Mage: Online Interference-Aware Scheduling in Multi-Scale Heterogeneous Systems

TL;DR

Mage is an interference-aware runtime system that optimizes application placement in multi-scale heterogeneous systems, significantly improving performance by minimizing destructive interference through online data mining and dynamic adjustments.

Contribution

Mage introduces a practical, online interference-aware scheduling approach that considers both intra- and inter-server heterogeneity for improved performance and efficiency.

Findings

38% performance improvement on heterogeneous CMPs

Up to 2x performance gain over greedy scheduler

11% improvement over combined Paragon approach

Abstract

Heterogeneity has grown in popularity both at the core and server level as a way to improve both performance and energy efficiency. However, despite these benefits, scheduling applications in heterogeneous machines remains challenging. Additionally, when these heterogeneous resources accommodate multiple applications to increase utilization, resources are prone to contention, destructive interference, and unpredictable performance. Existing solutions examine heterogeneity either across or within a server, leading to missed performance and efficiency opportunities. We present Mage, a practical interference-aware runtime that optimizes performance and efficiency in systems with intra- and inter-server heterogeneity. Mage leverages fast and online data mining to quickly explore the space of application placements, and determine the one that minimizes destructive interference between co-resident applications. Mage continuously monitors the performance of active applications, and, upon detecting QoS violations, it determines whether alternative placements would prove more beneficial, taking into account any overheads from migration. Across 350 application mixes on a heterogeneous CMP, Mage improves performance by 38% and up to 2x compared to a greedy scheduler. Across 160 mixes on a heterogeneous cluster, Mage improves performance by 30% on average and up to 52% over the greedy scheduler, and by 11% over the combination of Paragon [15] for inter- and intra-server heterogeneity.