  e-Infrastructures have powered the successful penetration of e-services
across domains, and form the backbone of the modern computing landscape.
e-Infrastructure is a broad term used for large, medium and small scale
computing environments. The increasing sophistication and complexity of
applications have led to even small-scale data centers consisting of thousands
of interconnects. However, efficient utilization of resources in data centers
remains a challenging task, mainly due to the complexity of managing physical
nodes, network equipment, cooling systems, electricity, etc. This results in a
very strong carbon footprint of this industry. In recent years, efforts based
on machine learning approaches have shown promising results towards reducing
energy consumption of data centers. Yet, practical solutions that can help data
center operators in offering energy efficient services are lacking. This
problem is more visible in the context of medium and small scale data center
operators (the long tail of e-infrastructure providers). Additionally, a
disconnect between solution providers (machine learning experts) and data
center operators has been observed. This article presents a community-driven
open source software framework that allows community members to develop better
understanding of various aspects of resource utilization. The framework
leverages machine learning models for forecasting and optimizing various
parameters of data center operations, enabling improved efficiency, quality of
service and lower energy consumption. Also, the proposed framework does not
require datasets to be shared, which alleviates the extra effort of organizing,
describing and anonymizing data in an appropriate format.
