ppbench - A Visualizing Network Benchmark for Microservices

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Abstract: Companies like Netflix, Google, Amazon, Twitter successfully exemplified elastic and scalable microservice architectures for very large systems. Microservice architectures are often realized in a way to deploy services as containers on container clusters. Containerized microservices often use lightweight and REST-based mechanisms. However, this lightweight communication is often routed by container clusters through heavyweight software defined networks (SDN). Services are often implemented in different programming languages adding additional complexity to a system, which might end in decreased performance. Astonishingly it is quite complex to figure out these impacts in the upfront of a microservice design process due to missing and specialized benchmarks. This contribution proposes a benchmark intentionally designed for this microservice setting. We advocate that it is more useful to reflect fundamental design decisions and their performance impacts in the upfront of a microservice architecture development and not in the aftermath. We present some findings regarding performance impacts of some TIOBE TOP 50 programming languages (Go, Java, Ruby, Dart), containers (Docker as type representative) and SDN solutions (Weave as type representative).

Details [KQ2016a]

Author(s):
Title: ppbench - A Visualizing Network Benchmark for Microservices
In book: Proceedings of the 6th International Conference on Cloud Computing and Services Science (CLOSER 2016)
Pages: 223-231
Doi: 10.5220/0005732202230231
Publication year: 2016

Bibtex

@inproceedings{KQ2016a,
  author = {Kratzke, Nane and Quint, Peter-Christian},
  title = {ppbench - A Visualizing Network Benchmark for Microservices},
  booktitle = {Proceedings of the 6th International Conference on Cloud Computing and Services Science (CLOSER 2016)},
  year = {2016},
  pages = {223-231},
  doi = {10.5220/0005732202230231},
  isbn = {978-989-758-182-3},
  project = {CloudTRANSIT},
  researchgate = {https://www.researchgate.net/publication/292657431}
}