English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

e-Infrastructures have powered the successful penetration of e-servicesacross domains, and form the backbone of the modern computing landscape.e-Infrastructure is a broad term used for large, medium and small scalecomputing environments. The increasing sophistication and complexity ofapplications have led to even small-scale data centers consisting of thousandsof interconnects. However, efficient utilization of resources in data centersremains a challenging task, mainly due to the complexity of managing physicalnodes, network equipment, cooling systems, electricity, etc. This results in avery strong carbon footprint of this industry. In recent years, efforts basedon machine learning approaches have shown promising results towards reducingenergy consumption of data centers. Yet, practical solutions that can help datacenter operators in offering energy efficient services are lacking. Thisproblem is more visible in the context of medium and small scale data centeroperators (the long tail of e-infrastructure providers). Additionally, adisconnect between solution providers (machine learning experts) and datacenter operators has been observed. This article presents a community-drivenopen source software framework that allows community members to develop betterunderstanding of various aspects of resource utilization. The frameworkleverages machine learning models for forecasting and optimizing variousparameters of data center operations, enabling improved efficiency, quality ofservice and lower energy consumption. Also, the proposed framework does notrequire datasets to be shared, which alleviates the extra effort of organizing,describing and anonymizing data in an appropriate format.

Towards Smart e-Infrastructures, A Community Driven Approach Based on Real Datasets