Efficient on-line processor scheduling for a class of IRIS ( increasing reward with increasing service ) real-time tasks

Abstract

In this paper we consider the problem of on-line scheduling of real-time tasks which receive a "reward" that depends on the amount of service received. In our model, tasks have associated deadlines at which they must depart the system. The task computations are such that the longer they are able to execute before their deadline, the greater the value of their computations, i.e., the tasks have the property that they receive increasing reward with increasing service (IRIS) . We focus on the problem of scheduling IRIS tasks in a system in which tasks arrive randomly over time, with the goal of maximizing the average reward accrued per task and per unit time. We describe and evaluate a two-level policy for this system. A top-level algorithm executes each time a task arrives and determines the amount of service to allocate to each task in the absence of future arrivals. A lower-level algorithm, an earliest deadline first (EDF) policy in our case, is responsible for the actual selection of tasks to execute. This two-level policy is evaluated through a combination of analysis and simulation, We observe that it provides nearly optimal performance when the variance in the interarrival times and/or laxities is low and that the performance is more sensitive to changes in the arrival process than the deadline distribution.