2015 - IEEE Fellow For contributions to parallel and distributed computing
2007 - ACM Senior Member
Keqin Li mainly focuses on Distributed computing, Scheduling, Cloud computing, Parallel computing and Dynamic priority scheduling. In his research on the topic of Distributed computing, Differential evolution and Bi objective is strongly related with Workflow. The Scheduling study combines topics in areas such as Symmetric multiprocessor system, Schedule and Heuristic.
The various areas that he examines in his Symmetric multiprocessor system study include Genetic algorithm and Heuristic. His Cloud computing research is multidisciplinary, incorporating perspectives in Quality of service, Computer network, Server and Virtual machine. In his research, Big data is intimately related to Computer cluster, which falls under the overarching field of Parallel computing.
His primary scientific interests are in Distributed computing, Parallel computing, Scheduling, Cloud computing and Algorithm. His Distributed computing research is multidisciplinary, relying on both Frequency scaling, Computer network, Directed acyclic graph and Mobile edge computing. His study in Parallel computing is interdisciplinary in nature, drawing from both Time complexity and Scalability.
His Scheduling research incorporates elements of Symmetric multiprocessor system, Schedule and Mathematical optimization. Keqin Li interconnects Virtual machine, Quality of service, Data center, Encryption and Server in the investigation of issues within Cloud computing. His study connects Fair-share scheduling and Dynamic priority scheduling.
His primary areas of investigation include Cloud computing, Distributed computing, Artificial intelligence, Scheduling and Quality of service. His Cloud computing study combines topics from a wide range of disciplines, such as Virtual machine, Scalability, Resource, Encryption and Server. The concepts of his Distributed computing study are interwoven with issues in Artificial neural network and Enhanced Data Rates for GSM Evolution, Mobile edge computing.
The study incorporates disciplines such as Machine learning and Pattern recognition in addition to Artificial intelligence. His study in Scheduling focuses on Job shop scheduling in particular. Keqin Li combines subjects such as Directed acyclic graph and Task with his study of Quality of service.
His primary areas of study are Cloud computing, Distributed computing, Quality of service, Scheduling and Mathematical optimization. His work deals with themes such as Scalability, Server, Encryption and Job shop scheduling, which intersect with Cloud computing. His Distributed computing study combines topics in areas such as Overhead, Computational complexity theory, Wireless sensor network, Approximation algorithm and Directed acyclic graph.
His studies in Quality of service integrate themes in fields like Variation, TOPSIS, Set, Selection and Mahalanobis distance. His biological study spans a wide range of topics, including Artificial neural network, Schedule, Access time and Downtime. As a part of the same scientific study, Keqin Li usually deals with the Mathematical optimization, concentrating on Stackelberg competition and frequently concerns with The Internet.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues
Yuming Xu;Kenli Li;Jingtong Hu;Keqin Li;Keqin Li.
Information Sciences (2014)
A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment
Jianguo Chen;Kenli Li;Zhuo Tang;Kashif Bilal.
IEEE Transactions on Parallel and Distributed Systems (2017)
Optimal dynamic mobility management for PCS networks
Jie Li;Hisao Kameda;Keqin Li.
IEEE ACM Transactions on Networking (2000)
A Comprehensive Survey of Network Function Virtualization
Bo Yi;Xingwei Wang;Keqin Li;Sajal k. Das.
Computer Networks (2018)
A two dimensional buddy system for dynamic resource allocation in a partitionable mesh connected system
Keqin Li;Kam Hoi Cheng.
conference on scientific computing (1990)
Optimal Multiserver Configuration for Profit Maximization in Cloud Computing
Junwei Cao;Kai Hwang;Keqin Li;Albert Y. Zomaya.
IEEE Transactions on Parallel and Distributed Systems (2013)
A two-dimensional buddy systems for dynamic resource allocation in a partitionable mesh connected system
Keqin Li;Kam-Hoi Cheng.
Journal of Parallel and Distributed Computing (1991)
Optimal Power Allocation and Load Distribution for Multiple Heterogeneous Multicore Server Processors across Clouds and Data Centers
Junwei Cao;Keqin Li;Ivan Stojmenovic.
IEEE Transactions on Computers (2014)
An Energy-Efficient Task Scheduling Algorithm in DVFS-enabled Cloud Environment
Zhuo Tang;Ling Qi;Zhenzhen Cheng;Kenli Li.
grid computing (2016)
Energy-Efficient Stochastic Task Scheduling on Heterogeneous Computing Systems
Kenli Li;Xiaoyong Tang;Keqin Li.
IEEE Transactions on Parallel and Distributed Systems (2014)
Profile was last updated on December 6th, 2021.
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The ranking h-index is inferred from publications deemed to belong to the considered discipline.
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