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Posted to commits@helix.apache.org by "Kanak Biscuitwala (JIRA)" <ji...@apache.org> on 2013/08/01 22:57:51 UTC

[jira] [Updated] (HELIX-152) Extend auto rebalance to accept replicas with non-unit weights and constraints

     [ https://issues.apache.org/jira/browse/HELIX-152?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Kanak Biscuitwala updated HELIX-152:
------------------------------------

    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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