Browsing by Author "Omwenga, Vincent O."

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    Autonomous Virtual Machine Sizing and Resource Usage Prediction for Efficient Resource Utilization in Multi-Tenant Public Cloud
    (I.J. Information Technology and Computer Science, 2019) Kenga, Derdus M.; Omwenga, Vincent O.; Ogao, Patrick J.
    In recent years, the use of cloud computing has increased exponentially to satisfy computing needs in both big and small organizations. However, the high amounts of power consumed by cloud data centres have raised concern. A major cause of power wastage in cloud computing is inefficient utilization of computing resources. In Infrastructure as a Service, the inefficiency is caused when users request for more resources for virtual machines than is required. In this paper, we propose a technique for automatic virtual machine sizing and resource usage prediction using neural networks, for multi tenant Infrastructure as a Service cloud service model. The proposed technique aims at reducing energy wastage in data centres by efficient resource utilization. An evaluation of our technique on CloudSim Plus cloud simulator and WEKA shows that effective VM sizing not only achieves energy savings but also reduces the cost of using cloud services from a customer perspective.
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    MFF: Performance Interference-Aware VM Placement Algorithm for Reducing Energy Consumption in Data Centers
    (Open Journal for Information Technology, 2020) Mosoti, Derdus; Omwenga, Vincent O.
    Virtualization is the main technology that powers cloud computing and has enabled the execution of multiple applications in same physical hardware using virtual machines (VM) for efficient utilization of resources and energy savings. Although virtualization successfully isolates coresident VMs from a security perspective, it does not offer a guarantee from a performance interference perspective. This means that sharing of resources results in competition, which is the cause of performance interference. Performance interference is more pronounced in homogenous workloads, where applications workloads contend to the same shared resource. In this case, application workloads run for longer times due to reduced performance and thus consume more energy. To address this problem, a VM allocation policy should ensure that VM running homogeneous workloads is not co-located. In this paper, we propose a VM allocation algorithm called Minimum Interference First Fit (MFF), which co-locates dissimilar workloads. The algorithm clusters VMs using K-means based on resources usage. Before a VM is placed into a physical machine (PM), similarity index (SI) of all the active PMs is computed, the VM is then placed in a PM with least SI. MFF has been evaluated on a simulated data center using CloudSim Plus cloud simulator on application workloads logs obtained from a production data center. Results show that MFF outperforms well-known VM allocations algorithms such as first fit (FF), worst fit (WF) and best fit (BF) from an energy consumption perspective.