Advanced Fusion ACO Approach for Memory Optimization in Cloud Computing Environment
Pooja Kumari, A. S. Saxena · 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) · 2021
A wide network of multi-core processors is installed on a scale-out platform such as the Cloud computing. The total device configuration, including the number of processor cores, core frequency, memory hierarchy, capability, the number of memory channels, and the memory data rate, depends on the performance, power usage, and capital costs of such facilities is increasing. This could also pose a much bigger threat to the efficiency of cloud apps and the expense of cloud computing, with the growth of big data and analytics apps. The role of the memory subsystem in the cloud infrastructure, and particularly for this evolving class of applications, is therefore important to understand. Despite the rising interest in recent years, little work has been done to understand patterns in memory specifications and build detailed and accurate models to forecast memory subsystem output and expense. Our proposed algorithm Fusion ACO is very effective for memory optimization in cloud computing environment.