Karl Taht's Research Paper Blog

Authors: Yu Gan, Yanqi Zhang, Kelvin Hu, Dailun Cheng, Yuan He, Meghna Pancholi, Christina Delimitrou Venue: ASPLOS 2018 Seer presents a framework to diagnose and avoid QoS violations in real-time. The motivation, design, and experimental framework in this paper are some of the best and most through I have seen in my recent reading. The work begins by discussing the microservice design of cloud providers. Such frameworks have numerous layers of abstraction, are often written in multiple programming languages, and have complex (and changing) dependency graphs. A performance bug in one microservice can cause QoS in many others, and diagnosing the root cause can be difficult. The work then builds a complex data collection framework which uses RPC-level and perf counters. When perf counters aren't available, the system uses microbenchmarks to diagnose the bottleneck. This area is particularly complex, and the authors even note that their system is similar to Dapper and Zipkin wh...

Authors: Abigail See, Minh-Thang Luong, Christopher D. Manning (Stanford) Venue:    Arxiv This paper applies pruning techniques to encoder-decoder deep multi-layer recurrent architecture with LSTM as the hidden unit type. The paper tries various pruning types, but finds the most effective to be simply pruning weights of least magnitude overall. While overall the techniques are mostly brought over from pruning techniques used in CNN's and other networks, the papers does make note of interesting artifacts of pruning. Firstly, deeper neurons are more sensitive to pruning that early neurons. In other words, the deeper units are actually of more importance and more sensitive to even low-magnitude weights. Additionally, they find that the sparse models can even out perform the originals, and claim that this is most likely due to the "generalizing" effect that pruning has. They say that while training set accuracy decreases, validation set accuracy actually increases! Addition...

Authors: Min Du, Feifei Li, Guineng Zheng, Vivek Srikumar Venue:    CCS  2017 (Conference on Computer and Communications Security) DeepLog presents a method for parsing system logs automatically using natural language processing techniques, and an LSTM. The paper makes a few assumptions, such as a fixed set of log entry types, called keys. DeepLog constructs workflows from the underlying system log so that once an anomaly is detected, root cause analysis can be performed. Need to do a deeper dive on this. Full Text