What is he best known for?
The fields of study he is best known for:
- Operating system
- Computer network
- The Internet
Li Zhang spends much of his time researching Distributed computing, Server, Computer network, Scheduling and Virtual machine.
His Distributed computing research is multidisciplinary, relying on both Scalability, Workload, Hill climbing, Fixed-priority pre-emptive scheduling and Cloud computing.
His Server study combines topics in areas such as Service provider, Real-time computing, Kernel virtual address space and Data diffusion machine.
His Computer network research is multidisciplinary, incorporating perspectives in The Internet, Service and Autonomic computing.
His studies deal with areas such as Mathematical optimization, Queue, Locality and Resource allocation as well as Scheduling.
His Virtual machine study integrates concerns from other disciplines, such as Resource, Multiplexing, Provisioning and Virtualization.
His most cited work include:
- Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement (906 citations)
- Efficient resource provisioning in compute clouds via VM multiplexing (352 citations)
- Consolidating virtual machines with dynamic bandwidth demand in data centers (295 citations)
What are the main themes of his work throughout his whole career to date?
Li Zhang mainly focuses on Distributed computing, Server, Computer network, Real-time computing and Workload.
His Distributed computing research incorporates themes from Scalability, Resource allocation, Parallel computing, Scheduling and Cloud computing.
His research integrates issues of Queue, Locality and Queueing theory in his study of Scheduling.
The Cloud computing study combines topics in areas such as Provisioning and Service.
His study on Server is mostly dedicated to connecting different topics, such as Virtual machine.
His Computer network study incorporates themes from Service provider and The Internet.
He most often published in these fields:
- Distributed computing (27.84%)
- Server (14.29%)
- Computer network (13.55%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (8.06%)
- Deep learning (3.66%)
- Lithium (2.56%)
In recent papers he was focusing on the following fields of study:
Li Zhang mostly deals with Artificial intelligence, Deep learning, Lithium, Electrolyte and Artificial neural network.
His Artificial intelligence study combines topics in areas such as Machine learning, Bandwidth and Server.
Li Zhang performs integrative Server and Training system research in his work.
His research in Deep learning tackles topics such as Cloud computing which are related to areas like Provisioning, Service, Workload and Distributed computing.
Li Zhang has included themes like Metadata, Aggregate, Memory management, File server and Cost reduction in his Distributed computing study.
His studies deal with areas such as Controller, Layer, Computer network and Visualization as well as Artificial neural network.
Between 2016 and 2021, his most popular works were:
- CAN: Constrained Attention Networks for Multi-Aspect Sentiment Analysis (21 citations)
- SparkBench : a spark benchmarking suite characterizing large-scale in-memory data analytics (20 citations)
- IBM deep learning service (16 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Computer network
- Artificial intelligence
His main research concerns Artificial intelligence, Deep learning, Distributed computing, Cloud computing and Workload.
His Deep learning research includes elements of Artificial neural network, Bandwidth and Server.
His Server research incorporates themes from Embedded system and Computer engineering.
His work in Distributed computing addresses issues such as Scalability, which are connected to fields such as Data analysis, Benchmark and Resource.
His Cloud computing research includes themes of Benchmarking, Provisioning and Service.
His Workload study integrates concerns from other disciplines, such as Kalman filter, Profiling, Elasticity, User space and Robustness.
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