Economic forms of resource management in which users can express their valuations for service, offer new possibilities for optimizing resource allocations in Grids. If users are to correctly express these valuations, quality of service guarantees need to be given with respect to the turnaround time of their workloads. Market mechanisms that support bidding and allocations in future time are crucial for delivering such guarantees. To deal with the significant delays that these mechanisms introduce in the allocation process, we present a hybrid market approach in which a low-latency spot market coexists with a higher latency futures market. Based on simulated market scenarios, we show how this combination can significantly increase the total value realized by the Grid infrastructure. We also demonstrate how providers can react to price dynamics in such a hybrid market setting.
Abstract. Economic principles are increasingly being regarded as a way to address conflicting user requirements, to improve the effectiveness of grid resource management systems, and to deliver incentives for providers to join virtual organizations. Because economic resource management mechanisms can encourage grid participants to reveal the true valuations of their jobs and resources, the system becomes capable of making better scheduling decisions. A lot of exploratory research into different market mechanisms for grids is ongoing. Since it is impractical to conduct analysis of novel mechanisms on operational grids, most of this research is being carried out using simulation. This paper presents the Grid Economics Simulator (GES) in support of such research. The key design goals of the framework are enabling a wide variety of economic and non-economic forms of resource management while simultaneously supporting distributed execution of simulations and exhibiting good scalability properties.
The introduction of economic principles in grid resource management provides an interesting avenue for efficiently addressing the problem of conflicting user requirements. In shared computing infrastructures such as grids, such conflicting requirements are prevalent and stem from the selfish actions users follow when formulating their service requests. We develop and analyze both a centralized and a decentralized algorithm for economic resource management in the context of consumer requests for CPU bound applications with deadline-based QoS requirements and nonmigratable workloads. A comparison with an algorithm recently proposed in the literature is presented with a focus on performance in terms of realized consumer value. We establish that our algorithms perform well and that they compare favorably to existing approaches.
SUMMARYGrid computing technology enables the creation of large-scale IT infrastructures that are shared across organizational boundaries. In such shared infrastructures, conflicts between user requirements are common and originate from the selfish actions that users perform when formulating their service requests. The introduction of economic principles in grid resource management offers a promising way of dealing with these conflicts. We develop and analyze both a centralized and a decentralized algorithm for economic grid resource management in the context of compute bound applications with deadline-based quality of service requirements and non-migratable workloads. Through the use of reservations, we co-allocate resources across multiple providers in order to ensure that applications finish within their deadline. An evaluation of both algorithms is presented and their performance in terms of realized user value is compared with an existing market-based resource management algorithm. We establish that our algorithms, which operate under a more realistic workload model, can closely approximate the performance of this algorithm. We also quantify the effect of allowing local workload preemption and different scheduling heuristics on the realized user value.
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