Quality optimizing real-time scheduling for incremental context services

A two phase task model was proposed in order to achieve optimal user experience for an incremental context service under resource constraints and with context timeliness requirements. The model divides each job of an incremental service into a primary part that provide the initial service and a optional part that provide the improvements. A corresponding two level scheduling theme was designed to utilize the model. Several scheduling algorithms were designed to execute the primary parts in a timely manner and the optional parts in a value maximizing manner by analyzing the schedulability of the primary parts and the value generation pattern of the optional parts. Simulation results show that all the three primary part scheduling algorithms can meet context timeliness requirements under resource constraints. The performances of the optional part scheduling algorithm were compared when using different value prediction methods. Results show that the simpler delta value method can achieve performance close to that of the fit curve method, and is more suitable for practical use.