Browsing by Author "Mosoti Derdus, Kenga"

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    Causes of Energy Wastage in Cloud Data Centre Servers : A Survey
    (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2019) Mosoti Derdus, Kenga; Oteke Omwenga, Vincent; Ogao, Patrick Job
    Datacenters are becoming the indispensable infrastructure for supporting the services offered by cloud computing. Unfortunately, datacenters consume a lot of energy, which currently stands at 3% of global electrical energy consumption. Consequently, cloud service providers (CSP) experience high operating costs (in terms of electricity bills), which is, in turn, passed to the cloud users. In addition, there is an increased emission of carbon dioxide to the environment. Before one embarks on addressing the problem of energy wastage in a datacenter, it is important to understand the causes of energy wastage in datacenter servers. In this paper, we elaborate on the concept of cloud computing and virtualization. Later, we present a survey of the main causes of energy wastage in datacenter servers as well as proposed solutions to address the problem.
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    The Effect of Cloud Workload Consolidation on Cloud Energy Consumption and Performance in Multi-Tenant Cloud Infrastructure
    (International Journal of Computer Applications, 2019) Mosoti Derdus, Kenga; Oteke Omwenga, Vincent; Ogao, Patrick Job
    As energy consumption is becoming a problem in cloud data centers, cloud service providers have adopted different techniques to address this problem. One of the most attractive technique is virtual machine (VM) consolidation. Apart from reducing energy consumption in computing platforms, this technique has other advantages such as reduced infrastructure costs and ease of virtual machine management. However, VM consolidation, which does not recognize workload characteristics may, in the long run, increase energy consumption and lead energy wastage. This paper investigates the relationship between different VM workload types and server energy consumption in a multi-tenant datacenters. Experiments are conducted using well known CPU, I/O, memory and network intensive workload benchmark obtained from Phoronix Test Suite (PTS). Results obtained show that there is a noticeable difference in the amount of energy consumed when VMs run workloads, which dominate the various server physical resources. Secondly, consolidating homogeneous workloads is disastrous in terms of energy consumption and performance over heterogeneous workloads. The latter can further reduce energy consumption and achieve acceptable performance levels if an optimum workload mix is reached.
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    Energy Consumption in Cloud Computing Environments
    (2017) Mosoti Derdus, Kenga; Omwenga, Vincent .; Ogao, Patrick
    Datacentres are becoming indispensable infrastructure for supporting the services offered by cloud computing. Unfortunately, they consume a great deal of energy accounting for 3% of global electrical energy consumption. The effect of this is that, cloud providers experience high operating costs, which leading to increased Total Cost of Ownership (TCO) of datacentre infrastructure. Moreover, there is increased carbon dioxide emissions that affects the universe. This paper presents a survey on the various ways in which energy is consumed in datacentre infrastructure. The factors that influence energy consumption within a datacentre is presented as well.
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    Measuring Inter-VM Performance Interference in IaaS Cloud
    (Computer Engineering and Applications Journal, 2019) Mosoti Derdus, Kenga; Oteke Omwenga, Vincent; Ogao, Patrick Job
    Virtualization has enabled cloud computing to deliver computing capabilities using limited computer hardware. Server virtualization provides capabilities to run multiple virtual machines (VMs) independently in a shared host leading to efficient utilization of server resources. Unfortunately, VMs experience interference from each other as a result of sharing common hardware. The performance interference arises from VMs having to compete for the hypervisor capacity and as a result of resource contention, which happens when resource demands exceed the allocated resources. From this viewpoint, any VM allocation policy needs to take into account VM performance interference before VM placement. Therefore, understanding how to measure performance interference is crucial. In this paper, we propose a simple experimental approach that can be used to measure performance interference in Infrastructure as a Service (IaaS) cloud during VM consolidation.
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    Virtual Machine Sizing in Virtualized Public Cloud Data Centres
    (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2019) Mosoti Derdus, Kenga; Oteke Omwenga, Vincent; Ogao, Patrick Job
    Virtual machine (VM) consolidation in data centres is a technique that is used to ensure minimum use of physical servers (hosts) leading to better utilization of computing resources and energy savings. To achieve these goals, this technique requires that the estimated VM size is on the basis of application workload resource demands so as to maximize resources utilization, not only at host-level but also at VM-level. This is challenging especially in Infrastructure as a Service (IaaS) public clouds where customers select VM sizes set beforehand by the Cloud Service Providers (CSPs) without the knowledge of the amount of resources their applications need. More often, the resources are overprovisioned and thus go to waste, yet these resources consume power and are paid for by the customers. In this paper, we propose a technique for determining fixed VM sizes, which satisfy application workload resource demands. Because of the dynamic nature of cloud workloads, we show that any resource demands that exceed fixed VM resources can be addressed via statistical multiplexing. The proposed technique is evaluated using VM usage data obtained from a production data centre consisting of 49 hosts and 520 VMs. The evaluations show that the proposed technique reduces energy consumption, memory wastage and CPU wastage by at least 40%, 61% and 41% respectively.