Fault Tolerance-Genetic Algorithm for Grid Task Scheduling using Check Point

Priya, S. Baghavathi; Prakash, M.; Dhawan, K.K.

doi:10.1109/gcc.2007.67

Cited by 39 publications

(17 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Baghavathi Priya and et al presented a fault tolerance approach for Task Scheduling by using Genetic algorithm on grid [5]. They have used checkpoint technique in their method that is a general-purpose method for providing fault tolerance in distributed systems.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The first step is resource discovery and filtering, and the second is selecting nodes and scheduling jobs to related nodes, and the last step is submitting and monitoring jobs. Surely, step 2 is vital because some nodes always have best behavior, while some others often have fault with low performance [5].In scheduling phase on grid, schedulers usually use some information about resources" attributes (CPU speed and load, memory) to do the scheduling. The information used by the schedulers is usually provided by an information service that is responsible for gathering data about all resources that compose the grid.…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Reliable and Fault Tolerant Job Scheduling System in Market-Based Grids Using Case-Based Reasoning Method

Bouyer¹

2013

IJMO

View full text Add to dashboard Cite

Abstract-In market-based Grid systems, a main aim is to execute jobs with considered quality of service requirements based on user defined budget. Since grid has heterogonous resource with unpredictable faults, the user cost constraints and expected service requirements may not provided. Therefore, using a better approach to resource scheduling to reduce fault is necessary. This paper presents a predictive approach on fault tolerance mechanisms for faultless job scheduling on market-based grids. The Case-Based Reasoning technique has been used for selecting fault tolerant nodes. This approach applies a specific structure in order to prepare fault tolerance between provider nodes to retain system in a safe state with minimum data transferring. Certainly, this algorithm increases fault tolerant confidence therefore, performance of grid will be high.Index Terms-Market-based grid, fault tolerance, case-based reasoning, job scheduling. I. INTRODUCTIONGrid computing is an amazing infrastructure to solve some problems that need to strong and heavy computation with very long time execution [1]. It is cooperation of different computers, for a specific task, so that the user acquires better performance for that specific task. In this environment, the resources are geographically distributed, but in logical aspect, these are as virtual single resource with high performance [2]. Grid computing allows a group of computers to share the system securely and optimizes their collective resources to meet required workloads by using open standards OGSA (Open Grid Services Architecture) [3]. The Grid allows executing jobs in different nodes. In order to perform job scheduling and resource management at Grid level, usually it has used a Resource scheduler or a meta-scheduler. A scheduler is fundamental in any large-scale Grid environment. The task of a Grid resource scheduler is to dynamically identify and characterize the available resources, and to select the most appropriate resources in order to submit jobs. In grid scheduling discussion, selecting best nodes with looking at economic and fault tolerance criteria is considerable [4]. Choosing the suitable fault tolerance resource for a user job to meet predefined constraints such as deadline, speedup and cost of execution is an important problem in the grids. In our approach, we highly have solved some of these problems.As known, grid scheduling consists of three steps. The first step is resource discovery and filtering, and the second is selecting nodes and scheduling jobs to related nodes, and the last step is submitting and monitoring jobs. Surely, step 2 is vital because some nodes always have best behavior, while some others often have fault with low performance [5].In scheduling phase on grid, schedulers usually use some information about resources" attributes (CPU speed and load, memory) to do the scheduling. The information used by the schedulers is usually provided by an information service that is responsible for gathering data about all resources that compose the grid. ...

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

A Reliable and Fault Tolerant Job Scheduling System in Market-Based Grids Using Case-Based Reasoning Method

Bouyer¹

2013

IJMO

View full text Add to dashboard Cite

show abstract

“…Saurabh Bilgaiyan et al [18] presented a study of various swarm-based and evolutionary optimization techniques effectively used for task-resource mapping and scheduling on parallel computing systems. M. Prakash et al [19] introduced DLBS optimization algorithm for optimum job allotment associated with resources upon every node. DLBS optimization algorithm selects the suitable source out of accessible resources.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Improved Hybrid DLBS Artificial Bee Colony Optimization Algorithm based on Parallel Computing Environment

Pathania¹,

Sharma²

2017

IJCA

View full text Add to dashboard Cite

This paper represents the Parallel Computing is now extremely popular because of its wide variety of applications through internet. The particular service based approaches which are aware from the server selection through the parallel can easily progress toward the cost and performance of parallel computing. A new hybrid DLBS Artificial bee colony optimization algorithm for parallel computing environment has been done. The overall objective of this paper is enlighten the performance analysis on dynamic load balancing strategy (DLBS) with ABC algorithm which enhances the results further by using mean flow time parameter.

show abstract

“…Min-min and Max-min need prediction information on processor speeds and task lengths. Priya et al [8] presented a genetic algorithm for scheduling the tasks. Along with scheduling of task on best suitable node, their algorithm accomplishes the recovery in grid environment by check pointing the state of the system on a regular basis.…”

Section: Related Workmentioning

confidence: 99%

A Novel Resource Scheduling Algorithm for Computational Grid

Aggarwal¹,

Kamboj²,

Singh³

et al. 2012

IJAIS

View full text Add to dashboard Cite

Grid is an emerging technology for enabling resource sharing and coordinated problem solving in dynamic multiinstitutional heterogeneous virtual organizations. However, the management of resources and computational tasks is a critical and complex undertaking as these resources and tasks are geographically distributed. A suitable and efficient scheduling algorithm is needed to schedule user tasks to heterogeneous resources distributed in the grid. We propose a genetic algorithm to solve the scheduling problems in computational grid. The proposed algorithm uses the optimal searching technique of genetic approach and takes different computing capabilities of nodes into consideration. The simulation result shows that proposed algorithm yields better performance when compared with other traditional heuristic approaches.

show abstract

Fault Tolerance-Genetic Algorithm for Grid Task Scheduling using Check Point

Cited by 39 publications

References 11 publications

A Reliable and Fault Tolerant Job Scheduling System in Market-Based Grids Using Case-Based Reasoning Method

A Reliable and Fault Tolerant Job Scheduling System in Market-Based Grids Using Case-Based Reasoning Method

Improved Hybrid DLBS Artificial Bee Colony Optimization Algorithm based on Parallel Computing Environment

A Novel Resource Scheduling Algorithm for Computational Grid

Contact Info

Product

Resources

About