Metascheduling of HPC Jobs in Day-Ahead Electricity Markets

TL;DR

This paper introduces a metascheduling algorithm for HPC jobs that optimizes electricity costs by leveraging day-ahead market prices, using predictions and flow optimization, demonstrated on real-world grids with significant savings.

Contribution

It presents a novel metascheduling approach formulated as a Minimum Cost Maximum Flow problem, integrating electricity price predictions for cost-effective HPC job placement.

Findings

Reduces electricity costs significantly in HPC grids.

Maintains low response times and fairness among users.

Demonstrates effectiveness on real-world, large-scale grids.

Abstract

High performance grid computing is a key enabler of large scale collaborative computational science. With the promise of exascale computing, high performance grid systems are expected to incur electricity bills that grow super-linearly over time. In order to achieve cost effectiveness in these systems, it is essential for the scheduling algorithms to exploit electricity price variations, both in space and time, that are prevalent in the dynamic electricity price markets. In this paper, we present a metascheduling algorithm to optimize the placement of jobs in a compute grid which consumes electricity from the day-ahead wholesale market. We formulate the scheduling problem as a Minimum Cost Maximum Flow problem and leverage queue waiting time and electricity price predictions to accurately estimate the cost of job execution at a system. Using trace based simulation with real and synthetic workload traces, and real electricity price data sets, we demonstrate our approach on two currently operational grids, XSEDE and NorduGrid. Our experimental setup collectively constitute more than 433K processors spread across 58 compute systems in 17 geographically distributed locations. Experiments show that our approach simultaneously optimizes the total electricity cost and the average response time of the grid, without being unfair to users of the local batch systems.