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Failure Analysis and Prediction for Big-Data Systems
تحلیل شکست و پیش بینی برای سیستم های داده های بزرگ-2016 Motivated by the high complexity of today’s datacenters, a large body of studies tries to understand workloads and resource
utilization in datacenters. However, there is little work on exploring unsuccessful job and task executions. In this article, we study and
predict three types of unsuccessful executions in traces of a Google datacenter, namely fail, kill, and eviction. We first quantitatively
show their strongly negative impact on machine time and the resulting task slowdown. We analyze patterns of unsuccessful jobs and
tasks, particularly focusing on their interdependencies, and we uncover their root causes by inspecting key workload and system
attributes. Furthermore, we develop three on-line prediction models that can classify jobs and events into four classes upon arrival
time, using independent or nested Neural Networks. We explore different combinations of feature sets and techniques to reduce the
computational overhead. Our evaluation results show that the proposed models can accurately classify 94.4% of jobs and 76.8% of
events into four classes.
Index Terms: Distributed Systems | Reliability | availability and serviceability | Neural nets. |
مقاله انگلیسی |