Search Results for author: George Iosifidis

Found 13 papers, 3 papers with code

Fair Resource Allocation in Virtualized O-RAN Platforms

no code implementations17 Feb 2024 Fatih Aslan, George Iosifidis, Jose A. Ayala-Romero, Andres Garcia-Saavedra, Xavier Costa-Perez

O-RAN systems and their deployment in virtualized general-purpose computing platforms (O-Cloud) constitute a paradigm shift expected to bring unprecedented performance gains.

Fairness

Workflow Optimization for Parallel Split Learning

1 code implementation1 Feb 2024 Joana Tirana, Dimitra Tsigkari, George Iosifidis, Dimitris Chatzopoulos

We propose a solution method based on the decomposition of the problem by leveraging its inherent symmetry, and a second one that is fully scalable.

Federated Learning Scheduling

Adaptive Online Non-stochastic Control

no code implementations2 Oct 2023 Naram Mhaisen, George Iosifidis

We tackle the problem of Non-stochastic Control (NSC) with the aim of obtaining algorithms whose policy regret is proportional to the difficulty of the controlled environment.

Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning

no code implementations4 Sep 2023 Michail Kalntis, George Iosifidis, Fernando A. Kuipers

Open Radio Access Network systems, with their virtualized base stations (vBSs), offer operators the benefits of increased flexibility, reduced costs, vendor diversity, and interoperability.

Meta-Learning

Reservation of Virtualized Resources with Optimistic Online Learning

no code implementations15 Mar 2023 Jean-Baptiste Monteil, George Iosifidis, Ivana Dusparic

The different service providers (SPs) have the opportunity to lease the network resources from the NO to constitute slices that address the demand of their specific network service.

Decision Making

Solving Recurrent MIPs with Semi-supervised Graph Neural Networks

no code implementations20 Feb 2023 Konstantinos Benidis, Ugo Rosolia, Syama Rangapuram, George Iosifidis, Georgios Paschos

We propose an ML-based model that automates and expedites the solution of MIPs by predicting the values of variables.

Bandit Convex Optimisation Revisited: FTRL Achieves $\tilde{O}(t^{1/2})$ Regret

no code implementations1 Feb 2023 David Young, Douglas Leith, George Iosifidis

We show that a kernel estimator using multiple function evaluations can be easily converted into a sampling-based bandit estimator with expectation equal to the original kernel estimate.

Energy-aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning

1 code implementation21 Aug 2022 Michail Kalntis, George Iosifidis

The design of Open Radio Access Network (O-RAN) compliant systems for configuring the virtualized Base Stations (vBSs) is of paramount importance for network operators.

Scheduling

Online Caching with no Regret: Optimistic Learning via Recommendations

no code implementations20 Apr 2022 Naram Mhaisen, George Iosifidis, Douglas Leith

We build upon the Follow-the-Regularized-Leader (FTRL) framework, which is developed further here to include predictions for the file requests, and we design online caching algorithms for bipartite networks with pre-reserved or dynamic storage subject to time-average budget constraints.

Edge-computing

Penalised FTRL With Time-Varying Constraints

no code implementations5 Apr 2022 Douglas J. Leith, George Iosifidis

In this paper we extend the classical Follow-The-Regularized-Leader (FTRL) algorithm to encompass time-varying constraints, through adaptive penalization.

Online Caching with Optimistic Learning

1 code implementation22 Feb 2022 Naram Mhaisen, George Iosifidis, Douglas Leith

The design of effective online caching policies is an increasingly important problem for content distribution networks, online social networks and edge computing services, among other areas.

Edge-computing

Lazy Lagrangians with Predictions for Online Learning

no code implementations8 Jan 2022 Daron Anderson, George Iosifidis, Douglas J. Leith

We consider the general problem of online convex optimization with time-varying additive constraints in the presence of predictions for the next cost and constraint functions.

Reinforcement Learning on Computational Resource Allocation of Cloud-based Wireless Networks

no code implementations10 Oct 2020 Beiran Chen, Yi Zhang, George Iosifidis, Mingming Liu

This paper models this dynamic computational resource allocation problem into a Markov Decision Process (MDP) and designs a model-based reinforcement-learning agent to optimise the dynamic resource allocation of the CPU usage.

Management Model-based Reinforcement Learning +2

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