Mage: Online Interference-Aware Scheduling in Multi-Scale Heterogeneous Systems
Francisco Romero, Christina Delimitrou

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…
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Taxonomy
TopicsCloud Computing and Resource Management · Caching and Content Delivery · Parallel Computing and Optimization Techniques
