Sikai Xing
Yanmin Zhu
ABSTRACT
Most medium-scale above corporations usually construct their own private clouds, utilizing commodity servers to provide computing resources. With the increase in computing demand, more and more servers are required. On the other hand, in these corporations, there are hundreds or thousands of desktops (physical or virtual personal computers) that are running with low resource utilizations. With the availability of container technologies, such as Docker, it is currently feasible to utilize these potential computing resources. To do so, it is critical to predict the resource consumption of desktops accurately before scheduling jobs for them, in order to improve the execution of jobs. Although some approaches have been proposed to predict the resource utilization of servers, they can't be directly applied to desktops due to the dynamics of desktops. To address this problem, we propose SAAP, a State-Aware Adaptive Prediction strategy for CPU load of desktops. SAAP is capable of dynamically selecting appropriate prediction algorithms to predict the CPU load, adapting to the state of desktops. Besides, two patterns that can improve prediction accuracy are found. To evaluate the effectiveness of SAAP, extensive experiments are conducted. The experimental results demonstrate that SAAP behaves much better than the Box-Jenkins models (AR, MA, ARMA, ARIMA) in prediction accuracy.
- Michael Armbrust, Armando Fox, Rean Griffith, Anthony D. Joseph, Randy H. Katz, Andy Konwinski, Gunho Lee, David A. Patterson, Ariel Rabkin, Ion Stoica, and Matei Zaharia. A view of cloud computing. Commun. ACM, 53(4):50--58, 2010. Google Scholar
Digital Library
- https://aws.amazon.com. {Online; accessed 08-August-2017}.Google Scholar
- Claus Pahl. Containerization and the paas cloud. IEEE Cloud Computing, 2(3):24--31, 2015.Google ScholarCross Ref
- What is docker? https://docker.com/whatisdocker/. {Online; accessed 08-August-2017}.Google Scholar
- https://kubernetes.io/docs/concepts/overview/what-is-kubernetes/. {Online; accessed 08-August-2017}.Google Scholar
- Ran Yang, Robert D. van der Mei, Dennis Roubos, Frank J. Seinstra, and Henri E. Bal. Resource optimization in distributed real-time multimedia applications. Multimedia Tools Appl., 59(3):941--971, 2012. Google ScholarDigital Library
- Peter A. Dinda and David R. O'Hallaron. Host load prediction using linear models. Cluster Computing, 3(4):265--280, 2000. Google ScholarDigital Library
- Lingyun Yang, Ian T. Foster, and Jennifer M. Schopf. Homeostatic and tendency-based CPU load predictions. In 17th International Parallel and Distributed Processing Symposium (IPDPS 2003). Google ScholarDigital Library
- Yuanyuan Zhang, Wei Sun, and Yasushi Inoguchi. CPU load predictions on the computational grid. IEICE Transactions, 90-D(1):40--47, 2007. Google ScholarDigital Library
- Jian Cao, Jiwen Fu, Minglu Li, and Jinjun Chen. CPU load prediction for cloud environment based on a dynamic ensemble model. Softw., Pract. Exper., 44(7):793--804, 2014. Google ScholarDigital Library
- Thomas G. Dietterich. Ensemble methods in machine learning. In Josef Kittler and Fabio Roli, editors, Multiple Classifier Systems, First International Workshop, MCS 2000, Cagliari, Italy, June 21-23, 2000, Proceedings, volume 1857 of Lecture Notes in Computer Science, pages 1--15. Springer, 2000. Google ScholarDigital Library
- Yves Grandvalet. Bagging equalizes influence. Machine Learning, 55(3):251--270, 2004. Google ScholarDigital Library
- Nicholas I. Sapankevych and Ravi Sankar. Time series prediction using support vector machines: A survey. IEEE Comp. Int. Mag., 4(2):24--38, 2009. Google ScholarDigital Library
- http://www.zbj.com/. {Online; accessed 08-August-2017}.Google Scholar
- Box, G. E. P., Jenkins, G. M., and Reinsel. Time Series Analysis: Forecasting and Control. Prentice Hall, 1994. Google ScholarDigital Library
- Peter A. Dinda. The statistical properties of host load. Scientific Programming, 7(3-4):211--229, 1999. Google ScholarDigital Library
- G. Udny Yule. On a method of investigating periodicities in disturbed series, with special reference to wolfer's sunspot numbers. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 226(636-646):267--298, 1927.Google ScholarCross Ref
- Gilbert Walker. On periodicity in series of related terms. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 131(818):518--532, 1931.Google ScholarCross Ref
- M. J. D. Powell. An efficient method for finding the minimum of a function of several variables without calculating derivatives. The Computer Journal, 7(2):155, 1964.Google ScholarCross Ref
- https://eclipse.org. {Online; accessed 08-August-2017}.Google Scholar
- https://www.mathsisfun.com/definitions/range-statistics.html. {Online; accessed 08-August-2017}.Google Scholar
- https://github.com/cs-sjtu/CPU-loads. {Online; accessed 08-August-2017}.Google Scholar
Index Terms
- SAAP: A State-Aware Adaptive Prediction Strategy for CPU Load of Desktops
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