SimCity Building B locks at the DICE team

1. SimCity Building Blocks at the DICE team Marian Bubak, BartoszBaliś, Marek Kasztelnik, Maciej Malawski, Piotr Nowakowski {bubak,balis, malawski}@agh.edu.pl, {m.kasztelnik, p.nowakowski}@cyfronet.pl Department of Computer Science and ACC Cyfronet AGH Krakow, PL dice.cyfronet.pl

2. Example application:Flood threat assessment Scenario: levee-protected area endangered by flood due to high water levels The user selects an area for flood threat assessment

3. Flood threat assessment: our solution

4. Data sets GIS data (levees, sensor locations) Sensor data Simulated sensor data Computation results Lots of metadata

5. (Virtual) Experiments Support for conducting virtual experiments: Flooding a reservoir protected by an artificial levee Observation of a levee during heavy rain Simulation of a flooding scenario Experiment lifecycle Creating a new experiment and defining its context Collecting information during the experiment Concluding the experiment Reusing experiment results in future experiments

6. HyperFlow: programming and execution of workflow-based scientific applications • Based on a formal model of computation (Process Networks) • Supports a rich set of complex workflow patterns B. Baliś, Increasing Scientific Workflow Programming Productivity with HyperFlow. In Proceedings of the 9th Workshop on Workflows in Support of Large-Scale Science. 2015 (In Print). Innovative programming approach and enactment engine for scientific workflows Combines declarative workflow description with low-level programming in JavaScript / node.js for implementing workflow activities Simple and concise syntax + mainstream scripting language & runtime platform = increased programming productivity

7. SimCity Requirements for “CIS” • Start a simulation based on user input. • Let an automated component start workflows within CIS with new parameter sets, and receive results asynchronously. All functions should be accessible from a user-friendly interface that is not concerned with how something is computed but with what is computed.

8. SimCity Requirements for “CIS” • Start an external component (for parameter exploration) via an external (custom) user interface. • Ideally, parameter exploration is exposed as a web service, so different algorithms can be started from a custom web interface. • The parameter exploration algorithm should be modifiable or at least selectableby the user.

9. SimCity Requirements for “CIS” • Stage data to and from clusters or cloud infrastructure (virtual machines). • For each job, the input and output files should end up at the correct places. • Construct input files based on parameter sets. • TRANSIMS, for example, works with control files to determine the parameters, but also with data files with parameters. Both would have to be editable from CIS.

10. SimCity Requirements for “CIS” • Automatically schedule workflows on cluster or cloud infrastructure. • Preferably, theuser should be able to select their own cluster.

11. SimCity Requirements for “CIS” • Providefeedback to a component based on user input. • Parameterexploration may be guided by theuser. In thiscase, ideally, a web service isprovidedfor real-time interaction with the parameter exploration component. • CIS givesfeedback to a component based on sensor data, which may then start new workflows.

12. Hybridcloud as a means of provisioningcomputingpower for virtualexperiments – theAtmosphereframework 128 CPU cores 96 CPU cores Massive (functionally limitless) hardware resource pool Head Node 184 GB RAM 256 GB RAM private IP space 4 TB storage private IP space 4 TB storage public IP space AtmosphereCore Services Host GUI host (provisionsend-userfeatures and accessoptions) Image store Cloud Management Portlets Provide GUI elements which enable service developers and end users to interact with the Atmosphere platform and create/deploy services on the available cloud resources OpenStackcloudsiteat ACC CYFRONET AGH Secure RESTful API (Cloud Facade) Head Node Atmosphere Core • Authentication and authorization logic • Communication with underlying computational clouds • Launching and monitoring service instances • Creating new service templates • Billing and accounting • Logging and administrative services Image store VPH-Share cloud site at UNIVIE Atmosphere Registry (AIR) Worker Node Worker Node Worker Node Worker Node Worker Node Worker Node Worker Node Worker Node Worker Node Worker Node Worker node w/large resource pool („fat node”) Worker node w/large resource pool („fat node”) API host user accounts Image store available cloud sites services and templates Amazon Elastic Compute Cloud (EC2) – European availability zone

13. Atmosphere platform interfaces Application -- or -- A full range of user-friendly GUIs is provided to enable service creation, instantiation and access. A comprehensive online user guide is also available. The GUIs work by invoking a secure RESTful API which is exposed by the Atmosphere host. We refer to this API as the Cloud Facade. Workflow environment Atmosphere Ruby on Rails controller layer (core Atmosphere logic) Any operation which can be performed using the GUI may also be invoked programmatically by tools acting on behalf of the platform user – this includes standalone applications and workflow management environments (which VPH-Share also provides). End user Atmosphere Registry (AIR) Cloud sites All operations on cloud hardware areabstracted by theAtmosphere platform whichexposes a unifiedRESTful API (with a suitable set of developer’sdocumentationavailable). For endusers, the API isconcealed by a layer of platform GUIsembeddedintheVPH-Share portal and providing a user-friendlywork environment - for domainscientists and service developersalike. The API canalso be directlyinvoked by external services as long as theypossesstherequired security credentials (Atmosphererelies on thewell-knownOpenIDauthentication standard).

14. Shared and scalable services – smart utilization of hardware resources Atmosphere Cloud Platform Atmosphere Atmosphere Scalable Published Shared Cloud Service Cloud Service Cloud Service Developer Scientist Scientist Scientist Scientist Scientist Scientist • A Shared service is backended by a single virtual machine which „mimics” multiple instances from the users’ point of view. • Shared services greatly conserve hardware resources and can be instantiated quickly. Shared VM Cloud WN • When a Scalable service is overloaded with requests Atmosphere can spawn additional instances in the cloud to handle the additional load. • The process is transparent from the user’s perspective. Separate VM Separate VM Cloud WN Cloud WN Published services becomevisible to non-developers and can be instantiatedusingtheGenericInvoker. Developersarefree to spawn „snapshot” images of theircloud services (e.g. for backup purposes) withoutexposingthem to externalusers.

15. More informationaboutthehybridcomputationalcloud platform A moredetailedintroduction to theAtmospherecloud platform (includingusermanuals) can be foundathttps://vph.cyfronet.pl/tutorial TheDIstributedComputingEnvironments (DICE) team homepageathttp://dice.cyfronet.plhasinformation on projectswhichuseAtmosphere for cloudresourceprovisioning

16. Costoptimization of applications on clouds M. Malawski, K.Figiela, J.Nabrzyski:Cost minimization for computational applications on hybrid cloud infrastructures, Future Generation Computer Systems, Volume 29, Issue 7, September 2013, Pages 1786-1794, ISSN 0167-739X, http://dx.doi.org/10.1016/j.future.2013.01.004 MaciejMalawski, KamilFigiela, Marian Bubak, EwaDeelman, JarekNabrzyski: Cost Optimization of Execution of Multi-level Deadline-Constrained Scientific Workflows on Clouds. PPAM (1) 2013: 251-260 http://dx.doi.org/10.1007/978-3-642-55224-3_24 https://github.com/kfigiela/optimization-models • Infrastructure model • Multiple compute and storage clouds • Heterogeneous instance types • Application model • Bag of tasks • Multi-level workflows • Modeling with AMPL (A Modeling Language for Mathematical Programming) and CMPL • Cost optimization underdeadline constraints • Mixed integer programming • Bonmin, Cplex solvers

17. Simulation and scheduling of large-scale scientific workflows on IaaS clouds Large-scale scientific workflows from Pegasus workflow management system Workflows of 100,000 tasks Workflow ensembles: schedule as many workflows as possible within a budget and deadline Cloud infrastructure simulated using CloudSim M. Malawski, G. Juve, E. Deelman, J. Nabrzyski: Cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds. SC 2012: 22 https://github.com/malawski/cloudworkflowsimulator

18. Cloud performance evaluation • Performance of VM deployment times • Virtualization overhead Evaluation of open source cloud stacks (Eucalyptus, OpenNebula, OpenStack) • Survey of European public cloud providers • Performance evaluation of top cloud providers (EC2, RackSpace, SoftLayer) • A grant from Amazon has been obtained M. Bubak, M. Kasztelnik, M. Malawski, J. Meizner, P. Nowakowski and S. Varma:Evaluation of Cloud Providers for VPH Applications, posterat CCGrid2013 - 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, Delft, the Netherlands, May 13-16, 2013

19. DICE team- http://dice.cyfronet.pl Main research interests: investigation of methods for building complex scientific collaborative applicationsand large-scale distributed computing infrastructures elaboration of environments and tools for e-Science development of knowledge-based approach to services, components, andtheir semantic composition and integration