Automatic healing of services in cloud computing environment

Cloud computing is an emerging technology which enables the cloud users to access the computing resources without having to pay huge capital expenses to scale up the IT infrastructure and reduces the management cost, in both hardware and software. A cloud introduces a resource rich computing model w...

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Bibliographic Details
Published in2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT) pp. 740 - 745
Main Authors Vijayalakshmi, M., Yakobu, D., Veeraiah, D., Rao, N. Gnaneswara
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2016
Subjects
Cloud computing
Computers
Elasticity
Measurement
Monitoring
Scalability
Server down
Servers
Virtual machining
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Summary:Cloud computing is an emerging technology which enables the cloud users to access the computing resources without having to pay huge capital expenses to scale up the IT infrastructure and reduces the management cost, in both hardware and software. A cloud introduces a resource rich computing model with features such as flexibility, pay per use, elasticity, scalability, and others. In this context, auto scaling and elasticity are methods used to assure the efficient use of resources. There are many factors related to the auto scaling mechanism that might affect the performance of the cloud services. Unfortunately, existing approaches to improve the performance of the cloud services are inadequate. In this paper, we proposed a mechanism called "automatic healing of services in a cloud computing environment" in which server downs are avoided and in turn, preserving the data loss. To prevent data loss during server crashes, we implemented a method of two folds. One is monitoring the process running on Port/PID, checking the CPU, Disk and memory within the instance and taking respective actions. Second one is automating the process mentioned in the first step. While monitoring, we got to know which service reaches CPU utilization 90%. Then automatic resume of such service takes place, avoiding server downs. One more contribution of our paper is users can define their own rules based on which actions will be triggered according to the requirement. We implemented our method in AWS cloud where CloudWatch is the monitoring tool. Likewise the proposed method can be implemented in any of the clouds. Compared with the previous efforts, our method is efficient and yields better results.
DOI:10.1109/ICACCCT.2016.7831738