FEDGEN Testbed: A Federated Genomics Private Cloud Infrastructure for Precision Medicine and Artificial Intelligence Research

The cloud computing space is enjoying a renaissance. Not long ago, cloud computing was confined to the wall of high-revenue companies, but in recent times a growing number of businesses, public and private institutions are turning to the cloud computing platform to reap the benefits of a self-service, scalable, and flexible infrastructure. Moreover, with the increased implementation, advantages, and popularity of artificial intelligence, the demand for computing environments to solve age-old problems such as malaria and cancer is on the rise. This paper presents the implementation of a cloud computing infrastructure, the FEDerated GENomics (FEDGEN) Testbed, to provide an adequate IT environment for cancer and malaria researchers. The cloud computing environment is built using Openstack middleware. OpenStack is deployed using Metal-As-A-Service (MAAS) and Juju. Virtual Machines (Instances) were deployed, and services (JupiterHub) were installed on the FEDGEN testbed. The built infrastructure would allow the running of models requiring high computing power and would allow for collaboration among teams

Evaluation of the Socio-Economic Impacts of Projects and Operations on the Ecosystem and Biodiversity in Ado-Ekiti.

<p><i>This research endeavours to assess the socio-economic ramifications resulting from the adverse effects of projects and operational activities on the ecosystem and biodiversity in Ado-Ekiti, Nigeria. Through a multidimensional analysis encompassing socio-economic surveys, community engagements, and ecological assessments, this study aims to unravel the intricate dynamics between development initiatives and their impacts on local livelihoods, health, and overall well-being. The findings of this research are anticipated to provide valuable insights for policy-makers, urban planners, and environmentalists, fostering a more comprehensive understanding of the interconnectedness between economic growth, environmental conservation, and community welfare.</i></p><p> </p&gt

Trends and patterns of broadband Internet access speed in a Nigerian university campus: A robust data exploration

Efficient broadband Internet access is required for optimal productivity in smart campuses. Besides access to broadband Internet, delivery of high speed and good Quality of Service (QoS) are pivotal to achieving a sustainable development in the area of education. In this data article, trends and patterns of the speed of broadband Internet provided in a Nigerian private university campus are largely explored. Data transmission speed and data reception speed were monitored and recorded on daily basis at Covenant University, Nigeria for a period of twelve months (January–December, 2017). The continuous data collection and logging were performed at the Network Operating Center (NOC) of the university using SolarWinds Orion software. Descriptive statistics, correlation and regression analyses, Probability Density Functions (PDFs), Cumulative Distribution Functions (CDFs), Analysis of Variance (ANOVA) test, and multiple comparison post-hoc test are performed using MATLAB 2016a. Extensive statistical visualizations of the results obtained are presented in tables, graphs, and plots. Availability of these data will help network administrators to determine optimal network latency towards efficient deployment of high-speed broadband communication networks in smart campuses. Keywords: Smart campus, Broadband internet access, Data bit rate, Mobile communication, Knowledge managemen

FEDARGOS-V1: A Monitoring Architecture for Federated Cloud Computing Infrastructures

Resource management in cloud infrastructure is one of the key elements of quality of services provided by the cloud service providers. Resource management has its taxonomy, which includes discovery of resources, selection of resources, allocation of resources, pricing of resources, disaster management, and monitoring of resources. Specifically, monitoring provides the means of knowing the status and availability of the physical resources and services within the cloud infrastructure. This results in making “monitoring of resources” one of the key aspects of the cloud resource management taxonomy. However, managing the resources in a secure and scalable manner is not easy, particularly when considering a federated cloud environment. A federated cloud is used and shared by many multi-cloud tenants and at various cloud software stack levels. As a result, there is a need to reconcile all the tenants’ diverse monitoring requirements. To cover all aspects relating to the monitoring of resources in a federated cloud environment, we present the FEDerated Architecture for Resource manaGement and mOnitoring in cloudS Version 1.0 (FEDARGOS-V1), a cloud resource monitoring architecture for federated cloud infrastructures. The architecture focuses mainly on the ability to access information while monitoring services for early identification of resource constraints within short time intervals in federated cloud platforms. The monitoring architecture was deployed in a real-time OpenStack-based FEDerated GENomic (FEDGEN) cloud testbed. We present experimental results in order to evaluate our design and compare it both qualitatively and quantitatively to a number of existing Cloud monitoring systems that are similar to ours. The architecture provided here can be deployed in private or public federated cloud infrastructures for faster and more scalable resource monitoring