• Journal of KIISE:Computer Systems and Theory
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• v.30 no.1
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• pp.12-21
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• 2003

In this paper, we propose an on-line scheduling algorithm with the goal of maximizing the total reward of IRIS (Increasing Reward with Increasing Service) real-time tasks that have reward functions and arrive dynamically into the system. We focus on enhancing the performance of scheduling algorithm, which W.: based on the following two main ideas. First, we show that the problem to maximize the total reward of dynamic tasks can also be solved by the problem to find minimum of maximum derivatives of reward functions. Secondly, we observed that only a few of scheduled tasks are serviced until a new task arrives, and the rest tasks are rescheduled with the new task. Based on our observation, the Proposed algorithm doesn't schedules all tasks in the system at every scheduling print, but a part of tasks. The performance of the proposed algorithm is verified through the simulations for various cases. The simulation result showed that the computational complexity of proposed algorithm is$O(N_2)$ in the worst case which is equal to those of the previous algorithms, but close to O(N) on the average.

• The KIPS Transactions:PartA
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• v.10A no.3
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• pp.181-188
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• 2003

We propose a heuristic on-line scheduling algorithm for the IRIS (Increasing Reward with Increasing Service) tasks, which has low computation complexity and produces total reward approximated to that of previous on-line optimal algorithms. The previous on-line optimal algorithms for IRIS tasks perform scheduling on all tasks in a system to maximize total reward. Therefore, the complexities of these algorithms are too high to apply them to practical systems handling many tasks. The proposed algorithm doesn´t perform scheduling on all tasks in a system, but on (constant) W´s tasks selected by a predefined task selection policy. The proposed algorithm is based on task selection policies that define how to select tasks to be scheduled. We suggest two simple and intuitive selection policies and a generalized selection policy that integrates previous two selection policies. By narrowing down scheduling scope to only W´s selected tasks, the computation complexity of proposed algorithm can be reduced to O(Wn). However, simulation results for various cases show that it is closed to O(W) on the average.