Analysis of Broadcast Delivery in a Videotex System
IEEE Transactions on Computers
R × W: a scheduling approach for large-scale on-demand data broadcast
IEEE/ACM Transactions on Networking (TON)
CSIM19: CSIM19: a powerful tool for building system models
Proceedings of the 33nd conference on Winter simulation
Scheduling algorithms for multiprogramming in a hard-real-time environment
Readings in hardware/software co-design
Efficient Data Allocation over Multiple Channels at Broadcast Servers
IEEE Transactions on Computers
Data on Air: Organization and Access
IEEE Transactions on Knowledge and Data Engineering
Multi-Level Multi-Channel Air Cache Designs for Broadcasting in a Mobile Environment
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
TOSA: a near-optimal scheduling algorithm for multi-channel data broadcast
Proceedings of the 6th international conference on Mobile data management
On-line balanced k-channel data allocation with hybrid schedule per channel
Proceedings of the 6th international conference on Mobile data management
Time-Critical On-Demand Data Broadcast: Algorithms, Analysis, and Performance Evaluation
IEEE Transactions on Parallel and Distributed Systems
Scheduling real-time requests in on-demand data broadcast environments
Real-Time Systems
Design and Performance Evaluation of Broadcast Algorithms for Time-Constrained Data Retrieval
IEEE Transactions on Knowledge and Data Engineering
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On-demand broadcast is an effective data dissemination approach in mobile computing. Recently, a large number of mobile applications have been developed in broadcast systems equipped with multiple channels. Therefore, it becomes a critical performance consideration for the underlying scheduling algorithm to utilize bandwidth efficiently in multi-channel on-demand broadcast environments. However, we find that existing scheduling algorithms fail to utilize bandwidth efficiently in this new environment and it results in a poor system performance. In this paper, we examine this bandwidth utilization problem. Based on the observation, we propose a bandwidth-efficient scheduling algorithm called COS in multi-channel on-demand broadcast environments. Results from our simulation study demonstrate the superiority of COS.