AI for Clouds: Predictive and Learning Centric Solutions for Edge and Cloud Computing Systems

Contemporary Distributed Systems such as Edge and Clouds are large scale, highly interconnected and complex infrastructures distributed over multiple networks. On the other hand, workloads from diverse users demand a high level of Service Level Agreements (SLAs) to satisfy the application requirements. Resource management systems should account these factors in managing workloads and resources efficiently. However, due to the massive complexity of these interconnected systems and heterogeneous workload characteristics, it is impossible to manually fine-tune the controllable parameters to efficiently manage the resources and simultaneously satisfy workload requirements. Hence, innovative data-driven Artificial Intelligence (AI)-Centric solutions are necessary. The AI-centric solutions can capture complex non-linear relationships between different elements and effectively configure the system for efficiency.

Team Members @ Melbourne CLOUDS Lab

External Collaborators


Datasets and other useful pointers

Cloud Computing and Distributed Systems (CLOUDS) Laboratory
School of Computing and Information Systems
The University of Melbourne, Australia