Affiliation:
1. Duke University, Durham, NC
Abstract
We draw on reinforcement learning frameworks to design and implement an adaptive controller for managing resource contention. During runtime, the controller observes the dynamic system conditions and optimizes control policies that satisfy latency targets yet improve server utilization. We evaluate a physical prototype that guarantees 95th percentile latencies for a search engine and improves server utilization by up to 70%, compared to exclusively reserving servers for interactive services, for varied batch workloads in machine learning.
Funder
Division of Computing and Communication Foundations
Division of Computer and Network Systems
Publisher
Association for Computing Machinery (ACM)
Subject
Hardware and Architecture,Information Systems,Software
Cited by
9 articles.
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