Fangxi Yin
Jianmei Guo
ABSTRACT
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 e-commerce 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.
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Digital Library
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Index Terms
- Java performance troubleshooting and optimization at alibaba
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ICPE '18: Companion of the 2018 ACM/SPEC International Conference on Performance EngineeringOn 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 e-commerce ...
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