Celer Network Review: Bring Internet Scale to Every Blockchain
Original article by EVALUAPE
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A. Introduction
An off-chain transaction system, which supports 1 billion of transactions per second. and brings the scalability of the Internet to blockchain.
B. Industry
Solving the challenge of scalability. Scalability is a big problem in blockchain currently. Related techniques include fragmentation, state channel, sidechain, etc. Once solved, the market space will be huge. The project proposes a solution of state channel. The current competing products mainly include lightning network (more inclined to simple payment) and the most similar is liquidity network.
C. Mode
Technical project. It mainly depends on whether it can solve or optimize the problem of the current state channel project. At present, the problems of the status channel mainly include: 1. One-way transmission channel; 2. Mortgage problem of channel tokens; 3. Topology problem of the relay; 4.Stability of multiple channels interworking. Celer network model adopts hierarchical modules and multi-hop relay to solve the above problems, and achieves high performance by improving the topology of the state channel network. On this basis, it also supports off-chain system and DAPP development. In the future, various types of dapp ecosystem can be developed under the chain.
D. Token economy model
In order to ensure the liquidity within the system, the project is divided into four participating parties, namely end users, liquidity fund providers, payment service providers, and state custodians. The capital flow is divided into three mechanisms, namely liquidity fund guarantee mechanism, liquidity fund auction mechanism, and off-chain status guarding network. They are used to provide status channels, liquidity funds in the status channels, and to ensure that the status channel is operating normally. The economic incentive program balances the interests of all parties and the overall needs of the system with reasonable design.
E. Technology
Celer proposes a generalized state channel model. The highlight lies in the design of the conditional state channel routing model, Croute (the team believes that the shortest routing algorithm can only be applied to stateless channel, so it can be solved by the local gradient algorithm), and the same set of independent architectures and hierarchies are established off the chain, which are divided into channel layer (solving problems on and off the chain), routing layer (solving problems of channel routing), and application layer (solving problems of upper-layer applications). The overall technical architecture is quite convincing.
F. Team
4 founding members:
Mo Dong, Ph.D. on distributed systems from UIUC, His research focuses on networking protocol design, distributed systems, formal verication and Game Theory. Dr. Dong led projects improving distributed network, and has won Internet Innovative Application Award. He has been teaching blockchain courses with over 7000 subscribers.
Dr. Junda Liu received his Ph.D. from UC Berkeley, and Master degree from Tsinghua University. He was the first to propose and develop DAG based routing to achieve nanosecond network recovery. Dr. Liu joined Google in 2011 and developed a dynamic datacenter topology. He holds 6 US patents and published numerous papers in top conferences.
Dr. Xiaozhou Li received his Ph.D. from Princeton University and is broadly interested in distributed systems, networking, storage, and data management research. He publishes at top venues and won the NSDI’18 best paper award for building a distributed coordination service
with multi-billion QPS. Xiaozhou specializes in developing scalable algorithms and protocols that achieve high performance at low cost, some of which have become core components of widely deployed systems. And he filed six U.S. patents.
Dr. Qingkai Liang received his Ph.D. degree from MIT in the field of distributed systems. He first-authored over 15 top-tier papers and invented 5 high-performance and highly-robust adversarial resistant routing algorithms.
Overall, the academic background of the entire team matches this project very well.
Consultants include Dr. Shousheng Zhang, Dr. Christos Kozyrakis from Stanford Computer Science and Electrical Engineering, and Dr. Alan Mishchenko, research scientist in the field of automated tools and formal verification at Berkeley.
Investors include top institutions such as Danhua Capital, FBG and Pantera.
G. Ecosystem
5917 followers on Twitter, 31814 followers on telegram, reddit technology discussion is hot.
H. Conclusion
Project advantages: 1) The second layer solving the problem of capacity expansion is in just need; 2) Excellent team, fitting well with the project; 3) The white paper has detailed and reasonable technical details; 4) Perfect economic model; 5) Top-level Capital support.
Project shortcomings: technical development is much more difficult.
Hype Score: High
Risk Score: Medium
Investment Score: High
Total Score:8.2
All information in this article is provided for reference only and does not constitute investment advice.
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