Modeling the cost of resource allocation in distributed control

Fraser, Martin D.; Gagliano, Ross A.; Schaefer, Mark E.

doi:10.1145/99637.99657

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(7 citation statements)

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“…A large range ol simttlalion parameters and initial values of variables were analyzed [8,9,12). Total systems resources were varied up to 4,000 anils, where the smallest integral unit ihat loiild be ac(|uireil was 1 unit.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Independent of the work at PARC, three different C;SU market paradigms have been developed: sealed-bid ciuclions, harter exchanges, and challenge protocols [8,9,11,12]. Some of the key ideas for these models originated in earlier explorations of systems synchronization, cybernetic structure, and organizational control [7,10].…”

Section: Definitions and Motivationsmentioning

confidence: 99%

“…One suite of simulation runs [9] was conducted not only to test the ability of the auction algorithm to allocate resources btit also to ascertain the ,sensitivity of the system to the cost and time devoted to auctions. The goal was to determine if the timeaveraged price has a rationing function tinder the effects of operating such an allocation algorithm in real time.…”

Section: Simulation Experimentsmentioning

confidence: 99%

“…In order to determine the relative overhead incurred by auctions, a series of experiments were designed to compute pro forma MP costs versus corresponding TM costs [8,9,12]. This also provided a measure of sensitivity to the relative nature of the time and effort, in terms of lost processing capacity, that would be dedicated to auctions.…”

Section: Figures Numberof Tasks Processingmentioning

confidence: 99%

“…environluftits, possibly due to their ready availability. Our attention, therefore, has been concentrated on the more coniputet-intensive applications, fxpfrinu'tititig with .sevetal different internal approaches through simulation [8,9,12]. We have chosen to pre-.scnt an atiction tiiodel in this article because of its intuitivt-appeal; that is, prices are naturally derived throtigh market interaction, and allocation is a function of demand [22].…”

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Auction allocation of computing resources

1995

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tandard methods for allocadng computing resources normally employ schedulers and either queue or priority schemes. Alternative methods utilizing marketlikc processes are being investigated, with direct applicability to evolving distributed systems. In this article, we present results of simulations of an auction allocation in which computing tasks are provided sufficient intclligcMUf to acquire resources by offering, bidding, and exchanging them {or funds.There are a number of important consequences of our experiments. First, we confirmed that a resource valuation variable in our model fiiin tions as a price, and is correlated to the arrival rates and complexities of the tasks. This price variable complements other measures, such as processing time, overhead costs, resource utilization, and efficiency, that assist in monitoring overall system performance. Second, such research provides a vicarious opportunity to explore potential non-von Neumann approaches to computer architectures and operating systems. Third, we note that these models are self-regulating, manifest bidding that increases with capacity, and successfully ration resources. Last, prior to their fully developed implementations, internal decentralized resource allocation clearly indicates how either external costing procedures or internal allocation in the operational phases of the lifecycle can be shifted leftward, or earlier, in the sy.stems development phase.During the past decade, there has been an enormous increase in the availability of computing resources, their connectivity through networks, and their complexity as seen in the variety of emerging parallel and distributed systems. Previously, standalone mainframes processed jobs (tasks) in batch mode, which economized on computing resources while wasting human resources. Time-sharing provided some improvement in system response, but its advantage was overcome by unmet demand, rising costs, and even technical difficulties such as thrashing. As personal computers (PCs) became available, they attracted small, independent tasks away from mainframes.However, complex tasks, particularly those requiring large amounts of numerical computation or extensive database access, usually exceeded the limits of these smaller machines. As PCs began to approach the size and power of workstations, many of these kinds of machines were then linked together in networks, which now demand more flexible and responsive access and allocation.In order to suggest changes in the allocation of computing resources and analyze the effects of their implementation, it is necessary to understand how allocation is characterized. Traditionally, computing resource allocation is accomplished cither internally, through scheduling in operating systems, or externally, through administrative policy. The general mode of external allocation in computing centers uses a combination of priority systems and pricing techniques to reduce utilization, thereby curtailing congestion [16].Other external approaches have attempted to utilize cost-shari...

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Definitions and Motivationsmentioning

confidence: 99%

Section: Simulation Experimentsmentioning

confidence: 99%

Section: Figures Numberof Tasks Processingmentioning

confidence: 99%

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confidence: 99%

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