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Efficient Edge-Cloud Resource Management for Latency-Sensitive Applications

Efficient Edge-Cloud Resource Management for Latency-Sensitive Applications

Great PhD Thesis on Efficient Edge-Cloud Resource Management for Latency-Sensitive Applications

Research Area:  Cloud Computing


   Internet of Things (IoT) is quickly evolving into a disruptive technology in recent years. For enhancing customer experience and accelerating job execution, IoT task offloading enables mobile end devices to release heavy computation and storage to the resource-rich nodes in collaborative Edges or Clouds. Resource management at the Edge-Cloud environment is challenging because it deals with several complex factors (e.g. different characteristics of IoT applications and heterogeneity of resources). Thus, efficient resource management will play an essential role in providing real-time or near real-time use for IoT applications. However, how different service architecture and offloading strategies quantitatively impact the end-to-end service time performance of IoT applications is still far from known particularly given a dynamic and unpredictable assortment of interconnected virtual and physical devices.
   This PhD thesis has investigated and modeled the delay within the Edge-Cloud environment as well as providing a detailed analysis of the main factors of service latency. Moreover, proposing a new task offloading approach for latency-sensitivity applications using fuzzy logic, where a decision is made as to whether we can offload the task to Local Edge, other Collaborative Edge or the Cloud depending on the current parameters of both application characteristics and the resources within the Edge-Cloud Environment. The proposed approach was compared against existing related works using a simulation tool, and it was evaluated in the domain of the edge-cloud environment where it was found to improve the overall service time for latency-sensitive applications, effectively utilizing the edge-cloud resources.

Name of the Researcher:  J Almutairi

Name of the Supervisor(s):  Xu, Jie

Year of Completion:  2020

University:  University of Leeds

Thesis Link:   Home Page Url