›› 2013,Vol. 28 ›› Issue (2): 357-365.doi: 10.1007/s11390-013-1336-2

所属专题: Computer Networks and Distributed Computing

• Special Section on Selected Paper from NPC 2011 • 上一篇    下一篇

面向分组交换网络的模糊动态分布式队列调度

Chollette C. Chude-Olisah1, Uche A. K. Chude-Okonkwo2, Member, IEEE Kamalrulnizam A. Bakar1, Member, ACM, and Ghazali Sulong1   

  • 收稿日期:2011-11-02 修回日期:2013-01-09 出版日期:2013-03-05 发布日期:2013-03-05

Fuzzy-Based Dynamic Distributed Queue Scheduling for Packet Switched Networks

Chollette C. Chude-Olisah1, Uche A. K. Chude-Okonkwo2, Member, IEEE Kamalrulnizam A. Bakar1, Member, ACM, and Ghazali Sulong1   

  1. 1 Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru 81110, Malaysia;
    2 Wireless Communication Center, Universiti Teknologi Malaysia, Johor Bahru 81110, Malaysia
  • Received:2011-11-02 Revised:2013-01-09 Online:2013-03-05 Published:2013-03-05
  • Supported by:

    This work was supported by the Ministry of Science and Teknologi Malaysia eScience under Grant No. 4S034 managed by Research Management Centre of Universiti Teknologi Malaysia.

解决分组交换系统的队列调度问题是拥塞控制的一个重要方面。为了通过使用预设参数对流量的不同服务质量需求实现一定的控制, 本文采用模糊逻辑决策方法进行队列调度。由于时变分组到达过程将影响网络状态和性能, 本文提出的模糊调度器充分考虑互联网流量的动态特性。定义了低、中、高三个优先级队列, 分组优先级的选择将影响队列的服务方式。在队列调度机制中, 模糊调度器不仅利用队列优先级, 同时也考虑分组丢失的敏感性以及队列限制。模拟显示模糊调度器更加适用于分组交换系统中互联网流量的动态特性。与优先级队列、先进先出队列以及加权公平队列相比, 模糊调度器的调度策略可以减少分组丢失, 提供良好的信道利用率并使报文延迟最小。

Abstract: Addressing the problem of queue scheduling for the packet-switched system is a vital aspect of congestion control. In this paper, the fuzzy logic based decision method is adopted for queue scheduling in order to enforce some level of control for traffic of different quality of service requirements using predetermined values. The fuzzy scheduler proposed in this paper takes into account the dynamic nature of the Internet traffic with respect to its time-varying packet arrival process that affects the network states and performance. Three queues are defined, viz low, medium and high priority queues. The choice of prioritizing packets in皍ences how queues are served. The fuzzy scheduler not only utilizes queue priority in the queue scheduling scheme, but also considers packet drop susceptibility and queue limit. Through simulation it is shown that the fuzzy scheduler is more appropriate for the dynamic nature of Internet traffic in a packet-switched system as compared with some existing queue scheduling methods. Results show that the scheduling strategy of the proposed fuzzy scheduler reduces packet drop, provides good link utilization and minimizes queue delay as compared with the priority queuing (PQ), first-in-first-out (FIFO), and weighted fair queuing (WFQ).

[1] Sheldon T. Encyclopedia of Networking and Telecom-munications (Network Professionals Library). USA:Osborne/McGraw-Hill Press, 2001.

[2] Guo Z, Zeng H. Simulation and analysis of weighted fair queu-ing algorithms in OPNET. In Proc. ICCMS, Feb. 2009,pp.114-118.

[3] Padjen R, Keefer L, Thurston S et al. Flannagan and MartinWalshaw. Cisco AVVID and IP Telephony Design & Imple-mentation.

[4] Cho H, Fadali M, Lee H. Dynamic queue scheduling usingfuzzy systems for Internet routers. In Proc. the 14th IEEEInt. Conf. Fuzzy Systems, May 2005, pp.471-476.

[5] Cho H, Fadali M, Lee J et al. Lyapunov-based fuzzy queuescheduling for Internet routers. Journal of Control, Automa-tion and Systems, 2007, 5(3): 317-323.

[6] Bolin N, Lemin L. Novel fuzzy scheduling supporting qualityof service for wideband CDMA cellular networks. In Proc.IEEE Int. Conf. Communications, Circuits and Systems,May 2005, pp.368-373.

[7] Gomathy C, Shanmugavel S. An efficient fuzzy based prio-rity scheduler for mobile ad hoc networks and performanceanalysis for various mobility models. In Proc. IEEE WCNC,March 2004, pp.1087-1092.

[8] Kazemian H B. A fuzzy approach to MPEG video trans-mission in ATM networks. Fuzzy Sets and Systems, 2006,157(16): 2259-2272.

[9] Bourenane M, Benhamamouch D, Hamadouch H. Inductiveapproach for QoS packet scheduling in dynamic networks. InProc. ICMCS, April 2009, pp.25-30.

[10] Bellman R E, Zadeh L A. Decision-making in a fuzzy envi-ronment. Management Science, 1970, 17(4): 141-164.

[11] Blake S, Black D, Carlson M et al. An architecture fordifferentiated services. Request for Comments (RFC) 2475,http://www.hjp.at/doc/rfc/rfc2475.html, Dec. 1998.

[12] Melo Jr. A, Coello J. Packet scheduling based on learning inthe next generation Internet architectures. In Proc. the 5thIEEE ISCC, July 2000, pp.773-778.

[13] Kanhere S S, Sethu H. Fair, efficient and low-latency packetscheduling using nested deficit round robin. In Proc. IEEEWorkshop on High Performance Switching and Routing, May2001, pp.6-10.

[14] Wakuda K, Kasahara S. A packet scheduling algorithm formaxmin fairness in multihop wireless LANs. Journal of Com-puter Communications, May 2009, pp.1437-1444.

[15] Nagle J. On packet switches with infinite storage. IEEETrans. Communication, 1987, 35(4): 435-438.

[16] Demers A, Keshav K, Shenker S. Analysis and simulation ofa fair queueing algorithm. Comput. Commun. Rev., 1989,19(4): 1-12.

[17] Stiliadis D, Varma A. Efficient fair queuing algorithm forpacket-switched networks. IEEE Trans. Networking, 1998,6(2): 175-185.

[18] Kesh S, Nerur S, Ramanujan S. Quality of service | Tech-nology and implementation. Journal of Information Manage-ment & Computer Security, 2002, 10(2): 85-91.

[19] Zadeh L A. Similarity relations and fuzzy orderings. Infor-mation Sciences, 1971, 3(2): 177-200.

[20] Cordon O, Herrera F, Villar P. Generating the knowledge baseof a fuzzy rule-based system by the genetic learning of thedata base. IEEE Trans. Fuzzy Systems, 2001, 9(4): 667-674.

[21] Mamdani E H, Assilian S. An experiment in linguistic syn-thesis with a fuzzy logic controller. Int. Journal on Man-Machine Studies, 1975, 7(1): 1-13.

[22] Kumar D, Pon D N, Murugesan K. Performance analysis ofneural networks based priority scheduler for WiMAX underbursty traffic conditions. Journal of Scientific Research, 2012,76(3): 351-365.

[23] McEachen J C, Zachary J M. Real-time representation of net-work traffic behavior for enhanced security. In Proc. the 3rdICITA, July 2005, pp.214-219.

[24] García-Galán S, Prado R P, Expósito J E M. Fuzzy schedul-ing with swarm intelligence-based knowledge acquisition forgrid computing. Engineering Applications of Artificial Intel-ligence, 2012, 25(2): 359-375.
No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 刘明业; 洪恩宇;. Some Covering Problems and Their Solutions in Automatic Logic Synthesis Systems[J]. , 1986, 1(2): 83 -92 .
[2] 陈世华;. On the Structure of (Weak) Inverses of an (Weakly) Invertible Finite Automaton[J]. , 1986, 1(3): 92 -100 .
[3] 高庆狮; 张祥; 杨树范; 陈树清;. Vector Computer 757[J]. , 1986, 1(3): 1 -14 .
[4] 陈肇雄; 高庆狮;. A Substitution Based Model for the Implementation of PROLOG——The Design and Implementation of LPROLOG[J]. , 1986, 1(4): 17 -26 .
[5] 黄河燕;. A Parallel Implementation Model of HPARLOG[J]. , 1986, 1(4): 27 -38 .
[6] 闵应骅; 韩智德;. A Built-in Test Pattern Generator[J]. , 1986, 1(4): 62 -74 .
[7] 唐同诰; 招兆铿;. Stack Method in Program Semantics[J]. , 1987, 2(1): 51 -63 .
[8] 闵应骅;. Easy Test Generation PLAs[J]. , 1987, 2(1): 72 -80 .
[9] 朱鸿;. Some Mathematical Properties of the Functional Programming Language FP[J]. , 1987, 2(3): 202 -216 .
[10] 李明慧;. CAD System of Microprogrammed Digital Systems[J]. , 1987, 2(3): 226 -235 .
版权所有 © 《计算机科学技术学报》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn
总访问量: