Journal of KIISE:Computer Systems and Theory (한국정보과학회논문지:시스템및이론)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지

Performance Enhancement of On-Line Scheduling Algorithm for IRIS Real-Time Tasks using Partial Solution

부분 해를 이용한 IRIS 실시간 태스크용 온-라인 스케줄링 알고리즘의 성능향상

  • 심재홍 (조선대학교 인터넷스프트웨어공학부) ;
  • 최경희 (아주대학교 정보통신전문대학원) ;
  • 정기현 (아주대학교 전자공학부)
  • Published : 2003.02.01
Download PDF
⟨ Previous Next ⟩

Abstract

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.

본 논문에서는 가치함수를 가지면서 동적으로 도착하는 IRIS(Increasing Reward with Increasing Service) 실시간 태스크들의 총 가치를 최대화하기 위한 온-라인 스케줄링 알고리즘을 제안한다. 본 논문은 스케줄링 알고리즘의 성능향상에 역점을 두고 있으며, 이는 다음 두 가지 아이디어를 기반으로 한다. 첫째, 총가치를 최대화하는 문제는 가치함수들의 최대 도함수 값들 중 최소 값을 찾는 문제를 해결함으로써 풀 수 있다는 것이다. 둘째, 새로운 태스크가 도착하기 전까지 이 전에 스케줄된 태스크들 중 소수만이 실제 실행되고, 나머지는 새로 도착한 태스크와 함께 다시 스케줄링 된다는 사실을 발견하고, 매 스케줄링 시 모든 태스크들을 스케줄링하는 것이 아니라, 일부 태스크들만 스케줄링하자는 것이다. 제안 알고리즘의 성능은 다양한 경우에 대한 모의실험으로 검증되었다. 실험 결과 제안 알고리즘의 계산 복잡도는 최악의 경우 기존 알고리즘과 동일한 $O(N_2)$이지만, 평균적으로 이 보다 낮은 O(N)에 가까운 것으로 확인되었다.

Keywords

References

  1. J.W.S. Liu, Real-Time Systems, Prentice-Hall, 2000
  2. E. Chang and A. Zakhor, 'Scalable Video Coding Using 3-D Subband Velocity Coding and Multi-Rate Quantization,' Proc. IEEE Int'l Conf. Acoustic, Speech, Signal Processing, Minneapolis, July, 1993
  3. G. Jung, K. Yim, J. Jung, J. Shin, K. Choi, D. Kim, and J. Shim, 'An Imprecise DCT Computation Model for Real-Time Applications,' Multimedia Technology and Applications, edited by V. Chow, pp.153-161, Springer, Dec. 1996
  4. J. Grass and S. Zilberstein, 'A Value-Driven System for Autonomous Information Gathering,' J. Intelligent Information Systems, Vol. 14, pp. 5-27, March 2000 https://doi.org/10.1023/A:1008718418982
  5. S.V. Vrbsky and J.W.S. Liu, 'APPROXIMATE - A Query Processor that Produces Monotonically Improving Approximate Answers,' IEEE Trans. Knowledge and Data Eng., Vol. 5, No.6, pp. 1056-1068, Dec. 1993 https://doi.org/10.1109/69.250091
  6. B. Hayes-Roth, 'Architectural Foundations for Real-Time Performance in Intelligent Agents,' J. Real-Time Systems, Vol. 2, No. 1, pp. 99-125, 1990 https://doi.org/10.1007/BF01840468
  7. R.E. Korf, 'Real-Time Heuristic Search,' Artificial Intelligence, Vol. 42, No. 2, pp. 189-212, 1990 https://doi.org/10.1016/0004-3702(90)90054-4
  8. E.J. Horvitz, 'Reasoning under Varying and Uncertain Resource Constraints,' Proc. 7th Nat'l Conf. Artificial Intelligence (AAAI-88), pp. 111-116, St. Paul, Minn., Aug. 1988
  9. M. Boddy and T. Dean, 'Deliberation Scheduling for Problem Solving in Time-Constrained Environments,' Artificial Intelligence, Vol. 67, No. 2, pp. 245-285, June 1994 https://doi.org/10.1016/0004-3702(94)90054-X
  10. J.K. Dey, J.F. Kurose, and D. Towsley, 'On-line Scheduling Policies for a Class of IRIS (Increasing Reward with Increasing Service) Real-Time Tasks,' IEEE Trans. Computers, Vol. 45, No. 7, 802-813, July 1996 https://doi.org/10.1109/12.508319
  11. J.W.S. Liu, K.J. Lin, W.K. Shih, A.C.S. Yu., J.Y. Chung, and W. Zhao, 'Algorithms for Scheduling Imprecise Computations,' IEEE Computer, Vol. 24, No. 5, pp. 58-68, May 1991 https://doi.org/10.1109/2.76287
  12. J.K. Dey, J.F. Kurose, D. Towsley, C.M. Krishna and M. Girkar, 'Efficient On-Line Processor Scheduling for a Class of IRIS (Increasing Reward with Increasing Service) Real-Time Tasks,' Proc. ACM Sigmetrics Conf. Measurement and Modeling of Computer Systems, pp. 217-228, Santa Clara, Calif., May 1993 https://doi.org/10.1145/166955.167013
  13. K. Choi and G. Jung, 'Comment on On-line Scheduling Policies for a Class of IRIS Real-Time Tasks,' IEEE Trans. Computers, Vol. 50, No. 5, pp. 526-528, May 2001 https://doi.org/10.1109/12.926165
  14. H. Aydin, R. Melhem, D. Mosse, and P. Mejia-Alvarez, 'Optimal Reward-based Scheduling for Periodic Real-Time Tasks,' IEEE Trans. Computers, Vol. 50, No. 2, pp. 111-130, Feb. 2001 https://doi.org/10.1109/12.908988
  15. C.L. Liu and J.W. Layland, 'Scheduling Algorithms for Multiprogramming in a Hard Real-Time Environment,' J. ACM, Vol. 20, No. 1, pp. 46-61, Jan. 1973 https://doi.org/10.1145/321738.321743