Blogs (1) >>
ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States
Thu 14 Nov 2019 14:00 - 14:20 at Hillcrest - Models and Logs Chair(s): Timo Kehrer

Execution logs record detailed runtime information of software systems and are used as the main data source for many tasks around software engineering. As modern software systems are evolving into large scale and complex structures, logs have become one type of fast-growing big data in industry. In particular, such logs often need to be stored for a long time in practice (e.g., a year), in order to analyze recurrent problems or track security issues. However, archiving logs consumes a large amount of storage space and computing resources, which in turn incurs high operational cost. Data compression is essential to reduce the cost of log storage. Traditional compression tools (e.g., gzip) work well for general texts, but are not tailed for execution logs. In this paper, we propose a novel and effective log compression method, namely logzip. Logzip is capable of extracting hidden structures from logs via fast iterative clustering and further generating coherent intermediate representations that can enable more effective compression. We evaluate logzip on five large log datasets of different types, with a total of 63.6 GB in size. The results show that, on average, logzip can save about half of the storage space over traditional compression tools. Meanwhile, the design of logzip is highly parallel and only incurs negligible overhead. In addition, we share the industrial experience of applying logzip in a global company.

Thu 14 Nov

13:40 - 15:20: Papers - Models and Logs at Hillcrest
Chair(s): Timo KehrerHumboldt-Universtität zu Berlin
ase-2019-papers13:40 - 14:00
Statistical Log Differencing
Lingfeng BaoInstitute of Information Engineering, Chinese Academy of Sciences, Nimrod BusanyTel Aviv University, David LoSingapore Management University, Shahar MaozTel Aviv University
ase-2019-papers14:00 - 14:20
Logzip: Extracting Hidden Structures via Iterative Clustering for Log Compression
Jinyang LiuSun Yat-Sen University, Jieming ZhuHuawei Noah's Ark Lab, Shilin HeChinese University of Hong Kong, Pinjia HeETH Zurich, Zibin ZhengSun Yat-Sen University, Michael LyuThe Chinese University of Hong Kong
ase-2019-papers14:20 - 14:40
Code-First Model-Driven Engineering: On the Agile Adoption of MDE Tooling
Artur BoronatUniversity of Leicester
ase-2019-papers14:40 - 15:00
Size and Accuracy in Model Inference
Nimrod BusanyTel Aviv University, Shahar MaozTel Aviv University, Yehonatan YulazariTel Aviv University
ase-2019-Demonstrations15:00 - 15:10
PMExec: An Execution Engine of Partial UML-RT Models
Mojtaba BagherzadehQueen's University, Karim JahedQueen's University, Nafiseh KahaniQueen's University, Juergen DingelQueen's University, Kingston, Ontario
ase-2019-Demonstrations15:10 - 15:20
mCUTE: A Model-level Concolic Unit Testing Engine for UML State Machines
Reza AhmadiQueen's University, Karim JahedQueen's University, Juergen DingelQueen's University, Kingston, Ontario