Maximizing online service profit for time-dependent applications

As computers and Internet technology advance, many time-dependent applications, such as mobile navigation and online gaming, are emerging. Time-dependent applications are associated with a pair of time-dependent functions representing accrued gain at the time when tasks complete or accrued cost when tasks fail to complete, respectively. For systems that provide time-dependent services, the optimization goal is to maximize the system profit when both potential gain and potential cost exist for each request the system accepts. This paper presents two scheduling algorithms, prediction-based highest gain density first (PHGDF) and iterative PHGDF (iPHGDF) scheduling algorithms with the objective to maximize the system's total accrued profit. Simulation results provide clear evidence that, with respect to the system total accrued profit, the proposed PHGDF and iPHGDF algorithms have advantages over other commonly used scheduling algorithm, such as the Earliest Deadline First (EDF), the Generic Utility Scheduling (GUS) and the Profit and Penalty aware scheduling (PP-aware) algorithms.