Preprint Article Version 1 This version is not peer-reviewed

Resource Demand Forecasting Model Based on Dynamic Cloud Workload

Version 1 : Received: 9 October 2017 / Approved: 9 October 2017 / Online: 9 October 2017 (12:40:34 CEST)

How to cite: An, C.; Zhou , J.. Resource Demand Forecasting Model Based on Dynamic Cloud Workload. Preprints 2017, 2017100051 (doi: 10.20944/preprints201710.0051.v1). An, C.; Zhou , J.. Resource Demand Forecasting Model Based on Dynamic Cloud Workload. Preprints 2017, 2017100051 (doi: 10.20944/preprints201710.0051.v1).

Abstract

The primary attraction of IaaS is providing elastic resources on demand. It becomes imperative that IaaS-users have an effective methodology for learning what resources they require, how many resources and for how long they need. However, the heterogeneity of resources, the diversity resource demands of different cloud applications and the variation of application-user behaviors pose IaaS-users big challenge. In this paper, we purpose a unified resource demand forecasting model suiting for different applications, various resources and diverse time-varying workload patterns. With the model, taking input from parameterized applications, resources and workload scenarios, the corresponding resources demands during any time interval can be deduced as output. The experiments configure concrete functions and parameters to help understanding the above model.

Subject Areas

cloud computing; workload model; workload-aware resource forecasting model

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