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.
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
Low-latency Stream Processing/Resilience/Elasticity in Edge Computing
Resource Allocation/Management/Sharing
Resource Allocation/Management/Sharing
RL on Systems
Incentive Deisign for Edge Resource Sharing
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.
Teaching Assistant , September 2015 - Present
Teaching Assistant , September 2013 - January 2014