Towards production in 2017

It has been a busy 2016 for the SSC team. We have served more than 60 pilot projects, conducted both beginning and more advanced level training at several locations in Sweden, and started working on a hardened infrastructure. Since competency renewal on OpenStack operations is expected to be one key challenge for SSC long-term, we have taken measures to standardize operations across regions to facilitate a joint, national responsibility for operations. At the end of the year, SNIC conducted a thorough evaluation of the project, looking specifically at whether the project had succeeded in creating services of value to the research community.

We are happy to announce that the outcome of this processes is a decision to converge towards production resources in 2017. This is great news for end-users, since this will mean a higher level of service and support.

SSC closes for new project request in Q1 order to transition to a production service

Early in 2017 we will upgrade the control planes at UPPMAX and C3SE with new hardware capable of supporting a larger amount of users and projects. The bulk of the compute nodes will continue to come from second generation HPC clusters but they will be modernized and expanded. Our two regions at C3SE and HPC2N will become available for general project requests. We will accelerate the work to integrate SSC into the SNIC ecosystem. In particular, we will redesign our temporary project and account handling. Security policies will also be documented and communicated to end users.

To free up time in the project to make this transitions as rapidly as possible, we will not accept any new pilot project requests until we are ready to announce the production services (the goal is early Q2 2017). During the transition period, we will keep supporting our existing pilot users on the same levels as they are now. When we reopen the services, it will be with the same best effort support levels as other SNIC resources.

Glenna 2

Glenna is a Nordic e-Infrastructure Collaboration (NeIC) initiative, with focus on knowledge exchange and Nordic collaboration on cloud computing. The first Glenna project has now concluded, and from January 2017 a new phase of the project, Glenna 2, starts. Glenna 2 will focus on four main aims:

  1. Supporting national cloud initiatives to sustain affordable IaaS cloud resources through financial support, knowledge exchange and pooling competency on cloud operations.
  2. Using such national resources to establish an internationally leading collaboration on data intensive computing in collaboration with user communities.
  3. Leveraging the pooled competency to take responsibility for assessing future hybrid cloud technology and communicate that to the national initiatives.
  4. Supporting use of resources by pooling national cloud application expert support and create a Nordic support channel for cloud and big data. The mandate is to sustain a coordinated training and dissemination effort, creating training material and providing application level support to cloud users in all countries.

In short, aim 1 ensures the availability of IaaS, aim 2 seeks to establish PaaS and SaaS services for Big Data Analytics, aim 3 investigates future emerging technology and HPC-as-a Service and aim 4 will provide advanced user support for research groups transitioning into cloud computing infrastructure. The project directive for Glenna 2 can be found here.

SNIC Science Cloud is a cloud computing infrastructure run by SNIC, the Swedish National Infrastructure for Computing. SNIC Science Cloud provides a national-scale IaaS cloud and associated higher-level services (PaaS), for the research community

SNIC Science Cloud Workshop (Fall 2016)


Instructor: Salman Toor.
Level: Basic.

Location: Chalmers University of Technology, Room Raven & Fox, Fysik forskarhus 5th floor.

Visiting address: Chalmers Campus Johanneberg, Room Raven & Fox, Fysik forskarhus, 5th floor entrance Fysikgränd 3.

Infrastructure: SNIC Science Cloud (OpenStack based Community Cloud).

Date & duration: 25:th November, (10:00 – 16:00).

Audience: Users and potential users of SNIC Science Cloud resources with no previous cloud experience.


Register here.


  • Brief overview of Cloud Computing.
  • Cloud offerings: Compute, Storage, Network as a Service (*aaS).
  • Brief description of IaaS, PaaS, SaaS etc.
  • How to access Cloud resources?
  • Introduction to SNIC Science Cloud initiative.

Hands-on session topics:

1 – How does the Horizon dashboard work?
2 – How to start a virtual machine (VM)?
3 – Instance snapshots.
4 – Access to cloud storage (volumes and Object store)
5 – Storage snapshot
5 – Network information
6 – Basic system interaction with APIs



First half (10:15 – 12:00): Lectures
Second half (13:00 – 16:00): Lab session


Who are the pilot users of the SNIC Science Cloud?

The SNIC Science Cloud is a project run by the Swedish National Infrastructure for Computing. The goal is to investigate if and how cloud resources should be provided as a complement to the more traditional HPC-resources. An important part of this investigation is our pilot users.  The project has run in two main phases:

1. 2013-2014: A small-scale pilot system and a small number (3-5) of predefined pilot use-cases, chosen to highlight the utility of cloud resources. Advanced user support in the form of SNIC Application Experts (AEs) were available to assist the pilot users.

2. 2015-2016: A scaled up infrastructure with more resources, and an open process for user-initiated project requests. In this phase, we have concentrated on training workshops and creating open source tutorials instead of targeted advanced support.

In the summer 2016 we reported on the growth of project requests and promised to follow up with a more in-depth analysis of who these users are and what they are doing. We have looked in our project database at all the projects that registered for SSC resources in 2015-2016 (as of Oct. 11). During the period, we have had 57 project requests.




As can be seen, Life Science users dominate. Of these, a large fraction are affiliated with SciLifeLab/NBIS. This is expected since the Bioinformatics community was an early adopter of service-oriented computing, and since their applications often have the need to integrate multiple software. Many Swedish universities are represented, but Uppsala University (UU), The Royal Institute of Technology (KTH) and the Karolinska Institute (KI) dominate. This is likely a consequence of the fact that these institutions’ involvement in SciLifeLab, and that both KTH and UU has served as hosts for the SSC project, presumably increasing awareness of SSC amongst the scientists.  

In the period Sep 11-Oct 11, a total of 47000 instance hours were deployed in 31 different projects. Using a reference instance type (flavor) with 4 VCPUs and 8GB RAM, this corresponds to an average of 130 instances continuously deployed during the month. We will follow up on the resource usage patterns over time in future posts, when we have more fine-grained data.

So what are the users doing with the SSC OpenStack resources? It appears that development and testing of software and services, as well as exploring the cloud computing paradigm for old and new types of applications are still the dominating use case. In common to most projects is the need for flexible customization of the computing environment, made possible by virtualization. Many projects also want to provide their solutions as services to serve their own specific community.

Some projects are making more substantial use of the IaaS resources, making use of advanced tools for contextualization, automation and orchestration to achieve quite a diverse range of objectives. In common to all these projects is that they have access to own expertise on distributed and cloud computing in the project groups. To serve as an inspiration to new users, we have during 2016 highlighted some of them as user success stories:

Elastic proteomics analysis in the Malmstroem Lab.

Processing ozone data from the Odin satellite at Chalmers University of Technology.

Estimation of failure probabilities with applications in underground porous media flows.  

Virtual Research Environment for Clinical Metabolomics.  

So what will happen in 2017? A projection is hard to provide, but given the global trend that private/community IaaS becomes more and more common also in academia, observations made by our partners in the Nordic Glenna project, and with the momentum created via the European OpenScienceCloud initiative, we believe that the interest in cloud resources will keep increasing rapidly. 

Fortunately, in the SSC project we are in a good position to meet an increased demand due to our architectural design based on regions, in which we can leverage previous generation HPC hardware at multiple geographic locations to quickly add compute hosts at low cost. We have now integrated resources at three HPC-centra, UPPMAX, C3SE and HPC2N and can if needed scale resources to over 5000 physical cores and 1PB of storage during 2017. This model also opens up for substantial user communities to enter SSC with their own dedicated regions. We also hope to start looking into public-private partnerships to secure a larger variety of SLA-backed resources and to allow for users to burst outside of the allocated quotas.

Scalable processing of ozone data from the Odin satellite

The Odin satellite has measured ozone and related gases in the stratosphere and mesosphere for more than 15 years. This is one of the longest data series with global coverage that exists in atmospheric science. To make sure we can provide the best possible reference data set to the science community ESA has funded a total overhaul of the data set. This includes everything from review of the calibration algorithm of the sensor, to a complete review of the algorithms that provide concentrations of the different species at different heights and locations in the atmosphere.

The instrument is a passive sub millimeter radiometer, measuring emitted energy from different molecules through the limb of the atmosphere. The atmosphere is scanned over different heights around 60 times an orbit and the satellite makes about 15 orbits a day. That way measurements quickly cover most of the Earth’s atmosphere. As an example we have been able follow the development of the ozone depletion over the polar regions. We can also follow the circulation of air masses globally.

The REST API is the interface for all communication between the different components in the system. As an example the number crunching “processors” are not aware of the technology behind the REST API - they only ask for data and deliver results back to the API. This means it is easy for us to scale, move or change the underlying data storage technology.
The REST API is the interface for all communication between the different components in the system. As an example the number crunching “processors” are not aware of the technology behind the REST API – they only ask for data and deliver results back to the API. This means it is easy for us to scale, move or change the underlying data storage technology.


The Odin science community is using the SNIC Science Cloud. This enables scalable processing of Odin data both for testing of alternative algorithms and standard products.

To be able to process the dataset as quickly as possible we have packaged a set of “processors” in docker images ready to be deployed at any docker enabled computer. These docker images are self-contained with auxiliary datasets and code ready to be fed with measurements from the Odin satellite. Once the docker images are deployed the container asks the Odin-API, a REST service, for the next available measurement and starts immediately to crunch the data. When the process is done it delivers the results back to the Odin-API and starts over with a new measurement.

Users can browse and download data from the user interface and power-users can communicate directly with the REST-API to analyse data programmatically or start new processing campaigns.

Proteomics analysis using cloud infrastructure

Proteomics is the study of the global protein expression of cells and tissues. In proteomics, measurements are often carried out using mass spectrometers and the resulting data is both complex and large in volume. Proteins are complex macromolecules consisting hundreds or thousands of 20 amino acid types. Each amino acid can also undergoes modifications and this result that an estimated 1 million different protein types exists in complex organisms such as humans and their abundance varies over 7 orders of magnitude.

Computational proteomics aims at generating interpretable information from the thousands of mass spectra produced each hour. In general, the computational workflows need to be adapted to new data acquisition strategies and sometimes even per project. To accommodate this, typical workflows consist of many tools produced by research groups, consortia or companies. Below, we describe the technology stack we use to provide stable workflows to both experienced and novice users, yet remain flexible to accommodate special analysis cases.

All produced data, both measured and derived, is ingested into a data manager referred to as openBIS (Bauch et al 2011), which is ultimately stored on Swestore. Workflows can automatically stage data on the computation infrastructure in use. GC3PIE is used to manage the workflow and to interact with the computational resources as follows; a new workflow is submitted by a user, the GC3PIE head node downloads the data, creates cloud workers that then executes the various tools that constitutes the workflow. The final result data is registered in the data manager in relation to the input data. The result data consist of both result data and interactive reports.

Johan Malmström (Lund University) and Lars Malmström (ETH Zurich)

Important milestone reached for SNIC Science Cloud!

Our work on the SNIC Science Cloud is progressing at a steady rate, and we are very happy to announce that we have achieved an important milestone: over 50 active projects with more than 200 users from different disciplines. As the figure shows, the number of projects has been growing steadily, with an average of 3 per month.  Early in the project, we were driven by pre-defined pilot use-cases, but as can be seen, users are since some time ago finding us rather than the opposite. The question now is what will happen later in 2016 and 2017, will the linear trend continue or will we see a more rapid increase in project requests?

Number of active projects on the SSC IaaS resources as registered in SUPR, the SNIC project management portal. The projects vary in size, and in total include over 200 users.

The IaaS is still based on best possible efforts and in kind contributions from three SNIC centers (UPPMAX, C3SE and HPC2N). We are currently working hard on implementing more extensive accounting, gathering data on resource usage patterns, and we will follow up with a more in-depth analysis later this year. This is an important activity in our efforts to better understand the operational costs and constraints of a cloud for science, and will form a basis for e.g. informed capacity planning. We plan to make all data publicly available in the hope that it will help other academic institutions in their e-infrastructure strategy work.

We have organized three workshops and there will be more to come in the fall. The aim of the workshops is to help the user community make productive use of clouds in their research.  The first workshop was held at the Department of Information Technology, Uppsala University (September 2015), the second was at SciLifeLab, Stockholm (March 2016) and recently the third workshop was held at Royal Institute of Technology (KTH), Stockholm (May 2016). The first two workshops were about introduction to Cloud computing and how to get started with OpenStack based IaaS. The third workshop address advanced concepts of virtualization, contextualization based on CloudInit and Ansible and orchestration using Heat.

Our senior cloud architect and UPPMAX application expert on cloud computing, Salman Toor, lecturing about cloud computing at KTH. In this advanced-level workshop, contextualization, orchestration and automation was on the agenda.
Our senior cloud architect and UPPMAX application expert on cloud computing, Salman Toor, lecturing about cloud computing at KTH. In this advanced-level workshop, contextualization, orchestration and automation was on the agenda.

SSC aims at being a very open project, sharing as much of our developed content and practices as open source. In line with this,  all the tutorial material is available for download, reuse, and importantly, contributions, from our GitHub repository:

Pull requests are much appreciated.

Have a nice summer!

SSC Team

Using cloud computing for estimating failure probabilities with applications in underground porous media flows

In this guest post, Fredrik Hellman, a PhD student at the Division of Scientific Computing, Department of Information Technology, Uppsala University, report on how cloud computing resources in SSC were used in recent work with collaborators at UU and Chalmers/GU. 

In many engineering applications the probability of system failures are of particular interest. A special application is the assessment of storage capacity of underground carbon dioxoide storage reservoirs,where a failure is that the capacity of the target reservoir is smaller than expected. Since the rock properties are generally uncertain, the uncertainty in the reservoir capacity is also large.

The SNIC Science Cloud was used in our work on estimating failure probability to assess the performance of four different Monte Carlo method setups for estimating failure probability in a porous media fluid flow simulation with uncertain rock properties. For all four methods, the basic algorithm was to generate a set of realizations of the uncertain rock properties and distribute the work of performing the simulation for each realization in a network of virtual machines in the SNIC Science Cloud. All algorithms thus exhibit single program, multiple data (SPMD) parallelism.


The code performing the simulations was written in Python, using
finite element assembly routines from the FEniCS project. The project benefited from using a cloud based service mainly for two reasons. First, the virtualization allowed for good control over the software environment. Experimental versions of software could easily be used without administrative overhead. Second, the IPython based MOLNs software for setting up and managing a virtual computing network for distributed computations was readily available and simplified the management of the computations.

Mini-workshop on microservice platforms

The Spjuth group is organizing a mini-workshop on microservice platforms on May 12 with talks 13:15-15:00 in ITC 2345, open to the public. SSC’s senior cloud architect Salman Toor will give a brief presentation about the cloud infrastructure. Agenda for the afternoon session is available here.

The type of microservice platforms presented offer higher-level functionality  by abstracting away most of the IaaS layer, allowing users to focus more on deploying applications than directly managing VMs. This makes it easier to build and deploy robust and scalable cloud computing applications. In the next SSC training session at KTH, there will be more information about MANTL, including a hands-on tutorial.