Learning in Market-based Resource Allocation

Load balancing is one of the most challenging problems facing large-scale computational systems. This paper introduces a competitive pricing model to achieve a general equilibrium between the global supply and demand in large scale computational commodity markets. Prices are adjusted according to a tâtonnement like process. A fully distributed pricing model is proposed based on an existing partially distributed version. While in this last version, the price of each commodity is computed by only one auctioneer, in our algorithm, a scalable number of auctioneers is used to support a massive number of suppliers and consumers.

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“…Like in the first case, each consumer aims to determine an optimum demand under the budgetary constraint as described by the expression (8).…”

Section: B the Second Case Study: Linear Preferencesmentioning

confidence: 99%

An equilibrium pricing model for large scale computational markets

Chourou

Jemni

Elleuch

2013

2013 International Conference on Electrical Engineering and Software Applications

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“…− S3) Bidding strategies should adapt to market dynamics [58] like price fluctuations, changing conditions and QoS of provided resources. Learning mechanisms will be adopted to aggregate information pertaining to past decisions, actions and available market information in order to "fine tune" the bid generation processes [24]. − S4) Bidding strategies should be flexible i.e.…”

Section: Requirements For the Bidding Strategiesmentioning

confidence: 99%

Automated bidding in computational markets: an application in market-based allocation of computing services

Borissov

Neumann

Weinhardt

2009

Auton Agent Multi-Agent Syst

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Autonomous agents are widely applied to automate interactions in robotics, e.g. for selling and purchasing goods on eBay, and in financial markets, e.g. in the form of quote machines and algorithmic traders. Current research investigates efficient economic mechanisms that fully automate the provisioning and usage processes of Grid-based services. On the one hand, consumers want to allocate resources on demand for their various applications, e.g. data sharing, stream processing, email, business applications and simulations. On the other hand, providers of Grid services want to govern business policies to meet their utilization and profit goals. The above-mentioned processes are not manually manageable, however, because decisions need to be taken within milliseconds. Therefore, such processes have to be automated to minimize human interactions. Hence, market mechanisms and strategic behavior play important roles when it comes to achieving automated and efficient allocation of Grid services. The paper begins by presenting a framework for automated bidding, providing a methodology for the design and implementation of configurable bidding strategies. Second, it presents a novel bidding strategy based on a reinforcement learning technique. This strategy is designed to automate the bid generation processes of consumers and providers in various market mechanisms. Third, the behavior and convergence of the strategy is evaluated in a centralized Continuous Double Auction and a decentralized on-line machine scheduling mechanism against selected benchmark bidding strategies. Fourth, we define a bidding language for communicating consumer and provider preferences to the market as well as report back the match of the market-based allocation process.

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“…In this section we compare the results of experiments for the individual and the social reward functions using the same amount of learning applied in [16,17]. We consider the case of a single IPA market with one type of resource, e.g.…”

Section: Learning the Ipa Marketmentioning

confidence: 99%

“…Our earlier works [16,17] evaluate the approach in two differ- ent cases. In one case the agents learn in the presence of agents with pre-defined static demand functions.…”

Section: Introductionmentioning

confidence: 99%

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Learning the IPA market with individual and social rewards

Gomes¹,

Kowalczyk

2009

Web Intelligence and Agent Systems: An International Journal

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Market-based mechanisms offer a promising approach for distributed resource allocation. In this paper we consider the Iterative Price Adjustment, a pricing mechanism that can be used in commodity-market resource allocation systems. We address the scenario where agents use utility functions to describe preferences in the allocation and learn demand functions optimized for the market by Reinforcement Learning. In particular, we investigate reward functions based on the individual utilities of the agents and the Social Welfare of the market. We also evaluate the quality of demand functions obtained throughout the learning process with the aim of analyzing its influence on the behavior of the agents and exploring how much learning is enough, so the amount required can be reduced. This investigation shows that both reward functions deliver similar results when the market consists of only learning agents. We further investigate this behavior and present its theoretical-experimental explanation.

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Learning in Market-based Resource Allocation

Cited by 7 publications

References 14 publications

An equilibrium pricing model for large scale computational markets

An equilibrium pricing model for large scale computational markets

Automated bidding in computational markets: an application in market-based allocation of computing services

Learning the IPA market with individual and social rewards

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