Performance Modeling and Analysis of Wireless Local Area Networks with Bursty Traffic

The explosive increase in the use of mobile digital devices has posed great challenges in the design and implementation of Wireless Local Area Networks (WLANs). Ever-increasing demands for high-speed and ubiquitous digital communication have made WLANs an essential feature of everyday life. With audio and video forming the highest percentage of traffic generated by multimedia applications, a huge demand is placed for high speed WLANs that provide high Quality-of-Service (QoS) and can satisfy end user’s needs at a relatively low cost. Providing video and audio contents to end users at a satisfactory level with various channel quality and current battery capacities requires thorough studies on the properties of such traffic. In this regard, Medium Access Control (MAC) protocol of the 802.11 standard plays a vital role in the management and coordination of shared channel access and data transmission. Therefore, this research focuses on developing new efficient analytical models that evaluate the performance of WLANs and the MAC protocol in the presence of bursty, correlated and heterogeneous multimedia traffic using Batch Markovian Arrival Process (BMAP). BMAP can model the correlation between different packet size distributions and traffic rates while accurately modelling aggregated traffic which often possesses negative statistical properties. The research starts with developing an accurate traffic generator using BMAP to capture the existing correlations in multimedia traffics. For validation, the developed traffic generator is used as an arrival process to a queueing model and is analyzed based on average queue length and mean waiting time. The performance of BMAP/M/1 queue is studied under various number of states and maximum batch sizes of BMAP. The results clearly indicate that any increase in the number of states of the underlying Markov Chain of BMAP or maximum batch size, lead to higher burstiness and correlation of the arrival process, prompting the speed of the queue towards saturation. The developed traffic generator is then used to model traffic sources in IEEE 802.11 WLANs, measuring important QoS metrics of throughput, end-to-end delay, frame loss probability and energy consumption. Performance comparisons are conducted on WLANs under the influence of multimedia traffics modelled as BMAP, Markov Modulated Poisson Process and Poisson Process. The results clearly indicate that bursty traffics generated by BMAP demote network performance faster than other traffic sources under moderate to high loads. The model is also used to study WLANs with unsaturated, heterogeneous and bursty traffic sources. The effects of traffic load and network size on the performance of WLANs are investigated to demonstrate the importance of burstiness and heterogeneity of traffic on accurate evaluation of MAC protocol in wireless multimedia networks. The results of the thesis highlight the importance of taking into account the true characteristics of multimedia traffics for accurate evaluation of the MAC protocol in the design and analysis of wireless multimedia networks and technologies

Performance and Security Trade-offs in High-Speed Networks. An investigation into the performance and security modelling and evaluation of high-speed networks based on the quantitative analysis and experimentation of queueing networks and generalised stochastic Petri nets.

Most used security mechanisms in high-speed networks have been adopted without adequate quantification of their impact on performance degradation. Appropriate quantitative network models may be employed for the evaluation and prediction of ¿optimal¿ performance vs. security trade-offs. Several quantitative models introduced in the literature are based on queueing networks (QNs) and generalised stochastic Petri nets (GSPNs). However, these models do not take into consideration Performance Engineering Principles (PEPs) and the adverse impact of traffic burstiness and security protocols on performance. The contributions of this thesis are based on the development of an effective quantitative methodology for the analysis of arbitrary QN models and GSPNs through discrete-event simulation (DES) and extended applications into performance vs. security trade-offs involving infrastructure and infrastructure-less high-speed networks under bursty traffic conditions. Specifically, investigations are carried out focusing, for illustration purposes, on high-speed network routers subject to Access Control List (ACL) and also Robotic Ad Hoc Networks (RANETs) with Wired Equivalent Privacy (WEP) and Selective Security (SS) protocols, respectively. The Generalised Exponential (GE) distribution is used to model inter-arrival and service times at each node in order to capture the traffic burstiness of the network and predict pessimistic ¿upper bounds¿ of network performance. In the context of a router with ACL mechanism representing an infrastructure network node, performance degradation is caused due to high-speed incoming traffic in conjunction with ACL security computations making the router a bottleneck in the network. To quantify and predict the trade-off of this degradation, the proposed quantitative methodology employs a suitable QN model consisting of two queues connected in a tandem configuration. These queues have single or quad-core CPUs with multiple-classes and correspond to a security processing node and a transmission forwarding node. First-Come-First-Served (FCFS) and Head-of-the-Line (HoL) are the adopted service disciplines together with Complete Buffer Sharing (CBS) and Partial Buffer Sharing (PBS) buffer management schemes. The mean response time and packet loss probability at each queue are employed as typical performance metrics. Numerical experiments are carried out, based on DES, in order to establish a balanced trade-off between security and performance towards the design and development of efficient router architectures under bursty traffic conditions. The proposed methodology is also applied into the evaluation of performance vs. security trade-offs of robotic ad hoc networks (RANETs) with mobility subject to Wired Equivalent Privacy (WEP) and Selective Security (SS) protocols. WEP protocol is engaged to provide confidentiality and integrity to exchanged data amongst robotic nodes of a RANET and thus, to prevent data capturing by unauthorised users. WEP security mechanisms in RANETs, as infrastructure-less networks, are performed at each individual robotic node subject to traffic burstiness as well as nodal mobility. In this context, the proposed quantitative methodology is extended to incorporate an open QN model of a RANET with Gated queues (G-Queues), arbitrary topology and multiple classes of data packets with FCFS and HoL disciplines under bursty arrival traffic flows characterised by an Interrupted Compound Poisson Process (ICPP). SS is included in the Gated-QN (G-QN) model in order to establish an ¿optimal¿ performance vs. security trade-off. For this purpose, PEPs, such as the provision of multiple classes with HoL priorities and the availability of dual CPUs, are complemented by the inclusion of robot¿s mobility, enabling realistic decisions in mitigating the performance of mobile robotic nodes in the presence of security. The mean marginal end-to-end delay was adopted as the performance metric that gives indication on the security improvement. The proposed quantitative methodology is further enhanced by formulating an advanced hybrid framework for capturing ¿optimal¿ performance vs. security trade-offs for each node of a RANET by taking more explicitly into consideration security control and battery life. Specifically, each robotic node is represented by a hybrid Gated GSPN (G-GSPN) and a QN model. In this context, the G-GSPN incorporates bursty multiple class traffic flows, nodal mobility, security processing and control whilst the QN model has, generally, an arbitrary configuration with finite capacity channel queues reflecting ¿intra¿-robot (component-to-component) communication and ¿inter¿-robot transmissions. Two theoretical case studies from the literature are adapted to illustrate the utility of the QN towards modelling ¿intra¿ and ¿inter¿ robot communications. Extensions of the combined performance and security metrics (CPSMs) proposed in the literature are suggested to facilitate investigating and optimising RANET¿s performance vs. security trade-offs. This framework has a promising potential modelling more meaningfully and explicitly the behaviour of security processing and control mechanisms as well as capturing the robot¿s heterogeneity (in terms of the robot architecture and application/task context) in the near future (c.f. [1]. Moreover, this framework should enable testing robot¿s configurations during design and development stages of RANETs as well as modifying and tuning existing configurations of RANETs towards enhanced ¿optimal¿ performance and security trade-offs.Ministry of Higher Education in Libya and the Libyan Cultural Attaché bureau in Londo

A Hardware Testbed for Measuring IEEE 802.11g DCF Performance

The Distributed Coordination Function (DCF) is the oldest and most widely-used IEEE 802.11 contention-based channel access control protocol. DCF adds a significant amount of overhead in the form of preambles, frame headers, randomised binary exponential back-off and inter-frame spaces. Having accurate and verified performance models for DCF is thus integral to understanding the performance of IEEE 802.11 as a whole. In this document DCF performance is measured subject to two different workload models using an IEEE 802.11g test bed. Bianchi proposed the first accurate analytic model for measuring the performance of DCF. The model calculates normalised aggregate throughput as a function of the number of stations contending for channel access. The model also makes a number of assumptions about the system, including saturation conditions (all stations have a fixed-length packet to send at all times), full-connectivity between stations, constant collision probability and perfect channel conditions. Many authors have extended Bianchi's machine model to correct certain inconsistencies with the standard, while very few have considered alternative workload models. Owing to the complexities associated with prototyping, most models are verified against simulations and not experimentally using a test bed. In addition to a saturation model we considered a more realistic workload model representing wireless Internet traffic. Producing a stochastic model for such a workload was a challenging task, as usage patterns change significantly between users and over time. We implemented and compared two Markov Arrival Processes (MAPs) for packet arrivals at each client - a Discrete-time Batch Markovian Arrival Process (D-BMAP) and a modified Hierarchical Markov Modulated Poisson Process (H-MMPP). Both models had parameters drawn from the same wireless trace data. It was found that, while the latter model exhibits better Long Range Dependency at the network level, the former represented traces more accurately at the client-level, which made it more appropriate for the test bed experiments. A nine station IEEE 802.11 test bed was constructed to measure the real world performance of the DCF protocol experimentally. The stations used IEEE 802.11g cards based on the Atheros AR5212 chipset and ran a custom Linux distribution. The test bed was moved to a remote location where there was no measured risk of interference from neighbouring radio transmitters in the same band. The DCF machine model was fixed and normalised aggregate throughput was measured for one through to eight contending stations, subject to (i) saturation with fixed packet length equal to 1000 bytes, and (ii) the D-BMAP workload model for wireless Internet traffic. Control messages were forwarded on a separate wired backbone network so that they did not interfere with the experiments. Analytic solver software was written to calculate numerical solutions for thee popular analytic models for DCF and compared the solutions to the saturation test bed experiments. Although the normalised aggregate throughput trends were the same, it was found that as the number of contending stations increases, so the measured aggregate DCF performance diverged from all three analytic model's predictions; for every station added to the network normalised aggregate throughput was measured lower than analytically predicted. We conclude that some property of the test bed was not captured by the simulation software used to verify the analytic models. The D-BMAP experiments yielded a significantly lower normalised aggregate throughput than the saturation experiments, which is a clear result of channel underutilisation. Although this is a simple result, it highlights the importance of the traffic model on network performance. Normalised aggregate throughput appeared to scale more linearly when compared to the RTS/CTS access mechanism, but no firm conclusion could be drawn at 95% confidence. We conclude further that, although normalised aggregate throughput is appropriate for describing overall channel utilisation in the steady state, jitter, response time and error rate are more important performance metrics in the case of bursty traffic

Shipment Consolidation in Discrete Time and Discrete Quantity: Matrix-Analytic Methods

Shipment consolidation is a logistics strategy whereby many small shipments are combined into a few larger loads. The economies of scale achieved by shipment consolidation help in reducing the transportation costs and improving the utilization of logistics resources. The fundamental questions about shipment consolidation are i) to how large a size should the consolidated loads be allowed to accumulate? And ii) when is the best time to dispatch such loads? The answers to these questions lie in the set of decision rules known as shipment consolidation policies. A number of studies have been done in an attempt to find the optimal consolidation policy. However, these studies are restricted to only a few types of consolidation policies and are constrained by the input parameters, mainly the order arrival process and the order weight distribution. Some results on the optimal policy parameters have been obtained, but they are limited to a couple of specific types of policies. No comprehensive method has yet been developed which allows the evaluation of different types of consolidation policies in general, and permits a comparison of their performance levels. Our goal in this thesis is to develop such a method and use it to evaluate a variety of instances of shipment consolidation problem and policies. In order to achieve that goal, we will venture to use matrix-analytic methods to model and solve the shipment consolidation problem. The main advantage of applying such methods is that they can help us create a more versatile and accurate model while keeping the difficulties of computational procedures in check. More specifically, we employ a discrete batch Markovian arrival process (BMAP) to model the weight-arrival process, and for some special cases, we use phase-type (PH) distributions to represent order weights. Then we model a dispatch policy by a discrete monotonic function, and construct a discrete time Markov chain for the shipment consolidation process. Borrowing an idea from matrix-analytic methods, we develop an efficient algorithm for computing the steady state distribution of the Markov chain and various performance measures such as i) the mean accumulated weight per load, ii) the average dispatch interval and iii) the average delay per order. Lastly, after specifying the cost structures, we will compute the expected long-run cost per unit time for both the private carriage and common carriage cases

Uplink multiple access techniques for satellite communication systems

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (leaves 90-92).by Christopher J. Karpinsky.M.S

Inferring Queueing Network Models from High-precision Location Tracking Data

Stochastic performance models are widely used to analyse the performance and reliability of systems that involve the flow and processing of customers. However, traditional methods of constructing a performance model are typically manual, time-consuming, intrusive and labour-intensive. The limited amount and low quality of manually-collected data often lead to an inaccurate picture of customer flows and poor estimates of model parameters. Driven by advances in wireless sensor technologies, recent real-time location systems (RTLSs) enable the automatic, continuous and unintrusive collection of high-precision location tracking data, in both indoor and outdoor environment. This high-quality data provides an ideal basis for the construction of high-fidelity performance models. This thesis presents a four-stage data processing pipeline which takes as input high-precision location tracking data and automatically constructs a queueing network performance model approximating the underlying system. The first two stages transform raw location traces into high-level “event logs” recording when and for how long a customer entity requests service from a server entity. The third stage infers the customer flow structure and extracts samples of time delays involved in the system; including service time, customer interarrival time and customer travelling time. The fourth stage parameterises the service process and customer arrival process of the final output queueing network model. To collect large-enough location traces for the purpose of inference by conducting physical experiments is expensive, labour-intensive and time-consuming. We thus developed LocTrack- JINQS, an open-source simulation library for constructing simulations with location awareness and generating synthetic location tracking data. Finally we examine the effectiveness of the data processing pipeline through four case studies based on both synthetic and real location tracking data. The results show that the methodology performs with moderate success in inferring multi-class queueing networks composed of single-server queues with FIFO, LIFO and priority-based service disciplines; it is also capable of inferring different routing policies, including simple probabilistic routing, class-based routing and shortest-queue routing