Linear programming for fun and profit

Modal uses a linear-programming-based resource solver to shield customers from volatile GPU markets while providing rapid, predictable access to large-scale compute capacity. The system evaluates real-time demand alongside cloud providers’ prices, availability, hardware performance, regional constraints, and CPU and memory requirements, then determines which instances to acquire or release at the lowest feasible cost. Unlike a simple price-based autoscaler, it must preserve spare capacity so containers can start in seconds despite cloud servers taking minutes to provision, while also handling users’ varied GPU preferences and rapidly changing workloads. Modal relies on Google’s GLOP solver, based on the simplex algorithm, and supplements it with heuristics that prune unnecessary instance options, prevent infeasible optimization problems, and keep decisions fast enough for production use. Background workers enact the solver’s recommendations and feed failed provisioning attempts back into future calculations as updated provider capacity limits, helping the platform exploit temporary price differences and maintain scalable service.

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