Deploying Lightweight Queue Management for improving performance of Mobile Ad-hoc Networks (MANETs)

Network based congestion avoidance which involves managing the queues in the network devices is an integral part of any network. Most of the mobile networks today use Droptail queue management where packets are dropped on queue overflow. Droptail, however, is known to suffer from the well known global synchronisation problem which is characterised by the phenomenon of alternating periods of empty and full queues and hence bursty losses. Especially in resource constrained networks such as MANETs, packet loss results in increased overhead in terms of energy wasted to forward a packet which was eventually dropped, additional energy required to retransmit this packet and the degraded service quality as experienced by the end user application. Active queue management (AQM) has been successfully demonstrated as a solution to the global synchronisation problem in the context of wired networks. However, if AQM is to be deployed in MANETs, it should be lightweight, proactive and easy to implement as mobile networks are resource constrained in terms of memory, processing power and battery life. To the best of our knowledge a study addressing the implications of AQM in mobile networks (MANETs in particular) does not exist. This paper presents a predictive queue management strategy named PAQMAN that proactively manages the queue, is simple to implement and requires negligible computational overhead (and hence uses the limited resources efficiently). The performance of PAQMAN (coupled with explicit congestion notification - ECN) has been compared with Droptail through ns2 simulations. Results from this study show that PAQMAN reduces packet loss ratio (and hence the fraction of retransmissions) while at the same time increasing transmission efficiency. Moreover, as its computational overhead is negligible, it is ideally suited for deployment in MANETs

[1]  QUTdN QeO,et al.  Random early detection gateways for congestion avoidance , 1993, TNET.

[2]  T. V. Lakshman,et al.  SRED: stabilized RED , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[3]  Sally Floyd,et al.  Adap-tive RED: An algorithm for increasing the robustness of RED , 2001 .

[4]  Kang G. Shin,et al.  A self-configuring RED gateway , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[5]  Deborah Estrin,et al.  Recommendations on Queue Management and Congestion Avoidance in the Internet , 1998, RFC.

[6]  Sally I. McClean,et al.  Proactive Predictive Queue Management for improved QoS in IP Networks , 2006, International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies (ICNICONSMCL'06).

[7]  David L. Black,et al.  The Addition of Explicit Congestion Notification (ECN) to IP , 2001, RFC.

[8]  Aura Ganz,et al.  Adaptive congestion control in infrastructure wireless LANs with bounded medium access delay , 2002, International Mobility and Wireless Access Workshop.

[9]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[10]  Sachin Garg,et al.  Proxy-RED: an AQM scheme for wireless local area networks , 2004, Proceedings. 13th International Conference on Computer Communications and Networks (IEEE Cat. No.04EX969).

[11]  R. Srikant,et al.  Analysis and design of an adaptive virtual queue (AVQ) algorithm for active queue management , 2001, SIGCOMM '01.

[12]  Donald F. Towsley,et al.  Analysis and design of controllers for AQM routers supporting TCP flows , 2002, IEEE Trans. Autom. Control..