Open AccessA Study And Analysis Of Forecasts In Resource Allocation Using ARIMA In Cloud Environment
Author(s) -
L. Rajalakshmi
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i5.2029
Subject(s) - cloud computing , computer science , scalability , metric (unit) , autoregressive integrated moving average , resource (disambiguation) , set (abstract data type) , data center , the internet , data science , world wide web , database , business , computer network , marketing , time series , machine learning , programming language , operating system
Cloud computing refers to the delivering or usage of hosted services over internet rather than a traditional data center. Hosted services can be renting infrastructure/resources on demand or using the cloud as a platform to develop applications or using the cloud to host software that are accessed by clients. The bottom line is to obtain affordable resources from a provider and pay as you go in a flexible manner. In doing so, not all resources need to be obtained upfront. The initial capacity can be rented out and the remaining can be scaled as per the need. To handle such scalability, auto-scaling systems helps tackling the need to maintain the finite set of resources that can serve the current need and on the other hand also reduce the resources when the current need decreases. Very often in a cloud based environment it makes sense to adapt proactive strategies to scale the resources than to react after the surge had occurred. The proactive strategies use a quantified metric as a input to provision resources on demand that could meet the future expectations. This metric is obtained by carefully analysing the historical data of the application and in turn can influence the scaling decisions. Conclusions are drawn about the accuracy of the metric based on different timelines of historical information along with the confidence levels with which the prediction is done.