An on‐demand data broadcasting scheduling algorithm based on dynamic index strategy

Abstract

On-demand data broadcasting scheduling is an effective wireless data dissemination technique. Existing scheduling algorithms usually have two problems: (1) with the explosive growth of mobile users and real-time individual requirements, broadcasting systems present a shortage of scalability, dynamics and timeliness (request drop ratio); (2) with the growth of intelligent and entertained application, energy consumption of mobile client cannot be persistent (tuning time). This paper proposes an effective scheduling algorithm LxRxW. It takes into account the number of lost requests during next item broadcasting time, the number of requests and the waiting time. LxRxW can reduce the request drop ratio. At the same time, the algorithm employs a dynamic index strategy to put forward a dynamic adjusting method on the index cycle length (DAIL) to determine the proper index cycle. Extensive experimental results show that the LxRxW algorithm has better performance than other state-of-the-art scheduling algorithms and can significantly reduce the drop ratio of user requests by 40%-50%. The request drop ratio and accessing time of LxRxW with index increase by 1%-2% than LxRxW algorithm without index, but the tuning time decreases by 70%. The index strategy shows that when the index cycle length is less than 20 units, it can significantly reduce the average tuning time but when the index cycle length continues increasing, the average tuning time will increase contrarily. DAIL can dynamically determine the length of index cycle. Moreover, it can reach optimal integrated performance of the request drop ratio, the average accessing time and the average tuning time.

Efficient Coding-Based Scheduling for Multi-Item Requests in Real-Time On-Demand Data Dissemination

“…On the contrary, on-demand data dissemination schedules requests in the service queue and broadcasts data based on various attributes of pending data items at the server. By considering different clients' requirements for data acquisition, on-demand broadcast is more widely used for dynamic, large-scale data dissemination and attracts more and more research interests in recent years [14][15][16][17]. Meanwhile, data items are related and have temporality for many popular information services, such as the traffic condition inquiries, the weather report queries, and the stock quotes.…”