大数据应用内存漏洞分析

2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshop (DSN-W) Pub Date : 2016-10-03 DOI:10.1109/DSN-W.2016.58

N. Rameshan, R. Birke, Leandro Navarro-Moldes, Vladimir Vlassov, B. Urgaonkar, G. Kesidis, M. Schmatz, L. Chen

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引用次数: 1

摘要

由于在云中托管内存大数据分析的日益普及，我们提出了一种分析方法，可以了解不同的内存子系统(即缓存和内存带宽)如何容易受到来自同址应用程序干扰的影响。我们首先描述了所建议的工具的设计，并演示了一个案例研究，该案例研究由五个基于现实数据集的Spark应用程序组成。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Profiling Memory Vulnerability of Big-Data Applications

Motivated by the increasing popularity of hosting in-memory big-data analytics in cloud, we present a profiling methodology that can understand how different memory subsystems, i.e., cache and memory bandwidth, are susceptible to the impact of interference from co-located applications. We first describe the design of the proposed tool and demonstrate a case study consisting of five Spark applications on real-life data set.

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2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshop (DSN-W)

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