Efficient data allocation over multiple channels at broadcast servers

Abstract

Broadcast is a scalable way of disseminating data because broadcasting an item satisfies all outstanding client requests for it. However, because the transmission medium is shared, individual requests may have high response times. In this paper, we show how to minimize the average response time given multiple broadcast channels by optimally partitioning data among them. We also offer an approximation algorithm that is less complex than the optimal and show that its performance is near-optimal for a wide range of parameters. Finally, we briefly discuss the extensibility of our work with two simple, yet seldom researched extensions, namely, handling varying sized items and generating single channel schedules.

On-demand data broadcast with deadlines for avoiding conflicts in wireless networks

“…Consequently, due to the parallelism of multiple channels, Vaidya and Hameed (1999) proposed several algorithms to determine data scheduling pattern that minimizes the waiting time of client in multiple channels. Yee et al (2002) also proposed a greedy scheme to broadcast data items in multiple channels. Schabanel (2000) studied a generalization of the model for scheduling indivisible messages.…”

“…Two known research streams on the data scheduling problem are push-based (Yee et al, 2002) and pull-based (Chen et al, 2010) data broadcast. In push-based data broadcast, the server periodically distributes some pre-selected data items at the channels according to a-priori knowledge of clients' requests, such as the access probability.…”

An Efficient Interaction Framework for Mobile Web Services

“…Another paper describes different broadcast channel structures which were invented to share any data through a cell phone broadcast network [9]. Yet another research introduces the concept of skewing the data to accommodate the fluctuations in the number of requests for different types of data [7,9,11].…”

“…Due to its scalability, the number of users does not affect the response time for retrieving the information. Also advantageous is the fact that broadcast does not consume significant amounts of power on the client side [7,9,11,12].…”

Scheduling Parallel Data Retrievals for Non-consecutive Broadcast in MIMO Wireless Networks

“…It commonly orders all data items from hot to cold and reasonable gives different type of data with * Corresponding author: hshen@bjtu.edu.cn different broadcasting probability. Consequently, [8], [16], [4], [10], [14], [12], [19], [11], [6], [5] proposed several schemes to accurately locate data in broadcasting channel and highly hoped to reduce access time. Some of these schemes generally assume data which can repeat or unrepeat, consecutive or nonconsecutive in a channel.…”