An Optimum Compute Resources Consolidation Framework for Cloud Data Center

Abstract

Cloud computing offers an efficient alternative for business enterprises as compared to traditional models for computing and data storage needs. The dynamic workload on cloud servers is one of the major reasons for the ineffective utilization of cloud resources. Therefore, a larger number of servers are active in cloud data centers (CDCs) to satisfy the cloud users demands, which severely enhances energy consumption and heat dissipation. Hence, effective resource management within the CDCs has become crucial for cloud service providers. To meet users requests of cloud services, it is essential to optimally allocate requested resources on physical machines (PMs) through virtual machines (VMs). The dynamic nature of workloads complicates initial VM allocation, which often results in the overutilization or underutilization of PMs. This leads to performance degradation, wastage of resources, increased operational costs, higher active servers, and energy consumption. Therefore, to address these challenges, effective resource management strategies are required to ensure the effective utilization of PM resources. With an intent to achieve e!ective resource management, this thesis proposed solutions to accurately predict the resource usage of machines and e!ectively utilize resources to balance the load of PMs, as well as reduce the total energy consumption of a data center. Firstly, in order to e!ectively utilize the resources of PMs in a data center, a hybridizing approach leveraging Gaussian Mixture Model (GMM) and Long Short-Term Memory (LSTM) model is proposed to predict resource usage of heterogeneous PMs. This research aims to capture the heterogeneity of available PMs in a data center using GMM based on mean CPU usage and memory usage of machines while capturing temporal dependencies and patterns in resource usage data using optimal hyperparameters of the LSTM model that enable more accurate prediction. Next, to capture both long-range and short-term dependencies, as well as input se quences of data to