FTM2: Fault Tolerant Batch Mode Heuristics in Computational Grid

2014 ◽  
pp. 98-104 ◽  
Author(s):  
Sanjaya Kumar Panda ◽  
Pabitra Mohan Khilar ◽  
Durga Prasad Mohapatra
Keyword(s):  
Fault Tolerant ◽  
Computational Grid ◽  
Batch Mode

Fault Tolerant Cloud Systems

2019 ◽  
pp. 171-190
Author(s):  
Sathish Kumar ◽  
Balamurugan B
Keyword(s):  
Cloud Computing ◽  
Fault Tolerant ◽  
Software As A Service ◽  
Arrival Process ◽  
Cloud Provider ◽  
Infrastructure As A Service ◽  
Batch Mode ◽  
Web Based ◽  
Platform As A Service ◽  
Cloud Systems

Cloud computing refers to a model for accessing computing resource like networks, servers, storage, applications, and services remotely. Cloud computing offers these resources as a service, namely infrastructure-as-a-service, platform-as-a-service, and software-as-a-service. To use these services, two roles involved: the cloud provider offers the service and the cloud customer consumes the service. These resources are efficiently shared and utilized by customers and it is called workload. The requirement of workload depends on customer demands that vary from higher to lower. Based on the customer demand, cloud provider makes the resource available efficiently. In the context of cloud, the workload is based on web-based service or jobs processed in batch mode. The arrival process of jobs in the cloud is not often deterministic. The irregular increase or decrease in workload has a vital impact on resource provision. Monitoring the resources helps in measuring the performance of the cloud so that the resource can be provisioned to customers efficiently.

A Replica Based Co-Scheduler (RBS) for Fault Tolerant Computational Grid

2011 ◽  
pp. 101-116
Author(s):  
Zahid Raza ◽  
Deo P. Vidyarthi
Keyword(s):  
Fault Tolerance ◽  
Performance Optimization ◽  
Large Scale ◽  
Heterogeneous Computing ◽  
Fault Tolerant ◽  
Turnaround Time ◽  
Computational Grid ◽  
Successful Execution ◽  
Computational Resources ◽  
The Cost

Grid is a parallel and distributed computing network system comprising of heterogeneous computing resources spread over multiple administrative domains that offers high throughput computing. Since the Grid operates at a large scale, there is always a possibility of failure ranging from hardware to software. The penalty paid of these failures may be on a very large scale. System needs to be tolerant to various possible failures which, in spite of many precautions, are bound to happen. Replication is a strategy often used to introduce fault tolerance in the system to ensure successful execution of the job, even when some of the computational resources fail. Though replication incurs a heavy cost, a selective degree of replication can offer a good compromise between the performance and the cost. This chapter proposes a co-scheduler that can be integrated with main scheduler for the execution of the jobs submitted to computational Grid. The main scheduler may have any performance optimization criteria; the integration of co-scheduler will be an added advantage towards fault tolerance. The chapter evaluates the performance of the co-scheduler with the main scheduler designed to minimize the turnaround time of a modular job by introducing module replication to counter the effects of node failures in a Grid. Simulation study reveals that the model works well under various conditions resulting in a graceful degradation of the scheduler’s performance with improving the overall reliability offered to the job.

Fault Tolerant Cloud Systems

2018 ◽  
pp. 1075-1090
Author(s):  
Sathish Kumar ◽  
Balamurugan B
Keyword(s):  
Cloud Computing ◽  
Fault Tolerant ◽  
Software As A Service ◽  
Arrival Process ◽  
Cloud Provider ◽  
Infrastructure As A Service ◽  
Batch Mode ◽  
Web Based ◽  
Platform As A Service ◽  
Cloud Systems

Cloud computing refers to a model for accessing computing resource like networks, servers, storage, applications and services by remotely. Cloud computing offers these resources as a service, namely infrastructure –as-a-service, platform-as-a-service, and software-as-a-service. To use these service two roles involved: the cloud provider offers the service and the cloud customer consumes the service. These resources are efficiently shared and utilized by customers and it is called workload. The requirement of workload depends on customer demands that vary from higher to lower. Based on the customer demand, cloud provider makes the resource available efficiently. In the context of cloud, the workload is based on web-based service or jobs processed in batch mode. The arrival process of jobs in the cloud is no often deterministic. The irregular increase or decrease in workload has a vital impact on resource provision. Monitoring the resources helps in measuring the performance of the cloud so that the resource can be provisioned to customers efficiently.

scholarly journals Dynamic and Adaptive Fault Tolerant Scheduling With QoS Consideration in Computational Grid

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 7853-7873 ◽  
Author(s):  
Sajjad Haider ◽  
Babar Nazir
Keyword(s):  
Fault Tolerant ◽  
Computational Grid

scholarly journals IMMEDIATE/BATCH MODE SCHEDULING ALGORITHMS FOR GRID COMPUTING: A REVIEW

2017 ◽  
Vol 5 (7) ◽  
pp. 1-13
Author(s):  
J.Y Maipan-uku ◽  
I Rabiu ◽  
Amit Mishra
Keyword(s):  
Execution Time ◽  
Completion Time ◽  
Computational Grid ◽  
Grid Environment ◽  
Open Problems ◽  
Batch Mode ◽  
Online Mode ◽  
On Line ◽  
Basic Concepts ◽  
Minimum Completion Time

Immediate/on-line and Batch mode heuristics are two methods used for scheduling in the computational grid environment. In the former, task is mapped onto a resource as soon as it arrives at the scheduler, while the later, tasks are not mapped onto resource as they arrive, instead they are collected into a set that is examined for mapping at prescheduled times called mapping events. This paper reviews the literature concerning Minimum Execution Time (MET) along with Minimum Completion Time (MCT) algorithms of online mode heuristics and more emphasis on Min-Min along with Max-Min algorithms of batch mode heuristics, while focusing on the details of their basic concepts, approaches, techniques, and open problems.

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