Wim Depoorter

Combining Futures and Spot Markets: A Hybrid Market Approach to Economic Grid Resource Management

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.

Scalability of Grid Simulators: An Evaluation

Due to the distributed nature of resources in grids that cover multiple administrative domains, grid resource management cannot be optimally implemented using traditional approaches. In order to investigate new grid resource management systems, researchers utilize simulators which allows them to efficiently evaluate new algorithms on a large scale. We have developed the Grid Economics Simulator (GES) in support of research into grid resource management in general and economic grid resource management in particular. This paper compares GES to SimGrid and GridSim, two established grid simulation frameworks. We demonstrate that GES compares favourably to the other frameworks in terms of scalability, runtime performance and memory requirements. We explain how these differences are related to the simulation paradigm and the threading model used in each simulator.

A Simulation Framework for Studying Economic Resource Management in Grids

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.

Economic Grid Resource Management for CPU Bound Applications with Hard Deadlines

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 non- migratable 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.

Advance reservation, co-allocation and pricing of network and computational resources in grids

Abstract Through the introduction of economic principles, a Resource Management System (RMS) can incorporate user valuations and externalities such as the usage cost of resources, into its scheduling logic. Consequently, an economic RMS can extract more value from the available infrastructure compared to traditional RMSs. In order to use the available infrastructure efficiently, a grid RMS must take an application’s data requirements into account. Although RMSs exist that support the co-allocation and advance reservation of both network and computational resources, they do not incorporate economic principles. In this paper we present ENARA, an economic RMS with advance reservation and co-allocation support for both network and computational resources. The RMS both allocates and prices resources in line with the demand and supply conditions in the network. Through the use of a novel algorithm that determines the limiting links within a network, the RMS is able to estimate prices of individual network links at the start of its planning phase. This resolves an interdependency issue between resource pricing and allocation. We demonstrate that ENARA is able to align resource allocations with user valuations, significantly increasing the user value generated compared to an online approach, while attaining high utilization levels of the infrastructure.

Evaluating the Divisible Load Assumption in the Context of Economic Grid Scheduling with Deadline-Based QoS guarantees

The ef¿cient scheduling of jobs is an essential part of any grid resource management system. At its core, it involves ¿nding a solution to a problem which is NP-complete by reduction to the knapsack problem. Consequently, this problem is often tackled by using heuristics to derive a more pragmatic solution. Other than the use of heuristics, simpli¿cations and abstractions of the workload model may also be employed to increase the tractability of the scheduling problem. A possible abstraction in this context is the use of Divisible Load Theory (DLT), in which it is assumed that an application consists of an arbitrarily divisible load (ADL). Many applications however, are composed of a number of atomic tasks and are only modularly divisible. In this paper we evaluate the consequences of the ADL assumption on the performance of economic scheduling approaches for grids, in the context of CPU-bound modularly divisible applications with hard deadlines. Our goal is to evaluate to what extent DLT can still serve as a useful workload abstraction for obtaining tractable scheduling algorithms in this setting. The focus of our evaluation is on the recently proposed tsfGrid heuristic for economic scheduling of grid workloads which operates under the assumptions of ADL. We demonstrate the effect of the ADL assumption on the actual instantiation of schedules and on the user value realized by the RMS. In addition we describe how the usage of a DLT heuristic in a high-level admission controller for a mechanism which does take into account the atomicity of individual tasks, can signi¿cantly reduce communication and computational overhead.

Economic co-allocation and advance reservation of network and computational resources in grids

The introduction of economic principles allows Resource Management Systems (RMS) to better deal with conflicting user requirements by incorporating user valuations and externalities such as the usage cost of resources into the planning and scheduling logic. This allows economic RMSs to create more value for the participants than traditional system centric RMSs. It is important for an RMS to take the data requirements of an application into account during the planning phase. Traditional RMSs have been presented supporting co-allocation and advance reservation of both network and computational resources. However, to the best of our knowledge no economic RMSs proposed in the literature possesses these capabilities. In this paper we present ENARA, an economic RMS with advance reservation and co-allocation support for both network and computational resources. We will demonstrate that ENARA can significantly increase the user value compared to an online approach.