Broadcast disks with polynomial cost functions

  • Authors:

  • Amotz Bar-Noy;Boaz Patt-Shamir;Igor Ziper

  • Affiliations:

  • Computer and Information Science Department, Brooklyn College, 2900 Bedford Avenue Brooklyn, NY;Department of Electrical Engineering, Tel Aviv University, Tel Aviv 69978, Israel;Department of Electrical Engineering, Tel Aviv University, Tel Aviv 69978, Israel

  • Venue:
  • Wireless Networks
  • Year:
  • 2004

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Abstract

In broadcast disks systems, information is broadcasted in a shared medium. When a client needs an item from the disk, it waits until that item is broadcasted. Broadcast disks systems are particularly attractive in settings where the potential customers have a highly-asymmetric communication capabilities, i.e., receiving is significantly cheaper than transmitting. This is the case with satellite networks, mobile hosts in wireless networks, and Teletext system.The fundamental algorithmic problem for such systems is to determine the broadcast schedule based on the demand probability of items, and the cost incurred to the system by clients waiting. The goal is to minimize the mean access cost of a random client. Typically, it was assumed that the access cost is proportional to the waiting time. In this paper, we ask what are the best broadcast schedules for access costs which are arbitrary polynomials in the waiting time. These may serve as reasonable representations of reality in many cases, where the "patience" of a client is not necessarily proportional to its waiting time.We present an asymptotically optimal algorithm for a fractional model, where the bandwidth may be divided to allow for fractional concurrent broadcasting. This algorithm, besides being justified in its own right, also serves as a lower bound against which we test known discrete algorithms. We show that the Greedy algorithm has the best performance in most cases. Then we show that the performance of other algorithms deteriorate exponentially with the degree of the cost polynomial and approaches the fractional solution for sub-linear cost. Finally, we study the quality of approximating the greedy schedule by a finite schedule.