Near Optimal Number of Replicas for Continuous Media in Ad-hoc Networks of Wireless Devices

This study investigates replication of data in a novel streaming architecture consisting of ad-hoc networks of wireless devices. One application of these devices is home-to-home (H2O) entertainment systems where a device collaborates with others to provide each household with on-demand access to a large selection of audio and video clips. These devices are configured with a substantial amount of storage and may cache several clips for future use. A contribution of this study is a technique to compute the number of replicas for a clip based on the square-root of the product of bandwidth required to display clips and their frequency of access , i.e., where . We provide a proof to show this strategy is near optimal when the objective is to maximize the number of simultaneous displays in the system with string and grid (both symmetric and asymmetric) topologies. We say “near optimal” because values of less than 0.5 may be more optimum. In addition, we use analytical and simulation studies to demonstrate its superiority when compared with other alternatives. A second contribution is an analytical model to estimate the theoretical upper bound on the number of simultaneous displays supported by an arbitrary grid topology of H2O devices. This analytical model is useful during capacity planning because it estimates the capabilities of a H2O configuration by considering: the size of an underlying repository, the number of nodes in a H2O cloud, the representative grid topology for this cloud, and the expected available network bandwidth and storage capacity of each device. It shows that one may control the ratio of repository size to the storage capacity of participating nodes in order to enhance system performance. We validate this analytical model with a simulation study and quantify its tradeoffs.

[1]  John C. S. Lui,et al.  Design of Scalable Continuous Media Servers , 2004, Multimedia Tools and Applications.

[2]  Shahram Ghandeharizadeh,et al.  An evaluation of alternative continuous media replication techniques in wireless peer-to-peer networks , 2003, MobiDe '03.

[3]  Leana Golubcidk.  Threshold-Based Dynamic Replication in Large-Scale Video-on-Demand Systems , 2000 .

[4]  Carey L. Williamson,et al.  Temporal locality and its impact on Web proxy cache performance , 2000, Perform. Evaluation.

[5]  Edith Cohen,et al.  Replication strategies in unstructured peer-to-peer networks , 2002, SIGCOMM.

[6]  P. Venkat Rangan,et al.  Multimedia Storage Servers: A Tutorial , 1995, Computer.

[7]  Azer Bestavros,et al.  Temporal Locality in Web Request Streams , 2000 .

[8]  Cyrus Shahabi,et al.  Pipelining Mechanism to Minimize the Latency Time in Hierarchical Multimedia Storage Managers , 1995, Comput. Commun..

[9]  Toshihide Ibaraki,et al.  Resource allocation problems - algorithmic approaches , 1988, MIT Press series in the foundations of computing.

[10]  Asit Dan,et al.  Buffering and caching in large-scale video servers , 1995, Digest of Papers. COMPCON'95. Technologies for the Information Superhighway.

[11]  John C. S. Lui,et al.  Threshold-Based Dynamic Replication in Large-Scale Video-on-Demand Systems , 2004, Multimedia Tools and Applications.

[12]  Shahram Ghandeharizadeh,et al.  Design and Implementation of Scalable Continuous Media Servers , 1998, Parallel Comput..

[13]  Shahram Ghandeharizadeh.  H2O clouds: issues, challenges and solutions , 2003, Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint.

[14]  Hu Yuqi,et al.  Caching on the World Wide Web , 2003 .

[15]  Sandy Irani,et al.  Cost-Aware WWW Proxy Caching Algorithms , 1997, USENIX Symposium on Internet Technologies and Systems.

[16]  Shahram Ghandeharizadeh.  H 2 O Clouds : Issues , Challenges and Solutions , 2003 .

[17]  Gordon Bell,et al.  MyLifeBits: fulfilling the Memex vision , 2002, MULTIMEDIA '02.

[18]  Vannevar Bush,et al.  As we may think , 1945, INTR.

[19]  Azer Bestavros,et al.  GreedyDual* Web caching algorithm: exploiting the two sources of temporal locality in Web request streams , 2001, Comput. Commun..

[20]  Philip S. Yu,et al.  DASD dancing: a disk load balancing optimization scheme for video-on-demand computer systems , 1995, SIGMETRICS '95/PERFORMANCE '95.

[21]  Li Fan,et al.  Web caching and Zipf-like distributions: evidence and implications , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).