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CrystalBall: Statically Analyzing Runtime Behavior via Deep Sequence Learning

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Author:  Stephen Zekany, Daniel Rings, Nathan Harada, Michael A. Laurenzano, Lingjia Tang, Jason Mars Intro: Understanding the runtime behavior of the software is critical in many aspects of program development. A conventional approach to this problem is using dynamic profiling, meaning we have to run the program multiple times with environments that representative enough for the actual working situation. Besides that, a dynamic profiling approach has other weakness such as cannot profiling a subset of functions or paths very economically and requires re-profiling each time any changes is applied to the code. This paper presents a statically profiling method, harnessing the ability of RNN to discover the hidden information of hot path in the code. Also, since the training process uses intermediate representation as input, the result will have better generalization power. Methodology: The dataset is in the form of a basic block feature; each sequence of basic blocks repr...
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A Machine Learning Approach to Live Migration Modeling

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