Showing papers by "Gyorgy Dan published in 2023"

Online Learning for Rate-Adaptive Task Offloading Under Latency Constraints in Serverless Edge Computing

Abstract: We consider the interplay between latency constrained applications and function-level resource management in a serverless edge computing environment. We develop a game theoretic model of the interaction between rate adaptive applications and a load balancing operator under a function-oriented pay-as-you-go pricing model. We show that under perfect information, the strategic interaction between the applications can be formulated as a generalized Nash equilibrium problem, and use variational inequality theory to prove that the game admits an equilibrium. For the case of imperfect information, we propose an online learning algorithm for applications to maximize their utility through rate adaptation and resource reservation. We show that the proposed algorithm can converge to equilibria and achieves zero regret asymptotically, and our simulation results show that the algorithm achieves good system performance at equilibrium, ensures fast convergence, and enables applications to meet their latency constraints.

A Reinforcement Learning Approach to Undetectable Attacks against Automatic Generation Control

Abstract: Automatic generation control (AGC) is an essential functionality for ensuring the stability of power systems, and its secure operation is thus of utmost importance to power system operators. In this paper, we investigate the vulnerability of AGC to false data injection attacks that could remain undetected by traditional detection methods based on the area control error (ACE) and the recently proposed unknown input observer (UIO). We formulate the problem of computing undetectable attacks as a multi-objective partially observable Markov decision process. We propose a flexible reward function that allows to explore the trade-off between attack impact and detectability, and use the proximal policy optimization (PPO) algorithm for learning efficient attack policies. Through extensive simulations of a 3-area power system, we show that the proposed attacks can drive the frequency beyond critical limits, while remaining undetectable by state-of-the-art algorithms employed for fault and attack detection in AGC. Our results also show that detectors trained using supervised and unsupervised machine learning can both significantly outperform existing detectors.