What is he best known for?
The fields of study he is best known for:
- Computer network
- The Internet
- Operating system
Zongpeng Li spends much of his time researching Distributed computing, Computer network, Cloud computing, Linear network coding and Provisioning.
His Distributed computing research includes elements of Overlay network, Competitive analysis, Optimization problem, Server and Online algorithm.
The concepts of his Cloud computing study are interwoven with issues in Scalability, Algorithm design, Schedule, Key and Big data.
His Linear network coding study combines topics from a wide range of disciplines, such as Wireless ad hoc network and Multicast.
The Provisioning study combines topics in areas such as Service, Profit, Network service, Job scheduler and Virtual network.
His Service research integrates issues from World Wide Web and Web 2.0.
His most cited work include:
- Youtube traffic characterization: a view from the edge (778 citations)
- A Cross-Layer Optimization Framework for Multihop Multicast in Wireless Mesh Networks (181 citations)
- Dynamic resource provisioning in cloud computing: A randomized auction approach (154 citations)
What are the main themes of his work throughout his whole career to date?
Zongpeng Li mostly deals with Distributed computing, Computer network, Cloud computing, Linear network coding and Multicast.
His research on Distributed computing also deals with topics like
- Online algorithm that intertwine with fields like Competitive analysis,
- Approximation algorithm together with Mechanism design.
His studies in Computer network integrate themes in fields like Wireless, Wireless ad hoc network, Throughput, Wireless network and Scalability.
The various areas that Zongpeng Li examines in his Cloud computing study include Virtual machine, Resource allocation, Provisioning, Server and Operations research.
His work in Linear network coding addresses subjects such as Unicast, which are connected to disciplines such as Routing.
His Multicast research is multidisciplinary, relying on both Network topology, Bounded function and Combinatorics.
He most often published in these fields:
- Distributed computing (39.69%)
- Computer network (39.30%)
- Cloud computing (25.29%)
What were the highlights of his more recent work (between 2018-2021)?
- Distributed computing (39.69%)
- Cloud computing (25.29%)
- Server (13.23%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Distributed computing, Cloud computing, Server, Scheduling and Online algorithm.
His work in the fields of Distributed computing, such as Distributed algorithm, intersects with other areas such as Load management.
His Cloud computing research incorporates elements of Payment, Provisioning and Approximation algorithm.
His Server research is included under the broader classification of Computer network.
His work on Encryption and Homomorphic encryption as part of general Computer network study is frequently connected to Correlation attack, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His studies deal with areas such as Stochastic programming and Competitive analysis as well as Online algorithm.
Between 2018 and 2021, his most popular works were:
- Circular-Shift Linear Network Coding (12 citations)
- A Truthful and Efficient Incentive Mechanism for Demand Response in Green Datacenters (8 citations)
- Scaling Geo-Distributed Network Function Chains: A Prediction and Learning Framework (7 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- The Internet
- Operating system
His primary scientific interests are in Distributed computing, Cloud computing, Scheduling, Cyclic permutation and Circular shift.
Zongpeng Li interconnects Key, Online algorithm and Reinforcement learning in the investigation of issues within Distributed computing.
His Online algorithm study incorporates themes from Virtual machine, Container, Competitive analysis and Block.
His research in Cloud computing intersects with topics in Incentive, Payment, Approximation algorithm and Provisioning.
His study on Approximation algorithm is covered under Mathematical optimization.
His Scheduling study integrates concerns from other disciplines, such as Stochastic programming and Mobile device.
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