Jinlai Xu (徐锦来)

PhD Candidate

University of Pittsburgh

Biography

Jinlai Xu is a PhD candidate of Information Science at University of Pittsburgh. His research interests include Distributed Systems, Fog/Edge and Cloud Computing, Stream Processing Optimization and Blockchain-based Techniques. His current advisor is Balaji Palanisamy.

Before coming to University of Pittsburgh, he graduated from China University of Geosciences and his undergraduate and graduate advisor is Zhongwen Luo.

Full Curriculum Vitae

Interests

Fog/Edge Computing
Cloud Computing
Distributed Systems
Stream Processing Optimization
Reinforcement Learning

Education

PhD in Infomation Science (Expected), 2021
University of Pittsburgh
M.Phil in Software Engineering, 2015
China University of Geosciences
BEng in Software Engineering, 2012
China University of Geosciences

Research Interests

Stream Processing

Low-latency Stream Processing/Resilience/Elasticity in Edge Computing

Edge Computing

Resource Allocation/Management/Sharing

Cloud Computing

Resource Allocation/Management/Sharing

Reinforcement Learning

RL on Systems

Blockchain

Incentive Deisign for Edge Resource Sharing

Featured Publications

Jinlai Xu, Balaji Palanisamy, Heiko Ludwig, Qingyang Wang

June 2017 In IEEE Edge 2017

Zenith: Utility-aware Resource Allocation for Edge Computing

In the Internet of Things(IoT) era, the demands for low-latency computing for time-sensitive applications (e.g., location-based augmented reality games, real-time smart grid management, real-time navigation using wearables) has been growing rapidly. Edge Computing provides an additional layer of infrastructure to fill latency gaps between the IoT devices and the back-end computing infrastructure. In the edge computing model, small-scale micro-datacenters that represent ad-hoc and distributed collection of computing infrastructure pose new challenges in terms of management and effective resource sharing to achieve a globally efficient resource allocation. In this paper, we propose Zenith, a novel model for allocating computing resources in an edge computing platform that allows service providers to establish resource sharing contracts with edge infrastructure providers apriori. Based on the established contracts, service providers employ a latency-aware scheduling and resource provisioning algorithm that enables tasks to complete and meet their latency requirements. The proposed techniques are evaluated through extensive experiments that demonstrate the effectiveness, scalability and performance efficiency of the proposed model.

Recent Publications

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Jinlai Xu, Balaji Palanisamy, Qingyang Wang (2021). Resilient stream processing in edge computing. In IEEE/ACM CCGrid 2021.

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Chao Li, Balaji Palanisamy, Runhua Xu, Jinlai Xu, Jingzhe Wang (2021). SteemOps: Extracting and Analyzing Key Operations in Steemit Blockchain-based Social Media Platform. IEEE CODASPY 2021.

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Jingzhe Wang, Balaji Palanisamy, Jinlai Xu (2020). Sustainability-aware Resource Provisioning in Data Centers. IEEE CIC 2020.

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Jinlai Xu, Balaji Palanisamy (2018). Optimized Contract-based Model for Resource Allocation in Federated Geo-distributed Clouds. IEEE TSC.

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Jinlai Xu, Balaji Palanisamy, Yuzhe Tang, S.D. Madhu Kumar (2017). PADS: Privacy-preserving Auction Design forAllocating Dynamically Priced Cloud Resources. IEEE CIC 2017.

PDF Project Slides

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Teaching Experience

Teaching Assistant , September 2015 - Present
- University of Pittsburgh
  - Cloud Computing (2017 Spring, 2018 Spring, 2019 Spring)
    - Instructor: Prof. Balaji Palanisamy
  - Information Security & Privacy (2017 Fall)
    - Instructor: Prof. Balaji Palanisamy
  - Information Security & Privacy (Online Course) (2018 Fall)
  - Algorithm Design (2018 Fall)
    - Instructor: Prof. Hassan Karimi
Teaching Assistant , September 2013 - January 2014
- China University of Geosciences
  - Advanced Programming Language (JAVA)
    - Instructor: Prof. Shengwen Li

Professional Services

Journal Review
- Transactions on Services Computing (TSC)
- Concurrency and Computation: Practice and Experience (CCPE)
- International Journal of Cooperative Information Systems (IJCIS)
- Information Systems Frontiers (ISFI) : IRI - Special Issue on Foundations of Reuse
- PLOS ONE
- TELKOMNIKA (Telecommunication, Computing, Electronics and Control)
Conference Review
- International Workshop on Internet-scale Clouds and Big Data (ISCBD)
- IEEE International Conference on Communications (ICC)
Conference External Review
- IEEE World Wide Web (WWW)
- International Conference on Distributed Computing Systems (ICDCS)
- ACM International Conference on Information and Knowledge Management (CIKM)
Conference Volunteer
- IEEE 18th International Conference on Information Reuse and Integration, San Diego, CA, USA. Aug 4 - 6, 2017
- The 37th International Conference on Distributed Computing Systems (ICDCS 2017), Atlanta, GA, USA. June 5 - 8, 2017
- IEEE 17th International Conference on Information Reuse and Integration (IRI 2016), Pittsburgh, PA, USA. Jul 28 - 30, 2016
- IEEE 2ed International Conference on Collaboration and Internet Computing (CIC 2016), Pittsburgh, PA, USA. Nov 1 - 3, 2016
Conference Webmaster

Research Experience

PhD Student

University of Pittsburgh

Sep 2015 – Present Pittsburgh, PA, USA

Reviewed related literatures (mainly in Cloud Computing, Edge Computing, Stream Processing, Reinforcement Learning and Blockchain-based Techniques)
Focus on resource management problems in Edge and Cloud Computing to achieve low-latency stream processing
Publish papers on these topics

Research Assistant

China University of Geosciences

Sep 2012 – Jun 2015 Wuhan, Hubei, China

Reviewed related literatures (mainly in Cloud Computing)
Constructed the cloud computing platform for our faculty:
- Designed the virtualization solution for the cluster. (based on Xen)
- Deployed Hadoop and related application(Hive, Spark, Solr …) on the cluster.
- Supported the experiment of Deep Learning in our lab.
Studied MapReduce programming model and did research on it:
- Read the source code of MapReduce in Hadoop project.
- Proposed a new method to reuse the intermediate results automatically and data-awarenessly and implemented the prototype system by modifying the core code of MapReduce.
- Evaluated the performance on the cluster and got the result that the system could improve the performance up to 24.6% compared with the previous optimization work.
- The paper is published on CCPE. (Title: MEMoMR: Accelerate MapReduce via reuse of intermediate results)
Managed the cluster in our faculty:
- Allocated the virtual machines and network resource.
- Supported a mirror site on the cluster (http://mirrors.cug.edu.cn).

Undergraduate Research Assistant

China University of Geosciences

Jul 2009 – Aug 2012 Wuhan, Hubei, China

Reviewed related literatures (mainly in Computer Vision and Robotics).
Participated in The 9 th Robot Soccer Tournament of China and The Tryouts for FIRA in Changchun in freshmen year.
Studied the architecture and implementation of ROS(The Robot Operating System) and preliminarily deployed it on the robots control panel (Version: RB100 by RoBoard).
Successfully applied for The National College Students Innovation Experiment Program (with funding):
- Topic: Small Model Aircraft Autopilot System and Aerial Photo Research
- Chose Quadrotor(an aircraft with four rotors) as the carrier platform of the research.
- Studied the theory of balancing the Quadrotor with MikroKopter(one of the most famous open source UAV projects).
- Studied and implemented the point clouds registration algorithm ICP and RANSAC on ROS.
- Used ASUS Xtion PRO (a device like Kinect) to get the point cloud data and evaluated the algorithm.
- Wrote graduation thesis based on this topic.(Title: the Design and Implementation of the Quadrotor Autopilot and 3-D Point Cloud Generation and Processing System)