260 citations · 506 across the 24 of their papers we have counts for
8 papers · 2 filters
Scission: Performance-driven and Context-aware Cloud-Edge Distribution of Deep Neural Networks
Luke Lockhart, Paul Harvey, Pierre Imai +2
Partitioning and distributing deep neural networks (DNNs) across end-devices, edge resources and the cloud has a potential twofold advantage: preserving privacy of the input data,…
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…
WattsApp: Power-Aware Container Scheduling
Hemant Mehta, Paul Harvey, Omer Rana +2
Containers are becoming a popular workload deployment mechanism in modern distributed systems. However, there are limited software-based methods (hardware-based methods are expensi…
A Survey on Edge Performance Benchmarking
Blesson Varghese, Nan Wang, David Bermbach +4
Edge computing is the next Internet frontier that will leverage computing resources located near users, sensors, and data stores to provide more responsive services. Therefore, it…
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…
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.…