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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
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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
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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
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