Adaptive Energy Efficiency Optimization Framework for Cloud Computing

Faculty Development Competitive Research Grant Program 2021-2023

In recent years, the usage of large-scale computing systems has exceeded the peak beyond remote data persistence and remote virtual instances. Nowadays, these systems are popularly used for computational and data-intensive processes, outgrowing themselves from traditional in-house clusters environments. All the major corporations, including Amazon, Microsoft, Google, and IBM, provide large-scale computing systems in the shape of cloud platforms designed for both scientific and personal needs. Rising as an operational model, cloud computing has gradually gained popularity and become the standard orchestrator for data centers. In essence, cloud computing indeed changes the routine of scaling the physical infrastructure and service level. Instead of irrationally utilizing many arbitrary configurations of servers, storage, and network facilities for similar purposes, it would be more convenient and robust to provide transparent access to the computing resources via the virtualization. This can be seen as an effort to unify the capacity of multiple computing nodes in order to achieve a much higher level of service composition. Furthermore, with the unification of facilities, cloud computing makes it possible to engage in various optimized strategies in massive-scale. These strategies mainly help to reduce the management cost as well as energy consumption. In this proposal, we would like to kindly introduce our effort to actualize one potential approach of these massive strategies, namely “Adaptive Energy Efficiency Optimization Framework for Cloud Computing”.