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A Preliminary Study of Machine Learning Workload Prediction Techniques for Cloud Applications.

, , , and . PDP, page 222-227. IEEE, (2019)

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An Interference-Aware Application Classifier Based on Machine Learning to Improve Scheduling in Clouds., , , and . PDP, page 80-87. IEEE, (2020)Optimizing multi-tier application performance with interference and affinity-aware placement algorithms., , , , and . Concurr. Comput. Pract. Exp., (2019)GerpavGrid: using the Grid to maintain the city road system., , , , , , , and . SBAC-PAD, page 73-80. IEEE Computer Society, (2006)Dynamic Processor Allocation in Large Mesh-Connected Multicomputers., and . Euro-Par, volume 2150 of Lecture Notes in Computer Science, page 783-792. Springer, (2001)A Preliminary Study of Machine Learning Workload Prediction Techniques for Cloud Applications., , , and . PDP, page 222-227. IEEE, (2019)Performance and Cost Analysis between Elasticity Strategies over Pipeline-structured Applications., , , , and . CLOSER, page 404-411. SciTePress, (2019)C-MemMAP: clustering-driven compact, adaptable, and generalizable meta-LSTM models for memory access prediction., , , , , and . Int. J. Data Sci. Anal., 13 (1): 3-16 (2022)Hugo: A Cluster Scheduler that Efficiently Learns to Select Complementary Data-Parallel Jobs., , , , , , , and . Euro-Par Workshops, volume 11997 of Lecture Notes in Computer Science, page 519-530. Springer, (2019)Understanding performance interference in multi-tenant cloud databases and web applications., , , and . IEEE BigData, page 2847-2852. IEEE Computer Society, (2016)Optimizing the management of a database in a virtual environment., , , and . ISCC, page 594-599. IEEE Computer Society, (2013)