jm + least-conns   2

Marc Brooker's "two-randoms" load balancing approach
Marc Brooker on this interesting load-balancing algorithm, including simulation results:
Using stale data for load balancing leads to a herd behavior, where requests will herd toward a previously quiet host for much longer than it takes to make that host very busy indeed. The next refresh of the cached load data will put the server high up the load list, and it will become quiet again. Then busy again as the next herd sees that it's quiet. Busy. Quiet. Busy. Quiet. And so on. One possible solution would be to give up on load balancing entirely, and just pick a host at random. Depending on the load factor, that can be a good approach. With many typical loads, though, picking a random host degrades latency and reduces throughput by wasting resources on servers which end up unlucky and quiet.

The approach taken by the studies surveyed by Mitzenmacher is to try two hosts, and pick the one with the least load. This can be done directly (by querying the hosts) but also works surprisingly well on cached load data. [...] Best of 2 is good because it combines the best of both worlds: it uses real information about load to pick a host (unlike random), but rejects herd behavior much more strongly than the other two approaches.


Having seen what Marc has worked on, and written, inside Amazon, I'd take this very seriously... cool to see he is blogging externally too.
algorithm  load-balancing  distcomp  distributed  two-randoms  marc-brooker  least-conns 
february 2013 by jm
Timelike 2: everything fails all the time
Fantastic post on large-scale distributed load balancing strategies from @aphyr. Random and least-conns routing comes out on top in his simulation (although he hasn't yet tried Marc Brooker's two-randoms routing strategy)
via:hn  routing  distributed  least-conns  load-balancing  round-robin  distcomp  networking  scaling 
february 2013 by jm

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