Search Results for author: Zacharaya Shabka

Found 5 papers, 0 papers with code

Network Aware Compute and Memory Allocation in Optically Composable Data Centres with Deep Reinforcement Learning and Graph Neural Networks

no code implementations26 Oct 2022 Zacharaya Shabka, Georgios Zervas

Resource-disaggregated data centre architectures promise a means of pooling resources remotely within data centres, allowing for both more flexibility and resource efficiency underlying the increasingly important infrastructure-as-a-service business.

One-shot, Offline and Production-Scalable PID Optimisation with Deep Reinforcement Learning

no code implementations25 Oct 2022 Zacharaya Shabka, Michael Enrico, Nick Parsons, Georgios Zervas

Furthermore, once trained (which takes $\mathcal{O}(hours)$), the model generates actuator-unique PID parameters in a one-shot inference process that takes $\mathcal{O}(ms)$ in comparison to up to $\mathcal{O}(week)$ required for conventional tuning methods, therefore accomplishing these performance improvements whilst achieving up to a $10^6\times$ speed-up.

reinforcement-learning Reinforcement Learning (RL)

Resource Allocation in Disaggregated Data Centre Systems with Reinforcement Learning

no code implementations4 Jun 2021 Zacharaya Shabka, Georgios Zervas

Resource-disaggregated data centres (RDDC) propose a resource-centric, and high-utilisation architecture for data centres (DC), avoiding resource fragmentation and enabling arbitrarily sized resource pools to be allocated to tasks, rather than server-sized ones.

reinforcement-learning Reinforcement Learning (RL)

Optimal Control of SOAs with Artificial Intelligence for Sub-Nanosecond Optical Switching

no code implementations22 Jun 2020 Christopher W. F. Parsonson, Zacharaya Shabka, W. Konrad Chlupka, Bawang Goh, Georgios Zervas

Novel approaches to switching ultra-fast semiconductor optical amplifiers using artificial intelligence algorithms (particle swarm optimisation, ant colony optimisation, and a genetic algorithm) are developed and applied both in simulation and experiment.

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