What is he best known for?
The fields of study he is best known for:
- Operating system
- Computer network
- Database
Dan Feng mostly deals with Data deduplication, Backup, Scalability, Computer data storage and Parallel computing.
His Data deduplication research is multidisciplinary, relying on both Redundancy, Cloud computing and Upload.
His work carried out in the field of Backup brings together such families of science as Distributed computing, RAID, Bandwidth, Server and Search engine indexing.
His work in Scalability covers topics such as Computer network which are related to areas like Graph, Computation, Vertex and Graph.
His Computer data storage study necessitates a more in-depth grasp of Operating system.
In his research on the topic of Parallel computing, Factor, Reduction and Chunking is strongly related with Hash function.
His most cited work include:
- Achieving page-mapping FTL performance at block-mapping FTL cost by hiding address translation (53 citations)
- Locality-Sensitive Bloom Filter for Approximate Membership Query (51 citations)
- iOSDC : A Novel Autonomous Intelligent OSD Cluster (49 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Distributed computing, Operating system, Computer data storage, Parallel computing and Computer network.
He combines subjects such as Scheduling, Workload, Scalability and Server with his study of Distributed computing.
His Computer data storage research is multidisciplinary, incorporating perspectives in Object, RAID and Embedded system.
His research integrates issues of Data deduplication and Latency in his study of Parallel computing.
His Data deduplication study incorporates themes from Redundancy and Hash function.
The various areas that Dan Feng examines in his Computer network study include Computer security and Cloud computing.
He most often published in these fields:
- Distributed computing (22.48%)
- Operating system (19.54%)
- Computer data storage (17.92%)
What were the highlights of his more recent work (between 2017-2021)?
- Distributed computing (22.48%)
- Parallel computing (17.59%)
- Cache (9.77%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Distributed computing, Parallel computing, Cache, Operating system and Overhead.
He interconnects Scalability, Graph partition, Redundancy, Scheduling and Bandwidth in the investigation of issues within Distributed computing.
His research in Parallel computing intersects with topics in Data deduplication, Hash function, Key and Bottleneck.
Dan Feng has researched Data deduplication in several fields, including Fragmentation, Storage efficiency and Backup.
His Operating system study combines topics from a wide range of disciplines, such as Queue and Partition.
His Overhead research includes themes of Replication, Metadata, Non-volatile memory, Embedded system and Scheme.
Between 2017 and 2021, his most popular works were:
- Using High-Bandwidth Networks Efficiently for Fast Graph Computation (25 citations)
- A communication-reduced and computation-balanced framework for fast graph computation (12 citations)
- CACF: A Novel Circuit Architecture Co-optimization Framework for Improving Performance, Reliability and Energy of ReRAM-based Main Memory System (11 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Computer network
- Database
Dan Feng focuses on Resistive random-access memory, Crossbar switch, Distributed computing, Power network design and Energy consumption.
His Distributed computing research integrates issues from Feature, Graph partition, Image sharing, Bandwidth and Server.
Dan Feng works mostly in the field of Graph partition, limiting it down to concerns involving Aggregate and, occasionally, Scalability.
Dan Feng has included themes like Random access memory and Computer hardware in his Energy consumption study.
Dan Feng works mostly in the field of Standby power, limiting it down to topics relating to Embedded system and, in certain cases, File system.
His Graph study combines topics in areas such as Algorithm, Computer network and Global Positioning System.
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.
