Resilient and energy-efficient multi-cloud service grid for 5G edge applications

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This research project aims to design, implement, and validate a controller framework for an edge-to-cloud continuum that illustrates the Sky Computing paradigm. The controller framework will optimize the multi-cloud, multi-access, multi-core network, named Multi-Cloud Service Grid. A distributed Grid weaves together a fabric of heterogeneous compute, data, and intelligence to enable context-aware, immersive, and distributed modern applications anytime, anywhere, on-demand. Such a framework faces challenges of specialized infrastructure, multi-tenant services and distributed control. The validation of this framework will be accomplished via applications that operate a swarm of robots in a warehouse situation; requiring new analytical models. For these models, we will focus on deep reinforced learning and convolutional neural networks. One of the key outputs will be a stationary optimal control policy with continuous and discrete actions, achieving the required optimization using the controller framework. This framework, along with the models, AI methods, and policies, will be deployed on the CEOSNet infrastructure at ÉTS that provides cloud manufacturing services. This deployment should demonstrate the feasibility, advantages, and potential limitations of an integrated solution. The project will contribute to VMware's multi-cloud and grid platform as well as facilitating a new class of innovative 5G applications in Canada.