Interoperability and scalability with microservices in science

Associate Professor at Uppsala University
Oct. 31, 2016
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
Interoperability and scalability with microservices in science
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Interoperability and scalability with microservices in science

Editor's Notes

  1. Idea with SOA (~2005) Achieve interoperability by exposing data and functionality as Web services Experts (scientists) should set up and host their own Web services Users should integrate a multitude of distributed services, connect into workflows (e.g. Taverna), and share (parts of) workflows What happened? Users could not rely on Web services (downtime, API changes, abandoned) and they could not be mirrored Workflows never gained widespread popularity Today, stable web services mainly remain at large data and tool providers (EBI, NCBI etc)
  2. Drop applications into VMs running Docker in different clouds.