Online resource matching for heterogeneous grid environments

Naik, Vijay K.; Liu, C.; Yang, Lingyun; Wagner, Jonathan

doi:10.1109/ccgrid.2005.1558620

Cited by 33 publications

(29 citation statements)

References 7 publications

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“…Any of the standard grid resource matching algorithms that accepts the policy descriptions passed as the parameters of the resource matching model and additionally provides execution time guarantees, can be used to solve the matching problems of the two phases. Some examples of grid resource matching algorithms that satisfy these criteria have been described in [8], [9].…”

Section: B Heuristic Approach To Resource Matchingmentioning

confidence: 99%

Efficient Resource Virtualization and Sharing Strategies for Heterogeneous Grid Environments

Garbacki

Naik

2007

2007 10th IFIP/IEEE International Symposium on Integrated Network Management

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Abstract-Resource virtualization has emerged as a powerful technique for customized resource provisioning in grid and data center environments. In this paper, we describe efficient strategies for policy-based controlling of virtualization of the physical resources. With these strategies, virtualization is controlled taking into account workload requirements, available capacities of physical resources, and the governing policies. Realizing this control requires simultaneous handling of three problems: (i) determining the virtual resource configurations, (ii) the mapping of resulting virtual resources to physical resources, and (iii) the mapping of workloads to the virtual resources. We pose this as an optimization problem and solve this problem using a linear programming (LP) based approach. We evaluate this approach by implementing it in the Harmony grid environment consisting of heterogeneous resources and heterogeneous workload. Experimental results indicate that our approach is efficient and effective. We extend this approach further by using a two-phase heuristic that allows the decision making component to scale up to handle large scale grid systems.

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Section: B Heuristic Approach To Resource Matchingmentioning

confidence: 99%

Efficient Resource Virtualization and Sharing Strategies for Heterogeneous Grid Environments

Garbacki

Naik

2007

2007 10th IFIP/IEEE International Symposium on Integrated Network Management

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“…Their results show that unrestricted co-allocation is not recommended and performance is improved by correctly adjusting the component size of the co-allocated jobs. Other studies used co-allocation to develop load balancing techniques [8,9] or optimize the application execution time by selecting the most suitable resources [4,10].…”

Section: Related Workmentioning

confidence: 99%

“…The method first searches for the most powerful nodes (lines 2-5), where Power (n) is the computational power for node n. Note that for parallel jobs, the execution time is given by the slowest computational node selected. Then, the method tries to shift the tasks from powerful nodes to available slow nodes if the execution time remains the same, which may free up the assigned powerful nodes (lines [6][7][8][9][10][11][12]. The solution is a list of assignments of every task to a node.…”

Section: Multi-objective Genetic Algorithm (Moga)mentioning

confidence: 99%

“…-Min-Min is a heuristic presented by Li et al on [30] that is able to consider a set of jobs with the aim of minimizing energy consumption. -JPR is a variant of the Naik et al heuristic [10], where the jobs are matched with the best available resources depending on just only one of the criteria selected, makespan or energy. -PSO is a particle swarm optimization presented by Oukfif et al in [31] focused on minimizing both parameters.…”

Section: Moga Performance Comparisonmentioning

confidence: 99%

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Blacklist muti-objective genetic algorithm for energy saving in heterogeneous environments

et al. 2016

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Reducing energy consumption in large-scale computing facilities has become a major concern in recent years. Most of the techniques have focused on determining the computing requirements based on load predictions and thus turning unnecessary nodes on and off. Nevertheless, once the available resources have been configured, new opportunities arise for reducing energy consumption by providing optimal matching of parallel applications to the available computing nodes. Current research in scheduling has concentrated on not only optimizing the energy consumed by the processors but also optimizing the makespan, i.e., job completion time. The large number of heterogeneous computing nodes and variability of application-tasks are factors that make the scheduling an NP-Hard problem. Our aim in this paper is a multi-objective genetic algorithm based on a weighted blacklist able to generate scheduling decisions that globally optimizes the energy consumption and the makespan.

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“…Previous work [3,14] illustrates the benefits of using integer programming techniques to solve scheduling problems. However, our model tries to fit computation and communication parallel job requirements to resource capacity, and considers the sharing of the resources between parallel and local applications.…”

Section: Introductionmentioning

confidence: 99%

Resource Matching in Non-dedicated Multicluster Environments

Lérida

Solsona

Giné

et al. 2008

High Performance Computing for Computational Science - VECPAR 2008

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Abstract. We are interested in making use of Multiclusters to execute parallel applications. The present work is developed within the M-CISNE project. M-CISNE is a non-dedicated and heterogeneous Multicluster environment which includes MetaLoRaS, a two-level MetaScheduler that manages the appropriate job allocation to available resources. In this paper, we present a new resource-matching model for MetaLoRaS, which is aimed at mitigating the degraded turnaround time of co-allocated jobs, caused by the contention on shared inter-cluster links. The model is linear programming based and considers the availability of computational resources and the contention of shared inter and intra-cluster links. Its goal is to minimize the average turnaround time of the parallel applications without disturbing the local applications excessively and maximize the prediction accuracy. We also present a parallel job model that takes both computation and communication characterizations into account. By doing this, greater accuracy is obtained than in other models only focused on one of these characteristics. Our preliminary performance results indicate that the linear programming model for on-line resource matching is efficient in speed and accuracy and can be successfully applied to co-allocate jobs across different clusters.

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Online resource matching for heterogeneous grid environments

Cited by 33 publications

References 7 publications

Efficient Resource Virtualization and Sharing Strategies for Heterogeneous Grid Environments

Efficient Resource Virtualization and Sharing Strategies for Heterogeneous Grid Environments

Blacklist muti-objective genetic algorithm for energy saving in heterogeneous environments

Resource Matching in Non-dedicated Multicluster Environments

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