Scheduling on-demand broadcasts: new metrics and algorithms
MobiCom '98 Proceedings of the 4th annual ACM/IEEE international conference on Mobile computing and networking
Scheduling data broadcast in asymmetric communication environments
Wireless Networks
Efficient algorithms for scheduling data broadcast
Wireless Networks
R × W: a scheduling approach for large-scale on-demand data broadcast
IEEE/ACM Transactions on Networking (TON)
A new hybrid broadcast scheduling algorithm for asymmetric communication systems
ACM SIGMOBILE Mobile Computing and Communications Review
Prefetching from Broadcast Disks
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
IWDC '02 Proceedings of the 4th International Workshop on Distributed Computing, Mobile and Wireless Computing
Pinwheel Scheduling for Fault-Tolerant Broadcast Disks in Real-time Database Systems
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
AIDA-based real-time fault-tolerant broadcast disks
RTAS '96 Proceedings of the 2nd IEEE Real-Time Technology and Applications Symposium (RTAS '96)
Airdisks and AirRAID: Modeling and scheduling periodic wireless data broadcast
Airdisks and AirRAID: Modeling and scheduling periodic wireless data broadcast
Hybrid polling and contention access scheduling in IEEE 802.11e WLANs
Journal of Parallel and Distributed Computing
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In this paper, we present a novel hybrid push-pull algorithm which combines broadcasting of push data items, with dissemination upon request of pull items in asymmetric communication environments. These environments are made up only of one database server and many clients. Requests made by the clients are queued up for the pull items. The (pull) item with the number of pending requests is the one selected to be pulled. We present a performance analysis of our scheme, and determine the individual response time for each item disseminated and the overall time for the pull queue to be flushed. Next, we extend our algorithm by incorporating quality of service (QoS) factors, and then, study its performance analytically.