Energy hardware and workload aware job scheduling towards interconnected HPC environments

TL;DR

This paper introduces EAMC, a job scheduling policy for heterogeneous multi-cluster HPC environments that optimizes energy consumption, response time, and makespan by predicting performance and energy use for different hardware configurations.

Contribution

The paper presents a novel energy-aware multi-cluster job scheduling policy (EAMC) that predicts performance and energy consumption to optimize resource allocation in heterogeneous HPC systems.

Findings

Up to 25% reduction in response time.

Up to 20% energy savings.

Improved cluster utilization and workload efficiency.

Abstract

New HPC machines are getting close to the exascale. Power consumption for those machines has been increasing, and researchers are studying ways to reduce it. A second trend is HPC machines' growing complexity, with increasing heterogeneous hardware components and different clusters architectures cooperating in the same machine. We refer to these environments with the term heterogeneous multi-cluster environments. With the aim of optimizing performance and energy consumption in these environments, this paper proposes an Energy-Aware-Multi-Cluster (EAMC) job scheduling policy. EAMC-policy is able to optimize the scheduling and placement of jobs by predicting performance and energy consumption of arriving jobs for different hardware architectures and processor frequencies, reducing workload's energy consumption, makespan, and response time. The policy assigns a different priority to each job-resource combination so that the most efficient ones are favored, while less efficient ones are still considered on a variable degree, reducing response time and increasing cluster utilization. We implemented EAMC-policy in Slurm, and we evaluated a scenario in which two CPU clusters collaborate in the same machine. Simulations of workloads running applications modeled from real-world show a reduction of response time and makespan by up to 25% and 6% while saving up to 20% of total energy consumed when compared to policies minimizing runtime, and by 49%, 26%, and 6% compared to policies minimizing energy.