Fuzzy Based Flow Management of Real-Time Traffic for Quality of Service in WLANs
Tapio Frantti, Mikko Majane · InTech eBooks · 2011
Expert Systems flow control is more a local, point to point, issue with some direct feedback from the receiver to the sender.The term cognition refers to the processing of information, applying knowledge, and changing preferences.In the communication networks, cognition can be used to improve the performance of resource management, quality of service, security, control algorithms, or many other network goals.Here we define cognitive flow management as a cognitive process that can perceive current network conditions, and then plan, decide, and act on those conditions for improved quality of service.In our earlier publications (Frantti & Majanen, 2010; Frantti et al., 2010) we presented and compared PID (Proportional, Integral, Derivative) and fuzzy control systems, which adjust packet size of UDP (User Datagram Protocol) based uni-or bidirectional traffic flow on WLANs according to prevailing channel conditions.They aimed to optimize packet sizes of real-time traffic flows for the prevailing connection for higher end-to-end throughput by fulfilling the overall application dependent delay requirement.In this chapter, the aim of the flow management system is to adjust appropriate packet size and transmission interval of the source node's constant bit rate traffic flows for prevailing network conditions to achieve application dependent quality of service requirements.Hence, the research question can be stated here as follows: "How to manage constant bit rate real-time traffic flows so that application dependent quality of service requirements are achieved with the optimal network capacity?".Although the main goal of this work is related to the quality of service of WLAN systems and the simulations and results were performed for the IEEE 802.11b system, the approach and the techniques are not limited to these systems, but are easily applicable to other packet switched networks as well.The organization of the rest of the chapter is as follows.Section 2 presents a literature review of the weak resource reservation and quality of service in communication networks.It also presents a review of the packet size optimization in wireless networks.Section 3 briefly summarizes the structure and channel access of the WLANs.Section 4 introduces the principles of service classification whereas Section 5 gives an introduction to weak resource reservation, like congestion prevention and control, flow control and denying and/or degrading services and reduction of channel access competition by admission control.In Sections 7 and 8 are briefly summarized the basic principles of the developed PID and fuzzy system based controllers.Section 9 depicts the developed simulation model and simulation scenarios.Section 10 comprises achieved results with the controllers.Finally, conclusions are presented in Section 11 . Literature review 2.1 Hard resource reservationFor the QoS guarantee, the IETF has worked on the transport layer protocol called resource reservation protocol (RSVP) that can be used to hard resource reservation across a network.Integrated Services is often associated with RSVP.The Integrated Services architecture divides the flows to different service classes (e.g.guaranteed service class for intolerant applications that require that a packet never arrives late), and then RSVP is used for reserving the needed resources for each service class. Weak resource reservation: packet scheduling and queueing methodsWeak resource allocation schemes without actual reserved virtual links closely includes packet scheduling schemes and queueing methods (Kleinrock, 1975).The queueing algorithm can be thought of as allocating bandwidth to packets on the intermediate nodes.The most popular queueing algorithm is First-In-First-Out (FIFO), which determines the service order of packets 352 Expert Systems for Human, Materials and Automation www.intechopen.comFuzzy Based Flow Management of Real-Time Traffic for Quality of Service in WLANs 3based on their arrival order.In Priority Queueing (PQ), traffic classes with the highest priority are forwarded with the least delay (Huitema, 2000;Nagle, 1987;Sanjay & Hassan, 2002).In Class Based Queueing (CBQ) traffic classes are forwarded with equal share (Floyd & Jacobson, 1995), e.g., Round Robin (RR) algorithms process packets in turn with equal share and achieve very high accuracy and fairness in the output bandwidth sharing but cannot provide tight delay guarantees (Nagle, 1985).In Fair Queueing (FQ) techniques, like the Weighted Fair Queueing (WFQ), are assigned a weight to each output queue (Demers et al., 1989).However, scheduling and queueing methods provide a rather weak form of resource reservation and cannot guarantee QoS, because weights are only indirectly related to the bandwidth the flow receives.The another problem of these methods and their modifications is that they are quite static in their operations.The latest development of scheduling methods is directing to the dynamic adaptation of scheduling parameters which gives better overall performance.There exists some related articles such as (Crawford & Marshall, 2001;Horng et al., 2001;Sayenko et al., 2006; 2003) devoted to the adaptive WFQ.In Horng et al. (2001) the developed adaptive approach to WFQ is a variation of fair queue algorithm with dynamic priority scheduling.An adaptive approach to WFQ that uses a concept of revenue to adapt weights is presented in Sayenko et al. (2003).This adaptive WFQ algorithm is later extended in (Sayenko et al., 2006) to an comparison and analysis of several adaptive scheduling algorithms: Revenue-based adaptive WFQ (RA-WFQ), revenue-based adaptive Weighted Round Robin (RA-WRR) and revenue-based adaptive Deficit Round Robin (RA-DRR).In Crawford & Marshall (2001) a new fast packet scheduling algorithm called Dynamic Weighted Fair Queuing (DWFQ) is created.We have considered in our previous publication fuzzy expert systems for adaptive weighted fair queueing and service classification (Frantti & Jutila, 2009). QoS in wireless networksWireless network protocols are designed based on a layered approach, where each layer in the protocol stack is designed and operated independently.The interfaces between layers are rather static.There are many studies that examine QoS provisioning in wireless networks with a layered perspective, concentrating only on one layer at the time, e.g. on power control or modulation/rate adaptation on the physical layer, scheduling or channel access on the MAC layer, admission control or routing on the network layer, rate or congestion control on the transport layer, or video and image coding schemes on the application layer.Perkins & Hughes (2002) includes a survey of QoS support for wireless mobile ad hoc networks including QoS routing protocols, resource reservation schemes, and QoS aware MAC layers.QoS aware MAC layers for wireless ad hoc networks are also reviewed in Kumar et al. (2006).However, strict layered design is not optimal for wireless multihop networks because of their dynamic nature.In wireless networks the layers should cooperate more closely to jointly optimize the overall performance, especially in case of real-time applications with high bandwidth and/or stringent delay requirements.Many studies, e.g.