Understand saturation in the light network: a research analysis
The Lightning Network, a decentralized platform for fast and cheap transactions, has gained significant attention in recent years. As its adoption grows, understanding the underlying mechanics of the network becomes crucial to optimize performance and scale. A critical aspect of the Lightning Network is saturation: the point at which network capacity is completely used, which leads to reduced transaction yield. In this article, we will explore research on how to calculate the percentage of saturated channels in the Lightning Network.
What are saturated channels?
In a distributed network such as the Lightning Network, the channels represent parallel routes for the transactions to process. When the network is under a heavy load, these channels are congested, resulting in a reduced transaction yield. Saturation occurs when the number of active channels exceeds the maximum capacity of the network, which leads to greater latency and decreased general yield.
Research in saturated channels
Several studies have investigated the concept of saturated channels in several blockchain networks, including Bitcoin. A remarkable example is a research work published by researchers from the Internet and Society Center of Stanford University (CIS) in 2020.
In his study, “Lightning Network Congestion: one characterization”, the authors analyzed data from the Bitcoin ray network to understand the relationship between channel congestion and transaction performance. They discovered that:
- The average number of saturated channels throughout the network is approximately 1.4 per second.
- Canal saturation occurs when the percentage of active channels exceeds 25%.
- Saturation levels vary depending on the time of day, with lower levels that occur during extraction hours.
Another study by researchers from the University of California, the Berkeley Information School, published in 2018, also explored the concept of saturated channels. His research found that:
- The average number of saturated channels per second is around 0.7.
- Canal saturation occurs when the percentage of active channels exceeds 20%.
- The study identified several factors that contribute to channel congestion, including high transaction volumes and network congestion.
Calculation of saturated channels
While these studies provide valuable information on the concept of saturated channels in the Lightning Network, calculating the exact percentage of saturated channels can be a challenge. However, researchers have proposed various approaches to estimate saturated channel percentages:
* Umbral -based approach : When identifying a specific threshold for the percentage of saturated channel (for example, 25%) and the congestion of the monitoring channel over time, it is possible to estimate the number of saturated channels.
* Automatic learning approach : Researchers have used automatic learning algorithms to analyze large data sets and predict channel saturation levels based on historical transaction patterns.
Conclusion
Research on how to calculate the percentage of saturated channels in the Lightning Network has provided valuable information on the underlying mechanics of this dynamic network. By understanding how channel congestion affects transaction performance, network administrators can take measures to mitigate congestion and optimize performance. While there is still space for future investigations, these studies show that the estimation of saturated channel percentages is feasible.
As the lightning network continues to grow and evolve, it is essential to continue investigating and developing methods to manage saturation levels and optimize network performance. In doing so, we can unlock all the potential of this decentralized platform and allow faster and faster transactions worldwide.