Average True Range Approach for Resource Scheduling and Allocation in Cloud Computing Networks
Keywords:
Cloud Resource, Exponential Moving Average (EMA), Average True Range (ATR), longest expected processing time (LEPT), Machine learningAbstract
Purpose: Resource allocation in cloud computing has significant importance, in adhering to optimal utilization of resources, and maintaining service quality among the service networks. Scores of machine learning models and other contemporary solutions were proposed for handling the resource allocation process. However, one of the challenges with many of the existing solutions is complexity in the process and occupying the space for internal application executions.
Approach: Thus, in this manuscript, the attempt is to propose a simplistic approach of statistical model in the combination of average true range (ATR) and exponential moving average (EMA) models for multi-layer decision-making process. The proposed system is about identifying the average true range of service requests received and processed over time and applying the decision-making matrix at multiple scenarios.
Findings: An experimental study of the model executed on a data generated from Cloudsim simulations, refers to the effectiveness of the system, wherein the accuracy is high in terms of indicating the potential need for alternate resources or optimal use of the existing resources.
Originality: Considering the linearity of the model, it is effective for small-scale IaaS environments. In future research, a potential mode of applying the solution over a machine learning approach to improve the overall efficacy of the system can be considered more pragmatic for enhancing the outcome.
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