Chenggang Li

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  1. Predicting Node Failure in Cloud Service Systems, Qingwei Lin et al. FSE/ESEC 2018.

    In recent years, many traditional software systems have migrated to cloud computing platforms and are provided as online services. A cloud service system typically contains a large number of computing nodes. In reality, nodes may fail and affect service availability. In this paper, we propose a failure prediction technique, which can predict the failure-proneness of a node in a cloud service system based on historical data, before node failure actually happens. The ability to predict faulty nodes enables the allocation and migration of virtual machines to the healthy nodes, therefore improving service availability.



Research & Projects

Past Projects

Anomaly Detection and Log Analysis, Spring 2019
Worked as an research intern at Beijing Bizseer Technologies to develop Deep learning based Anomaly Detection and Log Analysis Algorithms to help major commercial banks in china find out Anomaly in daily data and uncover root cause from massive heterogeneous data and log reports of their complex softwares and critical IT infrastructure.

AutoExavator, 2018
AutoExavator is an R&D project in iRootech Technology Co., Ltd and SANY, which enables autonomous driving and construction for heavy equipment

Azure Capacity Management at Microsoft Research Asia, 2017-2018
Predict capacity days-to-exhaustion and Develop Buffer Capacity Management strategy to reserve the empty node/core buffer to fit the tenant upgrade needs.

Compute and Storage Node Fault Prediction for Cloud Platforms, 2017
Proactive downtime prediction: our node fault and hardware fault prediction service can proactively reduce VM downtime, through guiding new VM allocation and removing risky nodes from rotation. Validation result shows our prediction technology can add 10+% more node fault reduction on top of existing advanced allocation strategy. The top 1,000 nodes with disk failures we predicted causes 21K+ min VM downtime within a month.

BoneyCare is designed to help people with speech disorders improve their confidence and speaking quality. The App records, transcribes and analyzes a users speech patterns, which speech pathologists can then use to improve and accelerate their diagnoses and treatment plans. This project is a collaboration between 2nd year computer science students from two Chinese universities and UC Berkeley.

LightItUp is a hackathon solution based on MITM(man-in-the-middle) attack that helps late-owl students gain access over their dorm electricity meter and bypass enforcement of dorm electricity off after 23:00.


School Email:
Personal Email: Execute below command to find out.
openssl enc -base64 -d <<< b3NjYXJ4dml0YUBnbWFpbC5jb20K