The design, implementation and evaluation of SMART: a scheduler for multimedia applications
Proceedings of the sixteenth ACM symposium on Operating systems principles
CPU reservations and time constraints: efficient, predictable scheduling of independent activities
Proceedings of the sixteenth ACM symposium on Operating systems principles
Proceedings of the seventeenth ACM symposium on Operating systems principles
A CPU Scheduling Algorithm for Continuous Media Applications
NOSSDAV '95 Proceedings of the 5th International Workshop on Network and Operating System Support for Digital Audio and Video
Think: A Software Framework for Component-based Operating System Kernels
ATEC '02 Proceedings of the General Track of the annual conference on USENIX Annual Technical Conference
Virtual-Time Round-Robin: An O(1) Proportional Share Scheduler
Proceedings of the General Track: 2002 USENIX Annual Technical Conference
RTAS '95 Proceedings of the Real-Time Technology and Applications Symposium
Understanding the Linux Kernel, 2nd Edition
Understanding the Linux Kernel, 2nd Edition
Optimizing Unix resource scheduling for user interaction
Usenix-stc'93 Proceedings of the USENIX Summer 1993 Technical Conference on Summer technical conference - Volume 1
Operating Systems: Internals and Design Principles
Operating Systems: Internals and Design Principles
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
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The process scheduling aims to arrange CPU time to multiple processes for providing users with more efficient throughput. Except the class of process set by user, conventional operating systems have applied the equivalent scheduling policy to every process. Moreover, if the scheduling policy is once determined, it is unable to change without resetting the operating system which takes much time. In this paper, we propose an intelligent CPU process scheduling algorithm using fuzzy inference with user models. It classifies processes into three classes, batch, interactive and real-time processes, and models user's preferences to each process class. Finally, it assigns the priority of each process according to the class of the process and user's preference through the fuzzy inference. The experimental result shows the proposed method can adapt to user and allow different scheduling policies to multiple users.