A Literature Review and Taxonomy on Workload Prediction in Cloud Data Center

Resource management is one of the most challenging task in the cloud data center. These challenges have raised from the dynamic nature and high uncertainty in the cloud environment. Moreover, allocating resources over time may lead the sub-optimal execution environment due to significant up and drop...

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Bibliographic Details
Published in2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) pp. 415 - 420
Main Authors Vashistha, Avneesh, Verma, Pushpneel
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2020
Subjects
Cloud computing
Data centers
Data models
Mathematical model
Non-Temporal
Prediction Accuracy
Predictive models
Taxonomy
Temporal
Workload Patterns
Workload Prediction
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Summary:Resource management is one of the most challenging task in the cloud data center. These challenges have raised from the dynamic nature and high uncertainty in the cloud environment. Moreover, allocating resources over time may lead the sub-optimal execution environment due to significant up and drop in the workload that have some time dependent patterns. Therefore, it requires some time-sensitive techniques for optimising the resources utilization in cloud data center. In this paper, we discuss the workload prediction techniques that forecast the workload in the cloud environment and the value of predicted workload guides for optimising the resources. Furthermore, we present the workload taxonomy which is classified into (i) workload predictor and (ii) model fitting. In addition, we provide an extensive discussion on the workload predictors and further classified into temporal and non-temporal.
DOI:10.1109/Confluence47617.2020.9057938