Rebalancer

Rebalancer is a resource-allocation library developed at Meta for assigning objects to containers under constraints and optimization goals. Its C++ core runs in one process with multiple threads; applications can use C++ or Python interfaces.^[2] Meta announced its open-source release on September 21, 2026, after more than nine years of internal use.^[1]

Modeling an assignment

The model describes what can be placed, where it can go, and the properties relevant to placement. The same task-to-server problem can express limits on individual servers and requirements across racks.^[3]

Dimensions can depend on both the object and its destination. Object groups need not be disjoint.^[3]

Goals and constraints

Reusable specifications, or specs, describe placement requirements and quantities to optimize. The reference catalog contains more than 25 specs. Some support both goals and constraints; others support only one.^[4]

A constraint's policy matters when the initial assignment is invalid.^[5]

Multiple goals can share a weighted sum or occupy ordered priority buckets. Buckets are compared lexicographically: a lower-priority goal can improve only when earlier buckets tie. A large weight alone does not establish strict priority.^[6]

Expression graph and solvers

Rebalancer translates specifications into a directed acyclic expression graph. The OSDI 2024 paper describes a representation that grows with objects plus bins, rather than the object-bin product of a direct mixed-integer formulation. The graph separates problem representation from the choice of solving algorithm.^[7]

The model can remain unchanged when switching between these families.^[2][8]

Local search is the default. Its configurable move types determine which neighboring assignments it evaluates. Time and applied-move limits provide stopping conditions; without configured stopping limits, it searches until reaching a local optimum. Discontinuous requirements can make incremental search get stuck.^[9]

Production use at Meta

Meta uses Rebalancer for database-shard placement, allocating servers to services, traffic routing, serverless-function locality, and balancing online machine-learning training across regions. Its September 2026 release post reported the following operating scale.^[1]

These figures describe Meta's workloads, not a guaranteed runtime for arbitrary models.^[1]

Research and evaluation

Neeraj Kumar and eight coauthors presented Rebalancer at OSDI 2024. In one Azure-data placement experiment involving 10,000 pods and 500 nodes, the paper reported 94.6% placement with an optimal MIP solver, 92.8% with SINGLE_GREEDY, and 93.2% with SINGLE_RANDOM. SINGLE_RANDOM ran faster but placed fewer pods than the MIP baseline in that experiment.^[7]

The paper's separate DCM comparison used published DCM measurements rather than running both systems on identical hardware, because of security and dependency restrictions.^[7]

Explorer and distribution

Rebalancer Explorer is a web interface for inspecting solver inputs, assignments, goals, constraints, and solution scores. Its Next.js frontend communicates through a JSON proxy with a C++ Thrift backend. Docker Compose can start these components with example problem bundles. The proxy URL and bearer token are configurable; the token is required unless proxy authentication has explicitly been disabled.^[10]

Rebalancer is distributed under Apache 2.0. Python installation uses pip install rebalancer; the repository also provides platform-specific native builds and packages. Building the C++ library from source does not itself create the Python package.^[2]

See also

• Meta AI

References

1. ^1 ^2 ^3Engineering at Meta. Open-Sourcing Rebalancer: A Generic, High-Performance Library for Solving Assignment Problems. September 21, 2026.
2. ^1 ^2 ^3Meta. Rebalancer repository and README. GitHub.
3. ^1 ^2Meta. Core Concepts. Rebalancer documentation.
4. ^Meta. Goals and Constraints. Rebalancer documentation.
5. ^Meta. Constraint policy. Rebalancer documentation.
6. ^Meta. Goal priorities. Rebalancer documentation.
7. ^1 ^2 ^3Kumar, Neeraj, et al. Optimizing Resource Allocation in Hyperscale Datacenters: Scalability, Usability, and Experiences. OSDI 2024, pp. 507-528.
8. ^Meta. Solver Overview. Rebalancer documentation.
9. ^Meta. Local Search Solver. Rebalancer documentation.
10. ^Meta. Rebalancer Explorer. Rebalancer documentation.