Optimal Control of Wireless Computing Networks

Augmented information (AgI) services allow users to consume information that results from the execution of a chain of service functions that process source information to create real-time augmented value. Applications include real-time analysis of remote sensing data, real-time computer vision, personalized video streaming, and augmented reality, among others. We consider the problem of optimal distribution of AgI services over a wireless computing network, in which nodes are equipped with both communication and computing resources. We characterize the wireless computing network capacity region and design a joint flow scheduling and resource allocation algorithm that stabilizes the underlying queuing system while achieving a network cost arbitrarily close to the minimum, with a tradeoff in network delay. Our solution captures the unique chaining and flow scaling aspects of AgI services, while exploiting the use of the broadcast approach coding scheme over the wireless channel.Comment: 30 pages, journa

Compute- and Data-Intensive Networks: The Key to the Metaverse

The worlds of computing, communication, and storage have for a long time been treated separately, and even the recent trends of cloud computing, distributed computing, and mobile edge computing have not fundamentally changed the role of networks, still designed to move data between end users and pre-determined computation nodes, without true optimization of the end-to-end compute-communication process. However, the emergence of Metaverse applications, where users consume multimedia experiences that result from the real-time combination of distributed live sources and stored digital assets, has changed the requirements for, and possibilities of, systems that provide distributed caching, computation, and communication. We argue that the real-time interactive nature and high demands on data storage, streaming rates, and processing power of Metaverse applications will accelerate the merging of the cloud into the network, leading to highly-distributed tightly-integrated compute- and data-intensive networks becoming universal compute platforms for next-generation digital experiences. In this paper, we first describe the requirements of Metaverse applications and associated supporting infrastructure, including relevant use cases. We then outline a comprehensive cloud network flow mathematical framework, designed for the end-to-end optimization and control of such systems, and show numerical results illustrating its promising role for the efficient operation of Metaverse-ready networks

Self-organized backpressure routing for the wireless mesh backhaul of small cells

The ever increasing demand for wireless data services has given a starring role to dense small cell (SC) deployments for mobile networks, as increasing frequency re-use by reducing cell size has historically been the most effective and simple way to increase capacity. Such densification entails challenges at the Transport Network Layer (TNL), which carries packets throughout the network, since hard-wired deployments of small cells prove to be cost-unfeasible and inflexible in some scenarios. The goal of this thesis is, precisely, to provide cost-effective and dynamic solutions for the TNL that drastically improve the performance of dense and semi-planned SC deployments. One approach to decrease costs and augment the dynamicity at the TNL is the creation of a wireless mesh backhaul amongst SCs to carry control and data plane traffic towards/from the core network. Unfortunately, these lowcost SC deployments preclude the use of current TNL routing approaches such as Multiprotocol Label Switching Traffic Profile (MPLS-TP), which was originally designed for hard-wired SC deployments. In particular, one of the main problems is that these schemes are unable to provide an even network resource consumption, which in wireless environments can lead to a substantial degradation of key network performance metrics for Mobile Network Operators. The equivalent of distributing load across resources in SC deployments is making better use of available paths, and so exploiting the capacity offered by the wireless mesh backhaul formed amongst SCs. To tackle such uneven consumption of network resources, this thesis presents the design, implementation, and extensive evaluation of a self-organized backpressure routing protocol explicitly designed for the wireless mesh backhaul formed amongst the wireless links of SCs. Whilst backpressure routing in theory promises throughput optimality, its implementation complexity introduces several concerns, such as scalability, large end-to-end latencies, and centralization of all the network state. To address these issues, we present a throughput suboptimal yet scalable, decentralized, low-overhead, and low-complexity backpressure routing scheme. More specifically, the contributions in this thesis can be summarized as follows: We formulate the routing problem for the wireless mesh backhaul from a stochastic network optimization perspective, and solve the network optimization problem using the Lyapunov-driftplus-penalty method. The Lyapunov drift refers to the difference of queue backlogs in the network between different time instants, whereas the penalty refers to the routing cost incurred by some network utility parameter to optimize. In our case, this parameter is based on minimizing the length of the path taken by packets to reach their intended destination. Rather than building routing tables, we leverage geolocation information as a key component to complement the minimization of the Lyapunov drift in a decentralized way. In fact, we observed that the combination of both components helps to mitigate backpressure limitations (e.g., scalability,centralization, and large end-to-end latencies). The drift-plus-penalty method uses a tunable optimization parameter that weight the relative importance of queue drift and routing cost. We find evidence that, in fact, this optimization parameter impacts the overall network performance. In light of this observation, we propose a self-organized controller based on locally available information and in the current packet being routed to tune such an optimization parameter under dynamic traffic demands. Thus, the goal of this heuristically built controller is to maintain the best trade-off between the Lyapunov drift and the penalty function to take into account the dynamic nature of semi-planned SC deployments. We propose low complexity heuristics to address problems that appear under different wireless mesh backhaul scenarios and conditions..

Dynamic control of wireless networks with confidential communications

Future wireless communication systems are rapidly transforming to satisfy everincreasing and varying mobile user demands. Cross-layer networking protocols have the potential to play a crucial role in this transformation by jointly addressing the requirements of user applications together with the time-varying nature of wireless networking. As wireless communications becoming an integral and crucial part of our daily lives with many of our personal data is being shared via wireless transmissions, the issue of keeping personal transactions confidential is at the forefront of any network design. Wireless communications is especially prone to attacks due to its broadcast nature. The conventional cryptographical methods can only guarantee secrecy with the assumption that it is computationally prohibitive for the eavesdroppers to decode the messages. On the other hand, information-theoretical secrecy as defined by Shannon in his seminal work has the potential to provide perfect secrecy regardless of the computational power of the eavesdropper. Recent studies has shown that information-theoretical secrecy is possible over noisy wireless channels. In this thesis, we aim to design simple yet provably optimal cross-layer algorithms taking into account information-theoretical secrecy as a Quality of Service (QoS) requirement. Our work has the potential to improve our understanding the interplay between the secrecy and networking protocols. In most of this thesis, we consider a wireless cellular architecture, where all nodes participate in communication with a base station. When a node is transmitting a confidential messages, other legitimate nodes are considered as eavesdroppers, i.e., all eavesdroppers are internal. We characterize the region of achievable open and confidential data rate pairs for a single and then a multi-node scenario. We define the notion of confidential opportunistic scheduler, which schedules a node that has the largest instantaneous confidential information rate, with respect to the best eavesdropper node, which has the largest mean cross-channel rate. Having defined the operational limits of the system, we then develop dynamic joint scheduling and flow control algorithms when perfect and imperfect channel state information (CSI) is available. The developed algorithms are simple index policies, in which scheduling and flow control decisions are given in each time instant independently. In real networks, instantaneous CSI is usually unavailable due to computational and communication overheads associated with obtaining this information. Hence, we generalize our model for the case where only the distributions of direct- and crosschannel CSI are available at the transmitter. In order to provide end-to-end reliability, Hybrid Automatic Retransmission reQuest (HARQ) is employed. The challenge of using HARQ is that the dynamic control policies proposed in the preceding chapter are no longer optimal, since the decisions at each time instant are no longer independent. This is mainly due to the potential of re-transmitting a variant of the same message successively until it is decoded at the base station. We solve this critical issue by proposing a novel queuing model, in which the messages transmitted the same number of times previously are stored in the same queue with scheduler selecting a head-of-line message from these queues. We prove that with this novel queuing model, the dynamic control algorithms can still be optimal. We then shift our attention to providing confidentiality in multi-hop wireless networks, where there are multiple source-destination pairs communicating confidential messages, to be kept confidential from the intermediate nodes. For this case, we propose a novel end-to-end encoding scheme, where the confidential information is encoded into one very long message. The encoded message is then divided into multiple packets, to be combined at the ultimate destination for recovery, and being sent over different paths so that each intermediate node only has partial view of the whole message. Based on the proposed end-to-end encoding scheme, we develop two different dynamic policies when the encoded message is finite and asymptotically large, respectively. When the encoded message has finite length, our proposed policy chooses the encoding rates for each message, based on the instantaneous channel state information, queue states and secrecy requirements. Also, the nodes keep account of the information leaked to intermediate nodes as well the information reaching the destination in order to provide confidentiality and reliability. We demonstrate via simulations that our policy has a performance asymptotically approaching that of the optimal policy with increasing length of the encoded message. All preceding work assumes that the nodes are altruistic and/or well-behaved, i.e., they cooperatively participate into the communication of the confidential messages. In the final chapter of the thesis, we investigate the case with non-altruistic nodes, where non-altruistic nodes provide a jamming service to nodes with confidential communication needs and receiving in turn the right to access to the channel. We develop optimal resource allocation and power control algorithms maximizing the aggregate utility of both nodes with confidential communication needs as well as the nodes providing jamming service

Contention techniques for opportunistic communication in wireless mesh networks

Auf dem Gebiet der drahtlosen Kommunikation und insbesondere auf den tieferen Netzwerkschichten sind gewaltige Fortschritte zu verzeichnen. Innovative Konzepte und Technologien auf der physikalischen Schicht (PHY) gehen dabei zeitnah in zelluläre Netze ein. Drahtlose Maschennetzwerke (WMNs) können mit diesem Innovationstempo nicht mithalten. Die Mehrnutzer-Kommunikation ist ein Grundpfeiler vieler angewandter PHY Technologien, die sich in WMNs nur ungenügend auf die etablierte Schichtenarchitektur abbilden lässt. Insbesondere ist das Problem des Scheduling in WMNs inhärent komplex. Erstaunlicherweise ist der Mehrfachzugriff mit Trägerprüfung (CSMA) in WMNs asymptotisch optimal obwohl das Verfahren eine geringe Durchführungskomplexität aufweist. Daher stellt sich die Frage, in welcher Weise das dem CSMA zugrunde liegende Konzept des konkurrierenden Wettbewerbs (engl. Contention) für die Integration innovativer PHY Technologien verwendet werden kann. Opportunistische Kommunikation ist eine Technik, die die inhärenten Besonderheiten des drahtlosen Kanals ausnutzt. In der vorliegenden Dissertation werden CSMA-basierte Protokolle für die opportunistische Kommunikation in WMNs entwickelt und evaluiert. Es werden dabei opportunistisches Routing (OR) im zustandslosen Kanal und opportunistisches Scheduling (OS) im zustandsbehafteten Kanal betrachtet. Ziel ist es, den Durchsatz von elastischen Paketflüssen gerecht zu maximieren. Es werden Modelle für Überlastkontrolle, Routing und konkurrenzbasierte opportunistische Kommunikation vorgestellt. Am Beispiel von IEEE 802.11 wird illustriert, wie der schichtübergreifende Entwurf in einem Netzwerksimulator prototypisch implementiert werden kann. Auf Grundlage der Evaluationsresultate kann der Schluss gezogen werden, dass die opportunistische Kommunikation konkurrenzbasiert realisierbar ist. Darüber hinaus steigern die vorgestellten Protokolle den Durchsatz im Vergleich zu etablierten Lösungen wie etwa DCF, DSR, ExOR, RBAR und ETT.In the field of wireless communication, a tremendous progress can be observed especially at the lower layers. Innovative physical layer (PHY) concepts and technologies can be rapidly assimilated in cellular networks. Wireless mesh networks (WMNs), on the other hand, cannot keep up with the speed of innovation at the PHY due to their flat and decentralized architecture. Many innovative PHY technologies rely on multi-user communication, so that the established abstraction of the network stack does not work well for WMNs. The scheduling problem in WMNs is inherent complex. Surprisingly, carrier sense multiple access (CSMA) in WMNs is asymptotically utility-optimal even though it has a low computational complexity and does not involve message exchange. Hence, the question arises whether CSMA and the underlying concept of contention allows for the assimilation of advanced PHY technologies into WMNs. In this thesis, we design and evaluate contention protocols based on CSMA for opportunistic communication in WMNs. Opportunistic communication is a technique that relies on multi-user diversity in order to exploit the inherent characteristics of the wireless channel. In particular, we consider opportunistic routing (OR) and opportunistic scheduling (OS) in memoryless and slow fading channels, respectively. We present models for congestion control, routing and contention-based opportunistic communication in WMNs in order to maximize both throughput and fairness of elastic unicast traffic flows. At the instance of IEEE 802.11, we illustrate how the cross-layer algorithms can be implemented within a network simulator prototype. Our evaluation results lead to the conclusion that contention-based opportunistic communication is feasible. Furthermore, the proposed protocols increase both throughput and fairness in comparison to state-of-the-art approaches like DCF, DSR, ExOR, RBAR and ETT