Dynamic Resource Management Across Cloud-Edge Resources for Performance-Sensitive Applications.

A large number of modern applications and systems are cloud-hosted, however, limitations in performance assurances from the cloud, and the longer and often unpredictable end-to-end network latencies between the end user and the cloud can be detrimental to the response time requirements of the applications, specifically those that have stringent Quality of Service (QoS) requirements. Although edge resources, such as cloudlets, may alleviate some of the latency concerns, there is a general lack of mechanisms that can dynamically manage resources across the cloud-edge spectrum. To address these gaps, this research proposes Dynamic Data Driven Cloud and Edge Systems (D3CES). It uses measurement data collected from adaptively instrumenting the cloud and edge resources to learn and enhance models of the distributed resource pool. In turn, the framework uses the learned models in a feedback loop to make effective resource management decisions to host applications and deliver their QoS properties. D3CES is being evaluated in the context of a variety of cyber physical systems, such as smart city, online games, and augmented reality applications.