A Market Design for Grid Computing

Abstract

Grid computing uses software to integrate computing resources, such as CPU cycles, storage, network bandwidth, and even applications, across a distributed and heterogeneous set of networked computers. It is now widely deployed by organizations and provides seamless temporary processing-capacity expansion to handle peak-period demand on e-commerce servers, distributed gaming, and content storage and distribution. We develop a market-based resource-allocation model that adds an economic layer to the current approach of treating resource allocation as primarily a scheduling issue. We design a value-elicitation and allocation scheme that provides the economic incentives for buyers and sellers of computing resources to exchange assets. We formulate the problem as a combinatorial call auction and present a portfolio of three solution approaches that trade off economic properties, such as allocative efficiency, incentive compatibility, and fairness in allocation, with computational efficiency. The first of these is an efficient solution that maximizes social welfare and yields incentive-compatible Vickrey-Clarke-Groves prices, but requires solving multiple instances of an NP-hard problem. For markets where having a commodity price is critical, we show how the addition of fairness constraints to the efficient model can somewhat reduce the computational burden and yet preserve incentive compatibility. Finally, for markets that require real-time fast solution techniques, we propose a time-sensitive fair Grid (tsfGRID) heuristic that relaxes the maximal allocation requirement of the welfare-maximizing fair solution. Its solution is not guaranteed to be incentive-compatible, but the heuristic is designed to be fast, maintain fairness in allocations, and yield commodity prices. Notably, while incentive compatibility is not guaranteed by tsfGRID, computational results comparing it with the efficient solution technique indicate that there are no significant differences in the expected-revenue and operational-allocative characteristics.

Existence of Market Equilibria for Grid Computing

“…Further, for many firms it has the potential to lower IT costs through outsourcing their IT functionality to the grid, perhaps as much as 30% [10]. As a result, grid computing is now widely deployed by organizations and provides seamless temporary processing capacity expansion to handle peak period demand on e-commerce servers, distributed gaming, and content storage and distribution [11]. This trend is evident through reports such as "Grid Computing: A Vertical Market Perspective 2005 -2010" which claimed that the worldwide Grid spending would increase from $714.9 million in 2005 to approximately $19.2 billion in 2010 [12].…”

SORMA – Business Cases for an Open Grid Market: Concept and Implementation

“…Thus, the allocation process is controlled by market mechanisms e.g. Vickrey, English, Dutch, and double auctions [12] as well as combinatorial mechanisms [4,24,25]. Prominent examples of market mechanisms for scheduling of computational resources like CPU and Memory are based on proportional share mechanisms [15], where the users receive a share of the computer resource proportional to their valuations fraction of the overall valuation across all users.…”

Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services

“…Market mechanisms e.g. Vickrey, English, Dutch, and Double Auctions [28] as well as combinatorial mechanisms [29,30] involve a scheduling problem that is NP-hard. The computational complexity of such mechanisms drives researchers and practitioners to look at simpler allocation models which can be adapted to real-world scenarios and requirements.…”