Di Niu
Hong Xu
ABSTRACT
Video-on-demand (VoD) servers are usually over-provisioned for peak demands, incurring a low average resource efficiency. However, bandwidth shortage may still occur for individual videos as they share and contend for server resources. In this position paper, we propose a predictive workload management system for VoD servers targeting bandwidth. The system draws belief about future demand as well as demand volatility based on demand history using time series forecasting techniques. The prediction enables dynamic and efficient server bandwidth reservation with QoS guarantees. More importantly, we use a hedging technique similar to investment portfolio management and distribute workloads to multiple servers exploiting demand anti-correlation. The proposed system consolidates the workloads, enhances resource utilization, while in the meantime effectively controlling risk of server overload. The proposed methods are evaluated based on real-world VoD traces.
- UUSee. http://www.uusee.com.Google Scholar
- T. Bollerslev. Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31:307--327, 1986.Google ScholarCross Ref
- S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004. Google ScholarDigital Library
- Z. Liu, C. Wu, B. Li, and S. Zhao. UUSee: Large-Scale Operational On-Demand Streaming with Random Network Coding. In Proc. IEEE INFOCOM, 2010. Google ScholarDigital Library
- H. Markowitz. Portfolio Selection. The Journal of Finance, 7(1):77--91, March 1952.Google Scholar
- D. Niu, B. Li, and S. Zhao. Understanding Demand Volatility in Large VoD Systems. In Proc. the 21st International workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV), 2011. Google ScholarDigital Library
- D. Niu, Z. Liu, B. Li, and S. Zhao. Demand Forecast and Performance Prediction in Peer-Assisted On-Demand Streaming Systems. In Proc. IEEE INFOCOM Mini-Conference, 2011.Google ScholarCross Ref
Index Terms
- Risk management for video-on-demand servers leveraging demand forecast
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