
The Red Wedding Problem: Write Spikes at the Edge and a Mitigation Strategy [pdf] - cmeiklejohn
http://christophermeiklejohn.com/publications/hotedge-2018-preprint.pdf
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peterwwillis
Another way to recap the paper is this: run small clusters at your PoPs,
aggregate the results of those clusters and replicate back to an upstream
cluster with eventual consistency. The PoP clusters throttle down their
replication under high load. They also scale small clusters of PoPs at
specific times, which saves them money at the same time as dealing with
traffic spikes.

When you have a flood of writes, sometimes those writes are identical or
nearly identical, or the data in each write barely changes. It makes no sense
to flood those writes upstream, because since it's barely changing, you may
not need it that urgently. Throttling lets you simply move the changes back
with eventual consistency.

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zzzcpan
Not just aggregating writes, but merging them.

It seems that high load high scale solutions [1] [2] start to converge on this
idea of merging updates and eventual consistency.

[1]
[https://arxiv.org/pdf/1708.06423.pdf](https://arxiv.org/pdf/1708.06423.pdf)

[2] [https://databeta.wordpress.com/2018/03/09/anna-
kvs/](https://databeta.wordpress.com/2018/03/09/anna-kvs/)

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rkachowski
For people like me -

The Red Wedding Problem: A huge spike in read / write traffic. Exemplified in
the paper as users viewing and editing the Game of Thrones wiki in the hours
before, during and after an episode (Also gives realtime sports commentary on
reddit as an example)

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evrydayhustling
So... Red Wedding problems are left behind by Bolt-on solutions that address
only only read-heavy spikes?

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tuna
Complex and great read, thanks. I suggest following @cmeik on twitter for good
content: [https://twitter.com/cmeik](https://twitter.com/cmeik)

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keyle
Solid write up. Btw if you wonder what 'at the edge' means, it's basically
what I knew as CDN so far.

~~~
godelmachine
Wikipedia comes to the rescue →
[https://en.wikipedia.org/wiki/Edge_computing](https://en.wikipedia.org/wiki/Edge_computing)

Edge computing is a method of optimizing cloud computing systems by performing
data processing at the edge of the network, near the source of the data. This
reduces the communications bandwidth needed between sensors and the central
data center by performing analytics and knowledge generation at or near the
source of the data.

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heathermiller
[https://www.youtube.com/watch?v=PJwt2dxx9yg](https://www.youtube.com/watch?v=PJwt2dxx9yg)

