IMPACT 2019: Java performance troubleshooting and optimization at scale - Kingsum Chow, Jonathan Lu, and Sanhong Li, Alibaba - Computer Measurement Group

IMPACT 2019: Java performance troubleshooting and optimization at scale – Kingsum Chow, Jonathan Lu, and Sanhong Li, Alibaba

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IMPACT 2019: Java performance troubleshooting and optimization at scale – Kingsum Chow, Jonathan Lu, and Sanhong Li, Alibaba

Alibaba is moving toward one of the most efficient cloud infrastructures for global online shopping. On the 2017 Double 11 Global Shopping Festival, Alibaba’s cloud platform achieved total sales of more than 25 billion dollars and supported peak volumes of 325,000 transactions and 256,000 payments per second. Most of the cloud-based eCommerce transactions were processed by hundreds of thousands of Java applications with above a billion lines of code.

It is challenging to achieve comprehensive and efficient performance troubleshooting and optimization for large-scale online Java applications in production. We proposed new approaches to method profiling and code warmup for Java performance tuning. Our fine-grained, low-overhead method profiler improves the efficiency of Java performance troubleshooting. Moreover, our approach to ahead-of-time code warmup significantly reduces the runtime overheads of just-in-time compiler to address the bursty traffic. Our approaches have been implemented in Alibaba JDK (AJDK), a customized version of OpenJDK, and have been rolled out to Alibaba’s cloud platform to support online critical business. To view the full video you must have a CMG membership. Sign up today!

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