HadoopWatch: A first step towards comprehensive traffic forecasting in cloud computing
Peng Yang, Kai Chen, Guohui Wang, Wei Bai, Zhiqiang Ma, Lin Gu · 2014
This paper presents our effort towards comprehensive traffic forecasting for big data applications using external, light-weighted file system monitoring. Our idea is motivated by the key observations that rich traffic demand information already exists in the log and meta-data files of many big data applications, and that such information can be readily extracted through run-time file system monitoring. As the first step, we use Hadoop1 as a concrete example to explore our methodology and develop a system called HadoopWatch to predict traffic demand of Hadoop applications. We further implement HadoopWatch in our real small-scale testbed with 10 physical servers and 30 virtual machines. Our experiments over a series of MapReduce applications demonstrate that HadoopWatch can forecast the traffic demand with almost 100% accuracy and time advance. Furthermore, it makes no modification of the Hadoop framework, and introduces little overhead to the application performance.