Efficient resource management using advance reservations for heterogeneous Grids

Castillo, Claris; Rouskas, George N.; Harfoush, Khaled

doi:10.1109/ipdps.2008.4536228

Cited by 35 publications

(27 citation statements)

References 31 publications

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“…Advanced reservation support is commonly used in distributed systems to provide performance predictability, meet resource requirements, and provide quality of service (QoS) guarantees [12], [24]. As Grid and cloud systems evolve the task of planning job requirements is becoming more complex, requiring fine grained coordination of interdependent jobs in order to achieve larger goals.…”

Section: Flexible Advanced Reservationsmentioning

confidence: 99%

High Performance Resource Allocation Strategies for Computational Economies

Chard

Bubendorfer

2013

IEEE Trans. Parallel Distrib. Syst.

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Abstract-Utility computing models have long been the focus of academic research, and with the recent success of commercial cloud providers, computation and storage is finally being realized as the fifth utility. Computational economies are often proposed as an efficient means of resource allocation, however adoption has been limited due to a lack of performance and high overheads. In this paper, we address the performance limitations of existing economic allocation models by defining strategies to reduce the failure and reallocation rate, increase occupancy and thereby increase the obtainable utilization of the system. The high-performance resource utilization strategies presented can be used by market participants without requiring dramatic changes to the allocation protocol. The strategies considered include overbooking, advanced reservation, just-in-time bidding, and using substitute providers for service delivery. The proposed strategies have been implemented in a distributed metascheduler and evaluated with respect to Grid and cloud deployments. Several diverse synthetic workloads have been used to quantity both the performance benefits and economic implications of these strategies.

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Section: Flexible Advanced Reservationsmentioning

confidence: 99%

High Performance Resource Allocation Strategies for Computational Economies

Chard

Bubendorfer

2013

IEEE Trans. Parallel Distrib. Syst.

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“…We will discuss the most relevant papers in the following. [7] investigates the influence of resource heterogeneity on reservation-aware schedulers. The presented algorithm uses computational geometry to manage advance reservations.…”

Section: Related Workmentioning

confidence: 99%

Advance Reservation-based Computational Resource Brokering using Earliest Start Time Estimation

Lu¹,

Ma²,

Luckow³

et al. 2012

JCP

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As a potential method for improving the resource utility, advance reservations is crucial for brokering computational resource in a predictable and efficient way. However, Conducting Advance Reservation efficiently in grid environment is not easy due to the insufficient support of the current grid architecture and the dynamic feature of grid resources. Based on a grid middleware Migol, this paper presents and evaluates a job broker architecture that addresses this deficit and provides support for advance reservation on grid-level. Using Earliest Start Time Estimation algorithm (ESE) to predict queuing delays at grid resources, this architecture is able to conduct advance reservation at lower cost and higher accuracy. Experiments show that this brokering architecture is scalable and the ESE algorithm is efficient in use

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“…Advanced reservation support in distributed resource allocation is desirable for performance predictability, meeting resource requirements, and providing Quality of Service (QoS) guarantees [8,15,25,18,19]. As Grid and Cloud systems evolve the task planning requirements become more complex, requiring fine grained coordination of interdependent jobs in order to achieve larger goals.…”

Section: Advanced Reservationsmentioning

confidence: 99%

High occupancy resource allocation for grid and cloud systems, a study with DRIVE

Chard

Bubendorfer

Komisarczuk

2010

Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing

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Economic models have long been advocated as a means of efficient resource allocation, however they are often criticized due to a lack of performance and high overheads. The widespread adoption of utility computing models as seen in commercial Cloud providers has re-motivated the need for economic allocation mechanisms. The aim of this work is to address some of the performance limitations of existing economic allocation models, by reducing the failure/reallocation rate, increasing occupancy and thereby increasing the obtainable utilization of the system. This paper is a study of high performance resource utilization strategies that can be employed in Grid and Cloud systems. In particular we have implemented and quantified the results for strategies including overbooking, advanced reservation, justin-time bidding and using substitute providers for service delivery. These strategies are analyzed in a meta-scheduling context using synthetic workloads derived from a production Grid trace to quantify the performance benefits obtained.

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Efficient resource management using advance reservations for heterogeneous Grids

Cited by 35 publications

References 31 publications

High Performance Resource Allocation Strategies for Computational Economies

High Performance Resource Allocation Strategies for Computational Economies

Advance Reservation-based Computational Resource Brokering using Earliest Start Time Estimation

High occupancy resource allocation for grid and cloud systems, a study with DRIVE

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