Dice presents-feb2014

418 views

Published on

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
418
On SlideShare
0
From Embeds
0
Number of Embeds
70
Actions
Shares
0
Downloads
6
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Dice presents-feb2014

  1. 1. Distributed Computing Environments Team Marian Bubak bubak@agh.edu.pl Department of Computer Science and Cyfronet AGH University of Science and Technology Krakow, Poland dice.cyfronet.pl
  2. 2. DICE Team Academic Computer Centre CYFRONET AGH (1973) 120 employees http://www.cyfronet.pl/en/ Department of Computer Science AGH (1980) 800 students, 70 employees http://www.ki.agh.edu.pl/uk/index.htm Faculty of Computer Science, Electronics and Telecommunication (2012) 2000 students, 200 employees http://www.iet.agh.edu.pl/ AGH University of Science and Technology (1919) 16 faculties, 36000 students; 4000 employees http://www.agh.edu.pl/en Other 15 faculties Distributed Computing Environments (DICE) Team http://dice.cyfronet.pl • Investigation of methods for building complex scientific collaborative applications • Elaboration of environments and tools for e-Science • Integration of large-scale distributed computing infrastructures • Knowledge-based approach to services, components, and their semantic composition
  3. 3. • Investigating applicability of cloud computing model for complex scientific applications • Optimization of resource allocation for applications on clouds • Resource management for services on heterogeneous resources • Urgent computing scenarios on distributed infrastructures • Billing and accounting models • Procedural and technical aspects of ensuring efficient yet secure data storage, transfer and processing • Methods for component dependency management, composition and deployment • Information representation model for cloud federating platform, its components and operating procedures Current research objectives
  4. 4. • Optimization of service deployment on clouds – Constraint satisfaction and optimization of multiple criteria (cost, performance) – Static deployment planning and dynamic auto-scaling • Billing and accounting model – Adapted for the federated cloud infrastructure – Handle multiple billing models • Supporting system-level (e)Science – tools for effective scientific research and collaboration – advanced scientific analyses using HPC/HTC resources • Cloud security – security of data transfer – reliable storage and removal of the data • Cross-cloud service deployment based on container model Topics for collaboration
  5. 5. seconds ~95% 3 hours 100 jobs 1 job <10% asynchronous and frequent failures and hardware/software upgrades long and unpredictable job waiting times J. T. Moscicki: Understanding and mastering dynamics in Computing Grids, UvA PhD thesis, promoter: M. Bubak, co-promoter: P. Sloot; 12.04.2011 Spatial and temporal dynamics in grids • Grids increase research capabilities for science • Large-scale federation of computing and storage resources – 300 sites, 60 countries, 200 Virtual Organizations – 10^5 CPUs, 20 PB data storage, 10^5 jobs daily • However operational and runtime dynamics have a negative impact on reliability and efficiency
  6. 6. Completion time with late binding. Completion time with early binding. 40 hours1.5 hours J. T. Moscicki, M. Lamanna, M. Bubak, P. M. A.Sloot: Processing moldable tasks on the Grid: late job binding with lightweight user-level overlay, FGCS 27(6) pp 725-736, 2011 User-level overlay with late binding scheduling • Improved job execution characteristics • HTC-HPC Interoperability • Heuristic resource selection • Application aware task scheduling
  7. 7. IaaS Provider EEA Zoning jClouds API Support BLOB storage support Per- hour instance billing API Access Published price VM Image Import / Export Relational DB support Score Weight 20 20 10 5 5 5 3 2 1 Amazon AWS 1 1 1 1 1 1 0 1 27 2 Rackspace 1 1 1 1 1 1 0 1 27 3 SoftLayer 1 1 1 1 1 1 0 0 25 4 CloudSigma 1 1 0 1 1 1 1 0 18 5 ElasticHosts 1 1 0 1 1 1 1 0 18 6 Serverlove 1 1 0 1 1 1 1 0 18 7 GoGrid 1 1 0 1 1 1 0 0 15 8 Terremark ecloud 1 1 0 1 1 0 1 0 13 9 RimuHosting 1 1 0 0 1 1 0 1 12 10 Stratogen 1 1 0 0 1 0 1 0 8 11 Bluelock 1 1 0 0 1 0 0 0 5 12 Fujitsu GCP 1 1 0 0 1 0 0 0 5 13 BitRefinery 0 0 0 0 0 1 0 1 0 14 BrightBox 1 0 0 1 1 1 1 0 0 15 BT Global Services 1 0 0 0 1 0 1 0 0 16 Carpathia Hosting 1 0 0 0 0 0 1 0 0 17 City Cloud 1 0 0 1 1 1 0 0 0 18 Claris Networks 0 0 0 1 0 0 0 0 0 19 Codero 0 0 0 1 1 1 0 0 0 20 CSC 1 0 0 0 0 0 1 0 0 21 Datapipe 1 0 0 1 1 0 0 0 0 22 e24cloud 1 0 0 1 0 1 0 0 0 23 eApps 0 0 0 0 0 1 0 0 0 24 FlexiScale 1 0 0 1 1 1 1 0 0 25 Google GCE 1 0 1 1 1 1 0 1 0 26 Green House Data 0 0 0 0 1 0 1 0 0 27 Hosting.com 0 0 0 0 0 1 1 1 0 28 HP Cloud 0 1 1 1 1 1 1 1 0 29 IBM SmartCloud 0 0 1 1 1 1 0 1 0 30 IIJ GIO 0 0 0 0 0 0 0 0 0 31 iland cloud 1 0 0 1 0 1 1 0 0 32 Internap 0 0 1 1 1 1 0 0 0 33 Joyent 0 0 0 1 1 1 0 0 0 34 LunaCloud 1 0 1 1 1 1 0 0 0 35 Oktawave 1 0 1 1 1 1 0 1 0 36 Openhosting.co.uk 1 0 0 0 0 1 0 0 0 37 Openhosting.com 0 1 0 1 1 1 1 0 0 38 OpSource 1 0 1 1 1 1 1 0 0 39 ProfitBricks 1 0 0 1 1 1 0 0 0 40 Qube 1 0 0 0 0 1 0 0 0 41 ReliaCloud 0 0 0 0 0 0 0 0 0 42 SaavisDirect 0 0 1 1 0 1 0 0 0 43 SkaliCloud 0 1 0 1 1 1 1 0 0 44 Teklinks 0 0 0 0 0 0 0 0 0 45 Terremark vcloud 0 1 0 1 1 1 1 0 0 46 Tier 3 0 0 0 0 1 0 0 0 0 47 Umbee 1 0 0 1 1 1 1 0 0 48 VPS.net 1 0 0 0 1 1 0 0 0 49 Windows Azure 1 0 1 1 1 1 0 1 0 • 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, poster at CCGrid2013 - 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, Delft, the Netherlands, May 13-16, 2013 Cloud performance evaluation
  8. 8. • Infrastructure model – Multiple compute and storage clouds – Heterogeneous instance types • Application model – Bag of tasks – Leyered workflows • Modeling with AMPL (A Modeling Language for Mathematical Programming) • Cost optimization under deadline constraints • Mixed integer programming • Bonmin, Cplex solvers 0 500 1000 1500 2000 2500 3000 0 10 20 30 40 50 60 70 80 90 100 Cost($) Time limit (hours) 20000 tasks, 512 MiB input and 512 MiB output, task execution time 0.1h @ 1ccu machine Rackspace instances Rackspace and private instances Amazon's and private instances Multiple providers Amazon S3 Rackspace Cloud Files Optimal Layer 1 A Layer 2 B B B C Layer 3 D Layer 4 E Layer 5 F 1h 2.5 h 0.5 h 0.3 h 2 h 6 h 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 Private cloud Compute private Amazon Storage Compute m1.small m1.large t1.micro m2.xlarge Task Input Output Application Rackspace Storage Compute rs.1gb rs.2gb rs.4gb rs.16gb Cost optimization of applications on clouds
  9. 9. VPH-Share Master Int. AdminDeveloper Scientist Development Mode VPH-Share Core Services Host OpenStack/Nova Computational Cloud Site Worker Node Worker Node Worker Node Worker Node Worker Node Worker Node Worker Node Worker Node Head Node Image store (Glance) Cloud Facade (secure RESTful API ) Other CS Amazon EC2 Atmosphere Management Service (AMS) Cloud stack plugins (Fog) Atmosphere Internal Registry (AIR) Cloud Manager Generic Invoker Workflow management External application Cloud Facade client Customized applications may directly interface Atmosphere via its RESTful API called the Cloud Facade The Atmosphere Cloud Platform is a one-stop management service for hybrid cloud resources, ensuring optimal deployment of application services on the underlying hardware. P. Nowakowski, T. Bartynski, T. Gubala, D. Harezlak, M. Kasztelnik, M. Malawski, J. Meizner, M. Bubak: Cloud Platform for Medical Applications, eScience 2012 (2012) Resource allocation management
  10. 10. DRI is a tool which can keeps track of binary data stored in a cloud infrastructure, monitor data availability and faciliate optimal deployment of application services in a hybrid cloud (bringing computations to data or the other way around). Binary data registry LOBCDER Amazon S3 OpenStack Swift Cumulus Register files Get metadata Migrate LOBs Get usage stats (etc.) Distributed Cloud storage Store and marshal data End-user features (browsing, querying, direct access to data, checksumming) VPH Master Int. Data management portlet (with DRI management extensions) DRI Service A standalone application service, capable of autonomous operation. It periodically verifies access to any datasets submitted for validation and is capable of issuing alerts to dataset owners and system administrators in case of irregularities.Validation policy Configurable validation runtime (registry-driven) Runtime layer Extensible resource client layer Metadata extensions for DRI Data reliability and integrity
  11. 11. Data security in clouds Jan Meizner, Marian Bubak, Maciej Malawski, and Piotr Nowakowski: Secure storage and processing of confidential data on public clouds. In: Proceedings of the International Conference On Parallel Processing and Applied Mathematics (PPAM) 2013 • To ensure security of data in transit • Modern applications use secure tranport protocols (e.g.TLS) • For legacy unencrypted protocols if absolutly needed, or as additional security measure: – Site-to-Site VPN, e.g. between cloud sites is outside of the instance, might use – Remote access – for individual users accessing e.g. from their laptops • Data should be secure stored and realiable deleted when no longer needed • Clouds not secure enough, data optimisations preventing ensuring that data were deleted • A solution: – end-to-end encryption (decryption key stays in protected/private zone) – data dispersal (portion of data, dispersed between nodes so it’s non-trivial/impossible to recover whole message)
  12. 12. • GworkflowDL language (with A. Hoheisel) • Dynamic, ad-hoc refinement of workflows based on semantic description in ontologies • Novelty – Abstract, functional blocks translated automatically into computation unit candidates (services) – Expansion of a single block into a subworkflow with proper concurrency and parallelism constructs (based on Petri Nets) – Runtime refinement: unknown or failed branches are re-constructed with different computation unit candidates T. Gubala, D. Harezlak, M. Bubak, M. Malawski: Semantic Composition of Scientific Workflows Based on the Petri Nets Formalism. In: "The 2nd IEEE International Conference on e-Science and Grid Computing", IEEE Computer Society Press, http://doi.ieeecomputersociety.org/10.1109/E-SCIENCE.2006.127, 2006 Semantic workflow composition
  13. 13. • Design of a laboratory for virologists, epidemiologists and clinicians investigating the HIV virus and the possibilities of treating HIV-positive patients • Based on notion of in-silico experiments built and refined by cooperating teams of programmers, scientists and clinicians • Novelty – Employed full concept-prototype- refinement-production circle for virology tools – Set of dedicated yet interoperable tools bind together programmers and scientists for a single task – Support for system-level science with concept of result reuse between different experiments T. Gubala, M. Bubak, P. M. A. Sloot: Semantic Integration of Collaborative Research Environments, chapter XXVI in “Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine and Healthcare”, Information Science Reference IGI Global 2009, ISBN: 978-1-60566-374-6, pages 514-530 Cooperative virtual laboratory for e-Science
  14. 14. T. Gubala, K. Prymula, P. Nowakowski, M. Bubak: Semantic Integration for Model-based Life Science Applications. In: SIMULTECH 2013 Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications, Reykjavik, Iceland 29 - 31 July, 2013, pp. 74-81 • Concept of describing scientific domains for in-silico experimentation and collaboration within laboratories • Based on separation of the domain model, containing concepts of the subject of experimentation from the integration model, regarding the method of (virtual) experimentation (tools, processes, computations) • Facets defined in integration model are automatically mixed-in concepts from domain model: any piece of data may show any desired behavior • Proposed, designed and deployed the method for 3 domains of science: – Computational chemistry inside InSilicoLab chemistry portal – Sensor processing for early warning and crisis simulation in UrbanFlood EWS – Processing of results of massive bioinformatic computations for protein folding method comparison – Composition and execution of multiscale simulations – Setup and management of VPH applications Semantic integration for science domains
  15. 15. GridSpace - platform for e-Science applications • Experiment: an e-science application composed of code fragments (snippets), expressed in either general- purpose scripting programming languages, domain-specific languages or purpose-specific notations. Each snippet is evaluated by a corresponding interpreter. • GridSpace2 Experiment Workbench: a web application - an entry point to GridSpace2. It facilitates exploratory development, execution and management of e-science experiments. • Embedded Experiment: a published experiment embedded in a web site. • GridSpace2 Core: a Java library providing an API for development, storage, management and execution of experiments. Records all available interpreters and their installations on the underlying computational resources. • Computational Resources: servers, clusters, grids, clouds and e- infrastructures where the experiments are computed. E. Ciepiela, D. Harezlak, J. Kocot, T. Bartynski, M. Kasztelnik, P. Nowakowski, T. Gubała, M. Malawski, M. Bubak: Exploratory Programming in the Virtual Laboratory. In: Proceedings of the International Multiconference on Computer Science and Information Technology, pp. 621- 628, October 2010, the best paper award.
  16. 16. Goal: Extending the traditional scientific publishing model with computational access and interactivity mechanisms; enabling readers (including reviewers) to replicate and verify experimentation results and browse large-scale result spaces. Challenges: Scientific: A common description schema for primary data (experimental data, algorithms, software, workflows, scripts) as part of publications; deployment mechanisms for on-demand reenactment of experiments in e-Science. Technological: An integrated architecture for storing, annotating, publishing, referencing and reusing primary data sources. Organizational: Provisioning of executable paper services to a large community of users representing various branches of computational science; fostering further uptake through involvement of major players in the field of scientific publishing. P. Nowakowski, E. Ciepiela, D. Harężlak, J. Kocot, M. Kasztelnik, T. Bartyński, J. Meizner, G. Dyk, M. Malawski: The Collage Authoring Environment. In: Proceedings of the International Conference on Computational Science, ICCS 2011 (2011), Winner of the Elseview/ICCS Executable Paper Grand Challenge E. Ciepiela, D. Harężlak, M. Kasztelnik, J. Meizner, G. Dyk, P. Nowakowski, M. Bubak: The Collage Authoring Environment: From Proof-of- Concept Prototype to Pilot Service in Procedia Computer Science, vol. 18, 2013 Collage - executable e-Science publications
  17. 17. 17 Jun 2012 • Goal: Extend the traditional way of authoring and publishing scientific methods with computational access and interactivity mechanisms thus bringing reproducibility to scientific computational workflows and publications • Scientific challenge: Conceive a model and methodology to embrace reproducibility in scientific worflows and publications • Technological challenge: support these by modern Internet technologies and available computing infrastructures • Solution proposed: • GridSpace2 – web-oriented distributed computing platform • Collage – authoring environment for executable publications Dec 2011 Jun 2011 GridSpace2 / Collage - Executable e-Science Publications
  18. 18. Results: • GridSpace2/Collage won Executable Paper Grand Challenge in 2011 • Collage was integrated with Elsevier ScienceDirect portal so papers can be linked and presented with corresponding computational experiments • Special Issue of Computers & Graphics journal featuring Collage- based executable papers was released in May 2013 • GridSpace2/Collage has been applied to multiple computational workflows in the scope of PL-Grid, PL-Grid Plus and Mapper projects E. Ciepiela, P. Nowakowski, J. Kocot, D. Harężlak, T. Gubała, J. Meizner, M. Kasztelnik, T. Bartyński, M. Malawski, M. Bubak: Managing entire lifecycles of e-science applications in the GridSpace2 virtual laboratory–from motivation through idea to operable web-accessible environment built on top of PL-grid e-infrastructure. In: Building a National Distributed e-Infrastructure–PL-Grid, 2012 P. Nowakowski, E. Ciepiela, D. Harężlak, J. Kocot, M. Kasztelnik, T. Bartyński, J. Meizner, G. Dyk, M. Malawski: The Collage Authoring Environment. In: Procedia Computer Science, vol. 4, 2011 GridSpace2 / Collage - Executable e-Science Publications E. Ciepiela, D. Harężlak, M. Kasztelnik, J. Meizner, G. Dyk, P. Nowakowski, M. Bubak: The Collage Authoring Environment: From Proof-of-Concept Prototype to Pilot Service. In: Procedia Computer Science, vol. 18, 2013
  19. 19. Common Information Space (CIS) • Facilitate creation, deployment and robust operation of Early Warning Systems in virtualized cloud environment • Early Warning System (EWS): any system working according to four steps: monitoring, analysis, judgment, action (e.g. environmental monitoring) B. Balis, M. Kasztelnik, M. Bubak, T. Bartynski, T. Gubala, P. Nowakowski, J. Broekhuijsen: The UrbanFlood Common Information Space for Early Warning Systems. In: Elsevier Procedia Computer Science, vol 4, pp 96-105, ICCS 2011. Common Information Space • connects distributed component into EWS and deploy it on cloud • optimizes resource usage taking into acount EWS importance level • provides EWS and self monitoring • equipped with autohealing
  20. 20. • Simple yet expressive model for complex scientific apps • App = set of processes performing well-defined functions and exchanging signals HyperFlow model JSON serialization { "name": "...",  name of the app "processes": [ ... ],  processes of the app "functions": [ ... ],  functions used by processes "signals": [ ... ],  exchanged signals info "ins": [ ... ],  inputs of the app "outs": [ ... ]  outputs of the app } • Supports a rich set of workflow patterns • Suitable for various application classes • Abstracts from other distributed app aspects (service model, data exchange model, communication protocols, etc.) HyperFlow: model & execution engine
  21. 21. • HyperFlow model & engine for distributed apps • App optimization & scheduling • Autoscaling and dynamic app reconfiguration • Multi-cloud resource provisioning Execution Platform Provisioning platform VM VM VM Cloud VM VM Executor Input data Trigger app execution Monitoring Provisioner Start/Stop/Reconfigure VM Autoscaler Optimizer & Scheduler Reconfigure app Scaling rules measuremants HyperFlow Enactment Engine Enact Execute App model App state Composite App Initial deployment Platform for distributed applications
  22. 22. Objectives • Provide means for ad-hoc metadata model creation and deployment of corresponding storage facilities • Create a research space for metadata model exchange and discovery with associated data repositories with access restrictions in place • Support different types of storage sites and data transfer protocols • Support the exploratory paradigm by making the models evolve together with data Architecture • Web Interface is used by users to create, extend and discover metadata models • Model repositories are deployed in the PaaS Cloud layer for scalable and reliable access from computing nodes through REST interfaces • Data items from Storage Sites are linked from the model repositories Colaborative metadata management
  23. 23. • MAPPER Memory (MaMe) a semantics- aware persistence store to record metadata about models and scales • Multiscale Application Designer (MAD) visual composition tool transforming high level description into executable experiment • GridSpace Experiment Workbench (GridSpace) execution and result management of experiments choose/add/delete Mapper A Mapper B Submodule A Submodule B MADGridSpace MaMe K. Rycerz, E. Ciepiela, G. Dyk, D. Groen, T. Gubala, D. Harezlak, M. Pawlik, J. Suter, S. Zasada, P. Coveney, M. Bubak: Support for Multiscale Simulations with Molecular Dynamics, Procedia Computer Science, Volume 18, 2013, pp. 1116-1125, ISSN 1877-0509 K. Rycerz, M. Bubak, E. Ciepiela, D. Harezlak, T. Gubala, J. Meizner, M. Pawlik, B.Wilk: Composing, Execution and Sharing of Multiscale Applications, submitted to Future Generation Computer Systems, after 1st review (2013) K. Rycerz, M. Bubak, E. Ciepiela, M. Pawlik, O. Hoenen, D. Harezlak, B. Wilk, T. Gubala, J. Meizner, and D. Coster: Enabling Multiscale Fusion Simulations on Distributed Computing Resources, submitted to PLGrid PLUS book 2014 • A method and an environment for composing multiscale applications from single-scale models • Validation of the the method against real applications structured using tools • Extension of application composition techniques to multiscale simulations • Support for multisite execution of multiscale simulations • Proof-of-concept transformation of high-level formal descriptions into actual execution using e-infrastructures Multiscale programming and execution tools
  24. 24. Research on Feature Modeling: • modelling eScience applications family component hierarchy • modelling requirements • methods of mapping Feature Models to Software Product Line architectures Research on adapting Software Product Line principles in scientific software projects: • automatic composition of distributed eScience applications based on Feature Model configuration • architectural design of Software Product Line engine framework B. Wilk, M. Bubak, M. Kasztelnik: Software for eScience: from feature modeling to automatic setup of environments, Advances in Software Development, Scientific Papers of the Polish Informations Processing, Society Scientific Council, 2013 pp. 83-96 Building scientific software based on Feature Model
  25. 25. CrossGrid 2002-2005 Interactive compute- and data-intensive applications K-Wf Grid 2004-2007 Knowledge-based composition of grid workflow applications CoreGRID 2004-2008 Problem solving environments, programming models for grid applications GREDIA 2006-2009 Grid platform for media and banking applications ViroLab 2006-2009 Script based composition of applications, GridSpace virtual laboratory PL-Grid; + 2009-2015 Advanced virtual laboratory, DataNet – metadata models (2 large Polish projects) gSLM 2009-2012 Service level management for grid and clouds UrbanFlood 2009-2012 Common Information Space for Early Warning Systems MAPPER 2010-2013 Computational strategies, software and services for distributed multiscale simulations VPH-Share 2011-2015 Federating cloud resources for VPH compute- and data intensive applications Collage 2011-2013 Executable Papers; 1st award of Elsevier Competition at ICCS2011 (Elsevier project) ISMOP 2013-2016 Management of cloud resources, workflows, big data storage and access, analysis tools (MCBiR) PaaSage 2013-2016 Optimization of workflow applications on cloud resources DICE team in EU projects
  26. 26. • Optimization of service deployment on clouds – Constraint satisfaction and optimization of multiple criteria (cost, performance) – Static deployment planning and dynamic auto-scaling • Billing and accounting model – Adapted for the federated cloud infrastructure – Handle multiple billing models • Supporting system-level (e)Science – tools for effective scientific research and collaboration – advanced scientific analyses using HPC/HTC resources • Cloud security – security of data transfer – reliable storage and removal of the data • Cross-cloud service deployment based on container model Topics for collaboration dice.cyfronet.pl

×