Interoperability and scalability with microservices

Ola Spjuth <ola.spjuth@farmbio.uu.se>Science for Life Laboratory

Uppsala University

Service-Oriented Architectures (SOA) in the life sciences

• Standardize– Agree on e.g. interfaces, data formats,

protocols etc.• Decompose and compartmentalize

– Experts (scientists) should provide services – do one thing and do it well

– Achieve interoperability by exposing data and tools as Web services

• Integrate– Users should access and integrate

remote services

API

Scientist

service

Scientist

consume

Service-Oriented Architectures (SOA) in the life sciences, ~2005

Scientist

downtime

API changed

Not maintained

Difficult to sustain,unreliable solutions

APIAPIAPI

Cloud Computing

• Cloud computing offers advantages over contemporary e-infrastructures in the life sciences– On-demand elastic resources and services– No up-front costs, pay-per-use

• A lot of businesses (and software development) moving into the cloud– Vibrant ecosystem of frameworks and tools, including for

big data• High potential for science

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Virtual Machines and Containers

Virtual machines• Package entire systems (heavy)• Completely isolated• Suitable in cloud environments

Containers:• Share OS• Smaller, faster, portable• Docker!

MicroServices

• Similar to Web services: Decompose functionality into smaller, loosely coupled services communicating via API– “Do one thing and do it well”

• Preferably smaller, light-weight and fast to instantiate on demand• Easy to replace, language-agnostic

– Suitable for loosely coupled teams (which we have in science)– Portable - easy to deploy and scale– Maximize agility for developers

• Suitable to deploy as containers in cloud environments

7

Scaling microservices

http://martinfowler.com/articles/microservices.html

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Shippingcontainers?

9

Orchestrating containers

Kubernetes: Orchestrating containers

• A declarative language for launching containers

• Start, stop, update, and manage a cluster of machines running containers in a consistent and maintainable way

• Origin: Google• Suitable for microservices

Containers

Scheduled and packed containers on nodes

11

Virtual Research Environment (VRE)

• Virtual (online) environments for research– Easy and user-friendly access to computational resources, tools and

data, commonly for a scientific domain

• Multi-tenant VRE – log into shared system• Private VRE

– Deploy on your favorite cloud provider

• Horizon 2020-project, €8 M, 2015-2018– “standardized e-infrastructure for the processing, analysis and information-mining

of the massive amount of medical molecular phenotyping and genotyping data generated by metabolomics applications.”

• Enable users to provision their own virtual infrastructure (VRE)– Public cloud, private cloud, local servers– Easy access to compatible tools exposed as microservices– Will in minutes set up and configure a complete data-center (compute

nodes, storage, networks, DNS, firewall etc)– Can achieve high-availability, scalability and fault tolerance

• Use modern and established tools and frameworks supported by industry– Reduce risk and improve sustainability

• Offer an agile and scalable environment to use, and a straightforward platform to extend

http://phenomenal-h2020.eu/

Users should not see this…

Deployment and user access

Launch on reference installation

Launch on public cloudPrivate VRE

In-house deployment scenarios

MRC-NIHR Phenome Centre

• Medium-sized IT-infrastructure

• Dedicated IT-personnel

• Users: ICL staff

Hospital environment

• Dedicated server

• No IT-personnel• User: Clinical

researcher

Private VRE

Build and test tools, images, infrastructure

Docker Hub

PhenoMeNalJenkins

PhenoMeNalContainer Hub

Development: Container lifecycle

Source code repositories

Two proof of concepts so far

Kultima group Pablo Moreno

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Implications

• Improve sustainability– Not dependent on specific data centers

• Improve reliability and security– Users can run their own service environments (VREs) within isolated

environments– High-availability and fault tolerance

• Scalability– Deploy in elastic environments

• Agile development– Automate “from develop to deploy”

• Agile science – Simple access to discoverable, scalable tools on elastic compute resources with

no up-front costs

• NB: Many problems of interoperability remains!– Data– APIs– etc.

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Ongoing research on VREs

Datafederation

Computefederation

Privacypreservation

Workflows

Big Dataframeworks

Data management and modeling

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Acknowledgements

Pharmb.ioWesley SchaalMaris LapinsJonathan AlvarssonArvid BergSamuel LampaMarco CapucciniMartin DahlöValentin GeorgievAnders LarssonPolina GeorgievStaffan ArvidssonLaeeq Ahmed

AstraZenecaLars CarlssonErnst Ahlberg-Helgee

University ViennaDavid KreilMaciej Kańduła

SNIC CloudSalman ToorAndreas Hellander

Caramba.clinicKim KultimaStephanie HermanPayam Emami

ToxHQ team