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$\mu$μ-DDRL: A QoS-Aware Distributed Deep Reinforcement Learning Technique for Service Offloading in Fog Computing Environments., , , and . IEEE Trans. Serv. Comput., 17 (1): 47-59 (January 2024)Multiple Workflows Scheduling in Multi-tenant Distributed Systems: A Taxonomy and Future Directions., , and . ACM Comput. Surv., 53 (1): 10:1-10:39 (2021)μ-DDRL: A QoS-Aware Distributed Deep Reinforcement Learning Technique for Service Offloading in Fog computing Environments., , , and . CoRR, (2023)Containers Orchestration with Cost-Efficient Autoscaling in Cloud Computing Environments., and . CoRR, (2018)Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions., , , , and . CoRR, (2021)Reinforcement Learning (RL) Augmented Cold Start Frequency Reduction in Serverless Computing., , and . CoRR, (2023)A Deep Reinforcement Learning Approach to Resource Management in Hybrid Clouds Harnessing Renewable Energy and Task Scheduling., , and . CLOUD, page 240-249. IEEE, (2021)Task-Based Budget Distribution Strategies for Scientific Workflows with Coarse-Grained Billing Periods in IaaS Clouds., , and . eScience, page 128-137. IEEE Computer Society, (2017)Scheduling dynamic workloads in multi-tenant scientific workflow as a service platforms., and . Future Gener. Comput. Syst., (2018)Task Runtime Prediction in Scientific Workflows Using an Online Incremental Learning Approach., , and . UCC, page 93-102. IEEE Computer Society, (2018)