A Novel Market Learning Algorithm for Grid Resource Allocation 
Saeed Parsa, Amin Shokri 
International Journal of Web Applications, Vol. 2, No. 1, 2010.
DOI: 
Link: http://dline.info/ijwa/fulltext/v2n107.pdf
Abstract:
The aim of this research has been to introduce a learning algorithm based on market concepts for grid resource allocation and resolve some incompetence with current mechanisms. Grid resource allocation could be considered as a double auction in which grid resource manager acts as an auctioneer and jobs and resource owners act as buyers (resource consumers) and sellers. In our novel algorithm resource allocation is based on activity history of each participant in the auction. Bids in the auctions are calculated in each round, based on proportion of former bid fluctuations and prior activity of each buyer and deadline issues of each buyerâ€™s job. As the same, asks in the auctions are calculated in each round, based on proportion of former ask fluctuations and prior activity of each seller probability of failure for each seller (resource owner) which is calculated by Poisson function. Practical evaluation results demonstrate that market equilibrium in our mechanism is improved in comparison with other mechanisms. Also essence of learning features in this novel algorithm increases competition incentive in participants which do selfishly in every round of an auction to maximize their utility.