Optimal Skewed Data Allocation on Multiple Channels with Flat Broadcast per Channel

Abstract

—Broadcast is an efficient and scalable way of transmitting data to an unlimited number of clients that are listening to a channel. Cyclically broadcasting data over the channel is a basic scheduling technique, which is known as flat scheduling. When multiple channels are available, a data allocation technique is needed to assign data to channels. Partitioning data among channels in an unbalanced way, depending on data popularities, is an allocation technique known as skewed allocation. In this paper, the problem of data broadcasting over multiple channels is considered, assuming skewed data allocation to channels and flat data scheduling per channel, with the objective of minimizing the average waiting time of the clients. First, several algorithms, based on dynamic programming, are presented which provide optimal solutions for N data items and K channels. Specifically, for data items with uniform lengths, an OðNK log NÞ time algorithm is proposed, which improves over the previously known OðN 2 KÞ time algorithm. When K 4, a simpler OðN log NÞ time algorithm is exhibited which requires only OðNÞ time if the data items are sorted. Moreover, for data items with nonuniform lengths, it is shown that the problem is NP-hard when K ¼ 2 and strong NP-hard for arbitrary K. In the former case, a pseudopolynomial algorithm is discussed whose time is OðNZÞ, where Z is the sum of the data lengths. In the latter case, an algorithm is devised with time exponential in the maximum data length, which can optimally solve, in reasonable time, only small instances. For larger instances, a new heuristic is devised which is experimentally tested on some benchmarks whose popularities are characterized by Zipf distributions. Such experimental tests reveal that the new heuristic proposed here always outperforms the best previously known heuristic in terms of solution quality.

DOI: 10.1109/TC.2005.81

Extracted Key Phrases

8 Figures and Tables

Showing 1-10 of 19 references

For more information on this or any other computing topic, please visit our Digital Library at www.computer.org/publications/dlib

  • 2005

Shashanka received the BE (Hons) in computer science from the Birla Institute of Technology and Science He is currently a graduate student

  • V S Madhusudana
  • 2003

Efficient Data Allocation for Broadcast Disk Arrays

  • W G Yee
  • 2001
Showing 1-10 of 34 extracted citations

Statistics

051015'05'06'07'08'09'10'11'12'13'14'15'16'17
Citations per Year

60 Citations

Semantic Scholar estimates that this publication has received between 35 and 108 citations based on the available data.

See our FAQ for additional information.