Paper Title :Node Failure Prediction in a Cloud Environment: A Comparative Analysis Using TSA
Author :Shaziya Kauser, Jeni Gracia Rc, Mathivanan, Lakshmi Kanthan Narayanan, T Suresh Bala Krishnan
Article Citation :Shaziya Kauser ,Jeni Gracia Rc ,Mathivanan ,Lakshmi Kanthan Narayanan ,T Suresh Bala Krishnan ,
(2024 ) " Node Failure Prediction in a Cloud Environment: A Comparative Analysis Using TSA " ,
International Journal of Advances in Computer Science and Cloud Computing (IJACSCC) ,
pp. 56-59,
Volume-12,Issue-2
Abstract : The role of cloud-based systems, which are composed of a very large number of computing nodes and are
capable of providing a wide range of services in a live environment, has increased as a result of the computing industry's
recent and spectacular rise. Despite the fact that nodes can be connected or wireless, additional failures are possible. Node
failure will cause services to be interrupted and destroyed, which is referred to as service downtime. Here, the issue of
Provider of Cloud Services (CSP) in terms of Quality of Service (QoS) occurs, causing the Cloud Service Vendors a lot of
trouble. The ability to foresee downtime before it really happens is crucial for overcoming the weakness of the high demand
computational model. In this paper, we propose a fault prediction technique that uses deep learning methods and is based on
historical metrics like CPU, memory, and power usage. This technique will make it easier for the fault prediction model to
identify the nodes that can migrate services to healthy nodes, which will be a key element in minimising the impact on the
services that are currently available. The moving average that is autoregressively integrated (ARIMA), a classic statistical
forecasting model, and the least square linear regression analysis, a regression-based model, are contrasted for fault
occurrence in clustered nodes on the basis of time series data.
Keywords - Time Series, ARIMA, Node Failure Prediction.
Type : Research paper
Published : Volume-12,Issue-2
Copyright: © Institute of Research and Journals
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Published on 2025-04-25 |
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