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https://issues.apache.org/jira/browse/HELIX-152?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Kanak Biscuitwala updated HELIX-152:
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Description:
The current auto rebalancing strategy assumes that each replica adds a
one-dimensional value of 1. It may make sense to make these costs weighted and
perhaps multi-dimensional. Arbitrary constraint specification is also useful.
This is similar to problems related to constraint satisfaction or knapsack
solving, and some existing implementations may be worth looking at. One
example: https://code.google.com/p/or-tools/
was:
The current auto rebalancing strategy assumes that each replica adds a
one-dimensional value of 1. It may make sense to make these costs weighted and
perhaps multi-dimensional.
This is similar to problems related to constraint satisfaction or knapsack
solving, and some existing implementations may be worth looking at. One
example: https://code.google.com/p/or-tools/
Summary: Extend auto rebalance to accept replicas with non-unit weights
and constraints (was: Extend auto rebalance to accept replicas with non-unit
weights)
> Extend auto rebalance to accept replicas with non-unit weights and constraints
> ------------------------------------------------------------------------------
>
> Key: HELIX-152
> URL: https://issues.apache.org/jira/browse/HELIX-152
> Project: Apache Helix
> Issue Type: Improvement
> Components: helix-core
> Reporter: Kanak Biscuitwala
> Priority: Minor
>
> The current auto rebalancing strategy assumes that each replica adds a
> one-dimensional value of 1. It may make sense to make these costs weighted
> and perhaps multi-dimensional. Arbitrary constraint specification is also
> useful.
> This is similar to problems related to constraint satisfaction or knapsack
> solving, and some existing implementations may be worth looking at. One
> example: https://code.google.com/p/or-tools/
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