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Your search keyword '"Beschastnikh, I."' showing total 2 results
Start Over Author "Beschastnikh, I."
2 results on '"Beschastnikh, I."'

1. VNF chain allocation and management at data center scale

Author

Kodirov, N., Bayless, S., Ruffy, F., Beschastnikh, I., Hoos, H.H., Hu, A.J., and Sierra C.

Subjects
Emulation, Network packet, Computer science, business.industry, Distributed computing, Scale (chemistry), 020206 networking & telecommunications, Throughput, 02 engineering and technology, Network topology, Chain (algebraic topology), 020204 information systems, Limit (music), 0202 electrical engineering, electronic engineering, information engineering, Data center, business
Abstract

Recent advances in network function virtualization have prompted the research community to consider data-center-scale deployments. However, existing tools, such as E2 and SOL, limit VNF chain allocation to rack-scale and provide limited support for management of allocated chains. We define a narrow API to let data center tenants and operators allocate and manage arbitrary VNF chain topologies, and we introduce NetPack, a new stochastic placement algorithm, to implement this API at data-center-scale. We prototyped the resulting system, dubbed Daisy, using the Sonata platform. In data-center-scale simulations on realistic scenarios and topologies that are orders of magnitude larger than prior work, we achieve in all cases an allocation density within 96% of a recently introduced, theoretically complete, constraint-solver-based placement engine, while being 82x faster on average. In detailed emulation with real packet traces, we find that Daisy performs each of our six API calls with at most one second of throughput drop.

Published
2018

2. Fairness in Classifying and Grouping Health Equity Information.

Author

Jin R, Li X, Block LJ, Beschastnikh I, Currie LM, and Ronquillo CE

Subjects
Humans, Social Determinants of Health, Health Equity, Electronic Health Records classification, Machine Learning
Abstract

This paper explores the balance between fairness and performance in machine learning classification, predicting the likelihood of a patient receiving anti-microbial treatment using structured data in community nursing wound care electronic health records. The data includes two important predictors (gender and language) of the social determinants of health, which we used to evaluate the fairness of the classifiers. At the same time, the impact of various groupings of language codes on classifiers' performance and fairness is analyzed. Most common statistical learning-based classifiers are evaluated. The findings indicate that while K-Nearest Neighbors offers the best fairness metrics among different grouping settings, the performance of all classifiers is generally consistent across different language code groupings. Also, grouping more variables tends to improve the fairness metrics over all classifiers while maintaining their performance.

Published
2024
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