A sustainable model for ICT capacity building in developing countries

System administrators are often asked to apply their professional expertise in unusual situations, or under tight resource constraints. What happens, though, when the “situation” is a foreign country with only basic technical infrastructure, and the task is to bauild systems which are able to survive and grow in these over-constrained environments? In this paper we report on our experiences in two very different countries – Cuba and Ethiopia – where we ran a number of ICT projects. In those projects we assisted local universities to upgrade their ICT infrastructure and services. This included skills and process building for local system administrators. Based on our experiences we formulate a model for sustainable ICT capacity building. We hope this model will be useful for other organizations doing similar projects

Computing server power modeling in a data center: survey,taxonomy and performance evaluation

Data centers are large scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT) and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power measurement techniques and error calculation formula on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power measurement technique and error formula, with the aim of achieving an objective comparison. We use different servers architectures to assess the impact of heterogeneity on the models' comparison. The performance analysis of these models is elaborated in the paper