What is he best known for?
The fields of study he is best known for:
- Computer network
- Operating system
- The Internet
His primary areas of study are Computer network, Distributed computing, Cloud computing, Quality of service and Routing protocol.
Haiying Shen has included themes like File system fragmentation, Self-certifying File System and Scalability in his Computer network study.
His study in Scalability is interdisciplinary in nature, drawing from both Bandwidth, Cluster analysis and Social network.
Haiying Shen combines subjects such as Node and Testbed with his study of Distributed computing.
His work deals with themes such as Virtual machine, Scheduling, Real-time computing and Data management, which intersect with Cloud computing.
The Quality of service study combines topics in areas such as PlanetLab, Information retrieval and Knowledge extraction.
His most cited work include:
- A Review of Communication, Driver Characteristics, and Controls Aspects of Cooperative Adaptive Cruise Control (CACC) (178 citations)
- Game-Theoretic Analysis of Cooperation Incentive Strategies in Mobile Ad Hoc Networks (139 citations)
- Load Rebalancing for Distributed File Systems in Clouds (117 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Computer network, Distributed computing, Cloud computing, Server and Scalability.
Computer network is closely attributed to File sharing in his study.
His File sharing research is multidisciplinary, incorporating perspectives in Computer security, Self-certifying File System and Social network.
His Distributed computing research is multidisciplinary, incorporating elements of Testbed, Resource and Resource allocation.
His biological study spans a wide range of topics, including Virtual machine, Scheduling and Bandwidth.
His study explores the link between Scalability and topics such as Overhead that cross with problems in Node.
He most often published in these fields:
- Computer network (53.22%)
- Distributed computing (39.22%)
- Cloud computing (20.45%)
What were the highlights of his more recent work (between 2018-2021)?
- Cloud computing (20.45%)
- Distributed computing (39.22%)
- Real-time computing (8.12%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Cloud computing, Distributed computing, Real-time computing, Reinforcement learning and Server.
His Cloud computing research includes themes of Virtual machine, Machine learning, Scalability and Artificial intelligence.
Haiying Shen does research in Distributed computing, focusing on Load balancing specifically.
His Server research integrates issues from Service-level agreement, Distributed database and Reservation.
Haiying Shen incorporates a variety of subjects into his writings, including Replica, Node and Computer network.
Haiying Shen studies Flooding, a branch of Computer network.
Between 2018 and 2021, his most popular works were:
- Stochastic Load Balancing for Virtual Resource Management in Datacenters (68 citations)
- Planning and Operation of Parking Lots Considering System, Traffic, and Drivers Behavioral Model (28 citations)
- Price-Controlled Energy Management of Smart Homes for Maximizing Profit of a GENCO (22 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Computer network
- The Internet
Haiying Shen mainly investigates Cloud computing, Reliability, Distributed computing, Server and Virtual machine.
He interconnects Machine learning, Artificial intelligence and Backup in the investigation of issues within Cloud computing.
Haiying Shen performs multidisciplinary studies into Distributed computing and Job performance in his work.
His Server research incorporates themes from Cloud storage, Data loss and Distributed database.
His Virtual machine research incorporates elements of Quality of service, Cluster analysis, Elasticity, Load balancing and Provisioning.
His work in the fields of Network Load Balancing Services overlaps with other areas such as Load management.
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