What is he best known for?
The fields of study he is best known for:
- Operating system
- Computer network
- Microsoft Windows
His primary areas of investigation include Data deduplication, Backup, Distributed computing, Scalability and Operating system.
The concepts of his Data deduplication study are interwoven with issues in Hash function, Search engine indexing and Computer data storage.
His Backup research is multidisciplinary, relying on both Exploit, Cloud computing, Overhead and Cache.
His research integrates issues of Solid-state drive, Block and Computer network in his study of Cloud computing.
His research investigates the connection between Distributed computing and topics such as Scheduling that intersect with issues in Fault tolerance.
He interconnects Metadata, Parallel computing, Bloom filter, Node and Server in the investigation of issues within Scalability.
His most cited work include:
- Performance impact and interplay of SSD parallelism through advanced commands, allocation strategy and data granularity (239 citations)
- SiLo: a similarity-locality based near-exact deduplication scheme with low RAM overhead and high throughput (172 citations)
- A dynamic and reliability-driven scheduling algorithm for parallel real-time jobs executing on heterogeneous clusters (114 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Distributed computing, Parallel computing, Operating system, Scalability and Computer network.
His biological study spans a wide range of topics, including Workload, Scheduling, Server and Network interface.
His Parallel computing research integrates issues from Input/output and Computer cluster.
His Operating system study frequently draws connections to other fields, such as Garbage collection.
His Scalability study integrates concerns from other disciplines, such as Data deduplication, Metadata, Computer data storage and Bloom filter.
Hong Jiang works mostly in the field of Computer network, limiting it down to topics relating to Cloud computing and, in certain cases, Backup, as a part of the same area of interest.
He most often published in these fields:
- Distributed computing (30.57%)
- Parallel computing (19.43%)
- Operating system (18.57%)
What were the highlights of his more recent work (between 2018-2021)?
- Data deduplication (12.29%)
- Operating system (18.57%)
- Cloud computing (12.00%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Data deduplication, Operating system, Cloud computing, Parallel computing and Server.
His Data deduplication study combines topics in areas such as Distributed database, Backup, Embedded system, Redundancy and Big data.
His work in the fields of Operating system, such as ext4 and Scheduling, intersects with other areas such as Buffer.
His studies deal with areas such as Computer network and Information privacy as well as Cloud computing.
His Computer network research focuses on Scalability and how it relates to Distributed computing and Shared resource.
His work on Cache as part of general Parallel computing research is often related to Interrupt, thus linking different fields of science.
Between 2018 and 2021, his most popular works were:
- Using High-Bandwidth Networks Efficiently for Fast Graph Computation (25 citations)
- Public Auditing for Shared Cloud Data with Efficient and Secure Group Management (22 citations)
- Application-Aware Big Data Deduplication in Cloud Environment (12 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Computer network
- Microsoft Windows
Hong Jiang mainly focuses on Cloud computing, Parallel computing, Server, Data deduplication and Scalability.
His Cloud computing study incorporates themes from Computer security, Information privacy, Scheduling and Latency.
The study incorporates disciplines such as Distributed memory, Distributed computing, Graph partition, Aggregate and Bandwidth in addition to Server.
He undertakes interdisciplinary study in the fields of Distributed computing and Time series database through his research.
His study in Data deduplication is interdisciplinary in nature, drawing from both Cache, Distributed database and Big data.
His research investigates the link between Scalability and topics such as Computer network that cross with problems in Computation, Vertex and Graph.
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