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20. KEA: Tuning an Exabyte-Scale Data Infrastructure

Author

Zhu, Yiwen, Krishnan, Subru, Karanasos, Konstantinos, Tarte, Isha, Power, Conor, Modi, Abhishek, Kumar, Manoj, Zhang, Deli, Muthyala, Kartheek, Jurgens, Nick, Sakalanaga, Sarvesh, Darbha, Sudhir, Iyer, Minu, Agarwal, Ankita, and Curino, Carlo

Subjects
Computer Science - Databases
Abstract

Microsoft's internal big-data infrastructure is one of the largest in the world -- with over 300k machines running billions of tasks from over 0.6M daily jobs. Operating this infrastructure is a costly and complex endeavor, and efficiency is paramount. In fact, for over 15 years, a dedicated engineering team has tuned almost every aspect of this infrastructure, achieving state-of-the-art efficiency (>60% average CPU utilization across all clusters). Despite rich telemetry and strong expertise, faced with evolving hardware/software/workloads this manual tuning approach had reached its limit -- we had plateaued. In this paper, we present KEA, a multi-year effort to automate our tuning processes to be fully data/model-driven. KEA leverages a mix of domain knowledge and principled data science to capture the essence of our cluster dynamic behavior in a set of machine learning (ML) models based on collected system data. These models power automated optimization procedures for parameter tuning, and inform our leadership in critical decisions around engineering and capacity management (such as hardware and data center design, software investments, etc.). We combine "observational" tuning (i.e., using models to predict system behavior without direct experimentation) with judicious use of "flighting" (i.e., conservative testing in production). This allows us to support a broad range of applications that we discuss in this paper. KEA continuously tunes our cluster configurations and is on track to save Microsoft tens of millions of dollars per year. At the best of our knowledge, this paper is the first to discuss research challenges and practical learnings that emerge when tuning an exabyte-scale data infrastructure.

Published
2021
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23. Control Simulation to Underwater Manipulator

Author

Huan, Shang, Chao, Jiangang, Xie, Lan, Zhang, Yu, Han, Liangliang, Wang, Zhiguo, Zhang, Deli, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Long, Shengzhao, editor, and Dhillon, Balbir S., editor

Published
2023
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24. Control Design to Underwater Robotic Arm

Author

She, Jiahong, Huan, Shang, Xie, Shaoli, Zhang, Deli, Han, Liangliang, Yang, Jian, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Long, Shengzhao, editor, and Dhillon, Balbir S., editor

Published
2023
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33. Safety Design to Underwater Robotic Arm

Author

Huan, Shang, Chao, Jiangang, Zhang, Yan, She, Jiahong, Han, Liangliang, Yang, Jian, Zhang, Deli, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Long, Shengzhao, editor, and Dhillon, Balbir S., editor

Published
2022
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39. Effect of Pretreatment on the Pyrolysis Kinetics of Corn Stalk: Comparison of Inert, Oxidative, and Wet Torrefaction.

Author

Gao, Haolei, Zhang, Deli, Liu, Zhenfei, Wang, Fang, Su, Xiaohong, Liu, Wei, and Yi, Weiming

Abstract

This study compared the pyrolysis behaviors of corn stalk (CS) and its torrefied biomass after inert torrefaction (IT), oxidative torrefaction (OT), and wet torrefaction (WT), focused on the kinetic parameters and reaction mechanisms. Inert and oxidative torrefaction reduced volatile matter while increasing ash content and fixed carbon. Wet torrefaction reduced both volatile matter and ash content while increasing fixed carbon. Three pretreatment methods decreased oxygen content, increased carbon content, and had a higher heating value. The materials were pyrolyzed in a thermogravimetric analyzer. For CS, the average activation energy (E) values calculated by the Flynn–Wall–Ozawa and Kissinger–Akahira–Sunosen methods were 62.5 and 60.07 kJ/mol. IT and WT showed increased trend, with values of 81.58, 81.48 kJ/mol and 69.75, 67.58 kJ/mol respectively. Conversely, OT decreased with the E values of 57.39 and 56.2 kJ/mol. Pyrolysis was divided into two stages based on various conversion rates (α) using Malek and Coats–Redfern methods. When α was below 0.5, a one-dimensional diffusion mathematical model described the pyrolysis process. When α was beyond 0.5, the pyrolysis of CS conformed to the cylindrical symmetric three-dimensional diffusion mathematical model, while IT, OT, and WT better fit the spherical symmetric three-dimensional diffusion mathematical model. However, the torrefaction atmosphere’s impact on the pyrolysis kinetic mechanism was limited, exhibiting no alterations in the diffusion model. Different torrefaction samples demonstrated a degree of homogeneity, considering the lower pretreatment temperatures and the economic feasibility of torrefaction atmospheres in oxidative torrefaction, coupled with the lowest activation energy of oxidative torrefaction products indicating more efficient pyrolysis, oxidative torrefaction was recommended as the torrefaction pretreatment process before pyrolysis engineering. [ABSTRACT FROM AUTHOR]

Published
2025
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45. Critical nonlocal Schrödinger-Poisson system on the Heisenberg group

Author

Liu Zeyi, Tao Lulu, Zhang Deli, Liang Sihua, and Song Yueqiang

Subjects
heisenberg group, schrödinger-poisson type system, mountain pass theorem, krasnoselskii genus theorem, clark critical point theorem, 35j20, 35r03, 46e35, Analysis, QA299.6-433
Abstract

In this paper, we are concerned with the following a new critical nonlocal Schrödinger-Poisson system on the Heisenberg group:

Published
2021
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50. Motion Prediction and Object Detection for Image-Based Visual Servoing Systems Using Deep Learning.

Author

Hao, Zhongwen, Zhang, Deli, and Honarvar Shakibaei Asli, Barmak

Subjects
PARTICLE swarm optimization, OBJECT recognition (Computer vision), TIME series analysis, ROBOTICS, ALGORITHMS
Abstract

This study primarily investigates advanced object detection and time series prediction methods in image-based visual servoing systems, aiming to capture targets better and predict the motion trajectory of robotic arms in advance, thereby enhancing the system's performance and reliability. The research first implements object detection on the VOC2007 dataset using the Detection Transformer (DETR) and achieves ideal detection scores. The particle swarm optimization algorithm and 3-5-3 polynomial interpolation methods were utilized for trajectory planning, creating a unique dataset through simulation. This dataset contains randomly generated trajectories within the workspace, fully simulating actual working conditions. Significantly, the Bidirectional Long Short-Term Memory (BILSTM) model was improved by substituting its traditional Multilayer Perceptron (MLP) components with Kolmogorov–Arnold Networks (KANs). KANs, inspired by the K-A theorem, improve the network representation ability by placing learnable activation functions on fixed node activation functions. By implementing KANs, the model enhances parameter efficiency and interpretability, thus addressing the typical challenges of MLPs, such as the high parameter count and lack of transparency. The experiments achieved favorable predictive results, indicating that the KAN not only reduces the complexity of the model but also improves learning efficiency and prediction accuracy in dynamic visual servoing environments. Finally, Gazebo software was used in ROS to model and simulate the robotic arm, verify the effectiveness of the algorithm, and achieve visual servoing. [ABSTRACT FROM AUTHOR]

Published
2024
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