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Fast DRL-based scheduler configuration tuning for reducing tail latency in edge-cloud jobs.

, , , and . J. Cloud Comput., 12 (1): 90 (December 2023)

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Usage patterns in multi-tenant data centers: a temporal perspective., , and . ICAC, page 161-166. ACM, (2012)Green MapReduce for heterogeneous data centers., , and . GLOBECOM, page 1120-1126. IEEE, (2014)Automating Deep Neural Network Model Selection for Edge Inference., , , and . CogMI, page 184-193. IEEE, (2019)On Fair Attribution of Costs Under Peak-Based Pricing to Cloud Tenants., , , , , and . ACM Trans. Model. Perform. Evaluation Comput. Syst., 2 (1): 3:1-3:28 (2016)Exploring and Exploiting Data-Free Model Stealing., , , and . ECML/PKDD (5), volume 14173 of Lecture Notes in Computer Science, page 20-35. Springer, (2023)TrustNet: Learning from Trusted Data Against (A)symmetric Label Noise., , and . BDCAT, page 52-62. ACM, (2021)EdgeVisionBench: A Benchmark of Evolving Input Domains for Vision Applications at Edge., , , , and . ICDE, page 3643-3646. IEEE, (2023)Fabricated Flips: Poisoning Federated Learning without Data., , , and . DSN, page 274-287. IEEE, (2023)FCT-GAN: Enhancing Global Correlation of Table Synthesis via Fourier Transform., , and . CIKM, page 4450-4454. ACM, (2023)Towards realizing a low cost and highly available datacenter power infrastructure., , , , and . HotPower@SOSP, page 7:1-7:5. ACM, (2011)