可用性数据

发布于 2024-11-16 13:19:22 字数 115 浏览 2 评论 0原文

我正在为大型 P2P 系统创建一个模拟器。为了使模拟尽可能好,我想使用来自现实世界的数据。我想使用这些数据来模拟每个节点的行为(主要是可用性)。是否有从大型 P2P 系统(例如 BitTorrent)记录的可用数据?

I'm creating a simulator for a large scale P2P-system. In order to make the simulations as good as possible I would like to use data from the real world. I'd like to use this data to simulate each node's behavior (primarily it's availability). Is there any availability-data that has been recorded from large P2P-systems (such as BitTorrent) available?

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檐上三寸雪 2024-11-23 13:19:22

我对其他 P2P 协议不太确定,但这里是回答 BitTorrent 问题的一个尝试:

您应该能够从 BitTorrent 跟踪器 日志,在跟踪器是集中式的情况下(与分散式跟踪器相反,或者使用分散式哈希表的情况)。

要了解日志,请查看众多日志分析器之一,例如 BitTorrent Tracker 日志分析器

至于实际数据,你可以在网上找到它们。例如,此处 ☆ 有一个巨大的 RedHat9 跟踪器日志。我会在谷歌上搜索“bittorrent tracker log”。

☆ 该页面上的文章剖析 BitTorrent:Torrent 生命周期的五个月看起来也很有趣。

I'm not too sure about other P2P protocols, but here's a stab at answering the question for BitTorrent:

You should be able to glean some stats from a BitTorrent tracker log, in the case where the tracker was centralised (as opposed to decentralised tracker, or where a decentralised hash table is used).

To wrap your head around the logs, have a look at one of the many log analyzers, like BitTorrent Tracker Log Analyzer.

As for actual data, you can find them all over the web. There's a giant RedHat9 tracker log here ☆, for instance. I'd search Google for "bittorrent tracker log".

☆ The article Dissecting BitTorrent: Five Months in a Torrent's Lifetime on that page also looks interesting.

怎言笑 2024-11-23 13:19:22

解决这个问题的另一种方法是用数学方法模拟可用性。可用性将遵循某种 powerlaw 分布,例如,绝大多数节点很少且持续时间很短时间,并且很少有节点几乎总是在很长一段时间内可用。

当然,现实世界的网络在数据中会有许多其他类型的模式,所以这不是一个完美的模拟,但我认为它非常好。

Another way of appropaching this is to simulate availability mathematically. Availability will follow some powerlaw distribution, e.g. the vast majority of nodes are available very rarely and for short periods of time, and a very few nodes are available nearly always over long periods.

Real world networks will of course have many other types of patterns in the data so this is not a perfect simulation, but I figure it's pretty good.

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