We’re open-sourcing Rebalancer , the assignment-problem solver that has been used to solve resource allocation problems throughout Meta for over nine years.

Rebalancer separates several related concerns: how to specify an assignment problem, how to store it efficiently in memory, how to solve it, and how to debug it.

This separation of concerns is crucial to Rebalancer’s usability, scalability, and extensibility.

For a more detailed technical exposition, see the accompanying paper , “ Optimizing Resource Allocation in Hyperscale Datacenters: Scalability, Usability, and Experiences ,” published at OSDI’24.

Given a set of objects and a set of bins, how do we assign objects to bins in a way that optimizes specific objectives while meeting certain constraints ?

This question arises at all layers of Meta’s infrastructure stack including in Hardware placement: racks (objects) need to be positioned in datacenters (bins) to optimize the spread of racks across electrical fault domains while honoring power and cooling limitations.

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