On-demand broadcast is a scalable approach to disseminating information to a large population of clients while satisfying dynamic needs of clients, such as in vehicular networks. However, in conventional broadcast approaches, only one data item can be retrieved by clients in one broadcast tick. To further improve the efficiency of wireless bandwidth, in this work, we conduct a comprehensive study on incorporating network coding with representative on-demand scheduling algorithms while preserving their original scheduling criteria. In particular, a graph model is derived to maximize the coding benefit based on the clients’ requested and cached data items. Furthermore, we propose a heuristic coding-based approach, which is applicable for all the on-demand scheduling algorithms with low computational complexity. In addition, based on various application requirements, we classify the existing on-demand scheduling algorithms into three groups—real-time, non-real-time and stretch optimal. In view of different application-specific objectives, we implement the coding versions of representative algorithms in each group. Extensive simulation results conclusively demonstrate the superiority of coding versions of algorithms against their non-coding versions on achieving their respective scheduling objectives.
REVIEW OF TASK SCHEDULING METHODS FOR REAL TIME TASKS IN CLOUD ENVIRONMENT