Michael E. Woodward

Performance evaluation of dynamic probabilistic broadcasting for flooding in mobile ad hoc networks

Abstract In mobile ad hoc networks (MANETs), flooding is a required message dissemination technique for network-wide broadcast. The conventional blind flooding algorithm causes broadcast storm problem, a high number of unnecessary packet rebroadcasts thus resulting in high contention and packet collisions. This paper proposes a new probabilistic approach that dynamically fine-tunes the rebroadcasting probability of a node for routing request packets (RREQs) according to the number of neighbour nodes. We evaluate the performance of the proposed approach for the ad hoc on demand distance vector (AODV) routing protocol and compared against the blind flooding, fixed probabilistic and adjusted probabilistic flooding [L.M.M.M. Bani-Yassein, M. Ould-Khaoua et al., Performance analysis of adjusted probabilistic broadcasting in mobile ad hoc networks, International Journal of Wireless Information Networks 13(2) (2006) 127–140; M.B. Yassein, M.O. Khaoua et al., Improving route discovery in on-demand routing protocols using local topology information in MANETs, Proceedings of the ACM international workshop on Performance Monitoring, Measurement, and Evaluation of Heterogeneous Wireless and Wired Networks, Terromolinos, Spain, ACM Press, 2006, pp. 95–99.] approaches. The simulation results show that our proposed approach demonstrates better performance than blind flooding, fixed probabilistic and adjusted flooding approaches.

A Comprehensive Analytical Model for IEEE 802.11e QoS Differentiation Schemes under Unsaturated Traffic Loads

Arbitrary inter-frame space (AIFS), contention window (CW) and transmission opportunity (TXOP) are three important quality-of-service (QoS) differentiation schemes specified in the IEEE 802.11e enhanced distributed channel access (EDCA) protocol for wireless local area networks (WLANs). Analytical models of EDCA in the current literature have been mainly developed for the AIFS, CW, and TXOP schemes, separately. This study proposes a comprehensive analytical model to accommodate the combination of these three QoS schemes in WLANs under unsaturated traffic loads. We derive the performance metrics in terms of throughput, end-to- end delay, and frame loss probability. Extensive simulation experiments are conducted to validate the accuracy of the model.

Prioritised best effort routing with four quality of service metrics applying the concept of the analytic hierarchy process

In this paper, a new approach that applies the concept of the Analytic Hierarchy Process (AHP) in the area of routing in communication networks is proposed. AHP is a well-known model in the area of decision making with multiple objectives. In addition, a new algorithm called Enhanced Best Effort Quality of Service Routing (QoS) with Multiple Prioritised Metrics is proposed for connection-oriented point-to-point communications. Four QoS metrics have been considered: delay, bandwidth, security and loss probability. The results presented and discussed in this paper are focussed on demonstrating the effects of metric prioritisation on the routing decisions. It is found that changing priority of a metric from 0 (the lowest priority) to 1 (the highest priority) applying the proposed algorithm improves the value of that metric by an average of (20-60)% for 90% of utilisation range.

Performance evaluation for DRED discrete-time queueing network analytical model

Due to the rapid development in computer networks, congestion becomes a critical issue. Congestion usually occurs when the connection demands on network resources, i.e. buffer spaces, exceed the available ones. We propose in this paper a new discrete-time queueing network analytical model based on dynamic random early drop (DRED) algorithm to control the congestion in early stages. We apply our analytical model on two-queue nodes queueing network. Furthermore, we compare between the proposed analytical model and three known active queue management (AQM) algorithms, including DRED, random early detection (RED) and adaptive RED, in order to figure out which of them offers better quality of service (QoS). We also experimentally compare the queue nodes of the proposed analytical model and the three AQM methods in terms of different performance measures, including, average queue length, average queueing delay, throughput, packet loss probability, etc., aiming to determine the queue node that offers better performance.

Analysis and Comparison of Burst Transmission Schemes in Unsaturated 802.11e WLANs

Contention free bursting (CFB) and block acknowledgement (BACK) are two efficient burst transmission schemes specified in the IEEE 802.11e standard for improving the utilization of scarce wireless bandwidth. Most existing performance models of the CFB and BACK schemes have focused on the analysis of system throughput and have not taken the realistic factors, such as unsaturated traffic loads and finite buffer capacity, into account. In this paper, we propose a comprehensive analytical model for evaluating the throughput, end-to-end delay, and loss probability of burst transmission schemes in wireless local area networks (WLANs) under unsaturated traffic conditions. The accuracy of the proposed model is validated through extensive ns2 simulation experiments. Performance results obtained from the model demonstrate that both schemes can substantially improve network performance. Moreover, the BACK scheme outperforms the CFB scheme when the transmission opportunity (TXOP) limit exceeds a certain threshold.

Discrete-time performance analysis of a congestion control mechanism based on RED under multi-class bursty and correlated traffic

Internet traffic congestion control using queue thresholds is a well known and effective mechanism. This motivates the stochastic analysis of a discrete-time queueing systems for the performance evaluation of the active queue management (AQM) based congestion control mechanism called Random Early Detection (RED) with bursty and correlated traffic using a two-state Markov-Modulated Bernoulli arrival process (MMBP-2) as the traffic source. A two-dimensional discrete-time Markov chain is introduced to model the RED mechanism for two traffic classes where each dimension corresponds to a traffic class with its own parameters. This mechanism takes into account the reduction of incoming traffic arrival rate due to packets dropped probabilistically with the drop probability increasing linearly with system contents. The stochastic analysis of the queue considered could be of interest for the performance evaluation of the RED mechanism for the multi-class traffic with short range dependent (SRD) traffic characteristics. The performance metrics including mean system occupancy, mean packet delay, packet loss probability and system throughput are computed from the analytical model for a dropping policy which is a function of the thresholds and maximum drop probability. Typical numerical results are included to illustrate the credibility of the proposed mechanism in the context of external bursty and correlated traffic. These results clearly demonstrate how different threshold settings can provide different trade-offs between loss probability and delay to suit different service requirements. The effects on various performance measures of changes in the input parameters and of burstiness and correlations exhibited by the arrival process are also presented. The model would be applicable to high-speed networks which use slotted protocols.

End-to-end delay in localized QoS routing

Quality of service (QoS) routing has been proposed for supporting the requirements of network applications and satisfying connection constraints. A large amount of information needs to be exchanged periodically among routers. Therefore, in order to satisfy such requirements localized QoS routing algorithms have been proposed. This is where source nodes make routing decisions based on statistics collected locally. Using local information for routing avoids the overheads of exchanging global information with other nodes. In this paper we present a localized delay-based QoS routing (DBR) algorithm which relies on delay constraint that each path satisfies to make routing decisions. We also modify credit based routing (CBR) so that this uses delay instead of bandwidth. The two localized algorithms and the commonly used global shortest path algorithm (Dijkstra¿s) are compared under different delay constraints and network topologies. We demonstrated through simulation that our scheme performs better than the modified CBR under different range of workloads and system parameters and outperforms the Dijkstra scheme in all network topologies, unless unrealistically small update intervals are used.

A Dynamic IEEE 802.11e TXOP Scheme in WLANs under Self-Similar Traffic: Performance Enhancement and Analysis

Transmission opportunity (TXOP) is a new scheme specified in the IEEE 802.11e standard which enables a station to transmit multiple frames consecutively within a burst after it gains the channel. Realistic traffic loads in wireless local area networks (WLANs) with multimedia applications often exhibit the bursty and self-similar properties which imply the frequent occurrence of the large bursts of frame arrivals and thus require the TXOP scheme to be dynamically adapted to the traffic characteristics. This paper presents a dynamic TXOP scheme which adjusts the TXOP limits of mobile stations according to the current status of their transmission queues. We further develop an analytical model to evaluate the performance of this scheme under self-similar traffic. QoS performance metrics in terms of throughput, end-to-end delay, and frame loss probability are derived and validated via NS2 simulation experiments. The numerical results reveal that the dynamic TXOP scheme achieves the better QoS than the original one under self-similar traffic.

A Discrete-time Queue Analytical Model based on Dynamic Random Early Drop

Congestion is one of the main problems in networks such as the Internet that has been studied by many researchers. Since the fast development in computer networks and the increase of demands on network resources such as bandwidth allocation and buffer spaces, congestion control becomes a crucial task. In this paper, we introduce a dynamic random early drop (DRED) discrete-time queue analytical model to deal with network congestion incipiently. We compare the proposed analytical model with the original DRED algorithm with reference to packet loss probability, average queue length, throughput, and average queuing delay. The experimental results clearly show that when the traffic load increases, DRED router buffer drops packets on a higher rate than the proposed analytical model, which consequently degrades the throughput performance. Furthermore, the packet loss rate for the proposed analytical model is often stable is not affected with the increase of the traffic loads, and thus stabilise the throughput performance

Localized Credit Based QoS Routing : Performance Evaluation Using

Localized Quality of Service (QoS) routing has recently been proposed as a viable alternative approach to traditional QoS routing algorithms that use global state information. In this approach, problems associated with maintaining global state information and the staleness of such information are avoided by having the source nodes to infer the network QoS state based on flow blocking statistics collected locally, and perform flow routing using this localized view of the network QoS state . In this paper we introduce a credit based routing algorithm (cbr) which is a simple yet effective localized QoS routing algorithm. We compare its performance against the localized proportional sticky routing (psr) algorithm same time complexity. using different types of network topologies, QoS requirements and traffic patterns and under a wide range of traffic loads. Extensive simulations show that our algorithm outperforms the psr algorithm with the