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Adaptive Workload Forecasting in Cloud Data Centers
- Source :
- Journal of Grid Computing. 18:149-168
- Publication Year :
- 2019
- Publisher :
- Springer Science and Business Media LLC, 2019.
-
Abstract
- Forecasting on different levels of the management system of a cloud data center has received increased attention due to its significant impact on the cloud services quality. Making accurate forecasts, however, is challenging due to the non-stationary workload and intrinsic complexity of the management system of a cloud data center. It is possible to prevent excessive resource allocation and service level agreement violations through workload forecasting for virtual machines and containers. In this paper, the authors propose the adaptive forecasting model and corresponding adaptive forecasting methods to apply in the management system of a cloud data center for workload forecasting, ensuring compliance with the service level agreement and power consumption decrease. The authors consider six alternative forecasting methods and 77 training data windows on each management step to determine the best combination of methods and the training set size that generates a most accurate forecast, thereby adapting to the current state of the physical or virtual server in a cloud data center. Through the comprehensive analysis, the authors also evaluate the proposed adaptive forecasting methods using real-world workload traces Bitbrains and demonstrate that combined forecasting methods outperform the individual forecasting methods significantly in terms of forecasting accuracy measured by Mean Absolute Percentage Error.
- Subjects :
- 020203 distributed computing
Computer Networks and Communications
Computer science
business.industry
media_common.quotation_subject
020206 networking & telecommunications
Workload
Cloud computing
02 engineering and technology
computer.software_genre
ComputingMilieux_GENERAL
Service-level agreement
Mean absolute percentage error
Hardware and Architecture
Virtual machine
Management system
0202 electrical engineering, electronic engineering, information engineering
Resource allocation
Quality (business)
Data mining
business
computer
Software
Information Systems
media_common
Subjects
Details
- ISSN :
- 15729184 and 15707873
- Volume :
- 18
- Database :
- OpenAIRE
- Journal :
- Journal of Grid Computing
- Accession number :
- edsair.doi...........3c49e62457bb9fc8eeabdb5e56ec301e