Spawn: a distributed computational economy

Waldspurger, Carl A.; Hogg, Tad; Huberman, Bernardo A.; Kephart, Jeffrey O.; Stornetta, W.S.

doi:10.1109/32.121753

Cited by 480 publications

(243 citation statements)

References 15 publications

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“…This invention combined with the Alto Workstation presented another opportunity for distributed computing and the PARC Worm [38] was the result. In the 1980s and early 1990s several academic projects developed distributed computing systems that supported one or several Unix systems [6,29,30,41,50]. Of these, the Condor Project is perhaps the best known and most widely used.…”

Section: Introductionmentioning

confidence: 99%

Entropia: architecture and performance of an enterprise desktop grid system

Chien¹,

Calder²,

Elbert³

et al. 2003

Journal of Parallel and Distributed Computing

267

143

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The exploitation of idle cycles on pervasive desktop PC systems offers the opportunity to increase the available computing power by orders of magnitude (10Â-1000Â). However, for desktop PC distributed computing to be widely accepted within the enterprise, the systems must achieve high levels of efficiency, robustness, security, scalability, manageability, unobtrusiveness, and openness/ ease of application integration.We describe the Entropia distributed computing system as a case study, detailing its internal architecture and philosophy in attacking these key problems. Key aspects of the Entropia system include the use of: (1) binary sandboxing technology for security and unobtrusiveness, (2) a layered architecture for efficiency, robustness, scalability and manageability, and (3) an open integration model to allow applications from many sources to be incorporated.Typical applications for the Entropia System includes molecular docking, sequence analysis, chemical structure modeling, ;and risk management. The applications come from a diverse set of domains including virtual screening for drug discovery, genomics for drug targeting, material property prediction, and portfolio management. In all cases, these applications scale to many thousands of nodes and have no dependences between tasks. We present representative performance results from several applications that illustrate the high performance, linear scaling, and overall capability presented by the Entropia system. r

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Section: Introductionmentioning

confidence: 99%

Entropia: architecture and performance of an enterprise desktop grid system

Chien¹,

Calder²,

Elbert³

et al. 2003

Journal of Parallel and Distributed Computing

267

143

View full text Add to dashboard Cite

show abstract

“…Popcorn [5] was a Web based computing market, which adopted both one-sided and twosided auctions to realize on-line resource allocation across the Internet. Spawn [6] managed heterogeneous computer resources based on Vickery auction, so did CORE [7]. Double auctions were used in JaWS [8] and preferred by many projects such as Tycoon [9].…”

Section: ⋆⋆ Email Address: Dding@bjtueducnmentioning

confidence: 99%

A Greedy Double Auction Mechanism for Grid Resource Allocation

Ding

Luo

Gao

2010

Job Scheduling Strategies for Parallel Processing

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Abstract. To improve the resource utilization and satisfy more users, a Greedy Double Auction Mechanism(GDAM) is proposed to allocate resources in grid environments. GDAM trades resources at discriminatory price instead of uniform price, reflecting the variance in requirements for profits and quantities. Moreover, GDAM applies different auction rules to different cases, over-demand, over-supply and equilibrium of demand and supply. As a new mechanism for grid resource allocation, GDAM is proved to be strategy-proof, economically efficient, weakly budgetbalanced and individual rational. Simulation results also confirm that GDAM outperforms the traditional one on both the total trade amount and the user satisfaction percentage, specially as more users are involved in the auction market.

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“…This market mechanism leads to an equitable temperature distribution in the system 135]. Other domains where market mechanisms were successfully applied include purchasing memory in an operating systems 53], allocating virtual circuits 89], \stealing" unused CPU cycles in a network of computers 79,269], predicting option futures in nancial markets 214], and numerous scheduling and distributed resource allocation problems 159,165,245,255,275,276].…”

Section: Computational Economicsmentioning

confidence: 99%

Theory of Collective Intelligence

Wolpert

2004

Collectives and the Design of Complex Systems

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This paper surveys the emerging science of how to design a \COllective INtelligence" (COIN). A COIN is a large multi-agent system where: i) There is little to no centralized, personalized communication and/or control. ii) There is a provided world utility function that rates the possible histories of the full system.In particular, we a r e i n terested in COINs in which e a c h a g e n t runs a reinforcement learning (RL) algorithm. The conventional approach to designing large distributed systems to optimize a world utility does not use agents running RL algorithms. Rather, that approach begins with explicit modeling of the dynamics of the overall system, followed by detailed hand-tuning of the interactions between the components to ensure that they \cooperate" as far as the world utility i s concerned. This approach is labor-intensive, often results in highly nonrobust systems, and usually results in design techniques that have limited applicability. In contrast, we w i s h t o s o l v e the COIN design problem implicitly, via the \adaptive" character of the RL algorithms of each of the agents. This approach i n troduces an entirely new, profound design problem: Assuming the RL algorithms are able to achieve high rewards, what reward functions for the individual agents will, when pursued by those agents, result in high world utility? In other words, what reward functions will best ensure that we d o n o t have phenomena like t h e tragedy of the commons, Braess's paradox, or the liquidity trap? Although still very young, research speci cally concentrating on the COIN design problem has already resulted in successes in arti cial domains, in particular in packet-routing, the leader-follower problem, and in variants of Arthur's El Farol bar problem. It is expected that as it matures and draws upon other disciplines related to COINs, this research will greatly expand the range of tasks addressable by human engineers. Moreover, in addition to drawing on them, such a fully developed science of COIN design may provide much insight into other already established scienti c elds, such as economics, game theory, and population biology.

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Spawn: a distributed computational economy

Cited by 480 publications

References 15 publications

Entropia: architecture and performance of an enterprise desktop grid system

Entropia: architecture and performance of an enterprise desktop grid system

A Greedy Double Auction Mechanism for Grid Resource Allocation

Theory of Collective Intelligence

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