His primary scientific interests are in Scheduling, Distributed computing, Dynamic priority scheduling, Symmetric multiprocessor system and Job shop scheduling. His Scheduling study combines topics from a wide range of disciplines, such as Big data, Computer cluster and Parallel computing. Kenli Li has researched Parallel computing in several fields, including Algorithm and Tuple.
His Distributed computing research incorporates elements of Workload and Real-time computing. His Dynamic priority scheduling study frequently draws connections to adjacent fields such as Fair-share scheduling. His research integrates issues of Mathematical optimization, Directed acyclic graph and Processor scheduling in his study of Job shop scheduling.
Kenli Li mostly deals with Distributed computing, Parallel computing, Algorithm, Scheduling and Artificial intelligence. His Distributed computing research is multidisciplinary, relying on both Workload, Real-time computing, Dynamic priority scheduling, Fair-share scheduling and Cloud computing. His Cloud computing study incorporates themes from Quality of service, Virtual machine and Server.
His research combines Mathematical optimization and Algorithm. His Scheduling research includes themes of Symmetric multiprocessor system, Schedule and Directed acyclic graph. His Artificial intelligence study incorporates themes from Machine learning and Pattern recognition.
His main research concerns Artificial intelligence, Artificial neural network, Distributed computing, Cloud computing and Pattern recognition. His Artificial intelligence research includes themes of Machine learning and Task. His research in Artificial neural network intersects with topics in Robustness, Control theory, Nonlinear system and Applied mathematics.
He interconnects Quality of service, Scheduling, Virtual machine and Mobile edge computing in the investigation of issues within Distributed computing. His specific area of interest is Scheduling, where Kenli Li studies Job shop scheduling. His Cloud computing research incorporates elements of Database, Resource, Encryption, Task and Nash equilibrium.
Kenli Li mainly investigates Cloud computing, Artificial intelligence, Scheduling, Convolutional neural network and Distributed computing. His work deals with themes such as Quality of service, Database, Server and Nash equilibrium, which intersect with Cloud computing. His study in the field of Deep learning also crosses realms of Occupancy.
His research on Scheduling often connects related topics like Swarm behaviour. His Convolutional neural network research is multidisciplinary, relying on both Spatial relation, Data mining and Hierarchy. His Distributed computing research is multidisciplinary, incorporating elements of Virtual machine, Overhead, Access time and Downtime.
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iProX: an integrated proteome resource.
Jie Ma;Tao Chen;Songfeng Wu;Chunyuan Yang.
Nucleic Acids Research (2019)
vCUDA: GPU-Accelerated High-Performance Computing in Virtual Machines
Lin Shi;Hao Chen;Jianhua Sun;Kenli Li.
IEEE Transactions on Computers (2012)
A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues
Yuming Xu;Kenli Li;Jingtong Hu;Keqin Li;Keqin Li.
A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment
Jianguo Chen;Kenli Li;Zhuo Tang;Kashif Bilal.
Hyperspectral Anomaly Detection With Attribute and Edge-Preserving Filters
Xudong Kang;Xiangping Zhang;Shutao Li;Kenli Li.
IEEE Transactions on Geoscience and Remote Sensing (2017)
An Energy-Efficient Task Scheduling Algorithm in DVFS-enabled Cloud Environment
Zhuo Tang;Ling Qi;Zhenzhen Cheng;Kenli Li.
A hybrid deep learning CNNELM for age and gender classification
Mingxing Duan;Kenli Li;Canqun Yang;Keqin Li.
Energy-Efficient Stochastic Task Scheduling on Heterogeneous Computing Systems
Kenli Li;Xiaoyong Tang;Keqin Li.
Scheduling Precedence Constrained Stochastic Tasks on Heterogeneous Cluster Systems
Kenli Li;Xiaoyong Tang;Bharadwaj Veeravalli;Keqin Li.
Performance Analysis and Optimization for SpMV on GPU Using Probabilistic Modeling
Kenli Li;Wangdong Yang;Keqin Li.
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