Avail Report Cal. Week 35 - 2024 #

General Information #

The following results show measurement data that were collected in calendar week 35 in 2024 from 2024-08-26 to 2024-09-02.

• Number of crawls 336
• Number of visits 5,494,861

  Visiting a peer means dialing or connecting to it. Every time the crawler or monitoring process tries to dial or connect to a peer we consider this as visiting it. Regardless of errors that may occur.

• Number of unique peer IDs visited 12,022
• Number of unique peer IDs discovered in the DHT 10,508
• Number of unique IP addresses found 6,997

Timestamps are in UTC if not mentioned otherwise.

Churn Analysis #

This visualizes the uptime of peers over a specific period of time. The plot may display trends in churn rate, distribution of uptime periods, or patterns in peer activity. It helps analyze the stability and reliability of the network by identifying fluctuations in peer participation and examining potential factors influencing churn.

Geolocation #

Geographical data is sourced from the MaxMind database, which maps IP addresses to corresponding countries.

This bar plot illustrates the distribution of observed nodes across different countries.

This plot displays the weekly geographical distribution of nodes, categorized by country.

Cloud Providers #

Cloud providers data is sourced from Udger, which maps IP addresses to known hosting providers.

Cloud Hosting Rate #

This line chart displays the count of nodes within the Avail network that are hosted on known commercial cloud providers, compared to those that are not. It tracks the distribution over a specified period, offering insights into the infrastructure preferences for node hosting.

Regular analysis of this chart can reveal trends in the adoption of cloud services for nodes. Such information is crucial for understanding the network’s resilience and the potential reliance on cloud infrastructure.

This bar chart presents the weekly distribution of Avail nodes among various cloud providers, including nodes not hosted within data centers (grouped under Non-Datacenter).

The line chart illustrates the trends in the distribution of all Avail nodes across cloud providers over the given time period. Note that nodes hosted outside of data centers are not included in this representation.

Crawls #

Protocols #

This plot illustrates the evolving count of nodes supporting each of the listed protocols over time. It exclusively presents data gathered from nodes accessible through a libp2p connection via our crawler. The identification of supported protocols relies on the libp2p identify protocol, hence necessitating a libp2p connection for discovery.

Errors #

Keyspace Density Monitoring #

💡 The latest keyspace density data is available here.

In Kademlia, every object indexed by the DHT requires a binary identifier. In the libp2p DHT implementation, peers are identified by the digest of sha256(peer_id) and CIDs are identified by the digest of sha256(cid). This Kademlia identifier determines the location of an object within the Kademlia XOR keyspace.

The following plots examine the peer distribution within the keyspace, aiding in the identification of potential Sybil and eclipse attacks.

Keyspace population distribution #

Description: The plot illustrates the number of peers whose Kademlia identifier matches each prefix for all prefixes of a given size, for a given network crawl. Since the density of keyspace regions follows a Poisson distribution, it is expected to observe some regions that are significantly more populated than others.

How to read the plot: The selected depth indicates the prefix size. There are 2^i distinct prefixes at depth i. The slider at the bottom of the plot enables visualization of the population evolution over time across multiple crawls.

What to look out for: The red dashed line represents the expected density per region, corresponding to the number of peers matching a prefix. A bar exceeding the expected density by more than twice suggests that a region of the keyspace might be under an eclipse attack.

Keyspace density distribution #

Description: As previously mentioned, the keyspace population follows a Poisson distribution, which can make identifying outliers challenging. The plot below counts the number of regions for each population size and facilitates the identification and analysis of outliers. While it is normal for some regions to have populations above the average, the plot enables us to quantify these deviations.

How to read the plot: The red dashed line represents the expected number of regions for each population size. Note that the Poisson distribution is more evident at greater depths (longer prefix size), while analyzing data at lower depths provides limited insights. It is recommended to read the plot for depths between 6 and 8.

What to look out for: If a bar significantly exceeds its expected value on the right side of the plot, or if an isolated bar appears on the far right, it may indicate a potential eclipse attack, warranting further investigation.

Stale Node Records #

All Peers #

This stacked plot depicts the count of node records stored within each node’s routing table and made accessible through the DHT. These node records serve as a mechanism through which nodes discover new remote nodes in the network.

Ensuring the reachability of referenced nodes shared within the network holds paramount importance. The plot delineates the occurrences of reachable and non-reachable (stale) node records. Note that nodes running behind a NAT are counted as unreachable even though they may be online.

For instance, if a node’s record is present in the routing tables of 100 other nodes and the node is reachable, the plot will reflect an increase of 100 in the online category.