Adaptive cloud resource management through workload prediction

Abstract

Resource management strategy in adaptive cloud provisions the needed resources dynamically to the end-users. To improve the runtime performance of adaptive cloud for service-based applications, two aspects of technical issues are required to be addressed. The first one is the balancing of a large amount of data on existing resources and the second is resource provisioning which can adjust the number of resources optimally to adapt the time-varying workload. As the growth of data is increasing tremendously, efficient resource management is the need in cloud computing. We build a cloud framework to process data in automation with adaptive resource and workload management strategy. Numbers of approaches are reviewed and applied for workload prediction. We developed the model Auto-Regressive Integrated Moving Average-workload Prediction for Efficient Resource Provisioning (ARIMA-PERP) and evaluated the results that can satisfy the on-demand need of end-users for efficient resource utilization. To serve the maximum number of user requests, performance metrics of the proposed approach are evaluated. It is observed that our evaluated results achieved an accurate prediction by 91.11%, which meets the efficient resource utilization for the demanded workload. As compared with the exiting approach, we achieved better performance by 0.11% for accurate prediction. The proposed architecture is intended to provide the resources dynamically and efficiently satisfying the demands of the user. To achieve this objective of efficient resource provisioning, algorithms are developed for workload prediction which helps in deciding optimum resource provisioning. Our system uses proactive approach resource management and deployment of the adaptive cloud system. In traditional systems, resources are managed based on demand, availability and the strategy of scheduling which results in delayed response time at large. We configured the ARIMA model to predict the future workload for provisioning the resources dynamically and remove the problem of over-provisioning and under-provisioning in a cloud environment.