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

Network level performance of differentiated services (diffserv) networks

The Differentiated Services (DiffServ) architecture is a promising means of providing Quality of Service (QoS) in Internet. In DiffServ networks, three service classes, or Per-hop Behaviors (PHBs), have been defined: Expedited Forwarding (EF), Assured Forwarding (AF) and Best Effort (BE). In this dissertation, the performance of DiffServ networks at the network level, such as end-to-end QoS, network stability, and fairness of bandwidth allocation over the entire network have been extensively investigated. It has been shown in literature that the end-to-end delay of EF traffic can go to infinity even in an over-provisioned network. In this dissertation, a simple scalable aggregate scheduling scheme, called Youngest Serve First (YSF) algorithm is proposed. YSF is not only able to guarantee finite end-to-end delay, but also to keep a low scheduling complexity. With respect to the Best Effort traffic, Random Exponential Marking (REM), an existing AQM scheme is studied under a new continuous time model, and its local stable condition is presented. Next, a novel virtual queue and rate based AQM scheme (VQR) is proposed, and its local stability condition has been presented. Then, a new AQM framework, Edge-based AQM (EAQM) is proposed. EAQM is easier to implement, and it achieves similar or better performance than traditional AQM schemes. With respect to the Assured Forwarding, a network-assist packet marking (NPM) scheme has been proposed. It has been demonstrated that NPM can fairly distribute bandwidth among AF aggregates based on their Committed Information Rates (CIRs) in both single and multiple bottleneck link networks

A control theoretic approach to achieve proportional fairness in 802.11e EDCA WLANs

This paper considers proportional fairness amongst ACs in an EDCA WLAN for provision of distinct QoS requirements and priority parameters. A detailed theoretical analysis is provided to derive the optimal station attempt probability which leads to a proportional fair allocation of station throughputs. The desirable fairness can be achieved using a centralised adaptive control approach. This approach is based on multivariable statespace control theory and uses the Linear Quadratic Integral (LQI) controller to periodically update CWmin till the optimal fair point of operation. Performance evaluation demonstrates that the control approach has high accuracy performance and fast convergence speed for general network scenarios. To our knowledge this might be the first time that a closed-loop control system is designed for EDCA WLANs to achieve proportional fairness

Resource Allocation in SDN/NFV-Enabled Core Networks

For next generation core networks, it is anticipated to integrate communication, storage and computing resources into one unified, programmable and flexible infrastructure. Software-defined networking (SDN) and network function virtualization (NFV) become two enablers. SDN decouples the network control and forwarding functions, which facilitates network management and enables network programmability. NFV allows the network functions to be virtualized and placed on high capacity servers located anywhere in the network, not only on dedicated devices in current networks. Driven by SDN and NFV platforms, the future network architecture is expected to feature centralized network management, virtualized function chaining, reduced capital and operational costs, and enhanced service quality. The combination of SDN and NFV provides a potential technical route to promote the future communication networks. It is imperative to efficiently manage, allocate and optimize the heterogeneous resources, including computing, storage, and communication resources, to the customized services to achieve better quality-of-service (QoS) provisioning. This thesis makes some in-depth researches on efficient resource allocation for SDN/NFV-enabled core networks in multiple aspects and dimensionality. Typically, the resource allocation task is implemented in three aspects. Given the traffic metrics, QoS requirements, and resource constraints of the substrate network, we first need to compose a virtual network function (VNF) chain to form a virtual network (VN) topology. Then, virtual resources allocated to each VNF or virtual link need to be optimized in order to minimize the provisioning cost while satisfying the QoS requirements. Next, we need to embed the virtual network (i.e., VNF chain) onto the substrate network, in which we need to assign the physical resources in an economical way to meet the resource demands of VNFs and links. This involves determining the locations of NFV nodes to host the VNFs and the routing from source to destination. Finally, we need to schedule the VNFs for multiple services to minimize the service completion time and maximize the network performance. In this thesis, we study resource allocation in SDN/NFV-enabled core networks from the aforementioned three aspects. First, we jointly study how to design the topology of a VN and embed the resultant VN onto a substrate network with the objective of minimizing the embedding cost while satisfying the QoS requirements. In VN topology design, optimizing the resource requirement for each virtual node and link is necessary. Without topology optimization, the resources assigned to the virtual network may be insufficient or redundant, leading to degraded service quality or increased embedding cost. The joint problem is formulated as a Mixed Integer Nonlinear Programming (MINLP), where queueing theory is utilized as the methodology to analyze the network delay and help to define the optimal set of physical resource requirements at network elements. Two algorithms are proposed to obtain the optimal/near-optimal solutions of the MINLP model. Second, we address the multi-SFC embedding problem by a game theoretical approach, considering the heterogeneity of NFV nodes, the effect of processing-resource sharing among various VNFs, and the capacity constraints of NFV nodes. In the proposed resource constrained multi-SFC embedding game (RC-MSEG), each SFC is treated as a player whose objective is to minimize the overall latency experienced by the supported service flow, while satisfying the capacity constraints of all its NFV nodes. Due to processing-resource sharing, additional delay is incurred and integrated into the overall latency for each SFC. The capacity constraints of NFV nodes are considered by adding a penalty term into the cost function of each player, and are guaranteed by a prioritized admission control mechanism. We first prove that the proposed game RC-MSEG is an exact potential game admitting at least one pure Nash Equilibrium (NE) and has the finite improvement property (FIP). Then, we design two iterative algorithms, namely, the best response (BR) algorithm with fast convergence and the spatial adaptive play (SAP) algorithm with great potential to obtain the best NE of the proposed game. Third, the VNF scheduling problem is investigated to minimize the makespan (i.e., overall completion time) of all services, while satisfying their different end-to-end (E2E) delay requirements. The problem is formulated as a mixed integer linear program (MILP) which is NP-hard with exponentially increasing computational complexity as the network size expands. To solve the MILP with high efficiency and accuracy, the original problem is reformulated as a Markov decision process (MDP) problem with variable action set. Then, a reinforcement learning (RL) algorithm is developed to learn the best scheduling policy by continuously interacting with the network environment. The proposed learning algorithm determines the variable action set at each decision-making state and accommodates different execution time of the actions. The reward function in the proposed algorithm is carefully designed to realize delay-aware VNF scheduling. To sum up, it is of great importance to integrate SDN and NFV in the same network to accelerate the evolution toward software-enabled network services. We have studied VN topology design, multi-VNF chain embedding, and delay-aware VNF scheduling to achieve efficient resource allocation in different dimensions. The proposed approaches pave the way for exploiting network slicing to improve resource utilization and facilitate QoS-guaranteed service provisioning in SDN/NFV-enabled networks

Modelling mixed autonomy traffic networks with pricing and routing control

Connected and automated vehicles (CAVs) are expected to change the way people travel in cities. Before human-driven vehicles (HVs) are completely phased out, the urban traffic flow will be heterogeneous of HVs, CAVs, and public transport vehicles commonly known as mixed autonomy. Mixed autonomy networks are likely to be made up of different route choice behaviours compared with conventional networks with HVs only. While HVs are expected to continue taking individually and selfishly selected shortest paths following user equilibrium (UE), a set of centrally controlled AVs could potentially follow the system optimal (SO) routing behaviour to reduce the selfish and inefficient behaviour of UE-seeking HVs. In this dissertation, a mixed equilibrium simulation-based dynamic traffic assignment (SBDTA) model is developed in which two classes of vehicles with different routing behaviours (UE-seeking HVs and SO-seeking AVs) are present in the network. The dissertation proposes a joint routing and incentive-based congestion pricing scheme in which SO-seeking CAVs are exempt from the toll while UE-seeking HVs have their usual shortest-path routing decisions are subject to a spatially differentiated congestion charge. This control strategy could potentially boost market penetration rate of CAVs while encouraging them to adopt SO routing behaviour and discouraging UE-seeking users from entering congested areas. The dissertation also proposes a distance-based time-dependent optimal ratio control scheme (TORCS) in which an optimal ratio of CAVs is identified and selected to seek SO routing. The objective of the control scheme is to achieve a reasonable compromise between the system efficiency (i.e., total travel time savings) and the control cost that is proportional to the total distance travelled by SO-seeking AVs. The proposed modelling frameworks are then extended to bi-modal networks considering three competing modes (bus, SO-seeking CAVs, and UE-seeking HVs). A nested logit-based mode choice model is applied to capture travellers’ preferences toward three available modes and elasticity in travel demand. A dynamic transit assignment model is also deployed and integrated into the mixed equilibrium SBDTA model to generate equilibrium traffic flow under different scenarios. The applicability and performance of the proposed models are demonstrated on a real large-scale network of Melbourne, Australia. The research outcomes are expected to improve the performance of mixed autonomy traffic networks with optimal pricing and routing control

Flow control of real-time unicast multimedia applications in best-effort networks

One of the fastest growing segments of Internet applications are real-time mul- timedia applications, like Voice over Internet Protocol (VoIP). Real-time multimedia applications use the User Datagram Protocol (UDP) as the transport protocol because of the inherent conservative nature of the congestion avoidance schemes of Transmis- sion Control Protocol (TCP). The e®ects of uncontrolled °ows on the Internet have not yet been felt because UDP tra±c frequently constitutes only » 20% of the total Internet tra±c. It is pertinent that real-time multimedia applications become better citizens of the Internet, while at the same time deliver acceptable Quality of Service (QoS). Traditionally, packet losses and the increase in the end-to-end delay experienced by some of the packets characterizes congestion in the network. These two signals have been used to develop most known °ow control schemes. The current research considers the °ow accumulation in the network as the signal for use in °ow control. The most signi¯cant contribution of the current research is to propose novel end- to-end °ow control schemes for unicast real-time multimedia °ows transmitting over best-e®ort networks. These control schemes are based on predictive control of the accumulation signal. The end-to-end control schemes available in the literature are based on reactive control that do not take into account the feedback delay existing between the sender and the receiver nor the forward delay in the °ow dynamics. The performance of the proposed control schemes has been evaluated using the ns-2 simulation environment. The research concludes that active control of hard real- time °ows delivers the same or somewhat better QoS as High Bit Rate (HBR, no control), but with a lower average bit rate. Consequently, it helps reduce bandwidth use of controlled real-time °ows by anywhere between 31:43% to 43:96%. Proposed reactive control schemes deliver good QoS. However, they do not scale up as well as the predictive control schemes. Proposed predictive control schemes are e®ective in delivering good quality QoS while using up less bandwidth than even the reactive con- trol schemes. They scale up well as more real-time multimedia °ows start employing them

Adaptive Resource Control in 2-hop Ad-Hoc Networks

This paper presents a simple resource control\ud mechanism with traffic scheduling for 2-hop ad-hoc networks, in\ud which the Request-To-Send (RTS) packet is utilized to deliver\ud feedback information. With this feedback information, the\ud Transmission Opportunity (TXOP) limit of the sources can be\ud controlled to balance the traffic. Furthermore, a bottleneck\ud transmission scheduling scheme is introduced to provide fairness\ud between local and forwarding flows. The proposed mechanism is\ud modeled and evaluated using the well-known 20-sim dynamic\ud system simulator. Experimental results show that a fairer and\ud more efficient bandwidth utilization can be achieved than\ud without the feedback mechanism. The use of the structured and\ud formalized control-theoretical modeling framework has as\ud advantage that results can be obtained in a fast and efficient way