Research on Fine-grained Job scheduling in Grid Computing

Liao, Yeqing; Liu, Quan

doi:10.5815/ijieeb.2009.01.02

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…Grouping-based Fine-grained Job Scheduling (GFJS) algorithm [14,23] is based on resource characteristics. This algorithm integrated with Greedy and FCFS algorithms improve the Fine-grained jobs.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of scheduling algorithms in data warehouse for query processing

Krishnaveni

Hemalatha

2012

2012 International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET)

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The data warehouse is a centralized repository for analyzing and storing huge amounts of data. In distributed data warehouse, data can be shared across multiple data repositories which belong to one or more organizations. In general, clients expect short response times for their queries. The scheduling algorithms are used to resolve the issues and to accomplish the task with quick manner. In this paper, we are dealing grid based scheduling algorithms for query scheduling and resource management in distributed data warehouses. The algorithms are Optimal Resource Constraints (ORC), Grouping based Fine-grained Job Scheduling (GFJS), Research on Novel Dynamic Resource Management (RNDRM) and New Resource Mechanism with Negotiate Solution (NRMNS).When we are combining both job and resource scheduling algorithms, the query processing time and memory size performances are lower than individual scheduling responses.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of scheduling algorithms in data warehouse for query processing

Krishnaveni

Hemalatha

2012

2012 International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET)

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“…In [13] grouping strategy does not utilize the resources sufficiently. Fine-grained jobs are scheduled in [11] according to processing capability and bandwidth of selected resources.A dynamic job scheduling approach which is based on grouping is proposed in [14] for deploying applications with fine-grained tasks on global grids and is based on the processing capability of resources. This model reduces the processing time and communication time but does not utilize the resources sufficiently.A time minimization dynamic job grouping based scheduling is proposed in [5].…”

Section: Previous Workmentioning

confidence: 99%

Grouping-based Scheduling with Load Balancing for Fine-Grained Jobs in Grid Computing

Ezzat¹,

Aboutabl²,

Mostafa³

2013

IJACSA

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Abstract-Grid computing is characterized by the existence of a collection of heterogeneous geographicallydistributed resources that are connected over high speed networks.Job scheduling and resource management have been a great challenge to researchers in the area of grid computing.Very often, there are applications having alarge number of fine-grainedjobs.Sending these finegrained jobsindividually to be executed on grid resources that have high processing power reduces resource utilization and is thus uneconomical. This paper presents efficient grouping-based scheduling models that group fine-grained jobs to form coarsegrained jobs which are sent for execution on grid resources. Our groupingstrategy is based on the processing capability of resources and the processing requirements of grouped jobs.A load balancing approach is also presented to achieve efficient utilization of resources. Simulation experiments were conducted using the Gridsim toolkit. Results show that thetotal simulation time and the cost are improved by grouping.Furthermore, our load balancing approach enhances resource utilization and achieves load balancing among resources.

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“…Grouping-based Fine-grained Job Scheduling (GFJS) algorithm [15,16] is based on resource characteristics. This algorithm integrated with Greedy and FCFS algorithms improve the Fine-grained jobs.…”

Section: Literature Reviewmentioning

confidence: 99%

Query Processing in Distributed Data Warehouse using Proposed Dynamic Task Dependency Scheduling Algorithm

Krishnaveni¹,

Hemalatha²

2012

IJCA

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A data warehouse is an electronic storage of huge amounts of data. It is also a system for retrieving and managing a data. In distributed data warehouse, data can be shared across multiple data repositories. Each may belong to one or more organizations. Query sorting is the problem of formatting the number of queries to be selected together. Reducing the usual completion period of a random order is a common concern. In this paper we propose Dynamic Task Dependency Scheduling (DTDS) Algorithm for query scheduling. Here our proposed algorithm takes the arrival time, size and also it considers the query dependency from the given query. It is also adaptable for all distributed data warehouse systems. Performance results show that the proposed algorithm gives less processing time and minimum query cost compared to others.

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Research on Fine-grained Job scheduling in Grid Computing

Cited by 6 publications

References 20 publications

Analysis of scheduling algorithms in data warehouse for query processing

Analysis of scheduling algorithms in data warehouse for query processing

Grouping-based Scheduling with Load Balancing for Fine-Grained Jobs in Grid Computing

Query Processing in Distributed Data Warehouse using Proposed Dynamic Task Dependency Scheduling Algorithm

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