Streamr Network 1.0: Successful Scaling Tests Validate Trackerless Architecture
Tuesday, August 19, 2025 12:00 AM
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In early 2025, extensive scaling tests were conducted on the new trackerless Streamr Network 1.0 to evaluate its performance and scalability. The tests involved deploying thousands of nodes across 17 global regions, utilizing data centers to simulate a live network environment. The experiments focused on validating the three-layer trackerless architecture, particularly the locality awareness in Layer 2, and measuring the network’s performance under load. Key metrics analyzed included join times, routing delays, message propagation delays, and time to data, all crucial for the network’s functionality.
The experiment setup involved running tests with up to 2,000 nodes, coordinated by a centralized script that tracked progress and issued instructions. Initial latency measurements established a baseline, revealing a mean one-way delay of 75 ms between AWS regions. The results indicated that join times for nodes joining the Layer 0 Distributed Hash Table (DHT) were efficient, with most nodes able to start participating in higher layers shortly after establishing a single DHT connection. Routing delays and hops were also measured, showing that the mean hop count grew logarithmically with the number of nodes, while the average round-trip time (RTT) remained around 360 ms at 2,000 nodes, confirming the efficiency of Layer 0 routing.
The tests further evaluated stream message propagation delays and time to data, with results indicating that the mean message propagation delay was lower for larger streams due to effective locality-aware clustering. The mean time to data consistently averaged around 1 second, significantly below the 2-second target. Overall, the new trackerless Streamr Network demonstrated stable performance, efficient routing, and low latency growth, confirming its capability to scale effectively while maintaining high data availability and performance standards. This success validates the architecture’s design and its potential for future applications in decentralized data streaming.
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