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20172021
most citedPower Modelling for Heterogeneous Cloud-Edge Data Centers

1 citations · 1 across the 1 of their papers we have counts for

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cs.DC2021

NEUKONFIG: Reducing Edge Service Downtime When Repartitioning DNNs

Ayesha Abdul Majeed, Peter Kilpatrick, Ivor Spence +1

Deep Neural Networks (DNNs) may be partitioned across the edge and the cloud to improve the performance efficiency of inference. DNN partitions are determined based on operational…

cs.DC2020

A Case For Adaptive Deep Neural Networks in Edge Computing

Francis McNamee, Schahram Dustadar, Peter Kilpatrick +3

Edge computing offers an additional layer of compute infrastructure closer to the data source before raw data from privacy-sensitive and performance-critical applications is transf…

cs.DC2020

Cross Architectural Power Modelling

Kai Chen, Peter Kilpatrick, Dimitrios S. Nikolopoulos +1

Existing power modelling research focuses on the model rather than the process for developing models. An automated power modelling process that can be deployed on different process…

cs.DC2020

Modelling Fog Offloading Performance

Ayesha Abdul Majeed, Peter Kilpatrick, Ivor Spence +1

Fog computing has emerged as a computing paradigm aimed at addressing the issues of latency, bandwidth and privacy when mobile devices are communicating with remote cloud services.…

cs.DC2019

Performance Estimation of Container-Based Cloud-to-Fog Offloading

Ayesha Abdul Majeed, Peter Kilpatrick, Ivor Spence +1

Fog computing offloads latency critical application services running on the Cloud in close proximity to end-user devices onto resources located at the edge of the network. The rese…

cs.DC2018

RADS: Real-time Anomaly Detection System for Cloud Data Centres

Sakil Barbhuiya, Zafeirios Papazachos, Peter Kilpatrick +1

Cybersecurity attacks in Cloud data centres are increasing alongside the growth of the Cloud services market. Existing research proposes a number of anomaly detection systems for d…