Genetic algorithm-based neural fuzzy decision tree for mixed scheduling in ATM networks

Chin Teng Lin*, I. Fang Chung, Her Chang Pu, Tsern-Huei Lee, Jyh Yeong Chang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Future broad-band integrated services networks based on the asynchronous transfer mode (ATM) technology are expected to support multiple types of multimedia information with diverse statistical characteristics and quality of service (QoS) requirements. To meet these requirements, efficient scheduling methods are important for traffic control in the ATM networks. Among the general scheduling schemes, the rate monotonic algorithm is simple enough to be used in high-speed networks, but it does not attain as high a system utilization as the deadline driven algorithm does. However, the deadline driven scheme is computationally complex and hard to implement in hardware. The mixed scheduling algorithm is the combination of the rate monotonic algorithm and the deadline driven algorithm; thus it can provide most of the benefits of these two algorithms. In this paper, we use the mixed scheduling algorithm to achieve high system utilization under the hardware constraint. Because there is no analytic method for the schedulability test of the mixed scheduling, we propose a genetic algorithm-based neural fuzzy decision tree (GANFDT) to realize it in a real-time environment. The GANFDT combines the GA and a neural fuzzy network into a binary classification tree. This approach also exploits the power of the classification tree. Simulation results show that the GANFDT provides an efficient way to carry out the mixed scheduling in the ATM networks.

Original languageEnglish
Pages (from-to)832-845
Number of pages14
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Issue number6
StatePublished - 1 Dec 2002


  • Binary decision tree
  • Deadline driven algorithm
  • Quality of service (QoS)
  • Rate monotonic algorithm
  • Recursive least square (RLS)
  • Schedulability test

Fingerprint Dive into the research topics of 'Genetic algorithm-based neural fuzzy decision tree for mixed scheduling in ATM networks'. Together they form a unique fingerprint.

Cite this