Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP

TL;DR

This paper introduces POP, a novel partitioned optimization approach for large-scale granular resource allocation problems, achieving near-optimal solutions efficiently by dividing and conquering the problem space.

Contribution

POP is a new method that reuses the original optimization formulation by randomly partitioning the problem, leading to better solutions than heuristics for large-scale systems.

Findings

POP achieves allocations within 1.5% of optimal

POP significantly reduces runtime compared to existing methods

Effective for cluster scheduling, traffic engineering, and load balancing

Abstract

Resource allocation problems in many computer systems can be formulated as mathematical optimization problems. However, finding exact solutions to these problems using off-the-shelf solvers is often intractable for large problem sizes with tight SLAs, leading system designers to rely on cheap, heuristic algorithms. We observe, however, that many allocation problems are granular: they consist of a large number of clients and resources, each client requests a small fraction of the total number of resources, and clients can interchangeably use different resources. For these problems, we propose an alternative approach that reuses the original optimization problem formulation and leads to better allocations than domain-specific heuristics. Our technique, Partitioned Optimization Problems (POP), randomly splits the problem into smaller problems (with a subset of the clients and resources in the system) and coalesces the resulting sub-allocations into a global allocation for all clients. We provide theoretical and empirical evidence as to why random partitioning works well. In our experiments, POP achieves allocations within 1.5% of the optimal with orders-of-magnitude improvements in runtime compared to existing systems for cluster scheduling, traffic engineering, and load balancing.