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Dynamic Scaling of Virtualized, Distributed Service Chains: A Case Study of IMS., , , , and . IEEE J. Sel. Areas Commun., 35 (11): 2501-2511 (2017)Optimus: an efficient dynamic resource scheduler for deep learning clusters., , , , and . EuroSys, page 3:1-3:14. ACM, (2018)Elastic parameter server load distribution in deep learning clusters., , , , , and . SoCC, page 507-521. ACM, (2020)HybridFlow: A Flexible and Efficient RLHF Framework., , , , , , , , and . CoRR, (2024)LLM-PQ: Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive Quantization., , , , and . CoRR, (2024)DL2: A Deep Learning-Driven Scheduler for Deep Learning Clusters., , , , , and . IEEE Trans. Parallel Distributed Syst., 32 (8): 1947-1960 (2021)POSTER: LLM-PQ: Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive Quantization., , , , and . PPoPP, page 460-462. ACM, (2024)CDMPP: A Device-Model Agnostic Framework for Latency Prediction of Tensor Programs., , , , , , and . EuroSys, page 1054-1074. ACM, (2024)Deep Learning-based Job Placement in Distributed Machine Learning Clusters., , and . INFOCOM, page 505-513. IEEE, (2019)Online Job Scheduling in Distributed Machine Learning Clusters., , , and . INFOCOM, page 495-503. IEEE, (2018)