Cloud computing. Intelligent Services for Energy-Efficient Design and Life Cycle Simulation. as used by the ISES project

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1 Intelligent Services for Energy-Efficient Design and Life Cycle Simulation Project number: Call identifier: FP7-ICT Project coordinator: Technische Universität Dresden, Germany Website: ises.eu-project.info Cloud computing as used by the ISES project Munich, Germany, BuildingSMART BIM week 2013 BIM for energy-efficient buildings Matevž Dolenc Robert Klinc

2 Overview Definitions - Cloud computing - Engineering in the cloud Cloud computing - overview ISES use of the cloud - overview, examples, benefits Summary

3 Definitions Cloud computing is a model for enabling ubiquitous, convenient, ondemand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. P. Mell and T. Grance, The NIST Definition of Cloud Computing: Recommendations of the National Institute of Standards and Technology, Engineering in the cloud is a combination of cloud services and rich interactive applications that provides integrated, intelligent, self-service engineering services over and above engineering- application hosting and computation allowing engineers to create, explore, and discover better designs faster. P. Williams and S. Cox, (June 2009), Engineering in the Cloud: An Engineering Software + Services Architecture Forged in Turbulent Times,

4 But it is more or less the same as...

5 But it is more or less the same as... Autonomic computing Minitel Grid computing Thin client Client-server model Utility computing Peer-to-peer Mainframe computing Service-oriented computing

6 Cloud computing landscape and benefits Cloud computing benefits - Access data/services at any place, from any device and at any time - Lower cost of entry - Reliability, scalability, security and sustainability - Minimize infrastructure risk - Reduce run time and response time - Increased pace of innovation J. Keith & N.L. Burkhard (2010), The Future of Cloud Computing: Opportunities for European Cloud Computing Beyond 2010,

7 Cloud deployment modes Public - General purpose, pricing; Ex. AWS, Google Apps, Microsoft Azure,... Private - Security, business related features, infrastructure cost; Ex. CloudStack, OpenNebula,... Hybrid - Mixed employment of private and public cloud, the best of both worlds Cloud Survey predicts in 5 years 75% hybrid cloud sytems NorthBridge and GigaOM, 2013 Cloud Survey,

8 Cloud computing features On-demand self-service - Automatic provisioning of comp. capabilities (e.g. server time, storage,...) as needed. Broad network access - Use of standard networking mechanisms - thin and thick clients. Resource pooling - Computing resources are pooled in a multi-tenant model - location independence. Rapid elasticity - Capabilities are elastically provisioned and released - a sense of unlimited resources. Measured service

9 Cloud computing system types Software as a Service (SaaS) - Use of the provider s applications running on a cloud infrastructure. Platform as a Service (PaaS) - Deploy consumer-created or acquired applications created using API supported by the cloud platform provider. Cloud Software as a service (SaaS) Attached services Platform as a Service (PaaS) Application Extra Functions Application Pltaform Infrastructure as a Service (IaaS) Local Browser / Client Application Application - Provision processing, storage, networks, and other fundamental computing resources. Users Users Developers

10 Cloud computing barriers to adoption Infrastructure - Network - Availability of a service Technology - Performance unpredictability - Scalability (computing, storage, bandwidth,...) - Bugs in large-scale distributed systems Social - Reputation, fate sharing - Security Trust Business - Software licensing - Business models - Data lock-in - Data confidentiality and auditability

11 Engineering in the cloud: examples Computational analyses - Parallel applications (HPC) - Parametric studies (HTC)! Building Information Modeling - Desktop virtualization - Data sharing - Visualization - Collaboration!!

12 ISES cloud requirements Applications - Transparent use of Windows / Linux applications - Console application - Integration with existing services Types of analyses - Stochastic / Parametric studies - Parallel applications (MPI) Scalability - Private cloud extensibility - Hybrid cloud Storage - Integration with public cloud storage systems User access - Web based - API

13 ISES cloud architecture and testbed LOCAL REMOTE Hardware specs local data local results external/remote data external/remote results - IntelR XeonR Processor (2.26 GHz), 8 GB RAM Web browser CPU cores - Fiber-Channel disk array 5 TB vel.ises.eu-project.info ISES Cloud API ISES app ISES app ISES app Software - Ubuntu Server LTS AWS resources ISES resources Parametric studies Parallel MPI applications - OpenStack cloud infrastructure, HTCondor, MPI enabled - General purpose software: MATLAB, BLAS, LINPACK, - ISES specific applications (energy related)

14 ISES cloud architecture and testbed Hardware specs - IntelR XeonR Processor L5520 (2.26 GHz) - 8MB shared L3 cache 8GB - Fiber-Channel disk array 5 TB Software - Ubuntu Server LTS - OpenStack cloud infrastructure - HTCondor, MPI enabled - General purpose software: MATLAB, BLAS, LINPACK,

15 ISES use of the cloud: analysis Parametric analysis - Generating large parametric studies - Parametric studies execute one application many times with different sets of input parameters - High-throughput computing environment - Example: Granlund Riuska CFD analysis - Computational fluid dynamics, time consuming - Parallel applications use traditional computational clusters - High-performance computing environment - Example: Sofistik CFD

16 ISES use of the cloud: HTC Granlund Riuska - Efficient and versatile comfort and energy simulation application. - Standalone solver - Windows application - Running on Ubuntu LTS (Wine environment) Parametric studies - Use of independent computer systems - Example: Run a parameter sweep of F(x,y,z) for 20 values of x, 10 values of y and 3 values of z (20*10*3 = 600 combinations) Benchmark (IDA curves) - Number of analyses: Average analysis time: ~13min Number of computers Analysis time [hours] Speed-up factor

17 ISES use of the cloud: HTC

18 ISES use of the cloud: HTC

19 ISES use of the cloud: HTC

20 ISES use of the cloud: HTC

21 ISES use of the cloud: HPC Sofistik CFD - Parallel CFD analysis tool for 3D unsteady, incompressible, turbulent, buoyancy-driven flows. - Complementary tools (geometrical modeler, mesh generation tools, post-processing tools, etc.) Parallel processing of CFD solver - MPI protocol (MPICH2, OpenMPI) - 64-bit Linux - Synchronization - restricted parallelization - Small number of large messages

22 ISES use of the cloud: HPC CFD simulations in the context of ISES - 3D air flow inside a room (indoor climate) - coupled flow-thermal problem Numerical mesh hexahedral elements / nodes

23 ISES use of the cloud: HPC CFD simulations in the context of ISES - 3D wind flow around a tall building (outdoor climate) Numerical mesh elements / nodes

24 ISES use of the cloud: HPC CFD simulations in the context of ISES - 3D wind flow around a tall building (outdoor climate) Numerical mesh elements / nodes

25 ISES use of the cloud: HPC CFD simulations in the context of ISES 3D wind flow around a block of buildings in a city environment (outdoor climate taking into account building's interference (runtime approx. 6 days - 16 CPUs / realtime: approx. 11 minutes ) Numerical mesh tetrahedral elements / nodes -

26 ISES use of the cloud: HPC Questions - How public cloud MPI clusters compare to traditional scientific HPC clusters? - What about use of public cloud virtual computers for HTC? we see approximately 40% 1000% performance degradation in the EC2 compared to the NCSA runs. Greater then 200% performance degrad seen in the programs CG, FT, IS, IU, and MG. Surprisingly, even EP ( rassingly parallel), where no message-passing communication is perfo during the computation and only a global reduction is performed at th exhibits approximately 50% performance degradation in the EC2 run Walker E., (2008),Benchmarking Amazon EC2 for high-performance scientific computing,

27 Summary Cloud technology - Front-end: Website, mobile, API - Back-end: virtualization, scalability, management, accounting, storage,... - New business opportunities for software/service providers, infrastructure providers Cloud Survey (in 5 years 75% - hybrid cloud systems) - Bring Your Own Device (BYOD) Engineering in the cloud - BIM, analyses (HPC / HTC), collaboration Start with the requirements / use-cases / user scenarios

28 Intelligent Services for Energy-Efficient Design and Life Cycle Simulation Project number: Call identifier: FP7-ICT Project coordinator: Technische Universität Dresden, Germany Website: ises.eu-project.info The End Munich, Germany, BuildingSMART BIM week 2013 BIM for energy-efficient buildings Matevž Dolenc Robert Klinc

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