DiG: enabling out-of-band scalable high-resolution monitoring for data-center analytics, automation and control (extended)
Author:
Funder
Swiss Federal Institute of Technology Zurich
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Software
Link
https://link.springer.com/content/pdf/10.1007/s10586-020-03219-7.pdf
Reference45 articles.
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3. Garg, R., Mittal, M., Son, L.H.: Reliability and energy efficient workflow scheduling in cloud environment. Clust. Comput. 22(4), 1283–1297 (2019). https://doi.org/10.1007/s10586-019-02911-7
4. Kumar, J., Singh, A.K.: Cloud datacenter workload estimation using error preventive time series forecasting models. Clust. Comput. (2019). https://doi.org/10.1007/s10586-019-03003-2
5. Li, H., Wang, H., Fang, S., Zou, Y., Tian, W.: An energy-aware scheduling algorithm for big data applications in Spark. Clust. Comput. (2019). https://doi.org/10.1007/s10586-019-02947-9
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