CloudIntelligence 2021 Contributors - CloudIntelligence 2021

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Results (94)

A Alexander AckerAcker, Alexander Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models Learning Dependencies in Distributed Cloud Applications to Identify and Localize Anomalies
Rama AkkirajuAkkiraju, Rama IBM AIOps: Automating and Optimizing IT Operations Management with AI Committee Member in Steering Committee within the CloudIntelligence-track
B Boris BaracaldoBaracaldo, Boris Facebook SEAT: statistically sound infra-side deployment and integration testing
Ethan BenowitzBenowitz, Ethan Facebook SEAT: statistically sound infra-side deployment and integration testing
Ranjita BhagwanBhagwan, Ranjita Microsoft Research IndiaIndia Panel #1 - How to accelerate collaboration on Cloud Intelligence across academia and industry? Committee Member in Program Committee within the CloudIntelligence-track
Ricardo BianchiniBianchini, Ricardo Microsoft Research Committee Member in Steering Committee within the CloudIntelligence-track
Jasmin BogatinovskiBogatinovski, Jasmin Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models MicroDiag: Fine-grained Performance Diagnosis for Microservice Systems
C Hongshu CheChe, Hongshu Microsoft Research, Beijing, China TraceLingo: Trace representation and learning for performance issue diagnosis in cloud services
Junjie ChenChen, Junjie Tianjin UniversityChina Committee Member in Program Committee within the CloudIntelligence-track
Pengfei ChenChen, Pengfei Sun Yat-Sen University Kmon: An In-kernel Transparent Monitoring System for Microservice Systems with eBPF
Ruichuan ChenChen, Ruichuan Nokia Bell Labs PerfEstimator: A Generic and Extensible Performance Estimator for Data Parallel DNN Training
Murali ChintalapatiChintalapati, Murali Microsoft Azure F3: Fault Forecasting Framework for Cloud Systems Infusing ML into VM Provisioning in Cloud Committee Member in Web Chair within the CloudIntelligence-track
Eli CortezCortez, Eli Microsoft Azure Infusing ML into VM Provisioning in Cloud Committee Member in Organizing Committee within the CloudIntelligence-track
Jieqi CuiCui, Jieqi Sun Yat-Sen University Kmon: An In-kernel Transparent Monitoring System for Microservice Systems with eBPF
D Mike DahlinDahlin, Mike Google Committee Member in Steering Committee within the CloudIntelligence-track
Yingnong DangDang, Yingnong Microsoft, USAUnited States Session Chair of Project Showcase Session (part of CloudIntelligence) F3: Fault Forecasting Framework for Cloud Systems TraceLingo: Trace representation and learning for performance issue diagnosis in cloud services Infusing ML into VM Provisioning in Cloud Committee Member in Program Committee within the CloudIntelligence-track Committee Member in Organizing Committee within the CloudIntelligence-track
Christina DelimitriouDelimitriou, Christina Cornell UniversityUnited States Panel #2 - How to accelerate collaboration on Cloud Intelligence across academia and industry? Leveraging ML to Handle the Increasing Complexity of the Cloud
E Erik Elmroth Elmroth , Erik Elastisys AB/Umea University MicroDiag: Fine-grained Performance Diagnosis for Microservice Systems
Oytun EskiyenenturkEskiyenenturk, Oytun Facebook SEAT: statistically sound infra-side deployment and integration testing
F Igal FiglinFiglin, Igal Microsoft Azure Committee Member in Publicity Chair within the CloudIntelligence-track
Marcus FontouraFontoura, Marcus Microsoft Azure Committee Member in Steering Committee within the CloudIntelligence-track
Xin FuFu, Xin Facebook SEAT: statistically sound infra-side deployment and integration testing
G Morgan Geldenhuys Geldenhuys , Morgan Technische Universität Berlin Learning Dependencies in Distributed Cloud Applications to Identify and Localize Anomalies
Raphael Ghelman Ghelman , Raphael Microsoft Azure Infusing ML into VM Provisioning in Cloud
Pulak Goyal Goyal , Pulak Microsoft Azure Infusing ML into VM Provisioning in Cloud
Antonio GulliGulli, Antonio Google Committee Member in Program Committee within the CloudIntelligence-track
H Ahmed E. HassanHassan, Ahmed E. School of Computing, Queen's University Committee Member in Steering Committee within the CloudIntelligence-track
Daud Howlader Howlader , Daud Microsoft Azure Infusing ML into VM Provisioning in Cloud
Ryan HuangHuang, Ryan Johns Hopkins UniversityUnited States Committee Member in Program Committee within the CloudIntelligence-track
K Yu KangKang, Yu Microsoft Research, Beijing, ChinaChina Proceedings Chair in Proceeding Chair within the CloudIntelligence-track
Odej KaoKao, Odej Technische Universität Berlin Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models MicroDiag: Fine-grained Performance Diagnosis for Microservice Systems Learning Dependencies in Distributed Cloud Applications to Identify and Localize Anomalies Panel #1 - How to accelerate collaboration on Cloud Intelligence across academia and industry? Committee Member in Program Committee within the CloudIntelligence-track
Jim KleeweinKleewein, Jim Microsoft Panel #2 - How to accelerate collaboration on Cloud Intelligence across academia and industry?
L Cheng LiLi, Cheng University of Science and Technology of China PerfEstimator: A Generic and Extensible Performance Estimator for Data Parallel DNN Training
Ning LiLi, Ning School of Computer Science, Northwestern Polytechnical UniversityChina Rapid Trend Prediction for Large-Scale Cloud Database KPIs by Clustering
Xiaolin Andy LiLi, Xiaolin Andy Tongdun Technology Committee Member in Program Committee within the CloudIntelligence-track
Ze LiLi, Ze Microsoft, USA TraceLingo: Trace representation and learning for performance issue diagnosis in cloud services
Zhehao LiLi, Zhehao University of Science and Technology of China PerfEstimator: A Generic and Extensible Performance Estimator for Data Parallel DNN Training
Qingwei LinLin, Qingwei Microsoft Research, Beijing, ChinaChina Session Chair of Keynote (part of CloudIntelligence) Session Chair of Technical Paper Session #2 (part of CloudIntelligence) Session Chair of Technical paper session #1 (part of CloudIntelligence) Session Chair of Invited Talk #2 (part of CloudIntelligence) Session Chair of Invited Talk #1 (part of CloudIntelligence) F3: Fault Forecasting Framework for Cloud Systems TraceLingo: Trace representation and learning for performance issue diagnosis in cloud services Infusing ML into VM Provisioning in Cloud Committee Member in Program Committee within the CloudIntelligence-track Committee Member in Organizing Committee within the CloudIntelligence-track
Shan LuLu, Shan University of ChicagoUnited States Panel #1 - How to accelerate collaboration on Cloud Intelligence across academia and industry?
Chuan LuoLuo, Chuan Microsoft Research, ChinaChina F3: Fault Forecasting Framework for Cloud Systems Infusing ML into VM Provisioning in Cloud
Karan LuthraLuthra, Karan Facebook SEAT: statistically sound infra-side deployment and integration testing
M Erik MeijerMeijer, Erik FacebookUnited States Committee Member in Steering Committee within the CloudIntelligence-track
N Sasho Nedelkoski Nedelkoski , Sasho TU Berlin Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models
Elena NovakovskaiaNovakovskaia, Elena Salesforce Committee Member in Program Committee within the CloudIntelligence-track
O Dan O'NeilO'Neil, Dan Stanford University Committee Member in Program Committee within the CloudIntelligence-track
Harald OttOtt, Harald TU Berlin Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models
P Dan PeiPei, Dan Tsinghua University Towards Autonomous IT Operations through Artificial Intelligence Panel #1 - How to accelerate collaboration on Cloud Intelligence across academia and industry?
Xin PengPeng, Xin Fudan University, ChinaChina Session Chair of Panel #1 (part of CloudIntelligence) Large-Scale Trace Analysis for Microservice Architecture Understanding and Fault Analysis Committee Member in Program Committee within the CloudIntelligence-track Committee Member in Organizing Committee within the CloudIntelligence-track
Q Bo QiaoQiao, Bo Microsoft Research, Beijing, China F3: Fault Forecasting Framework for Cloud Systems TraceLingo: Trace representation and learning for performance issue diagnosis in cloud services Infusing ML into VM Provisioning in Cloud
R Saravanakumar RajmohanRajmohan, Saravanakumar Microsoft Office, United States F3: Fault Forecasting Framework for Cloud Systems
Sushant Rewaskar Rewaskar , Sushant Microsoft Azure Infusing ML into VM Provisioning in Cloud
Chaoyi RuanRuan, Chaoyi University of Science and Technology of China PerfEstimator: A Generic and Extensible Performance Estimator for Data Parallel DNN Training
Nick RuffRuff, Nick Facebook SEAT: statistically sound infra-side deployment and integration testing
S Dominik ScheinertScheinert, Dominik Technische Universität BerlinGermany Learning Dependencies in Distributed Cloud Applications to Identify and Localize Anomalies
Siddhartha SenSen, Siddhartha Session Chair of Invited Talk #4 (part of CloudIntelligence) Session Chair of Panel #2 (part of CloudIntelligence) Session Chair of Invited Talk #3 (part of CloudIntelligence) Committee Member in Program Committee within the CloudIntelligence-track
Gil Shafriri Shafriri , Gil Microsoft Azure Infusing ML into VM Provisioning in Cloud
Weiyi ShangShang, Weiyi Concordia University Committee Member in Program Committee within the CloudIntelligence-track
Zheng ShaoShao, Zheng Uber Committee Member in Program Committee within the CloudIntelligence-track
T Nisha TalagalaTalagala, Nisha Pyxeda AI Committee Member in Program Committee within the CloudIntelligence-track
Nutcha TemiyasathitTemiyasathit, Nutcha Facebook SEAT: statistically sound infra-side deployment and integration testing
Lauritz ThamsenThamsen, Lauritz TU Berlin Learning Dependencies in Distributed Cloud Applications to Identify and Localize Anomalies
Johan TordssonTordsson, Johan Elastisys AB MicroDiag: Fine-grained Performance Diagnosis for Microservice Systems
Tri M. TranTran, Tri M. Microsoft Azure Infusing ML into VM Provisioning in Cloud
W Paul WangWang, Paul Microsoft 365 F3: Fault Forecasting Framework for Cloud Systems
Xiaoling WangWang, Xiaoling Northwestern Polytechnical University Rapid Trend Prediction for Large-Scale Cloud Database KPIs by Clustering
Tianjun WengWeng, Tianjun Sun Yat-Sen University Kmon: An In-kernel Transparent Monitoring System for Microservice Systems with eBPF
Rich WolskiWolski, Rich University of California Santa Barbara Committee Member in Program Committee within the CloudIntelligence-track
Li WuWu, Li Elastisys AB/Technische Universität Berlin MicroDiag: Fine-grained Performance Diagnosis for Microservice Systems
Youjiang WuWu, Youjiang Microsoft, USA F3: Fault Forecasting Framework for Cloud Systems
X Tao XieXie, Tao Peking University Committee Member in Steering Committee within the CloudIntelligence-track
Guanbin XuXu, Guanbin University of Science and Technology of China PerfEstimator: A Generic and Extensible Performance Estimator for Data Parallel DNN Training
Yong XuXu, Yong Microsoft, China TraceLingo: Trace representation and learning for performance issue diagnosis in cloud services
Y Feng YanYan, Feng University of Nevada RenoUnited States PerfEstimator: A Generic and Extensible Performance Estimator for Data Parallel DNN Training
Chengru YangYang, Chengru University of Science and Technology of China PerfEstimator: A Generic and Extensible Performance Estimator for Data Parallel DNN Training
Conan YangYang, Conan Salesforce Building a Secured Data Intelligence Platform
Tao YangYang, Tao Facebook SEAT: statistically sound infra-side deployment and integration testing
Wanqi YangYang, Wanqi Sun Yat-Sen University Kmon: An In-kernel Transparent Monitoring System for Microservice Systems with eBPF
Randolph YaoYao, Randolph Microsoft, USA Infusing ML into VM Provisioning in Cloud
Susy YiYi, Susy Microsoft 365 F3: Fault Forecasting Framework for Cloud Systems
Guangba YuYu, Guangba Sun Yat-Sen University Kmon: An In-kernel Transparent Monitoring System for Microservice Systems with eBPF
Z Chuanfu ZhangZhang, Chuanfu Sun Yat-Sen University Kmon: An In-kernel Transparent Monitoring System for Microservice Systems with eBPF
Dongmei ZhangZhang, Dongmei Microsoft ResearchChina From Software Analytics to Cloud Intelligence – Reflection and Path Forward  F3: Fault Forecasting Framework for Cloud Systems Infusing ML into VM Provisioning in Cloud Committee Member in Steering Committee within the CloudIntelligence-track
Hongyu ZhangZhang, Hongyu The University of Newcastle Panel #1 - How to accelerate collaboration on Cloud Intelligence across academia and industry? Committee Member in Program Committee within the CloudIntelligence-track
Jian ZhangZhang, Jian Microsoft Program Chair in Program Chair within the CloudIntelligence-track
Lijun ZhangZhang, Lijun Northwestern Polytechnical University Rapid Trend Prediction for Large-Scale Cloud Database KPIs by Clustering
Xiaofang ZhangZhang, Xiaofang Northwestern Polytechnical University Rapid Trend Prediction for Large-Scale Cloud Database KPIs by Clustering
Xu ZhangZhang, Xu Microsoft Research, Beijing, China TraceLingo: Trace representation and learning for performance issue diagnosis in cloud services
Ming ZhaoZhao, Ming Arizona State University Committee Member in Program Committee within the CloudIntelligence-track
Pu ZhaoZhao, Pu Microsoft Research, Beijing, China F3: Fault Forecasting Framework for Cloud Systems TraceLingo: Trace representation and learning for performance issue diagnosis in cloud services
Qiong ZhaoZhao, Qiong Bank of Communications Rapid Trend Prediction for Large-Scale Cloud Database KPIs by Clustering
Andrew ZhouZhou, Andrew Microsoft 365 F3: Fault Forecasting Framework for Cloud Systems
Yuanyuan ZhouZhou, Yuanyuan University of California, San DiegoUnited States Committee Member in Steering Committee within the CloudIntelligence-track
Yaokang ZhuZhu, Yaokang Microsoft Research Asia TraceLingo: Trace representation and learning for performance issue diagnosis in cloud services
Petar ZuljevicZuljevic, Petar Facebook SEAT: statistically sound infra-side deployment and integration testing