S
Sharda Tripathi
Researcher at Indian Institute of Technology Delhi
Publications - 14
Citations - 154
Sharda Tripathi is an academic researcher from Indian Institute of Technology Delhi. The author has contributed to research in topics: Smart grid & Radio access network. The author has an hindex of 5, co-authored 12 publications receiving 77 citations. Previous affiliations of Sharda Tripathi include Polytechnic University of Turin & Birla Institute of Technology and Science.
Papers
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Journal ArticleDOI
VERA: Resource Orchestration for Virtualized Services at the Edge
Sharda Tripathi,Corrado Puligheddu,Somreeta Pramanik,Andres Garcia-Saavedra,Carla-Fabiana Chiasserini +4 more
TL;DR: This paper develops a framework for resource orchestration at the edge that leverages a model-free reinforcement learning approach and a Pareto analysis, which is proved to make fair and efficient decisions.
Journal Article
Beyond Being Instinctive: An Inquiry into Incest through Media
Sharda Tripathi,Sandeep Kaur +1 more
TL;DR: Incest prohibition is contradictory in itself; despite being considered instinctive in nature, there still exists a wide circulation of societal and political laws against incest, putting forth valid reasons to justify actions as discussed by the authors.
Proceedings ArticleDOI
Versatile Multivariate Data Pruning in Smart Grid IoT Networks
TL;DR: A novel versatile algorithm for multivariate data pruning at the edge devices in smart grid IoT networks is presented via a two stage data reduction mechanism which first exploits the inter-variable correlation to cut down on the number of transmitted variables, followed by adaptive data compression in temporal domain using adaptive compressive sampling.
Journal ArticleDOI
Fair and Scalable Orchestration of Network and Compute Resources for Virtual Edge Services
Sharda Tripathi,Corrado Puligheddu,Somreeta Pramanik,Andres Garcia-Saavedra,Carla-Fabiana Chiasserini +4 more
TL;DR: Vera as mentioned in this paper is a scalable reinforcement learning framework for resource orchestration at the edge, which leverages a Pareto analysis for provable fair and efficient decisions, and it is shown through experimental tests the correlation between a video-based application and a vRAN.