Adaptive Grid Resource Selection Based on Job History Analysis Using Plackett-Burman Designs

Hur, Cinyoung; Kim, Chongam

doi:10.1007/978-3-642-04492-2_14

Cited by 2 publications

(4 citation statements)

References 5 publications

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“…Newly generated job profile is added to Job History by Profile Adder, and Error Calculation module periodically conducts estimation for maintaining accuracy of reference job profiles. On account of space considerations, refer to reference [2] for details of resource selection algorithms.…”

Section: Effect Of Factors and Statistical Methodologymentioning

confidence: 99%

“…The prior study [2] showed that job history provides knowledge of which resources provide good performance and stability. Thus it can be effectively used for resource selection.…”

Section: Experiments and Evaluationmentioning

confidence: 99%

“…Therefore, large-scale resources are allocated to more jobs without considering reliability. As a result of experiment in prior study [2], resources appropriate to applications of fluid dynamic research are high performance and fault-tolerant resources. In this regard, high performance and reliable resources are applicable to the CFD applications such as f32, komolongma and sirius.…”

Section: A Scenario 1: Comparison With Fluid Dynamic Researchmentioning

confidence: 99%

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On Evaluating JARS: Job history-based Adaptive grid resource selection

Hur

Kim

2010

2010 IEEE Network Operations and Management Symposium - NOMS 2010

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Grids have ensured to solve a large-scale complex scientific problem with utilizing many heterogeneous resources that are geographically separated. However, as grid resources and scientific applications become diverse, the difficulty of their execution management and control has been increased. In this paper, we propose JARS (Job history-based Adaptive grid Resource Selection) system and evaluate with different scenarios. Specifically, statistical technique, Plackett-Burman design with fold-over, is applied on analyzing grid environments and job history. Through JARS system, accumulated knowledge of applications' execution can be utilized in practical resource selection. As a proof-of-concept, we analyzed job history from an aerospace research grid system and obtained characteristics of grid resources and applications. The simulations present that resources with acceptable performance and reliability are selected under heavy resource failures.

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Section: Effect Of Factors and Statistical Methodologymentioning

confidence: 99%

“…The prior study [2] showed that job history provides knowledge of which resources provide good performance and stability. Thus it can be effectively used for resource selection.…”

Section: Experiments and Evaluationmentioning

confidence: 99%

Section: A Scenario 1: Comparison With Fluid Dynamic Researchmentioning

confidence: 99%

See 1 more Smart Citation

On Evaluating JARS: Job history-based Adaptive grid resource selection

Hur

Kim

2010

2010 IEEE Network Operations and Management Symposium - NOMS 2010

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“…In [15], the authors focused on resource selection using job history files. Each time a job is executed, the system records the job execution time (JET) and other data in a history file.…”

Section: Related Studiesmentioning

confidence: 99%

An enhanced meta-scheduling system for grid computing that considers the job type and priority

2011

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Meta-scheduling systems play a crucial role in scheduling jobs that are submitted for execution and require special attention because an increasing number of jobs are being executed using a limited number of resources. The primary problem of meta-scheduling is selecting the best resources (sites) to use to execute the underlying jobs while still achieving the following objectives: reducing the mean job turnaround time, ensuring site load balance, and considering job priorities. We introduce an enhanced meta-scheduling system, called Job Nature Meta-scheduling over Grid (JNMgrid), that achieves these objectives. JNMgrid consists of three components: (1) Job Analyzer and Monitor, which is responsible for determining the types of jobs in specific ratios; (2) Job Decider, which is responsible for matching the jobs with the appropriate resources; and (3) Job Batcher, which is responsible for determining the best number of jobs for execution. The performance of JNMgrid is compared with similar existing systems, such as Random, Queue Length, File Access Cost, and File Access Cost + Job Queue Access Cost. The simulation results demonstrate that JNMgrid outperforms these systems and can thus be deployed in any grid middleware to improve sharing of limited resources among grid users.

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Adaptive Grid Resource Selection Based on Job History Analysis Using Plackett-Burman Designs

Cited by 2 publications

References 5 publications

On Evaluating JARS: Job history-based Adaptive grid resource selection

On Evaluating JARS: Job history-based Adaptive grid resource selection

An enhanced meta-scheduling system for grid computing that considers the job type and priority

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