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Browsing PhD Theses by Author "Kenga, D. M."

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    Energy-efficient resources utilization algorithm in cloud data center servers
    (Strathmore University, 2019) Kenga, D. M.
    In recent years, the use of cloud computing has increased exponentially to satisfy computing needs and this is attributable to its success in delivering service on a pay-as-you-go basis. As a result, Cloud Service Providers (CSPs) are putting up more Data Centers to meet the demand. However, the high amount of energy consumed by cloud data center servers has raised concern because CSPs experience high operating costs (electricity bills), which reduces profits. The cause of high energy usage in data center servers is energy wastage, which results from the low level of server utilization. This problem is currently addressed through Virtual Machine (VM) consolidation and Dynamic Voltage and Frequency Scaling (DVFS). Unfortunately, VM consolidation does consider workload types and VM sizes, which are factors that affect the level of server utilization. On the other hand, DVFS is designed for processor-bound tasks because dynamic power ranges for other computing resources such as memory are narrower. In this study, the effect of workload types (heterogeneous or homogeneous) running in VM and VM sizing on data center server energy consumption was investigated. The results obtained from conducted experiments show that heterogeneous workload is consolidation friendly as compared to homogeneous workloads from a data center energy consumption perspective. Further, a review of the literature discovered that oversized VMs lead to a low level of server utilization and thus leads to energy wastage. Consequently, VM allocation and VM sizing algorithms have been proposed and tested. The VM allocation algorithm co-locates heterogeneous workloads whereas the VM sizing algorithm is used for VM right-sizing. To test the applicability of the proposed algorithms in the cloud, the algorithms were evaluated using simulations on a cloud simulator (CloudSim Plus) using workloads logs obtained from a production data center (Grid Workload Archive Trace 13 (GWA-T-13)). Results on the evaluations carried out on the designed VM allocation algorithm showed that data center server energy consumption was reduced by 4%, 11%, and 17% when compared with Worst Fit (WF), First Fist (FF), and Best Fit (BF) VM allocation algorithms respectively. On the other hand, the VM sizing algorithm reduced energy consumption, memory wastage, and CPU wastage by at least 40%, 61%, and 41% respectively. From the results, we concluded that workload types and VM sizes affect the level of server utilization, which in turn determines the amount of energy consumption. Thus, the right workload types combined with the right VM sizing leads to a high level of server utilization leading to energy savings.