Location-Aware Container Scaling (LACS) in Geo-distributed Clouds Article Swipe

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Tao Shi ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.5281/zenodo.8166602

Datasets and code for the problem of location-aware container scaling (LACS) in geo-distributed clouds: Randomly extracted one day’s workload from WikiBench and NASA HTTP: AppWorkload.py Facebook subscribers by January 2020 to simulate the distribution of application requests among different user regions: FacebookUserData.csv Sprint IP Network location as 82 user regions from 35 countries on 6 continents: SprintLocation.csv Observation on the network latency matrix among 82 user centres: LatencyMatrix.py Representative code using openAI's gym environment: deepscale.py

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Mechanical Engineering

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Container (type theory) Scaling Computer science Mathematics Engineering Geometry Mechanical engineering

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DOI: https://doi.org/10.5281/zenodo.8166602

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Title: Location-Aware Container Scaling (LACS) in Geo-distributed Clouds

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Type: dataset

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Language: en

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Publication year: 2023

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Publication date: 2023-07-20

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Authors: Tao Shi

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Landing page: https://zenodo.org/record/8166602

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Open access: Yes

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OA status: green

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Concepts: Container (type theory), Scaling, Computer science, Mathematics, Engineering, Geometry, Mechanical engineering

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