What is he best known for?
The fields of study he is best known for:
- Operating system
- Computer network
- The Internet
Ming Zhao focuses on Virtual machine, Virtualization, Operating system, Resource allocation and Server.
His study in Virtual machine is interdisciplinary in nature, drawing from both Virtual prototyping, Capacity planning, Real-time computing and Dynamic priority scheduling.
His Virtualization research includes themes of Iterative and incremental development and Distributed computing.
Ming Zhao has researched Operating system in several fields, including Layer and Key.
His biological study spans a wide range of topics, including Data modeling, Database, Quality of service, Memory management and Reservation.
The various areas that he examines in his Server study include Process, Provisioning and Data center.
His most cited work include:
- From virtualized resources to virtual computing grids: the In-VIGO system (215 citations)
- Experimental study of virtual machine migration in support of reservation of cluster resources (173 citations)
- On the Use of Fuzzy Modeling in Virtualized Data Center Management (168 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Operating system, Distributed computing, Virtual machine, Virtualization and Cloud computing.
His Distributed computing study integrates concerns from other disciplines, such as Node, Quality of service, Job scheduler and Network File System.
Ming Zhao combines subjects such as Overhead and Resource allocation with his study of Virtual machine.
The Resource allocation study combines topics in areas such as Database and Autonomic computing.
His Virtualization research incorporates themes from Utility computing, Provisioning and Latency.
His Cloud computing research integrates issues from Computer security and Deep learning, Artificial intelligence.
He most often published in these fields:
- Operating system (33.33%)
- Distributed computing (33.33%)
- Virtual machine (27.78%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (11.11%)
- Operating system (33.33%)
- Metadata (5.56%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Operating system, Metadata, Computer data storage and Cache.
His work is connected to Memory footprint, Flash memory and Virtual machine, as a part of Operating system.
His Metadata research incorporates elements of File system and Server.
His research integrates issues of Temporal isolation among virtual machines, Virtualization, Bandwidth allocation, I/O scheduling and Scheduling in his study of Server.
His Workflow research is multidisciplinary, relying on both Edge computing and Distributed computing.
His studies deal with areas such as Data deduplication and Enhanced Data Rates for GSM Evolution as well as Distributed computing.
Between 2018 and 2021, his most popular works were:
- Exploring the Use of Synthetic Gradients for Distributed Deep Learning across Cloud and Edge Resources (7 citations)
- GFCache: a Greedy Failure Cache Considering Failure Recency and Failure Frequency for an Erasure-coded Storage System (5 citations)
- SmartDedup: Optimizing deduplication for resource-constrained devices (4 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Computer network
- The Internet
His primary areas of investigation include Data deduplication, Deep learning, Artificial intelligence, Resource constrained and Distributed computing.
His work deals with themes such as Mode, Hash function, Embedded system and Encryption, which intersect with Data deduplication.
His Deep learning study combines topics in areas such as Enhanced Data Rates for GSM Evolution, Human–computer interaction, Computational science, Hardware acceleration and Cloud computing.
In general Artificial intelligence study, his work on Edge computing and Artificial neural network often relates to the realm of Personalization, thereby connecting several areas of interest.
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