further, a subjective reputation system, grounded in Bayesian statistics, allows traders to identify and ignore the opinions of those attempting to falsely damage or bolster marketplace reputation.