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Mihai Tiuca

Publications and source records attributed to Mihai Tiuca.

2 recordsLinked to original sources

Resource Allocation and Conversion along the Org Chart

We consider the allocation of multiple heterogeneous resources to agents who are organized according to an organizational hierarchy. In a company those correspond to business units, departments, and engineering teams. In government it corresponds to federal, state, and municipal levels as well as various agencies within each. In a university it corresponds to schools, departments, and research groups. The resources are also distributed along the same organizational tree: part of the supply is available only to certain sub-trees since it is purchased for the exclusive use of certain departments or units, and some supply is available to the entire tree. We consider the allocation with multiple types of resources where there is a possibility of converting between certain pairs of resources. We formulate this allocation problem as a market equilibrium problem and derive the necessary conditions to find a feasible solution. We prove that such a feasible solution always exists and provide an algorithm to compute it. Finally, we test our algorithm on a real dataset of the allocation of computing resources, such as TPUs and GPUs at Google. Our results rely on a novel two-step process, differentiating it from previous approaches. First, we show how to solve ``easy'' (harmonic) instances of our problem that satisfy certain structural properties. Then, we show how to efficiently approximate general instances by a series of easier harmonic instances, whose solutions converge efficiently to the solution of the original problem.

cs.GT

Quota Marketplace: Dynamic Pricing for Efficient Allocation of ML Training Resources

The escalating demand for Machine Learning (ML) training resources in recent years has resulted in a substantial gap between the high demand and the available supply. Efficient allocation of these scarce and expensive resources is crucial for organizations to maximize their return on investment. Existing resource allocation mechanisms, like Karma [OSDI'23], are designed to guarantee Pareto efficiency and max-min fairness in settings with dynamic (time-varying) user demands, but fail to preserve these key properties in the presence of demands with heterogeneous values. Given the ubiquity and inevitability of heterogeneity in organizational values of different workloads, effective resource allocation policies must accommodate these variations. In this paper, we describe the design, implementation, deployment, and theoretical analysis of Quota Marketplace, a market-based mechanism to efficiently allocate ML training chips (like GPUs), explicitly addressing scenarios with demands of heterogeneous value. We detail the implementation of this mechanism within Google and present metrics that demonstrate its impact. We also discuss many business-critical requirements that the Quota Marketplace handles quite effectively, and document the gains and opportunities it has unlocked. We establish theoretically how this market-based approach achieves the essential properties of Pareto efficiency and max-min fairness by allowing the users to express the value of their workloads and enabling dynamic resource pricing based on supply and demand fluctuations. Ultimately, the market facilitates resource allocation that aligns with organizational priorities.

cs.LG