What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Computer network
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 most cited work include:
- A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues (227 citations)
- A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues (227 citations)
- A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment (180 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Distributed computing (28.61%)
- Parallel computing (24.40%)
- Scheduling (23.14%)
What were the highlights of his more recent work (between 2019-2021)?
- Cloud computing (22.58%)
- Distributed computing (28.61%)
- Artificial intelligence (13.32%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing (59 citations)
- A Survey of Intrusion Detection for In-Vehicle Networks (38 citations)
- Minimizing Redundancy to Satisfy Reliability Requirement for a Parallel Application on Heterogeneous Service-Oriented Systems (30 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Computer network
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.
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