Configuration Management at the Edge Services
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Date
2020-12
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Publisher
The Ohio State University
Abstract
Internet services are increasingly pushed from the remote cloud to the edge sites close to data sources to offer fast response time and low energy footprint. However, software deployed at edge sites must be updated frequently. Performing updates as soon as they are available consumes a large amount of energy. Configuration management tools that install software updates and manage allowed staleness can inflate energy demands, especially when updates interrupt idle periods at the edge site and block processors from entering power-saving modes. Our research studies configuration management policies, their effect on energy footprint and strategies to optimize them. We have observed that policies yielding low energy footprint differ from site to site and over time. We propose a data-driven approach that uses data collected at each edge site to predict an energy-efficient policy and also guards against worst-case performance if data-driven predictions error occurs. We use a novel random-walk approach to manage data-driven policies that yield a low footprint for a representative trace of updates observed at an edge site. We are setting up 4 edge service benchmarks powered by AI inference to create realistic software update traces. Data-driven management achieves energy footprint 1.2-2X lower than the default context-adaptive scheduler (for example the first come first serve scheduler) and lower than approaches inspired by recent research and a little bit higher than the offline optimal.