What is he best known for?
The fields of study he is best known for:
- Operating system
- Computer network
- Artificial intelligence
His primary areas of study are Distributed computing, Cloud computing, Virtual machine, Computer network and Server.
The concepts of his Distributed computing study are interwoven with issues in Grid computing, Scheduling, Bandwidth and Workflow.
His biological study spans a wide range of topics, including Software as a service, Data center and Parallel computing.
His Virtual machine study contributes to a more complete understanding of Operating system.
His research integrates issues of Software deployment and Game theory in his study of Computer network.
His study in Server is interdisciplinary in nature, drawing from both Replication, Scalability, The Internet and Algorithm, Online algorithm.
His most cited work include:
- AnySee: Peer-to-Peer Live Streaming (348 citations)
- Color Image Segmentation Based on Mean Shift and Normalized Cuts (307 citations)
- Live virtual machine migration with adaptive, memory compression (268 citations)
What are the main themes of his work throughout his whole career to date?
Distributed computing, Computer network, Cloud computing, Operating system and Scalability are his primary areas of study.
His Distributed computing research includes themes of Grid computing, Scheduling, Service and Parallel computing.
As part of his studies on Grid computing, Hai Jin frequently links adjacent subjects like Semantic grid.
Server, Quality of service, Peer-to-peer and Bandwidth are subfields of Computer network in which his conducts study.
His study in Virtual machine and Hypervisor falls under the purview of Operating system.
His study on Virtual machine is mostly dedicated to connecting different topics, such as Virtualization.
He most often published in these fields:
- Distributed computing (28.61%)
- Computer network (18.40%)
- Cloud computing (13.98%)
What were the highlights of his more recent work (between 2018-2021)?
- Distributed computing (28.61%)
- Cloud computing (13.98%)
- Parallel computing (10.86%)
In recent papers he was focusing on the following fields of study:
His main research concerns Distributed computing, Cloud computing, Parallel computing, Computer network and Artificial intelligence.
Distributed computing is often connected to Scheduling in his work.
His Cloud computing research is multidisciplinary, incorporating elements of Virtual machine, Provisioning, Server and Quality of service.
His Server study which covers Integer programming that intersects with Approximation algorithm.
As part of one scientific family, he deals mainly with the area of Parallel computing, narrowing it down to issues related to the Graph, and often Field-programmable gate array, Theoretical computer science and Scalability.
His Artificial intelligence study combines topics in areas such as Machine learning, Source code and Pattern recognition.
Between 2018 and 2021, his most popular works were:
- A Game-Theoretical Approach for User Allocation in Edge Computing Environment (65 citations)
- Computation Offloading Toward Edge Computing (62 citations)
- Defending Against Flow Table Overloading Attack in Software-Defined Networks (35 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Computer network
- Artificial intelligence
Hai Jin mainly focuses on Cloud computing, Server, Distributed computing, Artificial intelligence and Parallel computing.
His work carried out in the field of Cloud computing brings together such families of science as Virtual machine, Enhanced Data Rates for GSM Evolution and Database.
His Virtual machine research entails a greater understanding of Operating system.
The subject of his Server research is within the realm of Computer network.
His Distributed computing study also includes fields such as
- Scheduling which intersects with area such as Efficient energy use,
- Quality of service which intersects with area such as Resource allocation.
His research investigates the connection with Artificial intelligence and areas like Machine learning which intersect with concerns in Key.
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.
