The evolution and deployment of fifth-generation (5G) and beyond (B5G) infrastructure will require a tremendous effort to specify the design, standards, and manufacturing. 5G is vital to modern technological evolution, including industry 4.0, automotive, entertainment, and health care. The ambitious and challenging 5G project is classified into three categories, which provide an essential supporting platform for applications associated with: Enhanced mobile broadband (eMBB) Ultra-reliable low-latency communication (URLLC) Massive machine-type communication (mMTC) The demand for URLLC grows, particularly for applications like autonomous guided vehicles (AGVs), unmanned aerial vehicles (UAVs), and factory automation, and has a strict requirement of low latency of 1 ms and high reliability of 99.999%. To meet the needs of communication-sensitive and computation-intensive applications with different quality-of-service (QoS) requirements, this evolution focuses on ultra-dense edge networks with multi-access edge computing (MEC) facilities. MEC emerges as a solution, placing resourceful servers closer to users. However, the dynamic nature of processing and interaction patterns necessitates effective network control, which is challenging due to stringent requirements on both communication and computation. In this context, we introduce a novel approach to optimally manage task offloading, considering the intricacies of heterogeneous computing and communication services. Unlike existing methods, our methodology incorporates the number of admitted service migrations and QoS upper and lower bounds as binding constraints. The comprehensive model encompasses agent positions, MEC servers, QoS requirements, edge network communication, and server computing capabilities. Formulated as a mixed-integer linear program (MILP), it provides an optimal schedule for service migrations and bandwidth allocation, addressing the challenges posed by computation-intensive and communication-sensitive applications. Moreover, tailoring to an indoor robotics environment, we explore optimization-based approaches seeking an optimal system-level architecture while considering QoS guarantees. Optimization tools, e.g., ARCHEX, prove their ability to capture cyber-physical systems (CPS) requirements and generate correct-by-construction architectural solutions. We propose an extension in ARCHEX by incorporating dynamic properties, i.e., robot trajectories, time dimension, application-specific QoS constraints, and finally, integrating the optimization tool with a discrete-event network simulator (OMNeT++). This extension automates the generation of configuration files and facilitates result analysis, ensuring a comprehensive evaluation. This part of the work focuses on the dynamism of robots, server-to-service mapping, and the integration of automated simulation. The proposed extension is validated by optimizing and analyzing various indoor robotics scenarios, emphasizing critical performance parameters such as overall throughput and end-to-end delay (E2E). This integrated approach addresses the complex interplay of computation and communication resources, providing a solution for dynamic mobility, traffic, and application patterns in edge server environments.

<p dir="ltr">Immersive applications such as augmented reality (AR) and mixed reality (MR) often need to perform latency-critical analytics tasks on every frame captured on camera. These tasks, often powered by deep neural networks (DNNs) for their superior accuracy, necessitate offloading to edge servers with GPUs due to their computational intensity. Achieving high accuracy and efficient AR task offloading faces two fundamental challenges untapped by prior work: (1) In practice, multiple DNN-supported tasks need to offload concurrently to achieve the app functionality -- how to schedule such offloaded tasks on the client which compete for shared edge server resources to maximize the app QoE? (2) Concurrent AR clients from a large user base offload to a cluster of GPU servers -- how to schedule the offloaded tasks on the servers to maximize the number of clients served and lower the operating cost?</p><p dir="ltr">To tackle the first challenge, we design a framework, AccuMO, that balances the offloading frequencies of different tasks by dynamically scheduling the offloading of multiple tasks from an AR client to an edge server, thereby optimizing the overall accuracy across tasks and hence app QoE. Our design employs two novel ideas: (1) task-specific lightweight models that predict offloading accuracy drop as a function of offloading frequency and frame content, and (2) a general two-level control feedback loop that concurrently balances offloading among tasks and adapts between offloading and using local algorithms for each task.</p><p dir="ltr">We tackle the challenge of supporting concurrent AR clients in two steps. We first focus on maximizing the capacity of individual edge servers, where we present ARISE, which untangles the intricate interplay between per-client offloading schedule and batched inference on the server by proactively coordinating offloading requests from different AR clients. In the second step, we focus on a cluster setup of heterogeneous GPU servers which exposes the synergy between diversity in both DNN layers and GPU architectures, manifesting as comparable inference latency for many layers in DNN models when running on low-class and high-class GPUs. We exploit such overlooked capability of low-class GPUs using pipeline parallelism and present a novel inference serving system, IPIPE, that employs pool-based pipeline parallelism with a mixed-integer linear programming (MILP)-based control plane and a data plane that performs resource reservation-based adaptive batching.</p>

<p>Recent advancements in deep learning (DL) and high-communication bandwidth access networks such as 5G enable applications that require intelligence and faster computational power at the edge with low power consumption. In this thesis, we study how to improve the Quality-of-Experience (QoE) of these emerging 5G applications, e.g., virtual reality (VR) and video analytics on edge devices. These 5G applications either require high-quality visual effects with a stringent latency requirement (for VR) or high analytics accuracy (for video analytics) while maintaining frame rate requirements under dynamic conditions. </p> <p>In part 1, we study how to support high-quality untethered immersive multiplayer VR on commodity mobile devices. Simply replicating the prior-art for a single-user VR will result in a linear increase in network bandwidth requirement that exceeds the bandwidth of WiFi (802.11ac). We propose a novel technique, <em>Coterie, </em>that splits the rendering of background environment (BE) frames between the mobile device and the edge server that drastically enhances the similarity of the BE frames and reduces the network load via frame caching and reuse. Our proposed VR framework, Coterie, reduces per-player network requirement by over 10x and easily supports 4 players on Pixel 2 over 802.11ac while maintaining the QoE constraints of 4K VR.</p> <p>In part 2, we study how to achieve high accuracy of analytics in video analytics pipelines (VAP). We observe that the frames captured by the surveillance video cameras powering a variety of 24X7 analytics applications are not always pristine -- they can be distorted due to environmental condition changes, lighting issues, sensor noise, compression, etc. Such distortions not only deteriorate the accuracy of deep learning applications but also negatively impact the utilization of the edge server resources used to run these computationally expensive DL models. First, we study how to dynamically filter out low-quality frames captured. We propose a lightweight DL-based quality estimator, <em>AQuA</em>, that can be used to filter out low-quality frames that can lead to high-confidence errors (false-positives) if fed into the analytic units (AU) in the VAP. AQuA-filter reduces false positives by 17% and the compute and network usage by up to 27% when used in a face-recognition VAP. Second, we study how to reduce such poor-quality frame captures by the camera. We propose <em>CamTuner, </em>a system that automatically and dynamically adapts the complex camera settings to changing environmental conditions based on analytical quality estimation to enhance the accuracy of video analytics. In a real customer deployment, <em>CamTuner</em> enhances VAP accuracy by detecting 15.9% additional persons and 2.6%–4.2% additional cars (without any false positives) than the default camera setting. While <em>CamTuner</em> focuses on improving the accuracy of single-AU running on a camera stream, next we present <em>Elixir</em>, a system that enhances the video stream quality for multiple analytics on a video stream by jointly optimizing different AUs’ objectives. In a real-world deployment, <em>Elixir</em> correctly detects 7.1% (22,068) and 5.0% (15,731) more cars, 94% (551) and 72% (478) more faces, and 670.4% (4975) and 158.6% (3507) more persons than the default-camera-setting and time-sharing approaches, respectively.</p>

Advantages of cache cooperation on edge cache networks serving dynamic web content were studied. Design of cooperative edge cache grid a large-scale cooperative edge cache network for delivering highly dynamic web content with varying server update frequencies was presented. A cache clouds-based architecture was proposed to promote low-cost cache cooperation in cooperative edge cache grid. An Internet landmarks-based scheme, called selective landmarks-based server-distance sensitive clustering scheme, for grouping edge caches into cooperative clouds was presented. Dynamic hashing technique for efficient, load-balanced, and reliable documents lookups and updates was presented. Utility-based scheme for cooperative document placement in cache clouds was proposed. The proposed architecture and techniques were evaluated through trace-based simulations using both real-world and synthetic traces. Results showed that the proposed techniques provide significant performance benefits. A framework for automatically detecting cache-effective fragments in dynamic web pages was presented. Two types of fragments in web pages, namely, shared fragments and lifetime-personalization fragments were identified and formally defined. A hierarchical fragment-aware web page model called the augmented-fragment tree model was proposed. An efficient algorithm to detect maximal fragments that are shared among multiple documents was proposed. A practical algorithm for detecting fragments based on their lifetime and personalization characteristics was designed. The proposed framework and algorithms were evaluated through experiments on real web sites. The effect of adopting the detected fragments on web-caches and origin-servers is experimentally studied.

Fifth Generation (5G) cellular networks will experience a thousand-fold increase in data traffic with over 100 billion connected devices by 2020. In order to support this skyrocketing traffic demand, smaller base stations (BSs) are deployed to increase capacity. However, more BSs increase energy consumption which contributes to operational expenditure (OPEX) and CO2 emissions. Also, an introduction of a plethora of 5G applications running in the mobile devices cause a significant amount of energy consumption in the mobile devices. This thesis presents a novel framework for energy efficiency in 5G cloud radio access networks (C-RAN) by leveraging cloud computing technology. Energy efficiency is achieved in three ways; (i) at the radio side of H-C-RAN (Heterogeneous C-RAN), a dynamic BS switching off algorithm is proposed to minimise energy consumption while maintaining Quality of Service (QoS), (ii) in the BS cloud, baseband workload consolidation schemes are proposed based on simulated annealing and genetic algorithms to minimise energy consumption in the cloud, where also advanced fuzzy based admission control with pre-emption is implemented to improve QoS and resource utilisation (iii) at the mobile device side, Mobile Edge Computing (MEC) is used where computer intensive tasks from the mobile device are executed in the MEC server in the cloud. The simulation results show that the proposed framework effectively reduced energy consumption by up to 48% within RAN and 57% in the mobile devices, and improved network energy efficiency by a factor of 10, network throughput by a factor of 2.7 and resource utilisation by 54% while maintaining QoS.

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<p>This paper aims to present a novel approach to real-time indoor temperature forecasting to meet energy consumption constraints in buildings, utilizing computing resources available at the edge of a network, close to data sources. This work was inspired by the irreversible effects of global warming accelerated by greenhouse gas emissions from burning fossil fuels. As much as human activities have heavy impacts on global energy use, it is of utmost importance to reduce the amount of energy consumed in every possible scenario where humans are involved. According to the US Environmental Protection Agency (EPA), one of the biggest greenhouse gas sources is commercial and residential buildings, which took up 13 percent of 2019 greenhouse gas emissions in the United States. In this context, it is assumed that information of the building environment such as indoor temperature and indoor humidity, and predictions based on the information can contribute to more accurate and efficient regulation of indoor heating and cooling systems. When it comes to indoor temperature, distributed IoT devices in buildings can enable more accurate temperature forecasting and eventually help to build administrators in regulating the temperature in an energy-efficient way, but without damaging the indoor environment quality. While the IoT technology shows potential as a complement to HVAC control systems, the majority of existing IoT systems integrate a remote cloud to transfer and process all data from IoT sensors. Instead, the proposed IoT system incorporates the concept of edge computing by utilizing small computer power in close proximity to sensors where the data are generated, to overcome problems of the traditional cloud-centric IoT architecture. In addition, as the microcontroller at the edge supports computing, the machine learning-based prediction of indoor temperature is performed on the microcomputer and transferred to the cloud for further processing. The machine learning algorithm used for prediction, ANN (Artificial Neural Network) is evaluated based on error metrics and compared with simple prediction models.</p>

Abstract The next-generation networks are set to support a high data rate, low latency, high reliability, and diverse types of services and use cases. These requirements come at the expense of a more complex network management, and asymmetric and time-varying traffic dynamics. Accordingly, future networks will operate at different duplexing modes and with multiple access techniques. This thesis proposes novel transmission strategies and methodologies to dynamically optimize the duplexing modes and allocate resources for small cell based cellular networks. The first part of the thesis studies dynamic time-division-duplex (TDD) operation in dynamic and asymmetric uplink (UL) and downlink (DL) traffic conditions. In this regard, we propose a dynamic TDD framework that optimizes the UL and DL frame configuration and power allocation. Due to the high interference coupling between neighboring small cells, we propose a load-aware clustering method that groups the small cell base stations (SBSs) based on their spatial and load similarities. To balance the UL and DL loads within each cluster, we study the potential of load-based UL/DL decoupled user association in balancing the traffic loads within clusters. In the second part, we study the problem of half-duplex (HD)/full-duplex (FD) mode selection and UL/DL resource and power optimization in small cell networks. Therein, SBSs operate in non-orthogonal multiple access (NOMA) in both UL and DL to schedule multiple users at the same time-frequency resource. The goal of the study is therefore to select the optimal duplexing and multiple access scheme, based on the traffic load and interference conditions, such that users’ data rates are maximized, while stabilizing traffic queues. Finally, the last part of the thesis looks beyond rate maximization and focuses on ensuring low latency and high reliability in small cell networks providing edge computing services. The problem of distributing wireless resources to users requesting edge computing tasks is cast as a delay minimization problem under stringent reliability constraints. The study investigates the role of proactive computing in ensuring low latency edge computing, while the concept of hedged requests is presented as an enabler for computing service reliability. / Tiivistelmä Seuraavan sukupolven verkot suunnitellaan tukemaan suuria tiedonsiirtonopeuksia, pientä latenssia, erinomaista luotettavuutta ja monentyyppisiä palveluja ja käyttötapauksia. Näiden vaatimusten täyttämisen kääntöpuolena ovat entistä monimutkaisemmat verkonhallintatoiminnot sekä epäsymmetrinen ja ajallisesti muuttuva dataliikenteen dynamiikka. Verkot toimivat tulevaisuudessa eri dupleksointitiloissa hyödyntämällä useita eri liittymätekniikoita. Tässä tutkielmassa ehdotetaan uusia siirtostrategioita ja menetelmiä dupleksointitilojen dynaamista optimointia ja resurssien allokointia varten piensoluperustaisissa solukkoverkoissa. Tutkielman alkuosassa tarkastellaan dynaamisen aikajakodupleksin (TDD) toimintaa dataliikenneympäristöissä, joissa on käytössä dynaaminen ja epäsymmetrinen lähetysyhteys (UL) ja laskeva siirtotie (DL). Ehdotamme tältä osin dynaamista TDD-kehystä, joka optimoi UL- ja DL-kehyksen konfiguroinnin ja tehon allokoinnin. Vierekkäisten pienten solujen välisten kytkösten suuren interferenssin takia ehdotamme kuormituksen huomioivaa klusterointimenetelmää, jossa piensolutukiasemat (SBS) ryhmitellään niiden tilallisten ja kuormitusominaisuuksien yhteneväisyyden perusteella. Tutkimme UL- ja DL-kuormitusten tasapainottamista kussakin klusterissa tarkastelemalla UL/DL-yhteyksistä irti kytketyn, kuormitukseen perustuvan käyttäjän yhdistämisen mahdollisuuksia dataliikennekuormituksen tasapainottamisessa. Tutkielman toisessa osassa tarkastellaan puolidupleksi (HD)- ja kaksisuuntaisen (FD) -tilan valinnan ongelmaa ja UL-/DL-resurssien ja tehon optimointia piensoluverkoissa. Siinä piensolutukiasemat toimivat ei-ortogonaalisessa moniliittymässä (NOMA) sekä UL- että DL-yhteyksissä useiden käyttäjien ajoittamiseksi samalle aika-taajuusresurssille. Tutkielman tavoitteena on siten valita optimaalinen dupleksointi- ja moniliittymäkaavio dataliikenteen kuormituksen ja interferenssin perusteella siten, että käyttäjän tiedonsiirtonopeudet voidaan maksimoida ja dataliikennejonot tasata. Lopuksi tutkielman viimeisessä osassa tarkastellaan tiedonsiirtonopeuden maksimoinnin lisäksi pienen latenssin ja suuren luotettavuuden varmistamista piensoluverkoissa, jotka tuottavat reunalaskentapalveluja. Langattomien resurssien jakelemista käyttäjille, jotka vaativat reunalaskentatehtäviä, käsitellään viiveen minimoinnin ongelmana soveltamalla tiukkoja luotettavuusrajoituksia. Tutkielmassa tarkastellaan proaktiivisen tietojenkäsittelyn roolia pienen latenssin reunalaskennassa.

Nowadays more and more data is generated at the edge of the network, and people are beginning to consider decentralizing computing tasks to the edge of the network. The network architecture of edge computing is different from the traditional network architecture. Its distributed configuration can make up for some shortcomings of traditional networks, such as data congestion, increased delay, and limited capacity. With the continuous development of 5G technology, satellite communication networks are also facing many new business challenges. By using idle computing power and storage space on satellites and integrating edge computing technology into satellite communication networks, it will greatly improve satellite communication service quality, and enhance satellite task processing capabilities, thereby improving the satellite edge computing system performance. The primary problem that limits the computing performance of satellite edge networks is how to obtain a more effective dynamic service offloading strategy. To study this problem, this thesis monitors the status information satellite nodes in different periods, such as service load and distance to the ground, uses the Markov decision process to model the dynamic offloading problem of the satellite edge computing system, and finally obtains the service offloading strategies. The deployment plan is based on deep reinforcement learning algorithms. We mainly study the performance of the Deep Q-Network (DQN) algorithm and two improved DQN algorithms Double DQN (DDQN) and Dueling DQN (DuDQN) in different service request types and different system scenarios. Compared with existing service deployment algorithms, deep reinforcement learning algorithms take into account the long-term service quality of the system and form more reasonable offloading strategies. / Med den snabba utvecklingen av mobil kommunikationsteknik genereras mer och mer data i utkanten av nätverket, och människor börjar överväga att decentralisera datoruppgifter till kanten av nätverket. Och byggde ett komplett mobilt edge computing -arkitektursystem. Edge -dators nätverksarkitektur skiljer sig från den traditionella nätverksarkitekturen. Dess distribuerade konfiguration kan kompensera för eventuella brister i traditionella nätverk, såsom överbelastning av data, ökad fördröjning och begränsad kapacitet. Med den ständiga utvecklingen av 5G -teknik står satellitkommunikationsnät också inför många nya affärsutmaningar. Genom att använda inaktiv datorkraft och lagringsutrymme på satelliter och integrera edge computing -teknik i satellitkommunikationsnät kommer det att förkorta servicetiden för traditionella mobila satelliter kraftigt, förbättra satellitkommunikationstjänstkvaliteten och förbättra satellituppgiftsbehandlingsförmågan och därigenom förbättra satelliten edge computing systemprestanda. Det primära problemet som begränsar datorprestanda för satellitkantnät är hur man får en mer effektiv dynamisk tjänstavlastningsstrategi. Detta papper övervakar servicebelastningen av satellitnoder i olika perioder, markpositionsinformation och annan statusinformation använder Markov - beslutsprocessen för att modellera den dynamiska distributionen av satellitkantstjänster och får slutligen en uppsättning tjänstedynamik baserad på modell och design . Distributionsplanen är baserad på en djupt förbättrad algoritm för dynamisk distribution av tjänster. Det här dokumentet studerar huvudsakligen prestandan för DQN -algoritmen och två förbättrade DQN - algoritmer Double DQN och Dueling DQN i olika serviceförfrågningstyper och olika systemscenarier. Jämfört med befintliga algoritmer för serviceutplacering är prestandan för algoritmer för djupförstärkning något bättre.

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