A
Alberto Leon-Garcia
Researcher at University of Toronto
Publications - 369
Citations - 12417
Alberto Leon-Garcia is an academic researcher from University of Toronto. The author has contributed to research in topics: Cloud computing & Quality of service. The author has an hindex of 37, co-authored 363 publications receiving 11422 citations.
Papers
More filters
Posted Content
Representation of Federated Learning via Worst-Case Robust Optimization Theory
TL;DR: This work evaluates the model using the MNIST data set versus the protection function parameters, e.g., regularization factors, to compare the performance of FL with its centralized counterpart, and to replace the uncertain function with a concept of protection functions leading to more tractable formulation.
Proceedings Article
Impact of tunable wavelength converter performance on all-optical wavelength-routing switches for data centers
TL;DR: In this paper, the loss performance of a wavelength-routing optical packet switch was examined by focussing on tunable wavelength converter parameters, revealing the stringent wavelength conversion requirements in terms of optical signal-to-noise ratio and conversion efficiency.
Proceedings Article
Improvement of WLAN QoS Capability via Statistical Multiplexing
TL;DR: This paper presents an analytical model for evaluating the capability of wireless LANs to provision quantitative quality of service (QoS) guarantees, and shows that the WLAN admission region under the QoS constraint can be significantly improved, when the statistical multiplexing effect is taken into account.
Proceedings ArticleDOI
Speedup of DTA-Based Simulation of Large Metropolises for Quasi Real-Time ITS Applications
Agop Koulakezian,Billy Graydon,Hossam Abdelgawad,Baher Abdulhai,Yi-Chang Chiu,Alberto Leon-Garcia +5 more
TL;DR: The performance results show that compiler optimizations and parallelism allow to double the speed required for a 4-hour simulation after 12 iterations to reach equilibrium, and bring down the initial simulation time by 2.5 times, enabling the testing of various real-time ITS applications.
Proceedings ArticleDOI
Reinforcement Learning-based Admission Control in Delay-sensitive Service Systems
TL;DR: In this article, a reinforcement learning-based admission controller is proposed to guarantee a probabilistic upper bound on the end-to-end delay of the service system, while minimizing the probability of unnecessary rejections.