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

Abstract

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.

Traffic and mobility aware resource prediction using cognitive agent in mobile ad hoc networks

“…Lightweight and efficient AQM scheme discussed in [26] and [27] for MANET is PAQMAN, which uses Recursive Least Squares (RLS) algorithm to predict the average queue length in the next prediction interval. The average queue length is used as the congestion indicator.…”

Balancing Tradeoffs in Network Queue Management Problem via Forward–Backward Sweeping with Finite Checkpoints

“…In [5], the authors point out that higher memory usage is not equivalent to a higher runtime of an algorithm. An AQM algorithm should be lightweight, proactive and plain to implement in wired or mobile ad hoc networks [8,9]. Performance balancing of an AQM algorithm should be reached as well, i.e., the balance between performance criteria results.…”