Fault Tolerance Aware Scheduling for Brokers in Cloud Computing Datacenters

Author(s): Archana Pandita*, Prabhat K. Upadhyay.

Journal Name: Recent Patents on Computer Science

Volume 10 , Issue 4 , 2017

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Abstract:

Background: Cloud computing is becoming prominent as it makes use of a model, in which consumer must pay according to its usage, as described in various patents. The user pays as per his demand and requirement. There are several issues faced by Datacenters for efficient scheduling of the workload. Task implementation failure is a very common property of cloud computing environment and is not given much attention in different scheduling techniques. In this research article, we propose a technique which takes the heed of defects using the concepts of autonomous computing.

Methods: To assure the services related to quality to the consumers, an important task is to plot the available resources according to the jobs. Clustering is used where the events are logged and the ranges for CPU, Memory and Bandwidth are set as well. Fault tracing methodology is used with the help of which, the violations are checked, and requests are scheduled according to the results obtained by comparing the request with the cluster data.

Result: In our proposed model (FSBD), we have tried to overcome the shortcomings of the existing techniques. The damage caused to the Service level agreement (SLA's) is less and at the same time, execution time is reduced and performance is enhanced. The experimental result shows that the computing which is sensitive towards the faults supports flexible contingency which is favourable in terms of lesser SLA violation, better time to respond (up to 4.46 ms) and shorter execution time. The proposed approach, when compared to the traditional approach of fault aware pattern recognition, showed better results in terms of forbearance of faults. Also, the number of failed cloudlets is significantly lesser in FSBD (4.9%) as compared to the traditional Round Robin (40%) approach.

Conclusion: As is evident from the results shown, we can conclude that faults are able to cause huge damage to SLA's and lead to a lower performance in cloud computing. Further, we compared a system having no fault with the system having faulty behaviour to quantify the damage. After justifying the seriousness of the damage caused due to the fault, the proposed model recognizes the pattern of the behaviours of each component of the virtual machine, thereby identifying the problematic Virtual machine (VM) in the system. Post identification, very little number of requests is being allocated to the faulty VMs to keep SLA intact. Experiments conducted for validating the architecture clearly showed the effectiveness of the scheme.

Keywords: Cloud computing, fault tolerance, virtual machine, performance metrics, fault tracing, QoS, scheduling.

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Article Details

VOLUME: 10
ISSUE: 4
Year: 2017
Page: [299 - 307]
Pages: 9
DOI: 10.2174/2213275911666180419152348
Price: $58

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