Computational Grids are a promising platform for executing large-scale resource intensive applications. However, resource management and scheduling in the Grid environment is a complex undertaking as resources are (geographically) distributed, heterogeneous in nature, owned by different individuals or organizations with their own policies, have differen t access and cost models, and have dynamically varying loads and availability. This introduces a number of challenging issues such as site autonomy, heterogeneous interaction, policy extensibility, resource allocation or coallocation, online control, scalability, transparency, resource brokering, and "computational economy". A number of Grid systems (such as Globus and Legion) have addressed many of these issues with exception of a computational economy. We argue that a computational economy is required in order to create a real world scalable Grid because it provides a mechanism for regulating the Grid resources demand and supply....

: Computational Grids, coupling geographically distributed resources such as PCs, workstations, clusters, and scientific instruments, have emerged as a next generation computing platform for solving large-scale problems in science, engineering, and commerce. However, application development, resource management, and scheduling in these environments continue to be a complex undertaking. In this article, we discuss our efforts in developing a resource management system for scheduling computations on resources distributed across the world with varying quality of service. Our service-oriented grid computing system called Nimrod-G manages all operations associated with remote execution including resource discovery, trading, scheduling based on economic principles and a user defined quality of service requirement. The Nimrod-G resource broker is implemented by leveraging existing technologies such as Globus, and provides new services that are essential for constructing industrial-strength Grids. We discuss results of preliminary experiments on scheduling some parametric computations using the Nimrod-G resource broker on a world-wide grid testbed that spans five continents. 1.

Global Computing achieves high throughput computing by harvesting a very large number of unused computing resources connected to the Internet. This parallel computing model targets a parallel architecture defined by a very high number of nodes, poor communication performance and continuously varying resources. The unprecedented scale of the Global Computing architecture paradigm requires to revisit many basic issues related to parallel architecture: programming models, performance models, and class of applications or algorithms suitable to this architecture. XtremWeb is an experimental Global Computing platform dedicated to provide a tool for such studies. This

The POPCORN project provides an infrastructure for globally distributed computation over the whole Internet. It provides any programmer connected to the Internet with a single huge virtual parallel computer composed of all processors on the Interne ~ which care to participate at any given moment. POPCORN provides a market-based mechanism for trade in CPU time to motivate processors to provide their CPU cycles for other peoples ’ computations. “Selling” CPU time is as easy as visiting a certain web site with a Java-enabled browser. “Buying” CPU time is done by writing a paraIIel program using the POPCORN paradigm. A third entity in the POPCORN system is a “market” for CPU time, which is where buyers and seIlers meet and trade. The POPCORN system may be visited on our web-site:

In this paper, we investigate G-commerce — computational economies for controlling resource allocation in Computational Grid settings. We define hypothetical resource consumers (representing users and Grid-aware applications) and resource producers (representing resource owners who “sell ” their resources to the Grid). We then measure the efficiency of resource allocation under two different market conditions: commodities markets and auctions. We compare both market strategies in terms of price stability, market equilibrium, consumer efficiency, and producer efficiency. Our results indicate that commodities markets are a better choice for controlling Grid resources than previously defined auction strategies. 1

This chapter identifies challenges in managing resources in a Grid computing environment and proposes computational economy as a metaphor for effective management of resources and application scheduling. It identifies distributed resource management challenges and requirements of economybased Grid systems, and discusses various representative economy-based systems, both historical and emerging, for cooperative and competitive trading of resources such as CPU cycles, storage, and network bandwidth. It presents an extensible, service-oriented Grid architecture driven by Grid economy and an approach for its realization by leveraging various existing Grid technologies. It also presents commodity and auction models for resource allocation. The use of commodity economy model for resource management and application scheduling in both computational and data grids is also presented.

Computationally expensive tasks that can be parallelized are most efficiently completed by distributing th computation...

In this chapter, we describe the use of economic principles as the basis for Grid resource allocation policies and mechanisms. A computational economy in which users “buy ” resources from their owners is an attractive method of controlling Grid resource allocation for several reasons. Economies are intuitively easy to understand, they fit the model of flexible resource usage under local control (which is fundamental to Grid computing), and they can be analyzed through a considerable body of extant theory. We discuss many of the fundamental characteristics of computational economies, particularly as they pertain to Grid computing. We also present G-commerce — a framework that we have used to investigate Grid resource economies — as an example of the type of results that are possible. Finally, we discuss several of the issues associated with empirical investigation of Grid economies as a motivation for future work. 1

We study truthful mechanisms for auctions in which the auctioneer is trying to hire a team of agents to perform a complex task, and paying them for their work. As common in the field of mechanism design, we assume that the agents are selfish and will act in such a way as to maximize their profit, which in particular may include misrepresenting their true incurred cost. Our first contribution is a new and natural definition of the frugality ratio of a mechanism, measuring the amount by which a mechanism “overpays”, and extending previous definitions to all monopoly-free set systems. After reexamining several known results in light of this new definition, we proceed to study in detail shortest path auctions and “r-out-of-k sets ” auctions. We show that when individual set systems (e.g., graphs) are considered instead of worst cases over all instances, these problems exhibit a rich structure, and the performance of mechanisms may be vastly different. In particular, we show that the wellknown VCG mechanism may be far from optimal in these settings, and we propose and analyze a mechanism that is always within a constant factor of optimal. 1

We study auctions with severe bounds on the communication allowed: each bidder may only transmit t bits of information to the auctioneer. We consider both welfare-maximizing and revenuemaximizing auctions under this communication restriction. For both measures, we determine the optimal auction and show that the loss incurred relative to unconstrained auctions is mild. We prove non-surprising properties of these kinds of auctions, e.g. that discrete prices are informationally ecient, as well as some surprising properties, e.g. that asymmetric auctions are better than symmetric ones.