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<title>Yufeng Zhan's Research</title>
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<table summary="Table for page layout." id="tlayout">
<tbody><tr valign="top">
<td id="layout-menu">
<div class="menu-category">Yufeng Zhan</div>
<div class="menu-item"><a href="https://ray-zhan.github.io/" class="current">Home</a></div>
<div class="menu-item"><a href="https://ray-zhan.github.io/publications">Publications</a></div>
<div class="menu-item"><a href="https://ray-zhan.github.io/students">Students</a></div>
<div class="menu-item"><a href="https://ray-zhan.github.io/research">Research</a></div>
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<p><br></p>
<div id="toptitle">
<h1><b>MLOps</b></h1>
</div>
<table class="imgtable"><tbody><tr>
<!--<td><a href="itcs.jpg"><img src="itcs.jpg" alt="alt text" width="300px""></a> </td>
<td align="left"> <b>Intelligent Transportation System</b><br>-->
<p>MLOps stands for Machine Learning Operations. MLOps is a core function of Machine Learning engineering,
focused on streamlining the process of taking machine learning models to production, and then maintaining and
monitoring them. MLOps is a collaborative function, often comprising data scientists, devops engineers, and IT.
MLOps is a useful approach for the creation and quality of machine learning and AI solutions. By adopting an MLOps
approach, data scientists and machine learning engineers can collaborate and increase the pace of model development
and production, by implementing continuous integration and deployment practices with proper monitoring, validation,
and governance of ML models.</p>
<!--<br>Office: 国防科技园6号楼1005室<br>
<br>Assistant Professor<br>
<br>School of Automation, <a href="http://www.bit.edu.cn/" target="blank">Beijing Institute of Technology</a><br>
<br>Beijing, China<br>
<br>Email: [email protected]<br>
</td>-->
</tr></tbody></table>
<h2><b>Distributed Machine Learning</b></h2>
<p>Overview: </p>
<ul>
<li>C. Li, et al., "PyramidFL: A Fine-grained Client Selection Framework for Efficient Federated Learning," in Proc. of ACM MobiCom, 2022.</li>
<li>A. Li, et al., "Hermes: An Efficient Federated Learning Framework for Heterogeneous Mobile Clients," in Proc. of ACM MobiCom, 2021.</li>
<li>Z. Chai, et al., "FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers," in Proc. of ACM SC, 2021.</li>
<li>F. Li, et al., "Oort: Efficient Federated Learning via Guided Participant Selection," in Proc. of USENIX OSDI, 2021.</li>
<li>E. Diao, et al., "HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients," in Proc. of ICLR, 2021.</li>
<li>Y. G. Kim, et al., "AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning," in Proc. of ACM MICRO, 2021.</li>
<li>Y. G. Kim, et al., "Autoscale: Energy Efficiency Optimization for Stochastic Edge Inference using Reinforcement Learning," in Proc. of ACM MICRO, 2020.</li>
<li>L. Li, et al., "SmartPC: Hierarchical Pace Control in Real-time Federated Learning System," in Proc. of IEEE RTSS, 2019.</li>
</ul>
<h2><b>Task Scheduling & Resource Management</b></h2>
<p style="color:red">Task Scheduling</p>
<ul>
<li>H. Mao, et al., "Learning Scheduling Algorithms for Data Processing Clusters," in Proc. of ACM SIGCOMM, 2019.</li>
<li>R. Grandl, et al., "Graphene: Packing and Dependency-aware Scheduling for Data-Parallel Clusters," in Proc. of USENIX OSDI, 2016.</li>
<li>R. Grandl, et al., "Altruistic Scheduling in Multi-Resource Clusters," in Proc. of USENIX OSDI, 2016.</li>
<li>J. Rasley, et al., "Efficient Queue Management for Cluster Scheduling," in Proc. of ACM EuroSys, 2016.</li>
<li>V. Jalaparti, et al., "Network-Aware Scheduling for Data-Parallel Jobs: Plan When You Can," in Proc. of ACM SIGCOMM, 2015.</li>
<li>K. Karanasos, et al., "Mercury: Hybrid Centralized and Distributed Scheduling in Large Shared Clusters," in Proc. of USENIX ATC, 2015.</li>
<li>X. Ren, et al., "Hopper: Decentralized Speculation-aware Cluster Scheduling at Scale," in Proc. of ACM SIGCOMM, 2015.</li>
<li>R. Grandl, et al., "Multi-Resource Packing for Cluster Schedulers," in Proc. of ACM SIGCOMM, 2014.</li>
</ul>
<p style="color:red">Resource Management: </p>
<ul>
<li>A. Mirhosseini, et al., "Parslo: A Gradient Descent-based Approach for Near-optimal Partial SLO
Allotment in Microservices," in Proc. of ACM SoCC, 2021.</li>
<li>A. F. Baarzi, et al., "SHOWAR: Right-Sizing and Efficient Scheduling of Microservices," in Proc. of ACM SoCC, 2021.</li>
<li>K. Rzadca, et al., "Autopilot: Workload Autoscaling at Google," in Proc. of ACM EuroSys, 2020.</li>
<li>Y. Peng, et al., "Optimus: An Efficient Dynamic Resource Scheduler for Deep Learning Clusters," in Proc. of ACM EuroSys, 2018.</li>
<li>A. Chung, et al., "Stratus: cost-aware container scheduling in the public cloud," in Proc. of ACM SoCC, 2018.</li>
<li>S. A. Jyothi, et al., "Morpheus: Towards Automated SLOs for Enterprise Clusters," in Proc. of USENIX OSDI, 2016.</li>
<li>M. Chowdhury, et al., "HUG: Multi-Resource Fairness for Correlated and Elastic Demands," in Proc. of USENIX NSDI, 2016.</li>
</ul>
<h2><b>Application</b></h2>
<p style="color:red">Robotics</p>
<ul>
<li>S. Laki, et al., "In-Network Velocity Control of Industrial Robot Arms," in Proc. of USENIX NSDI, 2022.</li>
<li>B. Liu, et al., "Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems With Heterogeneous Sensor Data," IEEE Robotics and Automation Letters, 2020.</li>
<li>B. Thananjeyan, et al., "<a href="https://sites.google.com/view/safetyaugmentedvalueestimation/home">Safety Augmented Value Estimation from Demonstrations (saved): Safe Deep Model-based RL for Sparse Cost Robotic Tasks</a>," IEEE Robotics and Automation Letters, 2020.</li>
<li>B. Liu, et al., "Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems," IEEE Robotics and Automation Letters, 2019.</li>
<li>G. Hu, et al., "Cloud Robotics: Architecture, Challenges and Applications," IEEE Network, 2012.</li>
<li>R. Arumugam, et al., "DAvinCi: A Cloud Computing Framework for Service Robots," in Proc. of IEEE ICRA, 2010.</li>
</ul>
<p style="color:red">Smart Transportation: </p>
<ul>
<li>H. Wei, et al., "Intellilight: A reinforcement learning approach for intelligent traffic light control," in Proc. of ACM KDD, 2018.</li>
<li>Y. Ye, et al., "FedLight: Federated Reinforcement Learning for Autonomous Multi-Intersection Traffic Signal Control," in Proc. of IEEE DAC, 2021.</li>
</ul>
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