Satellite-Terrestrial Integrated Networks (STINs) leverage the global reach of satellite systems to push onboard computing and caching resources toward the network edge, enabling truly ubiquitous, anytime-anywhere services for remote Internet of Things (IoT) applications. In this context, efficiently orchestrating the constrained onboard computing and caching resources under stochastic service demands in a scalable, low-complexity, and resilient manner remains a critical challenge. Existing solutions primarily rely on centralized optimization or multi-agent learning techniques, which struggle to cope with the non-stationarity arising from the interdependence of autonomous agent decisions. In this work, we take a step further and develop a distributed game-theoretic framework for joint task offloading and service caching in STINs, providing provable equilibrium guarantees. Specifically, the joint problem is formulated as a non-cooperative stochastic game among IoT devices that autonomously determine their computing, association, and satellite caching strategies to minimize their end-to-end latency subject to energy and cache capacity constraints. The formulated game is proven to converge to a Correlated Equilibrium (CE), which generalizes the Nash Equilibrium (NE) to correlated, probabilistic strategy profiles across devices. Two distributed no-regret learning algorithms, operating under different information availability and rationality regimes, are introduced to derive the CE. The effectiveness and efficiency of the two no-regret learning algorithms are validated through extensive simulations, considering alternative equilibria, learning-based methods, baseline computing schemes, and varying network and algorithm configurations.
Filothei Linardatou, Maria Diamanti, E. Tsiropoulou et al.· IEEE Open Journal of the Com...· 0 citations
Over-the-Air Computation (AirComp) Federated Learning (FL) is actively studied as a communication-efficient technique for distributed Artificial Intelligence (AI) model training. To mitigate the impact of wireless channels on the aggregated global model while addressing client energy sustainability, recent efforts have explored integrating Reconfigurable Intelligent Surfaces (RIS) and Simultaneous Wireless Information and Power Transfer (SWIPT) into AirComp FL. In this context, literature has mainly focused on radio resource allocation for optimized SWIPT and RIS-assisted Downlink (DL) model broadcasting and Uplink (UL) AirComp model aggregation. Nevertheless, existing works largely treat the communication design of AirComp FL in isolation, neglecting the tight coupling between radio and compute resource allocation. In this paper, we address this gap by modeling the radio-compute dependency in AirComp FL and optimizing harvested energy to sustain client-side local training and model transmissions. To this end, we jointly optimize the RIS configuration, SWIPT power-splitting ratio, DL transmission time, and local computing frequency to minimize the total communication and computation overhead in latency and energy. The original non-convex problem is decomposed into two independent subproblems, which are solved iteratively via a combination of low-rank optimization and min-max convex reformulation techniques. Numerical evaluations confirm that integrating RIS and SWIPT into AirComp FL leads to higher accuracy, and reduced latency and energy overheads across the FL pipeline.
Stefanos Voikos, P. Charatsaris, Maria Diamanti et al.· IEEE Transactions on Wireles...· 0 citations
The rapid development and scaling of mobile telecommunications networks, together with related domains such as the edge-cloud continuum have raised significant concerns regarding energy consumption and environmental sustainability. Addressing these concerns requires a focus on CPU energy consumption, as CPUs are among the largest energy consumers in these systems. This paper investigates existing techniques, with a focus on CPU idle states (C-states), performance states (P-states), and frequency scaling governors implemented at both hardware and software levels. These mechanisms enable the dynamic adjustment of CPU parameters, providing opportunities to optimize power consumption, frequency, voltage, and overall system performance. In this regard, three CPUs with different architectures from well-known manufacturers, Intel® and AMD®, are thoroughly examined. A comprehensive dataset, collected under three load scenarios (idle, medium, and high), is used to support the analysis, reflect realistic runtime conditions, and enable a comparison of the technological differences in how these parameters are exposed and utilized.
M. Akbari, R. Bolla, R. Bruschi et al.· IEEE Conference on Network S...· 0 citations