Agent-based Constraint Solving for Resource Allocation in Manycore Systems

TL;DR

This paper introduces an agent-based decentralized method using DCOPs and a local search algorithm to efficiently solve complex resource allocation constraints in heterogeneous manycore systems, demonstrating improved constraint support and scalability.

Contribution

It presents a novel DCOP-based approach with the RESMGM algorithm for resource allocation, supporting more constraints and scalable solutions in heterogeneous manycore systems.

Findings

Demonstrates the viability of DCOP for resource allocation in manycore systems

RESMGM supports a wider range of constraints than existing methods

Achieves superior results with comparable overheads

Abstract

For efficiency reasons, manycore systems are increasingly heterogeneous, which makes the mapping of complex workloads a key problem with a high optimization potential. Constraints express the application requirements like which core type to choose, how many cores to choose, exclusively or non-exclusively, using a certain core, etc. In this work, we propose a decentralized solution for solving application resource constraints by means of an agent-based approach in order to obtain scalability. We translate the constraints into a Distributed Constraint Optimization Problem (DCOP) and propose a local search algorithm RESMGM to solve them. For the first time, we demonstrate the viability and efficiency of the DCOP approach for heterogeneous manycore systems. Our RESMGM algorithm supports a far wider range of constraints than state-of-the-art, leading to superior results, but still has comparable overheads w.r.t. computation and communication.