Avail Report Cal. Week 46 - 2024 #

General Information #

The following results show measurement data that were collected in calendar week 46 in 2024 from 2024-11-11 to 2024-11-18.

• Number of crawls: 336

  The number of crawls executed during the week indicated above.

• Number of visits: 2,305,526

  Every attempt to dial or connect to a peer counts as a visit. Regardless of errors that may occur. We visit peers many times during a given week to extract information from them or probe for their uptime. We visit all peers that we discover in the DHT and peers that we found to be still online from the previous week.

• Number of unique peer IDs discovered in the DHT: 5,224

  The total number of unique peers that our crawler learned about from other peers over the period of the given week and attempted to dial or connect to (visit). This is not the total number of Peer IDs that existed simultaneously in the network at any point during the week. It is the aggregate over the entire week.

• Number of unique IP addresses discovered: 2,698

  The total number of unique IP addresses that the discovered peers advertised to the DHT network within the given week. IPv4 and IPv6 addresses are included in this number even if they address the same peer. This is not the total number of unique IP addresses that participated in the DHT simultaneously at any given point in time. It is the aggregate over the entire week.

• Network Size: 4,909.7708333333333333

  The average number of peers that were found simultaneously (i.e., throughout the duration of one crawl) connected to the network. This number signifies the average network size from all crawls, during the given week.

Timestamps are in UTC if not mentioned otherwise.

Agents versions #

This stacked plot shows the distribution of agent versions over time, it offers insights into the uptake of newer versions.

This chart presents the version distribution of avail nodes, with each bar representing the average number of online peers over the course of a week.

Churn Analysis #

This plot presents the Cumulative Distribution Function (CDF) of peer departure times from the network over a measurement period of one-week. The plot basically shows the percentage of nodes online at a point in time (t=0) that will have disconnected after 1 hour, 2 hours, 3 hours, .. up to 24 hours after t=0.

• X-Axis (Time in Hours): Represents the elapsed time since the reference point (t=0), measured in hours.
• Y-Axis (Percentage of Offline Peers): Indicates the cumulative percentage of peers that have disconnected from the network by the corresponding time interval. Specifically, it shows the proportion of peers that were online at t=0 and have since gone offline.

This visualization aids in understanding peer churn dynamics, which is crucial for optimizing network stability, resource allocation, and improving overall decentralized network performance.

Note that the sum of the CDF is NOT 100%, as it only includes peers that were online for up to 24h.

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 Peer Records #

This stacked plot depicts the count of peer records stored within each node’s routing table and made accessible through the DHT. These peer records are used to discover new remote peers within the network, enabling efficient and secure routing towards the target peer or content

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

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