Resource allocation and congestion control strategies for networked unmanned systems

It is generally agreed that communication is a critical technological factor in designing networked unmanned systems (NUS) that consist of a large number of heterogeneous assets/nodes that may be configured in ad-hoc fashion and that incorporate intricate architectures. In order to successfully carry out the NUS missions, communication among assets need to be accomplished efficiently. In contrast with conventional networks, NUSs have specific features that may render communication more complex. The main distinct characteristics of NUS are as follows: (a) heterogeneity of assets in terms of resources, (b) multiple topologies that can be fully-connected, (c) real-time requirements imposed by delivery timeliness of messages under evolving and uncertain environments, (d) unknown and random time-delays that may degrade the closed-loop dynamics performance, (e) bandwidth constraints reflecting differences in assets behavior and dynamics, and (f) protocol limitations for complying with the wireless features of these networks. The NUS system consists of clusters each having three nodes, namely, a sensor, a decision-maker, and an actuator. Inspired by networked control systems (NCS), we introduced a generic framework for NUSs. Using the fluid flow model (FFM), the overall dynamical model of our network cluster is derived as a time-delay dependent system. The following three main issues are investigated in this thesis, bandwidth allocation, an integrated bandwidth allocation and flow rate control, and congestion control. To demonstrate the difficulty of addressing the bandwidth allocation control problem, a standard PID is implemented for our network cluster. It is shown that in presence of feedback loops and time-delays in the network, this controller induces flow oscillations and consequently, in the worst-case scenario, network instability. To address this problem, nonlinear control strategies are proposed instead. These strategies are evaluated subject to presence of unknown delays and measurable/estimated input traffic. For different network configurations, the error dynamics of the entire controlled cluster is derived and sufficient stability conditions are obtained. In addition, our proposed bandwidth allocation control strategy is evaluated when the NUS assets are assumed to be mobile. The bandwidth allocation problem is often studied in an integrated fashion with the flow rate control and the connection admission control (CAC). In fact, due to importance of interaction of various components, design of the entire control system is often more promising than optimization of individual components. In this thesis, several robust integrated bandwidth allocation and flow rate control strategies are proposed. The third issue that is investigated in this thesis is the congestion control for differentiated-services (DiffServ) networks. In our proposed congestion control strategies, the buffer queue length is used as a feedback information to control locally the queue length of each buffer by acting on the bandwidth and simultaneously a feedback signaling notifies the ordinary sources regarding the allowed maximum rate. Using sliding mode generalized variable structure control techniques (SM-GVSC), two congestion control approaches are proposed, namely, the non degenerate and degenerate GVS control approaches. By adopting decentralized end-to-end, semi-decentralized end-to-end, and distributed hop-by-hop control approaches, our proposed congestion control strategies are investigated for a DiffServ loopless mesh network (Internet) and a DiffServ fully-connected NUS. Contrary to the semi-decentralized end-to-end congestion control strategy, in the distributed hop-by-hop congestion control strategy, each output port controller communicates the maximum allowed flow rate only to its immediate upstream node(s) and/or source(s). This approach reduces the required amount of information in the flow control when Compared to other approaches in which the allowed flow rate is sent to all the upstream sources communicating through an output port

How (Un)Fair are the ABR Binary Schemes, Actually?

It is well known that a simple binary feedback rate--based congestion avoidance scheme cannot ensure a fairness goal of the Available Bit Rate (ABR) service, namely, max--min fairness. In this paper we show how the rates are distributed for the network consisting of the binary switches, and end--systems employing an additive--increase/multiplicative decrease rate control. The modeling assumptions fairly resembles the ABR congestion avoidance, and applies to an arbitrary network topology. The results are obtained on the basis of a stochastic modeling, upon which we obtain certain analytical results, and conduct a numerical simulation. We validate the stochastic modeling through a discrete--event simulation. We believe that modeling presented in this paper enlight the performance issues of the binary ABR schemes. Keywords ABR, ATM, congestion control, binary scheme, EFCI, fairness, max--min, proportional fairness, stochastic approximation, ODE, Lyapunov, Runge-Kutta

Methods of Congestion Control for Adaptive Continuous Media

Since the first exchange of data between machines in different locations in early 1960s, computer networks have grown exponentially with millions of people now using the Internet. With this, there has also been a rapid increase in different kinds of services offered over the World Wide Web from simple e-mails to streaming video. It is generally accepted that the commonly used protocol suite TCP/IP alone is not adequate for a number of modern applications with high bandwidth and minimal delay requirements. Many technologies are emerging such as IPv6, Diffserv, Intserv etc, which aim to replace the onesize-fits-all approach of the current lPv4. There is a consensus that the networks will have to be capable of multi-service and will have to isolate different classes of traffic through bandwidth partitioning such that, for example, low priority best-effort traffic does not cause delay for high priority video traffic. However, this research identifies that even within a class there may be delays or losses due to congestion and the problem will require different solutions in different classes. The focus of this research is on the requirements of the adaptive continuous media class. These are traffic flows that require a good Quality of Service but are also able to adapt to the network conditions by accepting some degradation in quality. It is potentially the most flexible traffic class and therefore, one of the most useful types for an increasing number of applications. This thesis discusses the QoS requirements of adaptive continuous media and identifies an ideal feedback based control system that would be suitable for this class. A number of current methods of congestion control have been investigated and two methods that have been shown to be successful with data traffic have been evaluated to ascertain if they could be adapted for adaptive continuous media. A novel method of control based on percentile monitoring of the queue occupancy is then proposed and developed. Simulation results demonstrate that the percentile monitoring based method is more appropriate to this type of flow. The problem of congestion control at aggregating nodes of the network hierarchy, where thousands of adaptive flows may be aggregated to a single flow, is then considered. A unique method of pricing mean and variance is developed such that each individual flow is charged fairly for its contribution to the congestion

Some aspects of traffic control and performance evaluation of ATM networks

The emerging high-speed Asynchronous Transfer Mode (ATM) networks are expected to integrate through statistical multiplexing large numbers of traffic sources having a broad range of statistical characteristics and different Quality of Service (QOS) requirements. To achieve high utilisation of network resources while maintaining the QOS, efficient traffic management strategies have to be developed. This thesis considers the problem of traffic control for ATM networks. The thesis studies the application of neural networks to various ATM traffic control issues such as feedback congestion control, traffic characterization, bandwidth estimation, and Call Admission Control (CAC). A novel adaptive congestion control approach based on a neural network that uses reinforcement learning is developed. It is shown that the neural controller is very effective in providing general QOS control. A Finite Impulse Response (FIR) neural network is proposed to adaptively predict the traffic arrival process by learning the relationship between the past and future traffic variations. On the basis of this prediction, a feedback flow control scheme at input access nodes of the network is presented. Simulation results demonstrate significant performance improvement over conventional control mechanisms. In addition, an accurate yet computationally efficient approach to effective bandwidth estimation for multiplexed connections is investigated. In this method, a feed forward neural network is employed to model the nonlinear relationship between the effective bandwidth and the traffic situations and a QOS measure. Applications of this approach to admission control, bandwidth allocation and dynamic routing are also discussed. A detailed investigation has indicated that CAC schemes based on effective bandwidth approximation can be very conservative and prevent optimal use of network resources. A modified effective bandwidth CAC approach is therefore proposed to overcome the drawback of conventional methods. Considering statistical multiplexing between traffic sources, we directly calculate the effective bandwidth of the aggregate traffic which is modelled by a two-state Markov modulated Poisson process via matching four important statistics. We use the theory of large deviations to provide a unified description of effective bandwidths for various traffic sources and the associated ATM multiplexer queueing performance approximations, illustrating their strengths and limitations. In addition, a more accurate estimation method for ATM QOS parameters based on the Bahadur-Rao theorem is proposed, which is a refinement of the original effective bandwidth approximation and can lead to higher link utilisation

Reactive traffic control mechanisms for communication networks with self-similar bandwidth demands

Communication network architectures are in the process of being redesigned so that many different services are integrated within the same network. Due to this integration, traffic management algorithms need to balance the requirements of the traffic which the algorithms are directly controlling with Quality of Service (QoS) requirements of other classes of traffic which will be encountered in the network. Of particular interest is one class of traffic, termed elastic traffic, that responds to dynamic feedback from the network regarding the amount of available resources within the network. Examples of this type of traffic include the Available Bit Rate (ABR) service in Asynchronous Transfer Mode (ATM) networks and connections using Transmission Control Protocol (TCP) in the Internet. Both examples aim to utilise available bandwidth within a network. Reactive traffic management, like that which occurs in the ABR service and TCP, depends explicitly on the dynamic bandwidth requirements of other traffic which is currently using the network. In particular, there is significant evidence that a wide range of network traffic, including Ethernet, World Wide Web, Varible Bit Rate video and signalling traffic, is self-similar. The term self-similar refers to the particular characteristic of network traffic to remain bursty over a wide range of time scales. A closely associated characteristic of self-similar traffic is its long-range dependence (LRD), which refers to the significant correlations that occur with the traffic. By utilising these correlations, greater predictability of network traffic can be achieved, and hence the performance of reactive traffic management algorithms can be enhanced. A predictive rate control algorithm, called PERC (Predictive Explicit Rate Control), is proposed in this thesis which is targeted to the ABR service in ATM networks. By incorporating the LRD stochastic structure of background traffic, measurements of the bandwidth requirements of background traffic, and the delay associated with a particular ABR connection, a predictive algorithm is defined which provides explicit rate information that is conveyed to ABR sources. An enhancement to PERC is also described. This algorithm, called PERC+, uses previous control information to correct prediction errors that occur for connections with larger round-trip delay. These algorithms have been extensively analysed with regards to their network performance, and simulation results show that queue lengths and cell loss rates are significantly reduced when these algorithms are deployed. An adaptive version of PERC has also been developed using real-time parameter estimates of self-similar traffic. This has excellent performance compared with standard ABR rate control algorithms such as ERICA. Since PERC and its enhancement PERC+ have explicitly utilised the index of self-similarity, known as the Hurst parameter, the sensitivity of these algorithms to this parameter can be determined analytically. Research work described in this thesis shows that the algorithms have an asymmetric sensitivity to the Hurst parameter, with significant sensitivity in the region where the parameter is underestimated as being close to 0.5. Simulation results reveal the same bias in the performance of the algorithm with regards to the Hurst parameter. In contrast, PERC is insensitive to estimates of the mean, using the sample mean estimator, and estimates of the traffic variance, which is due to the algorithm primarily utilising the correlation structure of the traffic to predict future bandwidth requirements. Sensitivity analysis falls into the area of investigative research, but it naturally leads to the area of robust control, where algorithms are designed so that uncertainty in traffic parameter estimation or modelling can be accommodated. An alternative robust design approach, to the standard maximum entropy approach, is proposed in this thesis that uses the maximum likelihood function to develop the predictive rate controller. The likelihood function defines the proximity of a specific traffic model to the traffic data, and hence gives a measure of the performance of a chosen model. Maximising the likelihood function leads to optimising robust performance, and it is shown, through simulations, that the system performance is close to the optimal performance as compared with maximising the spectral entropy. There is still debate regarding the influence of LRD on network performance. This thesis also considers the question of the influence of LRD on traffic predictability, and demonstrates that predictive rate control algorithms that only use short-term correlations have close performance to algorithms that utilise long-term correlations. It is noted that predictors based on LRD still out-perform ones which use short-term correlations, but that there is Potential simplification in the design of predictors, since traffic predictability can be achieved using short-term correlations. This thesis forms a substantial contribution to the understanding of control in the case where self-similar processes form part of the overall system. Rather than doggedly pursuing self-similar control, a broader view has been taken where the performance of algorithms have been considered from a number of perspectives. A number of different research avenues lead on from this work, and these are outlined

Theories and Models for Internet Quality of Service

We survey recent advances in theories and models for Internet Quality of Service (QoS). We start with the theory of network calculus, which lays the foundation for support of deterministic performance guarantees in networks, and illustrate its applications to integrated services, differentiated services, and streaming media playback delays. We also present mechanisms and architecture for scalable support of guaranteed services in the Internet, based on the concept of a stateless core. Methods for scalable control operations are also briefly discussed. We then turn our attention to statistical performance guarantees, and describe several new probabilistic results that can be used for a statistical dimensioning of differentiated services. Lastly, we review recent proposals and results in supporting performance guarantees in a best effort context. These include models for elastic throughput guarantees based on TCP performance modeling, techniques for some quality of service differentiation without access control, and methods that allow an application to control the performance it receives, in the absence of network support

Advances in Internet Quality of Service

We describe recent advances in theories and architecture that support performance guarantees needed for quality of service networks. We start with deterministic computations and give applications to integrated services, differentiated services, and playback delays. We review the methods used for obtaining a scalable integrated services support, based on the concept of a stateless core. New probabilistic results that can be used for a statistical dimensioning of differentiated services are explained; some are based on classical queuing theory, while others capitalize on the deterministic results. Then we discuss performance guarantees in a best effort context; we review: methods to provide some quality of service in a pure best effort environment; methods to provide some quality of service differentiation without access control, and methods that allow an application to control the performance it receives, in the absence of network support