AI Optimisation Approach for Autonomic Cloud Computing

Cloud computing has led to exponential growth in large scale data centers and warehouses, which form the paradigms substratum layer, Infrastructure as a Service. These large scale server warehouses consume substantial energy, not only to power servers, but also affiliated processes such as cooling. Dynamic consolidation of virtual machines using live migration and switching idle nodes to the sleep mode allows cloud providers to optimize resource usage and reduce energy consumption. The following research proposes a novel reinforcement learning approach for the selection of virtual machines for migration. Due to low level of abstraction, the proposed algorithm provides a decision support system which supports efficient and open application deployment, monitoring, and execution across different cloud service providers and results in lowering energy consumption without negatively effecting service level agreements.