Scheduling big data applications within advance reservation framework in optical grids

Abouelela, Mohamed; El-Darieby, Mohamed

doi:10.1016/j.asoc.2015.08.032

Cited by 10 publications

(2 citation statements)

References 11 publications

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“…The application-aware Grid job scheduling problem reported in the literature was proposed by Hu and Veeravalli [ 10 ], whose RAPAR and RAKAR algorithms addressed the scheduling of applications with heterogeneous processing requirements on a Grid. Paper [ 23 ] considered the geographically distributed data feature of Big Data applications and proposed an advance reservation scheduling framework in optical Grid. Xu and Yang proposed a heuristic multi-objective scheduling algorithm to optimize both Grid users’ applications and Grid resource providers’ incentives, such as cost [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

Application-aware deadline constraint job scheduling mechanism on large-scale computational grid

Tang

Liao

2018

PLoS ONE

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Recently, computational Grids have proven to be a good solution for processing large-scale, computation intensive problems. However, the heterogeneity, dynamics of resources and diversity of applications requirements have always been important factors affecting their performance. In response to these challenges, this work first builds a Grid job scheduling architecture that can dynamically monitor Grid computing center resources and make corresponding scheduling decisions. Second, a Grid job model is proposed to describe the application requirements. Third, this paper studies the characteristics of commercial interconnection networks used in Grids and forecast job transmission time. Fourth, this paper proposes an application-aware job scheduling mechanism (AJSM) that includes periodic scheduling flow and a heuristic application-aware deadline constraint job scheduling algorithm. The rigorous performance evaluation results clearly demonstrate that the proposed application-aware job scheduling mechanism can successful schedule more Grid jobs than the existing algorithms. For successful scheduled jobs, our proposed AJSM method is the best algorithm for job average processing time and makespan.

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Section: Related Workmentioning

confidence: 99%

Application-aware deadline constraint job scheduling mechanism on large-scale computational grid

Tang

Liao

2018

PLoS ONE

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“…Mohamed et. al., [10] proposes the advance resource reservation framework in multidomain scheduling approach for data intensive application. The iterative scheduling algorithm and K-shortest paths algorithm are used for reservation.…”

Section: Literature Surveymentioning

confidence: 99%

DRR: Dynamic Resource Reservation in Grid Computing

Devi¹,

Pethalakshmi²

2017

IJCA

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The computationally hazardous problems necessitate deploying the complexity in the grid environment for the earlier execution. This can only be achieved by resource sharing. To ensure the availability of resources at the required time, the resources are reserved in advance. The available advance resource reservation schemes are FCFS, priority based reservation, reservation based on negotiation, time slice based advance resource reservation and optimized resource reservation. In all the reservations, it is assumed that the reservations done are utilized, but there are some situations where the reserved resources are kept idle. This paper analyzes the reservations which are unutilized and allocates the unutilized reservations to the current requirements.

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A multi-parameter scheduling method of dynamic workloads for big data calculation in cloud computing

2017

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Scheduling big data applications within advance reservation framework in optical grids

Cited by 10 publications

References 11 publications

Application-aware deadline constraint job scheduling mechanism on large-scale computational grid

Application-aware deadline constraint job scheduling mechanism on large-scale computational grid

DRR: Dynamic Resource Reservation in Grid Computing

A multi-parameter scheduling method of dynamic workloads for big data calculation in cloud computing

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