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DTSTART;TZID=Europe/Paris:20260507T100000
DTEND;TZID=Europe/Paris:20260507T120000
DTSTAMP:20260507T125935Z
CREATED:20260507T125211Z
LAST-MODIFIED:20260507T125935Z
UID:8091-1778148000-1778155200@www.tecosa.center.kth.se
SUMMARY:PhD Defence – Nils Jörgensen
DESCRIPTION:Ämnesområde: Maskinkonstruktion \nRespondent: Nils Jörgensen \, Mekatronik och inbyggda styrsystem \nOpponent: Dr. Federico Rossi\, California Institute of Technology\, USA \nHandledare: Docent Fredrik Asplund\, Mekatronik och inbyggda styrsystem; Adjunkt. Prof. Rafia Inam\, Mekatronik och inbyggda styrsystem; Ph.D. Ajay Kattepur\, Ericsson Research\, Bangalore\, India \n  \nAbstract \nDen fjärde industriella revolutionen innebär flexibel tillverkning där autonoma mobila robotar samverkar över delade trådlösa nätverk. Femte generationens (5G) mobilnätsteknik—och dess efterföljare Beyond 5G och 6G—har lyfts fram som möjliggörande infrastruktur\, med löften om nätverksslicing (network slicing)\, differentierad tjänstekvalitet och edge computing som skulle kunna frigöra industrirobotar från kablar utan att offra reglerprecision. Den samtida framväxten av physical AI och paradigmet med AI-nativa radioaccessnätverk\, som sammanför AI-beräkningar med nätverksinfrastruktur\, förstärker ytterligare både löftet och komplexiteten i denna integration. Denna avhandling undersöker huruvida den nuvarande forskningen är rustad att utnyttja denna infrastruktur—och identifierar flera strukturella felanpassningar som fältet måste adressera. \nAvhandlingen består av fyra faser. En systematisk översikt av edge computing för cyberfysiska system visar att industriell tillverkning är den främsta drivkraften för tillämpningar\, men att edge computing i 5G-bemärkelse och systematisk behandling av tillförlitlighetsaspekter till stor del saknas i litteraturen. En fokuserad granskning avslöjar vidare ett fält som är terminologiskt fragmenterat\, metodologiskt begränsat till optimering på rörelsenivå med signalstyrka som fokus\, och nästan helt frånkopplat från faktisk telekommunikationsinfrastruktur. Granskningen resulterar i en taxonomi och utvärderingskriterier som tillhandahåller en strukturerad terminologi för att karakterisera och jämföra tillvägagångssätt inom robotik- och telekommunikationsområdena. \nSom svar demonstrerar ett planeringsramverk gemensam kommunikations- och uppdragsplanering genom att formulera koordinering av flera robotar i planeringsspråket PDDL (Planning Domain Definition Language)\, där 5G:s fysiska resursblock behandlas som explicita beslutsvariabler i planeringen tillsammans med uppgiftstilldelning och sekvensering. Detta minskar spektrumbehovet med femtio procent och uppfyller samtidigt planens restriktioner inom acceptabel beräkningstid. \nEn empirisk mätstudie på en privat 5G-testbädd i industriell miljö utmanar sedan ett centralt antagande som delas av de flesta befintliga kommunikationsmedvetna planeringsmetoder: att gynnsamma signalförhållanden på ett tillförlitligt sätt resulterar i faktisk systemprestanda. En kommersiell strålgångsbaserad simulator förutspådde signalkvaliteten med rimlig noggrannhet\, men överskattade systematiskt den tillgängliga datahastigheten\, och den dominerande källan till prediktionsfel visade sig vara anpassning av antalet samtidiga dataströmmar vid flerantennöverföring (MIMO). En modell baserad på Gaussisk processregression minskade prediktionsfelet med ungefär två tredjedelar och eliminerade systematiskt fel. Testbädden saknade dessutom stöd för nätverksslicing på radioaccessnätverksnivå\, vilket både planeringsramverket och den bredare litteraturen förutsätter—en klyfta mellan standardiserade gränssnitt och faktiskt tillgänglig funktionalitet. \nSammanfattningsvis kulminerar inte avhandlingen i ett enskilt överlägset ramverk utan i en mer mödosamt vunnen insikt: fältets algoritmiska sofistikering döljer diskrepanser från industriell verklighet som verkar på flera nivåer samtidigt—konceptuell\, modellerings-\, infrastruktur- och utvärderingsnivå. På konceptuell nivå utökas rörelseplanerare med signalnivåmätetal när uppdragsplanerare borde utökas med abstraktioner på nätverkstjänstnivå. På modelleringsnivå optimerar planerare för signalstyrka medan de fenomen som bestämmer datahastigheten i moderna cellulära system ligger bortom signalstyrka-baserade modeller. På infrastrukturnivå förblir de nätverksfunktioner som antas i litteraturen ofullständigt realiserade i kommersiell utrustning. Detta arbetes praktiska bidrag ligger i att systematiskt synliggöra dessa diskrepanser och i att erbjuda både analytiska verktyg och en konstruktiv artefakt som tillsammans stakar ut en mer verklighetsförankrad väg framåt. Att förverkliga visionen om den smarta fabriken kräver inte enbart bättre algoritmer utan en omorientering av antaganden\, formalismer och utvärderingsmetoder—från rörelseplanering till uppdragsplanering\, från signalstyrka till nätverkstjänstabstraktioner\, och från simuleringsbaserad validering till grundad på mätningar. \nLink to DiVA \n 
URL:https://www.tecosa.center.kth.se/event/phd-defence-nils-jorgensen/
CATEGORIES:PhD defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20260206T093000
DTEND;TZID=Europe/Paris:20260206T123000
DTSTAMP:20260120T122502Z
CREATED:20260120T122502Z
LAST-MODIFIED:20260120T122502Z
UID:7928-1770370200-1770381000@www.tecosa.center.kth.se
SUMMARY:Welcome to the dissertation by Ahmad Ishtar Terra\, KTH\, division of Mechatronics and industrial PhD student with Ericsson research
DESCRIPTION:Key people \nCandidate: Ahmad Terra (industrial PhD student KTH and Ericsson)\nOpponent: Professor Kary Främling (Umeå University) \nGrading committee:\nAssociate Professor Sepideh Pashami\, (Halmstad University/RISE)\nProfessor Kerstin Bach (NTNU)\nProfessor Fredrik Heintz (LIU) \nSpare committee member: Associate Professor Hossein Azizpour\nKTH Dissertation chair: Associate Professor Martin Grimheden\, head of Department\nSupervisors: Martin Törngren and Rafia Inam\, KTH \nVenue: KTH – F3 (Flodis)\, Lindstedtsvägen 26 & 28\, Stockholm\nJoin by link: https://kth-se.zoom.us/j/63010540491
URL:https://www.tecosa.center.kth.se/event/welcome-to-the-dissertation-by-ahmad-ishtar-terra-kth-division-of-mechatronics-and-industrial-phd-student-with-ericsson-research/
CATEGORIES:PhD defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20260116T100000
DTEND;TZID=Europe/Paris:20260116T120000
DTSTAMP:20260113T140440Z
CREATED:20260113T140002Z
LAST-MODIFIED:20260113T140440Z
UID:7920-1768557600-1768564800@www.tecosa.center.kth.se
SUMMARY:PhD Defence – Vishnu Moothedath
DESCRIPTION:Welcome to the PhD defence of Vishnu Moothedath\nTitle: Towards Efficient Distributed Intelligence: Cost-Aware Sensing and Offloading for Inference at the Edge \nLink to the thesis: https://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A2017058&dswid=8369 \nRespondent: Vishnu Moothdath\nOpponent: Professor Ayalvadi Ganesh\, University of Bristol\, Bristol\, United Kingdom\nCommittee:\nResearch Leader Dr. Beatriz Grafulla (Ericsson\, Kista)\nAssociate Professor Praveen Kumar Donta (Stockholm University)\nAssociate Professor Salman Toor (Uppsala University\nStand-In member: Professor Rolf Stadler (KTH\, Stockholm\, Sweden) \nChair: Mikael Skolgund (KTH) \nSupervisors: James Gross\, György Dan \nLocation (Hybrid meeting): Salongen\, Osquars backe 31\, KTH Campus; and\nhttps://kth-se.zoom.us/j/61617488895
URL:https://www.tecosa.center.kth.se/event/phd-defence-vishnu-moothedath/
CATEGORIES:PhD defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250618T100000
DTEND;TZID=UTC:20250618T110000
DTSTAMP:20250610T085839Z
CREATED:20250610T084829Z
LAST-MODIFIED:20250610T085839Z
UID:7742-1750240800-1750244400@www.tecosa.center.kth.se
SUMMARY:PhD Defense: “Situation Awareness for Autonomous Agents under Limited Sensing”
DESCRIPTION:TECoSA PhD student José Manuel Gaspar Sánchez  will defend his thesis at Kollegiesalen\, Brinellvägen 6\, KTH Campus.  Contact Martin Törngren (martint@kth.se) if you are interested in attending. Zoom link for the defense is found here. \nAbstract: Autonomous agents\, such as robots and automated vehicles\, rely on their ability to perceive and interpret their environment to make informed decisions and execute actions that align with their goals. A key aspect of this capability is situation awareness\, which involves understanding the current state of the environment and predicting its future evolution. Traditional autonomous systems address perception and prediction as separate tasks within a sequential pipeline\, where raw sensor data is processed into increasingly abstract representations. While this structured approach has driven significant advancements\, it remains constrained by sensor limitations\, including occlusions\, measurement uncertainty\, and adverse weather conditions. \nThis thesis investigates how predictions from past observations can enhance perception algorithms\, enabling agents to infer missing information\, reduce uncertainty\, and better anticipate changes. To support this integration\, alternative environment representations are explored that allow feedback between prediction and perception while capturing uncertainty. This tighter coupling improves decision-making\, particularly in complex and partially observable environments. \nThe contributions include: (1) a reachability-based reasoning framework for tracking possible hidden obstacles; (2) its extension to handle delayed and partial external data; (3) a probabilistic mapping method\, Transitional Grid Maps (TGM)\, that jointly models static and dynamic occupancy; and (4) an extension of TGM to mitigate weather-induced sensor noise. \nThe proposed methods are evaluated in simulated and real scenarios where traditional perception pipelines struggle\, such as occluded\, highly dynamic and noisy environments. By bridging the gap between perception and prediction\, this work contributes to the development of more robust and intelligent autonomous systems. \nDetails of the panel are shown below: \nSupervisor: Professor Martin Törngren\, KTH. \nOpponent: Professor Jonas Sjöberg\, Chalmers University of Technology.
URL:https://www.tecosa.center.kth.se/event/phd-defense-situation-awareness-for-autonomous-agents-under-limited-sensing/
CATEGORIES:PhD defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250610T140000
DTEND;TZID=UTC:20250610T150000
DTSTAMP:20250531T095953Z
CREATED:20250514T105248Z
LAST-MODIFIED:20250531T095953Z
UID:7704-1749564000-1749567600@www.tecosa.center.kth.se
SUMMARY:PhD Defense: “Joint Optimization of Pricing and Resource Allocation in Serverless Edge Computing: A Game-Theoretic Perspective”
DESCRIPTION:TECoSA PhD student Feridun Tütüncüoğlu will defend his thesis at F3\, KTH Campus. Contact György Dán (gyuri@kth.se) if you are interested in attending. \n \nAbstract: The rapid advancement of Internet of Things (IoT)\, Augmented Reality (AR)\, autonomous systems\, and intelligent automation is transforming daily lifeand revolutionizing industrial processes. These technologies demand significant computational resources while also imposing stringent latency requirements. A common approach to meet computational resource demand is leveraging Cloud Computing (CC)\, which offers scalable processing capabilities through centralized data centers. Nonetheless\, this centralized approach often fails to meet stringent latency requirements due to communication delays caused by the geographical distance between cloud servers and end users. This limitation has led to the emergence of the novel paradigm of Edge Computing (EC)\, which addresses the latency issue by placing compute units closer to end users. \nEC server clusters are expected to be smaller in scale and geographically more dispersed compared to CC data centers. This introduces new challenges\, including limited computational and storage capacity\, making efficient resource allocation crucial. Additionally\, the network operator managing edge infrastructure must maintain the financial sustainability under different workload characteristics and application requirements of users\, necessitating joint and adaptable resource management and pricing strategies. Function-as-a-Service (FaaS) offers a promising approach in this regard\, as its pay-as-you-go pricing model allows users to pay only for the resources they consume\, while also enabling dynamic resource management by shifting the entire responsibility of application deployment to the operator. However\, this flexibility also makes the choice of computation\, memory\, and bandwidth resources price-dependent\, further complicating resource management and pricing. \nIn the first part of the thesis\, we consider a setting where Wireless Devices (WDs) minimize energy and the monetary costs of computing tasks\, while the operator maximizes revenue by optimizing pricing and application caching under memory constraints. We consider a dynamic setting where the operator has no prior knowledge of the varying availability of WDs over time. We model this interaction as a Stackelberg Game (SG) and demonstrate the existence of an equilibrium. To address information asymmetry\, we use Bayesian optimization to learn pricing strategies\, establish an upper bound on its asymptotic regret\, and propose a greedy approximation algorithm for application caching. We then investigate the joint optimization of compute\, communication\, and memory resources in a static network setting\, where WDs minimize the costs of executing the tasks of their applications\, including monetary and energy expenses. We model this interaction as a SG\, show the existence of an equilibrium\, and prove that computing an equilibrium is NP-hard. We propose an efficient approximation algorithm with a bounded approximation ratio. An interesting feature of our solution is that the operator’s revenue is maximized when the WDs maximize their energy savings through computation offloading. Furthermore\, we investigate the rate-adaptation problem\, where WDs adjust their offloading rates based on available compute resources and pricing. We model the interaction as a SG and propose a Stackelberg gradient play algorithm that computes the operator’s implicit revenue function with respect to rate selection of the WDs. \nThe second part of this thesis explores a dynamic network and pricing setting where WDs arrive at the edge cell according to a non-homogeneous stochastic process\, and the operator sets prices based on the availability of WDs and their heterogeneous workload characteristics. We formulate the problem of maximizing the revenue of the operator as a sequential decision making problem under uncertainty\, where the operator’s price can be piecewise linear or non-linear and could vary over time. In a Markovian steady-state setting\, we derive analytical results for the optimal pricing strategy\, which also serve as a heuristic for the general case. To address the general case\, we introduce a Generalized Hidden Parameter Markov Decision Process and propose a dual Bayesian neural network approximator that approximates the state transitions and the revenue to accelerate the learning of the optimal pricing policy. This approach enables pre-training on synthetic traces while adapting quickly to unseen workload patterns. \nThe third part addresses overlooked computational challenges by examining the impact of server contention on both operator revenue and application latency constraints. To address this\, we propose a contention model validated through experiments across applications with varying compute demands\, including L1/L2/L3 caches\, I/O\, and memory bus usage. We develop a novel model-based Bayesian optimization algorithm to maximize operator revenue while ensuring that latency and resource capacity constraints are met. \nThe algorithmic contributions in the area of pricing and resource management are intended to serve as efficient\, deployable\, and scalable solutions that enhance the robustness and efficiency of resource allocation and pricing in EC. \nDetails of the panel are shown below: \nRespondent: Feridun Tütüncüoğlu \nSupervisors:  György Dan\, James Gross
URL:https://www.tecosa.center.kth.se/event/phd-defense-joint-optimization-of-pricing-and-resource-allocation-in-serverless-edge-computing-a-game-theoretic-perspective/
CATEGORIES:PhD defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250609T100000
DTEND;TZID=UTC:20250609T110000
DTSTAMP:20250516T082156Z
CREATED:20250514T102806Z
LAST-MODIFIED:20250516T082156Z
UID:7700-1749463200-1749466800@www.tecosa.center.kth.se
SUMMARY:PhD Defense: "Predictability\, Prediction\, and Control of Latency in 5G and Beyond: From Theoretical to Data-Driven Approaches"
DESCRIPTION:TECoSA PhD student Samie Mostafavi will defend his thesis at Hybrid\, Room D3\, Lindstedtsvägen 9\, KTH Campus and on zoom\, via this link.  Contact James Gross (jamesgr@kth.se) if you are interested in attending. \n \nAbstract: The explosive growth of mobile communication and the proliferation of real-time applications\, such as industrial automation and extended reality (XR)\, have created unprecedented demands for ultra-reliable low-latency communication (URLLC) in wireless networks. For example\, in industrial closed-loop control systems\, data must be transmitted within a target delay of at most a few milliseconds; violations can lead to costly failures and\, therefore\, must occur with probabilities below 0.0001 (or\, reliability above 0.9999). \nThis dissertation addresses the critical challenge of end-to-end latency prediction and control in these dynamic and stochastic environments\, bridging the gapbetween the inherent randomness of wireless communication and the deterministic performance guarantees required by time-sensitive applications. In this thesis\, we adopt a twofold approach\, combining rigorous theoretical analysis with practical\, data-driven methodologies. First\, we introduce a framework for analyzing predictability that quantifies the inherent limits of latency forecasting in communication networks. Through analysis of Markovian systems\, including single-hop and multi-hop queues\, exact expressions and spectral-based upper bounds for predictability are derived\, revealing the crucial influence of network topology\, state transitions\, and observation defects. \nBuilding on this foundation\, we developed and implemented data-driven techniques for probabilistic delay prediction. A key contribution is a tail-optimized prediction method that integrates Extreme Value Theory (EVT) within a mixture density network framework\, significantly enhancing the accuracy of predicting rare\, high-latency events critical for URLLC. To demonstrate the practical utility of these predictions\, ”Delta\,” a novel active queue management scheme\, is introduced. Delta integrates real-time delay violation probability predictions into packet-dropping decisions\, dynamically adapting to delay variations and significantly reducing delay violations. \nTo validate these approaches\, the ExPECA testbed and EDAF framework were developed\, enabling fine-grained delay measurement and decomposition in real 5G systems. Extensive experiments on both commercial off-the-shelf 5G and software-defined radio-based OpenAirInterface platforms confirmed the superior accuracy and efficiency of the proposed EVT-enhanced models. Furthermore\, temporal prediction models\, leveraging LSTM and Transformer architectures\, were developed and shown to achieve higher accuracy compared to the baseline approaches in real 5G network experiments\, capturing the time-varying dynamics of wireless networks and providing accurate multi-step forecasts. \nThis dissertation advances latency prediction and control for wireless networks\, offering both theoretical foundations and practical solutions for time-sensitive applications. These findings have significant implications for designing and operating next-generation wireless networks\, paving the way for more dependable communication. Future work should focus on integrating these prediction models to optimize the network and extending the framework to encompass broader quality of service metrics and emerging wireless technologies. \n  \nDetails of the panel are shown below: \nRespondent: Samie Mostafavi\nOpponent: Roberto Verdone\, University of Bologna\nCommittee: Viktoria Fodor\, KTH; Holger Rootzen\, Chalmers; Serveh Shalmashi\, Ericsson Research; (stand-in Mats Bengtsson\, KTH)\nChair: Mikael Skolgund\nSupervisors: James Gross\, György Dan
URL:https://www.tecosa.center.kth.se/event/phd-defense-predictability-prediction-and-control-of-latency-in-5g-and-beyond-from-theoretical-to-data-driven-approaches/
CATEGORIES:PhD defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20250124T090000
DTEND;TZID=UTC:20250124T100000
DTSTAMP:20250115T175325Z
CREATED:20250114T115229Z
LAST-MODIFIED:20250115T175325Z
UID:7590-1737709200-1737712800@www.tecosa.center.kth.se
SUMMARY:PhD Defense: "AI Driven Smart Tightening and Feature Management System for Agile Assembly"
DESCRIPTION:TECoSA industrial PhD student (with Atlas Copco) Lifei Tang will defend his thesis at Gladan\, Brinellvägen 85 at the Dept. of Engineering design\, KTH Campus.  Contact Lei Feng (lfeng@kth.se) if you are interested in attending. \n \nAbstract: The convergence of Industry 4.0\, digitization\, and the concepts of agile andsmart manufacturing is shaping a future driven by cyber-physical systems (CPS)\,the industrial internet of things (IIoT)\, and artificial intelligence (AI). This evo-lution promises unprecedented efficiency\, agility\, and intelligence in manufac-turing. Correspondingly\, these advancements also present new challenges forthe assembly industry. This thesis tackles these challenges in two key areas:trustworthy feature management for agile assembly\, and AI-powered tighteningdiagnosis for smart assembly.Agile manufacturing prioritizes flexibility for uncertain markets. To achievethis\, assembly device maker are increasingly shifting focus to software thatlargely defines hardware functionality. This shift allows companies to offer plat-forms with customizable features rather than just hardware\, which in tern\,reduces operational expenses and allows customers to dynamically configuretheir assembly lines via software. This transition also necessitates a trustwor-thy Feature Management System (FMS) to control feature activation throughsoftware licensing. However\, existing server-based solutions pose trust issues:sellers fear license abuse\, while buyers worry about single points of failure duringserver outages.This thesis contributes a novel permissioned blockchain system designedto address trust concerns in feature management for assembly devices. Theproposed solution combines software licensing for feature control with secureownership transaction records on the blockchain. By leveraging the trust\, trans-parency\, and security of permissioned blockchain technology\, the system ensuressecure and controlled access to license information for authorized parties.Integrating software and physical assembly devices into CPSs enables seam-less data acquisition through communication protocols. Once this data is col-lected\, AI becomes a powerful tool for decision-making. In smart tighteningsystems\, accurately diagnosing tightening results is critical. Modern assemblylines use advanced electric tightening tools equipped with torque and angletransducers to capture detailed data after each operation\, enabling the evalua-tion of tightening performance.Currently\, accurate evaluation still requires manual analysis by tighteningexperts\, resulting in inefficient quality checks that are limited to small samplesof production units. This thesis introduces innovative deep learning methods toautomate the tightening quality assessment process\, achieving expert-level ac-curacy across all manufactured units and reducing the risk of defective productsreaching consumers.Towards AI-powered smart assembly\, our initial research contribution fo-cused on developing a sensor fusion approach using convolutional neural networkand transformer-based architectures for diagnosing tightening results throughsupervised learning. However supervised learning requires labeled data\, andlabeling tightening results requires significant manual work from tightening ex-perts\, leading to a scarcity of labeled datasets. Furthermore\, in sensitive assem-bly applications\, regulatory constraints may prevent sensor data from leaving theshop floor\, where computational resources are often limited. To address thesechallenges\, this thesis also contributes novel self-supervised learning\, data aug-mentation and data augmentation scheduling methods that reduce reliance onlabeled data and computational resources. These innovations ultimately resultin a robust deep learning solution for diagnosing tightening results in real-worldmanufacturing environments. \nDetails of the panel are shown below: \nSupervisors: Professor Martin Törngren\, Associate Professor Lei Feng and Professor Lihui Wang \nOpponent: Professor Knut Åkesson\, Chalmers University of Technology
URL:https://www.tecosa.center.kth.se/event/phd-defense-ai-driven-smart-tightening-and-feature-management-system-for-agile-assembly/
CATEGORIES:PhD defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20230615T130000
DTEND;TZID=UTC:20230615T160000
DTSTAMP:20230524T080447Z
CREATED:20230523T141549Z
LAST-MODIFIED:20230524T080447Z
UID:6478-1686834000-1686844800@www.tecosa.center.kth.se
SUMMARY:PhD Defense: “An Emulation-Based Performance Evaluation Methodology for Edge Computing and Latency Sensitive Applications”
DESCRIPTION:TECoSA PhD student Manuel Olguin will defend his thesis at KTH Campus (U61).  All with an interest in this topic are welcome to attend. \nABSTRACT:  Cloud Computing has greatly impacted our daily lives by providing global accessibility and virtually unlimited scalability. However\, its centralized architecture prioritizing availability and scale has limitations for real-time processing and low-latency applications like Cyber-Physical Systems (CPSs) and mobile eXtended Reality (XR). Edge Computing is emerging as a solution to these limitations by bringing computation closer to the network edge\, enabling real-time decision making. A key challenge to mass deployment of Edge Computing infrastructure relates to the complexity of evaluation the performance of these systems\, which stems from the tight interaction of network and compute. This dissertation addresses this challenge by introducing a methodological approach for studying trade-offs in latency-sensitive applications deployed on Edge Computing which involves emulating the client-side workload while retaining the rest of the system. This maintains the realism of network and compute effects\, offering advantages in efficiency with respect to fully experimental approaches and capturing complex factors that are challenging to model analytically or in simulations. \nFurthermore\, this dissertation explores the implications of this methodology on the potential for optimization in Edge Computing deployments\, in particular with respect to improved accuracy in the emulation. In that context\, the dissertation provides a realistic model of human timings for a specific class of Mobile Augmented Reality (MAR) applications which is combined with a mathematical framework for the optimization of sampling systems. The results show that the introduced methodology improves efficiency\, repeatability\, and replicability compared to existing methods. By integrating workload components into the emulated software domain\, it reduces complexity while considering the complex effects of network and compute factors. The dissertation emphasizes the importance of incorporating enhanced realism in client-side emulation\, which enables the implementation of optimization approaches that would otherwise be infeasible\, and further highlights the significance of human behavior in addition to system-related metrics in the context of MAR\, a killer use-case for Edge Computing. \nDetails of the panel are shown below: \nPrincipal Supervisor: Professor James Gross\nDefense Chair: Professor Mats Bengtsson\nOpponent: Associate Professor Yu Xiao\, Aalto University\, Esbo\, Finland \nMembers of the Grading Committee:\nProfessor Ana Aguiar\, Universidade do Porto\, Portugal\nProfessor Per Gunningberg\, Uppsala University\, Sweden\nProfessor Klaus Wehrle\, RWTH Aachen University\, Germany
URL:https://www.tecosa.center.kth.se/event/phd-defense-an-emulation-based-performance-evaluation-methodology-for-edge-computing-and-latency-sensitive-applications/
CATEGORIES:PhD defense
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20220615T130000
DTEND;TZID=UTC:20220615T150000
DTSTAMP:20220518T110315Z
CREATED:20220517T131920Z
LAST-MODIFIED:20220518T110315Z
UID:5134-1655298000-1655305200@www.tecosa.center.kth.se
SUMMARY:Efficient Strategies for Safety Assurance of Automated Driving - half-time PhD seminar and discussion (Magnus Gyllenhammar with Prof Mario Trapp)
DESCRIPTION:This session takes place in conjunction with a Guest Seminar (10-11 Engineering Resilience for Cognitive Systems) and an Interactive Workshop (15.30-17.00 Holistic Technological Perspectives on Safety of Automated Driving Systems -Methods for Provision of evidence).  If you are interested in joining for some or all of this Resilience Meets Assurance day\, please email tecosa-admin@kth.se to register\, stating which session(s) and whether you would like to join in real life (KTH Campus) or via Zoom.  (Members can accept the Outlook invitations.) \n  \n \nMagnus Gyllenhammar\nABSTRACT: Automated Driving Systems (ADSs) promise enormous benefits to society in terms of increased comfort\, safety and efficiency of the transportation system\, effectively by relieving the vehicle operator from the responsibility of driving the vehicle. Contrary to previous generations of automotive systems\, common development and safety assurance practises no longer suffice to accommodate the increased system complexity and operational uncertainty inherent to an ADS. This is why we have yet to see a large scale deployment of ADSs on public roads\, despite recent technological progress and the promises made by several high-profile auto-makers. For that purpose\, I have in my research explored the research question: What are efficient strategies for safety assurance of ADSs? In this seminar\, I present my contributions to-date\, wherein some insights I have gained when tackling this research question are collected. In particular\, I divide my contributions into three themes: understanding the completeness of the development and verification task of the ADS; deriving a driving policy for the ADS with respect to quantitative safety requirements while incorporating knowledge from operational data\, and surveying the state-of-the art\, both general methods providing evidence for safety of the ADS as well as assurance methods. Leaning on these insights\, I present my intended next steps of my thesis: my intended research direction and the proposed research questions to guide me through to the dissertation. More specifically\, I intend to merge the existing approach of dynamic risk assessment\, wherein the decisions of the ADS are conditioned on the current operating conditions; and the precautionary safety approach\, where quantitative safety requirements are fulfilled through a probabilistic view of risk. Thereby\, I hope to provide an assurance approach that is both effective and efficient by considering risk\, from a statistical standpoint\, while\, in run-time\, conditionally deriving appropriate actions based on the experienced operating conditions of the ADS. \nThe presentation will be followed by a discussion led by Professor Mario Trapp\, Director of the Fraunhofer Institute for Cognitive Systems at IKS (Munich\, Germany).    \nBIO:  Magnus Gyllenhammar pursues a PhD at KTH Royal Institute of Technology as part of his employment at Zenseact in Gothenburg. His research focuses on finding efficient strategies for safety argumentation of ADSs\, especially focusing on dynamic risk assessment in relation to the fulfilment of a quantitative risk norm. He received a MSc. in Engineering Physics\, major in Complex Adaptive System\, from Chalmers University of Technology\, in 2016. In 2018 he joined Zenseact (then Zenuity) and has since worked on creating and realising data-driven strategies for verification and safety argumentation of ADSs.
URL:https://www.tecosa.center.kth.se/event/efficient-strategies-for-safety-assurance-of-automated-driving-half-time-phd-seminar-and-discussion-magnus-gyllenhammar-with-prof-mario-trapp/
CATEGORIES:PhD defense,Talks,Workshops
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DTSTART;TZID=UTC:20210415T130000
DTEND;TZID=UTC:20210415T170000
DTSTAMP:20210310T091529Z
CREATED:20210310T091414Z
LAST-MODIFIED:20210310T091529Z
UID:3379-1618491600-1618506000@www.tecosa.center.kth.se
SUMMARY:Resilient Resource Allocation for Service Placement in Mobile Edge Clouds
DESCRIPTION:TECoSA PhD student Peiyue Zhao will defend his thesis – abstract below.  All are welcome to join. Please contact peiyue @ kth.se for the link. \nAbstract \nMobile edge computing makes available distributed computation and storage resources in close proximity to end users and allows to provide low-latency and high-capacity services within mobile networks. Therefore\, mobile edge computing is emerging as a promising architecture for hosting critical services with stringent latency and performance requirements\, which otherwise are challenging to be addressed in conventional cloud computing architectures. Notable use cases of mobile edge computing include real-time data analytic services\, industrial process control\, and computation offloading for massive Internet of things devices. However\, those services rely on efficient resource management\, including resource dimensioning and service placement\, and require to be resilient to cyber-attacks\, to faulty components and to operation mistakes. The work in this thesis proposes models of resilient resource management that support rapid response to incidents in mobile edge computing and develops efficient algorithms for the resulting resource management problems.​ \nIn the first part of the thesis\, we consider resilient resource management for edge computing systems in which failover is realized by restoring additional service instances in different mobile edge computing nodes in case of failures. We first develop a placement algorithm based on Benders decomposition and linear relaxation to determine the mobile edge computing nodes to be opened and to compute the placement of the service instances with respect to a set of considered failure scenarios\, with the objective of minimizing operation costs. Upon the occurrence of a failure scenario\, service migration is to be triggered to migrate the service instances from one placement to another placement\, for which we further develop service migration algorithms to schedule migration under time constraints\, so as to minimize service interruptions. \nIn the second part of the thesis\, we consider resilient resource management in mobile edge computing for services with different levels of resilience requirements. Resilience is achieved by synchronizing states of the services to two types of standby instances that maintain the trade-off between energy consumption and activation time such that the standby instances can take over the service seamlessly as an instantaneous failure response. We formulate the joint problem of resource dimensioning and service placement for minimizing energy consumption and prove that it is NP-hard. We propose an efficient approximation algorithm based on Lagrangian relaxation to decide the type\, amount\, and locations of the computation resources and to compute the placement of service instances and their associated standby instances. We then consider the same resilience model but for hosting periodic services in mobile edge computing systems with resources portioned into availability zones\, under schedulability constraints. We formulate the corresponding resilient resource management problem as a non-linear programming problem and prove that it is NP-hard. We propose efficient solutions based on approximation programming and primal-dual approaches for resilient service placement. \nBy considering different models of resilient service placement in mobile edge computing\, the results in this thesis provide effective\, efficient\, and scalable resource management algorithms for emerging mobile edge computing systems.
URL:https://www.tecosa.center.kth.se/event/resilient-resource-allocation-for-service-placement-in-mobile-edge-clouds/
CATEGORIES:PhD defense
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DTSTART;TZID=UTC:20200527T140000
DTEND;TZID=UTC:20200527T140000
DTSTAMP:20200520T091219Z
CREATED:20200520T090444Z
LAST-MODIFIED:20200520T091219Z
UID:2774-1590588000-1590588000@www.tecosa.center.kth.se
SUMMARY:PhD defense: Task Placement and Resource Allocation in Edge Computing Systems
DESCRIPTION:Candidate: Sladana Josilo\, KTH/EECS/NSE \nChairperson: Prof. Carlo Fischione\, KTH/EECS/NSE \nFaculty opponent: Prof. Ben Liang\, Univ. of Toronto\, Canada \nDate: 14:00 on Wednesday\, 27 May 2020 \nRegister here: https://kth-se.zoom.us/webinar/register/WN_EQCltecySbSMoEQiRztIZg \n  \nGrading committee members:\nProf. Albert Banchs\, IMDEA and UC3M\, Spain\nAssoc. Prof. Valeria Cardellini\, Univ. Rome Tor Vergata\, Italy\nAdj. Prof. Johan Eker\, Lund University and Ericsson Research\, Sweden \nGrading committee stand-by members:\nProf. Mats Bengtsson\, KTH\, Sweden\nProf. James Gross\, KTH\, Sweden \nAdvisors: Prof. György Dán (KTH/EECS/NSE)\, Prof. Viktoria Fodor\n(KTH/EECS/NSE) \nAbstract:\nThe evolution of wireless and hardware technology has led to the rapid development of a variety of mobile applications. Common to these applications is that they have low latency and high computational requirements that often cannot be fulfilled by individual devices due to their insufficient computational power\, memory and battery capacity. An emerging approach to meet increasing user demand for delay sensitive and computationally intensive applications is mobile edge computing. The core paradigm of mobile edge computing is to bring computing and storage resources close to the end users and by doing so to relieve devices from computationally heavy workloads while meeting delay requirements of applications. However\, the overall performance of edge computing systems is determined by the efficiency of the joint allocation of wireless and computing resources. The work in this thesis proposes decentralized algorithms for allocating these two resources in edge computing infrastructures. \nIn the first part of the thesis\, we consider the resource allocation and computational task scheduling problem in an edge computing system in which wireless devices can use cloud resources and the resources of each other with the objective to minimize their own perceived response times. We develop a game theoretical model of the problem\, prove the existence of equilibrium task allocations and propose an efficient decentralized algorithm that computes an equilibrium based on average system parameters. \nIn the second part of the thesis\, we consider the resource allocation and computational task assignment problem in an edge computing system that consists of an edge cloud that can be accessed by devices through multiple wireless links. We model the problem as a strategic game\, in which each device aims at minimizing a combination of its response time and energy consumption. We prove the existence of equilibrium task allocations\, and use game theoretical tools for designing polynomial time decentralized algorithms with a bounded approximation ratio. We then extend the analysis to a system with periodic tasks\, and show that equilibrium task allocations still exist. Furthermore\, we propose a polynomial complexity decentralized algorithm and characterize the structure of equilibria computed by the algorithm. \nIn the third part of the thesis\, we consider the resource allocation and computational task assignment problem in an edge computing system that consists of multiple wireless links and multiple edge clouds managed by a single network operator. We model the interaction between the operator and devices that aim at minimizing their response times as a Stackelberg game. We express the optimal resource allocation policies in closed form\, prove the existence of Stackelberg equilibria and propose an efficient decentralized algorithm with a bounded approximation ratio. Finally\, we consider the same edge computing system under network slicing\, and based on a game theoretic treatment of the problem we develop an approximation algorithm for assigning tasks to slices and managing the resources across and within slices. \nBy providing constructive equilibrium existence proofs\, the results in this thesis provide low complexity decentralized algorithms for allocating edge computing resources in a variety of edge computing infrastructures. \nSmartGridComm 2020: sgc2020.ieee-smartgridcomm.org/
URL:https://www.tecosa.center.kth.se/event/phd-defense-task-placement-and-resource-allocation-in-edge-computing-systems/
CATEGORIES:PhD defense
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