Open AccessPredictive Cloud resource management framework for enterprise workloads
Author(s) -
Mahesh Balaji,
Ch. Aswani Kumar,
G. Subrahmanya V.R.K. Rao
Publication year - 2016
Publication title -
journal of king saud university - computer and information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.617
H-Index - 33
eISSN - 2213-1248
pISSN - 1319-1578
DOI - 10.1016/j.jksuci.2016.10.005
Subject(s) - autoregressive integrated moving average , metric (unit) , provisioning , cloud computing , key (lock) , computer science , workload , data mining , time series , machine learning , engineering , operations management , operating system
The study proposes an innovative Predictive Resource Management Framework (PRMF) to overcome the drawbacks of the reactive Cloud resource management approach. Performance of PRMF was compared with that of a reactive approach by deploying a timesheet application on the Cloud. Key metrics of the simulated workload patterns were monitored and analyzed offline using information gain module present in PRMF to determine the key evaluation metric. Subsequently, the best-fit model for the key evaluation metric among Autoregressive Integrated Moving Average (ARIMA) (1⩽p⩽4, 0
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