This Article 
 Bibliographic References 
 Add to: 
Optimal Lexicographic Shaping of Aggregate Streaming Data
April 2005 (vol. 54 no. 4)
pp. 398-408
We investigate the problem of smoothing multiplexed network traffic when either a streaming server transmits data to multiple clients or a storage server accesses data from multiple storage devices or other servers. We introduce efficient algorithms for lexicographically optimally smoothing the aggregate bandwidth requirements over a shared network link. Possible applications include improvement in the bandwidth utilization of network links and reduction in the energy consumption of server hosts. In the data transmission problem, we consider the case in which the clients have different buffer capacities and unlimited bandwidth constraints or unlimited buffer capacities and different bandwidth constraints. For the data access problem, we handle the general case of a shared buffer capacity and individual network bandwidth constraints. Previous approaches for the data access problem handled either the case of only a single stream or did not compute the lexicographically optimal schedule. By provably minimizing the variance of the required aggregate bandwidth, lexicographically optimal smoothing makes the maximum resource requirements within the network more predictable and increases the useful resource utilization. It also improves fairness in sharing a network link among multiple users and makes new requests from future clients more likely to be successfully admitted without the need for rescheduling previously accepted traffic. With appropriate hardware and system support, data traffic smoothing can also reduce the energy consumption of the host processor and the communication links. Overall, we expect that efficient resource management at the network edges will better meet quality of service requirements without restricting the scalability of the system.

[1] M. Elnozahy, M. Kistler, and R. Rajamony, “Energy Conservation Policies for Web Servers,” Proc. USENIX Symp. Internet Technologies and Systems, pp. 99-112, Mar. 2003.
[2] I. Stoica, S. Shenker, and H. Zhang, “Core-Stateless Fair Queueing: Achieving Approximately Fair Bandwidth Allocations in High Speed Networks,” Proc. ACM SIGCOMM Conf., pp. 118-130, Sept. 1998.
[3] B. Yener, G. Su, and E. Gabber, “Smart Box Architecture: A Hybrid Solution for IP QoS Provisioning,” Computer Networks J., vol. 3, no. 3, pp. 357-375, 2001.
[4] S.-B. Lee, G.-S. Ahn, X. Zhang, and A.T. Campbell, “Insignia: An IP-Based Quality of Service Framework for Mobile Ad Hoc Networks,” J. Parallel and Distributed Computing, vol. 60, no. 4, pp. 374-406, Apr. 2000.
[5] J.D. Salehi, Z.-L. Zhang, J.F. Kurose, and D. Towsley, “Supporting Stored Video: Reducing Rate Variability and End-to-End Resource Requirements through Optimal Smoothing,” IEEE/ACM Trans. Networking, vol. 6, no. 4, pp. 397-410, Aug. 1998.
[6] W. Zhao and S.K. Tripathi, “Bandwidth-Efficient Continuous Media Streaming through Optimal Multiplexing,” Proc. ACM SIGMETRICS Conf., pp. 13-22, June 1999.
[7] T.D. Burd, T.A. Pering, A.J. Stratakos, and R.W. Brodersen, “A Dynamic Voltage Scaled Microprocessor System,” IEEE J. Solid-State Circuits, vol. 35, no. 11, pp. 1571-1580, Nov. 2000.
[8] L. Shang, L.-S. Peh, and N.K. Jha, “Dynamic Voltage Scaling with Links for Power Optimization of Interconnection Networks,” Proc. Int'l Symp. High Performance Computer Architecture, pp. 91-102, Feb. 2003.
[9] A.R. Reibman and A.W. Berger, “Traffic Descriptors for VBR Video Teleconferencing over ATM Networks,” IEEE/ACM Trans. Networking, vol. 3, no. 3, pp. 329-339, June 1995.
[10] S. Gringeri, K. Shuaib, R. Egorov, A. Lewis, B. Khasnabish, and B. Basch, “Traffic Shaping, Bandwidth Allocation, and Quality Assessment for MPEG Video Distribution over Broadband Networks,” IEEE Network, vol. 12, no. 6, pp. 94-107, Nov./Dec. 1998.
[11] T.V. Lakshman, A. Ortega, and A.R. Reibman, “VBR Video: Tradeoffs and Potentials,” Proc. IEEE, vol. 86, no. 5, pp. 952-973, May 1998.
[12] T. Ibaraki and N. Katoh, Resource Allocation Problems: Algorithmic Approaches. Series on the Foundations of Computing, MIT Press, 1988.
[13] D.T. Hoang and J.S. Vitter, Efficient Algorithms for MPEG Video Compression. New York: John Wiley & Sons, 2002.
[14] W.-C. Feng and J. Rexford, “Performance Evaluation of Smoothing Algorithms for Transmitting Prerecorded Variable-Bit-Rate Video,” IEEE Trans. Multimedia, vol. 1, no. 3, pp. 302-313, 1999.
[15] J. McManus and K. Ross, “A Dynamic Programming Methodology for Managing Prerecorded VBR Sources in Packet-Switched Networks,” Telecomm. Systems, vol. 9, pp. 223-247, 1998.
[16] D.T. Hoang, P.M. Long, and J.S. Vitter, “Efficient Cost Measures for Motion Compensation at Low Bit Rates,” Proc. IEEE Data Compression Conf., Apr. 1996.
[17] J. Rexford, S. Sen, J. Dey, W. Feng, J. Kurose, J. Stankovic, and D. Towsley, “Online Smoothing of Live, Variable-Bit-Rate Video,” IEEE Trans. Multimedia, vol. 2, no. 1, pp. 37-48, Mar. 2000.
[18] Y. Mansour, B. Patt-Shamir, and O. Lapid, “Optimal Smoothing Schedules for Real-Time Streams,” Proc. ACM Symp. Principles of Distributed Computing, pp. 21-29, July 2000.
[19] S. Paek and S.-F. Chang, “Video Server Retrieval Scheduling for Variable Bit Rate Scalable Video,” Proc. IEEE Int'l Conf. Multimedia Computing and Systems, pp. 108-112, June 1996.
[20] S. Sahu, Z.-L. Zhang, J. Kurose, and D. Towsley, “On the Efficient Retrieval of VBR Video in a Multimedia Server,” Proc. IEEE Int'l Conf. Multimedia Computing and Systems, pp. 46-53, June 1997.
[21] A.L.N. Reddy and R. Wijayaratne, “Techniques for Improving the Throughput of VBR Streams,” Proc. SPIE/ACM Multimedia Computing and Networking Conf., pp. 216-227, Jan. 1999.
[22] S.V. Anastasiadis, K.C. Sevcik, and M. Stumm, “Shared-Buffer Smoothing of Variable Bit-Rate Streams,” Performance Evaluation, vol. 59, no. 1, pp. 47-72, Jan. 2005.
[23] S.V. Anastasiadis, P. Varman, J.S. Vitter, and K. Yi, “Lexicographically Optimal Smoothing for Broadband Traffic Multiplexing,” Proc. ACM Symp. Principles of Distributed Computing, pp. 68-77, July 2002.
[24] F. Yao, A. Demers, and S. Shenker, “A Scheduling Model for Reduced CPU Energy,” Proc. IEEE Symp. Foundations of Computer Science, pp. 374-382, Oct. 1995.
[25] J.R. Lorch and A.J. Smith, “Improving Dynamic Voltage Scaling Algorithms with Pace,” Proc. ACM SIGMETRICS Conf., pp. 50-61, June 2001.
[26] R.L. Graham and F.F. Yao, “Finding the Convex Hull of a Simple Polygon,” J. Algorithms, vol. 4, no. 4, pp. 324-331, Dec. 1983.
[27] D.T. Lee and F.P. Preparata, “Euclidean Shortest Path in the Presence of Rectilinear Barriers,” Networks, vol. 14, pp. 393-410, 1984.

Index Terms:
Data smoothing, streaming, prefetching, energy efficiency, content distribution networks, client/server systems, optimization.
Stergios V. Anastasiadis, Peter Varman, Jeffrey Scott Vitter, Ke Yi, "Optimal Lexicographic Shaping of Aggregate Streaming Data," IEEE Transactions on Computers, vol. 54, no. 4, pp. 398-408, April 2005, doi:10.1109/TC.2005.67
Usage of this product signifies your acceptance of the Terms of Use.