Two Level Job Scheduling and Data Replication in Data Grid

Abdi, Somayeh; Pedram, Hossein; Mohamadi, Somayeh

doi:10.5121/ijgca.2010.1103

Cited by 8 publications

(7 citation statements)

References 16 publications

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“…Moreover, the assumption on fragmented replicas being adjacent might not be easily possible in a data grid environment. In addition, the authors have proposed the hierarchical cluster scheduling strategy (HCS) and the hierarchical replication strategy (HRS) in [33][34][35][36], to improve the efficiency rate of accessing the data in the grid. HCS uses hierarchical scheduling and involves the information of a cluster to reduce the time searching for the appropriate computing node.…”

Section: Related Workmentioning

confidence: 99%

The Impact of the Implementation Cost of Replication in Data Grid Job Scheduling

Nazir

Ishaq

Shamshirband

et al. 2018

MCA

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Data Grids deal with geographically-distributed large-scale data-intensive applications. Schemes scheduled for data grids attempt to not only improve data access time, but also aim to improve the ratio of data availability to a node, where the data requests are generated. Data replication techniques manage large data by storing a number of data files efficiently. In this paper, we propose centralized dynamic scheduling strategy-replica placement strategies (CDSS-RPS). CDSS-RPS schedule the data and task so that it minimizes the implementation cost and data transfer time. CDSS-RPS consists of two algorithms, namely (a) centralized dynamic scheduling (CDS) and (b) replica placement strategy (RPS). CDS considers the computing capacity of a node and finds an appropriate location for the job. RPS attempts to improve file access time by using replication on the basis of number of accesses, storage capacity of a computing node, and response time of a requested file. Extensive simulations are carried out to demonstrate the effectiveness of the proposed strategy. Simulation results demonstrate that the replication and scheduling strategies improve the implementation cost and average access time significantly.

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Section: Related Workmentioning

confidence: 99%

The Impact of the Implementation Cost of Replication in Data Grid Job Scheduling

Nazir

Ishaq

Shamshirband

et al. 2018

MCA

View full text Add to dashboard Cite

show abstract

“…Also, their assumption that fragmented replicas are contiguous may not always hold in data grids. Further, they developed a job scheduling policy HCS (Hierarchical Cluster Scheduling) and a dynamic data replication strategy HRS (Hierarchical Replication Strategy) to improve the data access efficiencies in a cluster grid [20,21,22]. The rationale behind the tow coordinated algorithms is that job scheduling in concert whit data replication can significantly reduce data access time and the amount of inter-cluster-communications.…”

Section: Related Workmentioning

confidence: 99%

Improvement of Job Scheduling and Tow Level Data Replication Strategies in Data Grid

Jolfaei¹

2012

IJMNCT

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With the development of grid computing, Data Grid, as an important branch of Grid Computing focuses on supporting an efficient management mechanism for controlled sharing and large amounts of distributed data. Data intensive jobs are one major kind of jobs in Data Grid, and their scheduling strategies are regarded as one of the most important research fields. Dynamic replication in data grid aims to reduce data access time and to utilize network and storage resources efficiently. This paper proposes a novel dynamic replication strategy, called ERS(Enhancement of Replication Strategy), which reduces data access time by avoiding network congestions in a data grid network and we develop a job scheduling policy, called ESS (Enhancement of Scheduling strategy).

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“…For example, achieving lower execution time requires scheduling jobs to powerful CNs; whereas, achieving lower transfer times requires using powerful links with higher bandwidths in a system. Similar to other approaches [2,7,8,19,[22][23][24][25][26], we also assume that if several jobs in a CN require the same datafile, the requested datafile will be downloaded once and then stored in a local repository (cache) for further local requests.…”

Section: Problem Statement: Data Aware Job Schedulingmentioning

confidence: 99%

“…In this work, we tried to incorporate as many features as possible form approaches previously proposed to solve the DAJS problem [2,4,7,8,19,[23][24][25][26]28] and design our framework to consist of heterogeneous: (i) CNs, (ii) SNs, (iii) interconnecting network, (iv) schedulers, (v) users, (vi) jobs, and (vii) datafiles. Figure 1 shows components of our framework to model both types of grids.…”

Section: Frameworkmentioning

confidence: 99%

“…Such alignment, (i) will allow us to check the performance of GA-ParFnt, and (ii) verify the quality of answers found by already designed algorithm for the DAJS problem. BestMap [5], DIANA [2,8], JDS-BC [6], Chameleon/FLOP [22], MinTrans [7,19,23,26], and MinExe [7,26] are six approaches that support the batch mode for scheduling and thus are selected to directly evaluate the performance of GA-ParFnt.…”

Section: Algorithm Comparisonmentioning

confidence: 99%

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Genetic algorithm in finding Pareto frontier of optimizing data transfer versus job execution in grids

Taheri

Zomaya

Khan

2012

Concurrency and Computation

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SUMMARYThis work presents a genetic algorithm (GA)-based optimization technique, called GA-ParFnt, to find the Pareto frontier for optimizing data transfer versus job execution time in grids. As the performance of a generic GA is not suitable to find such Pareto relationship, major modifications are applied to it so that it can efficiently discover such relationship. The frontier curve representing this relationship is then matched against performance of several scheduling techniques-for both data intensive and computationally intensive applications-to measure their overall performances. Results show that few of these algorithms are far from the Pareto front despite their claims of being efficient in optimizing their targeted objectives. Results also provide invaluable insights into this formidable problem and should aid in the design of future schedulers.

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Two Level Job Scheduling and Data Replication in Data Grid

Abstract: Data Grid environment is a geographically distributed that deal with date-intensive application in

Cited by 8 publications

References 16 publications

The Impact of the Implementation Cost of Replication in Data Grid Job Scheduling

The Impact of the Implementation Cost of Replication in Data Grid Job Scheduling

Improvement of Job Scheduling and Tow Level Data Replication Strategies in Data Grid

Genetic algorithm in finding Pareto frontier of optimizing data transfer versus job execution in grids

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