Predictive modeling of streaming servers

ABSTRACT

In this paper, we describe our approach to deriving saturation models for streaming servers from vector-labeled training data. If a streaming server is driven into saturation by accepting too many clients, the quality of service degrades across the sessions. The actual saturating load on a streaming server depends on the detailed characteristics of the client requests: the content location (local disk or stream relay), the relative popularity, and the bit and packet rates [1]. Previous work in streaming-server models has used carefully selected, low-dimensional measurements, such as client jitter and rebuffering counts [2], or server memory usage [3]. In contrast, we collect 30 distinct low-level measures and 210 nonlinear derivative measures each second. This provides us with robustness against outliers, without reducing sensitivity or responsiveness to changes in load. Since the measurement dimensionality is so high, our approach requires the modeling and learning framework described in this paper.

REFERENCES

• 1: M. Covell, B. J. Seo, S. Roy, M. Spasojevic, L. Kontothanassis, N. Bhatti, and R. Zimmermann, "Calibration and Prediction of Streaming-Server Performance," Tech. Rep. HPL-2004-206R1, Hewlett-Packard Laboratories, 2004.
• 2: A. C. Dalal and E. Perry, "A new architecture for measuring and assessing streaming media quality," in Passive and Active Measurement Workshop, (La Jolla CA), 2003.
• 3: L. Cherkasova, W. Tang, and A. Vahdat, "Mediaguard: a model-based framework for building qos-aware streaming media services," in SPIE Conference on Multi-Media Computing and Networking, 2005.
• 4: Laurent El Ghaoui , Hervé Lebret, Robust Solutions to Least-Squares Problems with Uncertain Data, SIAM Journal on Matrix Analysis and Applications, v.18 n.4, p.1035-1064, Oct. 1997 [doi>10.1137/S0895479896298130]
• 5: M. Knop, J. Schopf, and P. Dinda, "Windows Performance Monitoring and Data Reduction using WatchTower," in Proc. of the Workshop on Self-Healing, Adpative, and Self-Managed Systems, (June), 2002.
• 6: Y. Feng, V. Zarzoso, and A. K. Nandi, "Quality Monitoring of WDM Channels with Blind Signal Separation Methods," Journal of Opitcal Networking, 2004.
• 7: RealNetworks Inc., "Helix Universal Server." http://realnetworks.com/products/media_delivery.html.

INDEX TERMS

Primary Classification:
  C. Computer Systems Organization
  C.5 COMPUTER SYSTEM IMPLEMENTATION
      C.5.5 Servers

Additional Classification:
  I. Computing Methodologies
  I.6 SIMULATION AND MODELING
      I.6.4 Model Validation and Analysis
      I.6.5 Model Development

General Terms:
Design, Performance, Theory