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This document provides an overview of the CERN School of Computing (CSC) held in August 2010. It discusses the organization of the CSC which covers introductory physics computing, common tools and techniques like ROOT, and data analysis. It also covers various base computing technologies like computer architecture, creating secure software, virtualization, and networking quality of service. Finally, it discusses data technologies and storage systems. The overall goal of the CSC is to provide students and researchers an overview of computing technologies relevant to particle physics experiments and concepts in particle physics.
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- 1. CSC 2010 Brunel University, London August 2010 Almudena Montiel Gonzalez GSI 1
- 2. What is CSC? CERN school of Computing For postgraduate students and research workers To give an overview of some computing technologies involved in particle physics and some concepts concerning this kind of physics 2
- 3. Organization Physics Base Data Computing Technologies Technologies - Computer Architecture - Intro to physics and Performance Tuning computing - Creating Secure - Tools and techniques ROOT Software - Data Technologies - Tools and techniques ROOT - Virtualization - Data Analysis - Networking QoS and Performance 3
- 4. Organization Physics Computing Physics Base Data Computing Technologies Technologies - Intro to physics - IntroComputer Architecture - to physics and Performance Tuning computing computing Secure - Creating - Tools and techniques - ROOT - ROOTSoftware - Data Technologies - Virtualization - Data Analysis - ToolsNetworking QoS and - and techniques Performance - Data Analysis 3
- 5. General introduction to Physics Computing Software and hardware components required for the processing of the experimental data, from the source to the physics analysis The main goal is data reduction: Very high event rate (40MHz) Event size (>10MB) Large background 4
- 6. General introduction to Physics Computing Online processing Trigger: Event selection Data acquisition: Interface to detector HW Monitoring Control 5
- 7. General introduction to Physics Computing Subdetector at CMS https://cms.web.cern.ch/cms/Resources/Website/Media/Videos/Animations/files/CMS_Slice.gif 6
- 8. General introduction to Introduction to Physics Computi CERN School of Computing 2010, Physics Computing CMS L1 trigger example back-to-back opposite sign isol CMS Level 1 trigger muons input rate 40MHz output rate 30-100kHz 2 detector systems: muons/ calorimeters High level filter input rate 30-100kHz output rate 100-150Hz CSC 2010 29 Rudi Frühwirth, HEPHY Vienna CSC 2010 Physics Computing General Introduction to Phys 7 197
- 9. General introduction to Introduction to Physics Computi CERN School of Computing 2010, Physics Computing CMS L1 trigger example back-to-back opposite sign isol CMS Level 1 trigger muons input rate 40MHz output rate 30-100kHz 2 detector systems: muons/ calorimeters High level filter input rate 30-100kHz output rate 100-150Hz CSC 2010 29 Rudi Frühwirth, HEPHY Vienna Raw Data sent to Physics Computing 325MB/s CSC 2010 Tier-0 farm General Introduction to Phys 7 197
- 10. General introduction to Physics Computing Offline processing Calibration Alignment Event Reconstruction Simulation Physics analysis 8
- 11. Introduction to Physics Computing General introduction to CERN School of Computing 2010, Uxbridge Physics Computing Silicon Tracker calibration Incoming particle creates electric Offline processing strips or p charge in g p pixels Calibration Alignment Event Reconstruction Simulation Incoming particle CSC 2010 Physics analysis 45 Rudi Frühwirth, HEPHY Vienna CSC 2010 Physics Computing General Introduction to Physics Computing Lect 8 213
- 12. General introduction to Physics Computing Offline processing Calibration Alignment Event Reconstruction Simulation Physics analysis 8
- 13. General introduction to Physics Computing Introduction to Physics Computing CERN School of Computing 2010, Uxbridge Neutral particles (ctd) Offline processing Calibration Alignment Event Reconstruction Simulation Physics analysis CSC 2010 Rudi Frühwirth, HEPHY Vienna 61 8
- 14. General introduction to Physics Computing Offline processing Calibration Alignment Event Reconstruction Simulation Physics analysis 8
- 15. ROOT It is an object-oriented program and library developed by CERN for particle physics analysis. Developed in 1995, but from 2003 written in C++. What does it provides: Data storage, access and query system. Statistical analysis algorithms. Scientific visualization: 2D, 3D, PDF, LateX Geometrical modeler PROOF parallel query engine 9
- 16. ROOT It is an object-oriented program and library developed by CERN for particle physics analysis. Developed in 1995, but from 2003 written in C++. What does it provides: Data storage, access and query system. Statistical analysis algorithms. Scientific visualization: 2D, 3D, PDF, LateX Geometrical modeler PoD PROOF parallel query engine 9
- 17. ROOT 10
- 18. ROOT 10
- 19. histograms, functions, parametric e ROOT and to visualize 3D objects (geome 10
- 20. ROOT 10
- 21. Tools and Techniques Software design and modern tools for Physics Computing. As individual Testing: Junit, CppUnit Memory related problems - allocation, memory leaks - malloc, MALLOC_CHECK, memprof, ccmalloc, etc. Performance tools: perfAnal. As part of large code projects Controlling and versioning: CVN, SVN Releases and configuration management of systems: CMS 11
- 22. Organization Physics Base Data Computing Technologies Technologies - Computer Architecture - Intro to physics and Performance Tuning computing - Creating Secure - Tools and techniques Software - Data Techonlogies - ROOT - Virtualization - Data Analysis - Networking QoS and Performance 12
- 23. Organization Physics Base Data Computing Base Technologies Technologies Technologies - Computer Architecture - Intro to physics computing - Computer Secure Tuning and Performance - Creating Architecture and - Tools and techniques - ROOT Performance Tuning - Data Techonlogies Software - Virtualization - Data Analysis - Creating Secure and - Networking QoS Software - Virtualization Performance - Networking QoS and Performance 12
- 24. Computer Architecture Seven dimensions of performance Computer Architecture and Performance Tuning and performance tuning Computer Architecture and Performance Tuning nsions of performance First three dimensions: Superscalar Pipelining p g imensions: Pipelining Pipelining p g Computational width/SIMD Introduction to processor layout. chitecture and Performance Tuning Superscalar performance al width/SIMD dimension is a “pseudo” Next dimension: SIMD width s:is a “pseudo”dimensions of performance 7Hardware multithreading Superscalar Multithreading n SIMD width Nodes Pipelining p g ast three dimensions: Multithreading ultithreadingLast t ee d e s o s Multiple cores Nodes D so s e ensions: Sockets Multiple sockets Superscalar s do” Multiple compute nodesSockets kets SIMD width Multicore 19 Multithreading pute nodes= Single Instruction Multiple Data SIMD Sverre Jarp - CERN NodesOverall impact of programming styles and compilers Multicore Data CSC 2010 Base CERN Sverre Jarp - Technologies Computer Architecture and Performance Tuning Lecture 1 & 2 817 s Metrics to define application performance: CPI, #branch Computer Architecture and Performance Tuning Lecture 1 & 2 Sockets 817 instructions, mispredicted branches, #SSE instructions, fails cache. Multicore Jarp - CERN Performance monitoring with pfmon and Perfmon2 chitecture and Performance Tuning Lecture 1 & 2 17 13
- 25. Creating secure software Protection, detection, reaction Threats (and solutions) are not only technical: social engineering 14
- 26. Network QoS and performance RSVP / NSIS protocols (simplified) Base Technologies / Networking QoS and Performance QoS options: Flow RES R R R Flow senderTechnologies / Networking QoS and Performance RESP Receiver Base Diffserv PrincipleNSIS/RSVP R Base Technologies / Networking QoS and Performance MPLS NSIS/RSVP Priority Mark Priority traffic P2 Priority traffic P1 RESERVE control message sent periodically byflowing inserted Create a “circuit” Traffic source before Pkts enter the (called MPLS path) R over th the “QoS core” MPLS path Diffserv Regular traffic Force all traffic with R Simple examination “Marked” destination of mark provides R same priority receiver replies with a RESPONSE control message R packets same Qos requirement MPLS RESPONSE reserve resources on Rthe route back to follow the same DiffServ path MPLS MPLS path if RESERVE not repeated after time-out, resources released TCP, UDP and RTP protocols in real-time 37 François Fluckiger – CERN CSC 2010 Base Technologies Networking QoS and PerformanceFrançois Fluckiger – 2 42 Lecture 1 and CERN CSC 2010 Base Technologies Networking QoS and Performance Lecture 1 and 2 28 972 François Fluckiger – CERN streaming traffic over the Internet 977 CSC 2010 Base Technologies Networking QoS and Performance 963 15
- 27. Virtualization Virtualization refers to technologies designed to provide a layer of abstraction between computer hardware systems and the software running on them. 16
- 28. Virtualization Memory Resource Virt. mem Network Storage Virtualization Platform OS level Partial Full virtualization Application Paravirtualization HW assisted 17
- 29. Virtualization: Introduction to virtualization technology Hypervisor Architecture Virtualization A technique that all (software based) virtualization solutions Platform virtualization use is ring deprivileging: the It p operating system that runs g y hides the physical originally on ring of is computing characteristics 0 a moved to another less privileged ring like platform from the users ring 1. This allows the (hypervisor or l Thi Host softwareVMM to control ll th t t the guest OS access to VMM) creates a simulated resources. computer environment, a It avoids one guestfor itskicking virtual machine, OS guest another out of memory, or a OS. guest OS controlling the hardware directly. 18
- 30. Virtualization Partial virtualization The machine simulates only some parts of the host hardware environment. Does not allow any “guest” operating system to work. 19
- 33. Virtualization Why? Server consolidation Isolated sandboxes per user. Running untrusted applications will not risk the entire box Provisioning with no need of up-front purchase 22
- 34. Virtualization ...Why? Disaster recovery: the restarting and relocating of a VM is faster Developing: being able to run on different platforms Easier management, it is easier to automate, easier to scale the number of VMs up and down 23
- 35. Virtualization Use Cases Software testing: ETICS Software development: CernVM Volunteering computing: BOINC 24
- 36. Virtualization Use Case: Cloud Computing Get services on demand over the network Service: Software, Platform or Infrastructure 25
- 37. Virtualization: Application of the virtualization technology Virtualization Rethinking Application Deployment Use Case: CernVM Virtual Machine Application mphasis in the ‘Application’ Virtual appliance Libraries The application dictates the platform and not the contrary Runs on any virtualization platform and Tools provides consistent and effortless pplication (e.g. of experiment SW installation simulation) is Databases undled with its libraries, services OS nd bitsConfiguration of a CernVM image for a of OS Self-contained, self-describing, deployment ready specific experiment such as ALICE or LHCb and run some experiment specific What makes the Application ready to run in any target application xecution environment? e.g. Traditional, Grid, Cloud26
- 38. and group to ‘alice’ (we will need this for the next p Virtualization 27
- 39. Organization Physics Base Data Computing Technologies Technologies - Computer Architecture - Intro to physics and Performance Tuning computing - Creating Secure - Tools and techniques Software - Data Technologies - ROOT - Virtualization - Data Analysis - Networking QoS and Performance 28
- 40. Organization Physics Data Technologies Base Data Computing Technologies Technologies - Computer Architecture - Intro to physics and Performance Tuning computing - Creating Secure - Tools and techniques Software - Data Technologies - ROOT - Data Technologies - Virtualization - Data Analysis - Networking QoS and Performance 28
- 41. Data technologies Storage Technologies Physical and logical connectivity Complexity Hardware Components CPU, disk, memory, Storage Technologies PC, disk server motherboard O Network, Storage devices Cluster, Interconnects Local fabric RAID Wide area network World Wide G Cluster Man File Systems (local, 5 Bernd Panzer-Steindel - CERN network and cluster) CSC 2010 Data Technologies Storage Techn 1019 And many other concepts.. 29
- 42. Data technologies Storage Technologies Physical and logical connectivity Complexity Hardware Components CPU, disk, memory, Storage Technologies PC, disk server motherboard O Network, Storage devices Cluster, Interconnects Local fabric RAID Wide area network World Wide G Cluster Man File Systems (local, 5 Bernd Panzer-Steindel - CERN network and cluster) CSC 2010 Data Technologies Storage Techn 1019 And many other concepts.. 29
- 43. Data technologies Storage Technologies Physical and logical connectivity Complexity Hardware Components CPU, disk, memory, Storage Technologies PC, disk server motherboard O Network, Storage devices Cluster, Interconnects Local fabric RAID Wide area network World Wide G Cluster Man File Systems (local,systems I Cluster file Storage Technologies 5 Bernd Panzer-Steindel - CERN network and cluster) Aggregation of local file systems and Server nodes Clients CSC 2010 Data Technologies Storage Techn 1019 Meta-data server is the new important component Mapping of files to locations And many other Data base implementation (Oracle, MySQL, ….) Data base Control data flow between the clients and the concepts.. Meta-data server Data flow directly between clients and disk server Server S Two types of implementations : 1. Device driver implementation via the virtual file system the application accesses the data via a file system syntax th li ti th d t i fil t t mount point, looks like a local file system, same commands (ls, rm, mkdir, etc.) 2. Translation of application IO commands ( p , read, write, seek, close) via pp (open, , , , ) special IO library linked into the executable. Special commands for ls/rm/mkdir … 42 Bernd Panzer-Steindel - CERN 29
- 44. If you are interested: http://www-linux.gsi.de/~amontiel/CSC2010.pdf.gz 30
- 47. Any questions? 32
Editor's Notes
- \n
- the school has been run for more than 30 years, i attended the 33rd edition. \nOrganized since 1970. Director Francois Fluckiger.\nWe were about 50 students. \nThe teachers are experts in the field, normally from CERN collaboration institutions and also past students.\n2 weeks long \n 2-weeks long, from 8.30 to 19\n
- The lectures were divided into theoretical and practical sessions. \nI would be impossible to mention everything in this presentation, so i will just mention some keywords and main concepts learnt for each module\n
- The lectures were divided into theoretical and practical sessions. \nI would be impossible to mention everything in this presentation, so i will just mention some keywords and main concepts learnt for each module\n
- \n
- Decisions quick, crucial, data discard lost forever\nTrigger\nDAQ\nMonitoring the detector status, the DAQ performance , the trigger performance, data quality check\nControl: Configure system, Start/Stop data taking, initiate special runs, upload trigger tables\n
- Inner Tracker (pixels + strips): momentum + position of charged tracks\nElectromagnetic calorimeter: energy of photons, electrons and positrons.\nHadron calorimeter: energy of charged neutral hadrons\nMuon system: momentum and position of muons\n
- the trigger conditions are defined by the physics of the experiment\n\nCalorimeter trigger:  Two types of calorimeters: hadronic,\nelectromagnetic\n Local: Computes energy deposits\n Regional: Finds candidates for electrons, photons, jets, isolated hadrons; computes transverse energy sums\n Global: Sorts candidates in all categories, does total and missing transverse energy sums, computes jet multiplicities for different thresholds\n\n Muon trigger:  Three types of muon detectors\n Local: Finds track segments\n Regional: Finds tracks\n Global: Combines information from all regional triggers, selects best four muons, provides energy and direction\n\n Global trigger:\n Final decision logic\n 28 input channels (muons, jets, electrons, photons, total/missing ET)\n 128 trigger algorithms running in parallel  128 decision bits  Apply conditions (thresholds, windows, deltas)  Check isolation bits  Apply topology criteria (close/opposite)\n\n\n
- Until here online, everything was about data taken in real time. \nNow offline. COMPRESSING\nCalibration: convert the raw data, analogical or digital, to physical quantities, like energy or position. Silicon Tracker calibration-> Solve inverse problem: reconstruct crossing point from charge distribution and crossing angle. Very detector dependent.using partile track data to check if all the current detector settings and postition and stuff is correct\nAlignment: find out precise detector positions\nEvent reconstruction: reconstruct particle tracks and vertices off all particle trajectories participating in one event. \nfind out which particles have been created where and with which momentum. Many can be observed directly. Some are short-lived and have to be reconstructed from their decay products.The difficulties: background from low-momentum, additional background from other interactions, energy loss.\nSimulation: simulate trayectories of particles where u take into account interactions that u cannot calculate\nNeed: - optimization of detector in design phase. \n- testing , validation, optimization of trigger and reconstruction algorithm.\n- compute acceptance corrections.\n generate artificial events resembling real data as closely as possible.\nPhysics analisys: ROOT\n
- Until here online, everything was about data taken in real time. \nNow offline. COMPRESSING\nCalibration: convert the raw data, analogical or digital, to physical quantities, like energy or position. Silicon Tracker calibration-> Solve inverse problem: reconstruct crossing point from charge distribution and crossing angle. Very detector dependent.using partile track data to check if all the current detector settings and postition and stuff is correct\nAlignment: find out precise detector positions\nEvent reconstruction: reconstruct particle tracks and vertices off all particle trajectories participating in one event. \nfind out which particles have been created where and with which momentum. Many can be observed directly. Some are short-lived and have to be reconstructed from their decay products.The difficulties: background from low-momentum, additional background from other interactions, energy loss.\nSimulation: simulate trayectories of particles where u take into account interactions that u cannot calculate\nNeed: - optimization of detector in design phase. \n- testing , validation, optimization of trigger and reconstruction algorithm.\n- compute acceptance corrections.\n generate artificial events resembling real data as closely as possible.\nPhysics analisys: ROOT\n
- Until here online, everything was about data taken in real time. \nNow offline. COMPRESSING\nCalibration: convert the raw data, analogical or digital, to physical quantities, like energy or position. Silicon Tracker calibration-> Solve inverse problem: reconstruct crossing point from charge distribution and crossing angle. Very detector dependent.using partile track data to check if all the current detector settings and postition and stuff is correct\nAlignment: find out precise detector positions\nEvent reconstruction: reconstruct particle tracks and vertices off all particle trajectories participating in one event. \nfind out which particles have been created where and with which momentum. Many can be observed directly. Some are short-lived and have to be reconstructed from their decay products.The difficulties: background from low-momentum, additional background from other interactions, energy loss.\nSimulation: simulate trayectories of particles where u take into account interactions that u cannot calculate\nNeed: - optimization of detector in design phase. \n- testing , validation, optimization of trigger and reconstruction algorithm.\n- compute acceptance corrections.\n generate artificial events resembling real data as closely as possible.\nPhysics analisys: ROOT\n
- Until here online, everything was about data taken in real time. \nNow offline. COMPRESSING\nCalibration: convert the raw data, analogical or digital, to physical quantities, like energy or position. Silicon Tracker calibration-> Solve inverse problem: reconstruct crossing point from charge distribution and crossing angle. Very detector dependent.using partile track data to check if all the current detector settings and postition and stuff is correct\nAlignment: find out precise detector positions\nEvent reconstruction: reconstruct particle tracks and vertices off all particle trajectories participating in one event. \nfind out which particles have been created where and with which momentum. Many can be observed directly. Some are short-lived and have to be reconstructed from their decay products.The difficulties: background from low-momentum, additional background from other interactions, energy loss.\nSimulation: simulate trayectories of particles where u take into account interactions that u cannot calculate\nNeed: - optimization of detector in design phase. \n- testing , validation, optimization of trigger and reconstruction algorithm.\n- compute acceptance corrections.\n generate artificial events resembling real data as closely as possible.\nPhysics analisys: ROOT\n
- Data storage: sofisticated data structures optimized for Write once read many (WROM) Ttrees\nMathematical library with advance algorithms statistics useful for simulation\nrendering openGL\n– geometrical modeller –Allows visualization of detector geometries\n - PROOF parallel query engine:start analysis locally ("client"),\n• PROOF distributes data and code, • lets CPUs ("workers") run the analysis, • collects and combines (merges) data, • shows analysis results locally\nPoD which allows starting a PROOF cluster at user request on any resource management system.\ndoesn’t require administrator privileges\n\n
- - histogramming and graphing to visualize and analyze distributions and functions,\n - curve fitting (regression analysis) and minimization of functionals,\n - statistics tools used for data analysis,\n - matrix algebra,\n - four-vector computations, as used in high energy physics,\n - standard mathematical functions,\n - multivariate data analysis, e.g. using neural networks,\naccess to distributed data (in the context of the Grid),\ndistributed computing, to parallelize data analyses,\npersistence and serialization of objects, which can cope with changes in class definitions of persistent data,\naccess to databases,\n3D visualizations (geometry)\ncreating files in various graphics formats, like PostScript, JPEG, SVG,\ninterfacing Python and Ruby code in both directions,\ninterfacing Monte Carlo event generators.\n\n
- - histogramming and graphing to visualize and analyze distributions and functions,\n - curve fitting (regression analysis) and minimization of functionals,\n - statistics tools used for data analysis,\n - matrix algebra,\n - four-vector computations, as used in high energy physics,\n - standard mathematical functions,\n - multivariate data analysis, e.g. using neural networks,\naccess to distributed data (in the context of the Grid),\ndistributed computing, to parallelize data analyses,\npersistence and serialization of objects, which can cope with changes in class definitions of persistent data,\naccess to databases,\n3D visualizations (geometry)\ncreating files in various graphics formats, like PostScript, JPEG, SVG,\ninterfacing Python and Ruby code in both directions,\ninterfacing Monte Carlo event generators.\n\n
- - histogramming and graphing to visualize and analyze distributions and functions,\n - curve fitting (regression analysis) and minimization of functionals,\n - statistics tools used for data analysis,\n - matrix algebra,\n - four-vector computations, as used in high energy physics,\n - standard mathematical functions,\n - multivariate data analysis, e.g. using neural networks,\naccess to distributed data (in the context of the Grid),\ndistributed computing, to parallelize data analyses,\npersistence and serialization of objects, which can cope with changes in class definitions of persistent data,\naccess to databases,\n3D visualizations (geometry)\ncreating files in various graphics formats, like PostScript, JPEG, SVG,\ninterfacing Python and Ruby code in both directions,\ninterfacing Monte Carlo event generators.\n\n
- - histogramming and graphing to visualize and analyze distributions and functions,\n - curve fitting (regression analysis) and minimization of functionals,\n - statistics tools used for data analysis,\n - matrix algebra,\n - four-vector computations, as used in high energy physics,\n - standard mathematical functions,\n - multivariate data analysis, e.g. using neural networks,\naccess to distributed data (in the context of the Grid),\ndistributed computing, to parallelize data analyses,\npersistence and serialization of objects, which can cope with changes in class definitions of persistent data,\naccess to databases,\n3D visualizations (geometry)\ncreating files in various graphics formats, like PostScript, JPEG, SVG,\ninterfacing Python and Ruby code in both directions,\ninterfacing Monte Carlo event generators.\n\n
- - histogramming and graphing to visualize and analyze distributions and functions,\n - curve fitting (regression analysis) and minimization of functionals,\n - statistics tools used for data analysis,\n - matrix algebra,\n - four-vector computations, as used in high energy physics,\n - standard mathematical functions,\n - multivariate data analysis, e.g. using neural networks,\naccess to distributed data (in the context of the Grid),\ndistributed computing, to parallelize data analyses,\npersistence and serialization of objects, which can cope with changes in class definitions of persistent data,\naccess to databases,\n3D visualizations (geometry)\ncreating files in various graphics formats, like PostScript, JPEG, SVG,\ninterfacing Python and Ruby code in both directions,\ninterfacing Monte Carlo event generators.\n\n
- Also ROOT as a big software project inside a collaboration, needed to follow some GOOD PRACTICES to be able to develop in such a environment. \n\n
- The lectures were divided into theoretical and practical sessions. \nI would be impossible to mention everything in this presentation, so i will just mention some keywords and main concepts learnt for each module\n
- The lectures were divided into theoretical and practical sessions. \nI would be impossible to mention everything in this presentation, so i will just mention some keywords and main concepts learnt for each module\n
- Superscalar:executes more than one instruction during a clock cycle by simultaneously dispatching multiple instructions to redundant functional units on the processor,the CPU checks for dependencies between instructions\nPipelining: instructions are divided into stages and we can execute several instructions at a time.\nSIMD:using vectors\nHW multithreading: \nStreaming SIMD Extensions packed vectors, registers with 128bits\nImpact of programming styles: taking into account the memory hierarchy and the fails in memory. Use vectorization. GNU compiler and Intel compiler: automatic autovector and autoparallelization. \n
- Threat modeling: what threats will the system face? – what could go wrong? – how could the system be attacked and by whom?\nRisk assessment: how much to worry about them?\n– calculate or estimate potential loss and its likelihood\nSecurity is a process, not a product . It should be present in any stage of SW development.\nHUMAN FACTOR\n\n
- Quality of Service\nto provide different priority to different applications, users, or data flows, or to guarantee a certain level of performance to a data flow\n\nRSVP:Resource Reservation Protocol, is the mechanism defined by the Integrated Services for reserving resources in the network. It is called a signaling protocol, because its aim is to signal to the network that a given flow is going to require certain guarantees for latencies and loss ratio, if the flow respects a certain bit rate.\nNSIS:Next Step in Signaling (NSIS)\n\nDiffserv, which stands for Differentiated Services, is another recent technique aiming at overcoming the problem of heavy classification -that is the process for routers of knowing which service class a packet belongs to. The idea it to "mark" the packets with and indication of their priority in order to avoid having routers examining multiple fields. This mark is called a "differentiated mark", or a Diffserv Code Point (DSCP) and serves to map to a differentiated treatment to be applied to the packet.\n\nMPLS stands for Multi-Protocol Label Switching. LABEL in the header of the packet , NETWORKING NODES = neither pure IP routers nor pure switches, rather hybrid objects which try and combine the good points of both systems. FAST FORWARDING DECISION. MPLS routers are provided with forwarding tables that map the incoming label to an outgoing link.\n\nTransmission Control Protocol\nUser Datagram Protocol\nMost applications use RTP (Real-Time Transport Protocol)\nReal Time audio or video application\ntime-stamp packet loss detectio\n
- \n
- Memory Virtualization: aggregating RAM resources from networked systems into a single memory pool.\nVirt. Memory: giving an application program the impression that it has contiguous working memory, isolating it from the underlying physical memory implementation.\nNetwork: creation of a virtualized network addressing space within or across network subnet. external network virtualization, in which one or more local networks are combined or subdivided into virtual networks, with the goal of improving the efficiency of a large corporate network or data center. internal network virtualization. Here a single system is configured with containers, such as the Xen domain, combined with hypervisor control programs or pseudo-interfaces such as the VNIC, to create a “network in a box.”\nStorage: the process of completely abstracting logical storage from physical storage\nCluster: high-availability\nGrid: throughput\nApp. Virtualization: encapsulate the app from the OS, so that they could be executed everywhere: wine, Java VM.\n
- VMM:Virtual machine monitor\nPlatform virtualization approaches \nOperating system-level virtualization: the kernel of an operating system allows for multiple isolated user-space instances, instead of just one.\nPartial virtualization: Provides only a partial simulation of the underlying hardware. that entire operating systems cannot run in the virtual machine but that many applications can run.\n\n
- \n
- Full virtualization: emulates enough hardware to allow an unmodified "guest" OS to run. The challenge of emulate privileged operations. QEMU,Parallels Desktop for Mac, VirtualBox, Virtual Iron, Oracle VM.\nHardware-assisted virtualization: VMM can efficiently virtualize the entire x86 instruction set by handling these sensitive instructions using a classic trap-and-emulate model in hardware, as opposed to software. Linux KVM.\n
- Paravirtualization: the virtual machine does not necessarily simulate hardware, but instead (or in addition) offers a special API that can only be used by modifying the "guest" OS. This system call to the hypervisor is called a "hypercall"\n\n
- List of reasons that maybe they overlap in between each other\nServer consolidation is an approach to the efficient usage of computer server resources in order to reduce the total number of servers or server locations that an organization requires\n\n
- \n
- SW testing: Virtual machines can cut time and money out of the software development and testing process. Set of virtual machines that run a variety of platforms attached to an Execution Engine where Build and Test Jobs are executed on behalf of the submitting users.\nSW development: Software @ LHC\n Millions of lines of code  Different packaging and software distribution models  Complicated software installation/update/configuration procedure  Long and slow validation and certification process  Very difficult to roll out major OS upgrade (SLC4 -> SLC5)  Additional constraints imposed by the grid middleware development Effectively locked on one Linux flavour  Whole process is focused on middleware and not on applications\n\nDONATION\n
- SaaS: The capability provided to the consumer is is to use the provider’s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browse.\nPaaS: to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider.\nIaaS:to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications\n
- Virtual Software Appliance is a lightweight Virtual Machine image that combines\n Minimal operating environment  Specialized application functionality  Easy end user configuration\nThese appliances are designed to run under one or more of the various virtualization technologies, such as\n VMware , Xen, Parallels, Microsoft Virtual PC, QEMU, User mode Linux, CoLinux\nVirtual Software Appliances also aim to eliminate the issues related to deployment in a traditional server environment\n Simplify configuration procedure  Ease maintenance effort\n
- \n
- The lectures were divided into theoretical and practical sessions. \nI would be impossible to mention everything in this presentation, so i will just mention some keywords and main concepts learnt for each module\n
- The lectures were divided into theoretical and practical sessions. \nI would be impossible to mention everything in this presentation, so i will just mention some keywords and main concepts learnt for each module\n
- Storage Technologies\nFile systems: Make the storage devices available to the user applications\nPhysical: Mapping of disk blocks to files\nLogical: Hierarchical arrangement of directories Stores the actual file data and structural file system meta-data\nRAID: redundant array of independent (or inexpensive) disks is a technology that provides increased storage reliability through redundancy, combining multiple relatively low-cost, less-reliable disk drives components into a logical unit where all drives in the array are interdependent.\n
- Storage Technologies\nFile systems: Make the storage devices available to the user applications\nPhysical: Mapping of disk blocks to files\nLogical: Hierarchical arrangement of directories Stores the actual file data and structural file system meta-data\nRAID: redundant array of independent (or inexpensive) disks is a technology that provides increased storage reliability through redundancy, combining multiple relatively low-cost, less-reliable disk drives components into a logical unit where all drives in the array are interdependent.\n
- Storage Technologies\nFile systems: Make the storage devices available to the user applications\nPhysical: Mapping of disk blocks to files\nLogical: Hierarchical arrangement of directories Stores the actual file data and structural file system meta-data\nRAID: redundant array of independent (or inexpensive) disks is a technology that provides increased storage reliability through redundancy, combining multiple relatively low-cost, less-reliable disk drives components into a logical unit where all drives in the array are interdependent.\n
- \n
- Motivation: hands-on exercises in shape of contests\nExamination -> csc diploma\nown presentations\nexcursion to oxford and bletchley park, dinner at a college\ndinner at cruiser in the thames\nsports activities everyday\n\n\n
- Motivation: hands-on exercises in shape of contests\nExamination -> csc diploma\nown presentations\nexcursion to oxford and bletchley park, dinner at a college\ndinner at cruiser in the thames\nsports activities everyday\n\n\n
- Motivation: hands-on exercises in shape of contests\nExamination -> csc diploma\nown presentations\nexcursion to oxford and bletchley park, dinner at a college\ndinner at cruiser in the thames\nsports activities everyday\n\n\n
- Motivation: hands-on exercises in shape of contests\nExamination -> csc diploma\nown presentations\nexcursion to oxford and bletchley park, dinner at a college\ndinner at cruiser in the thames\nsports activities everyday\n\n\n
- Motivation: hands-on exercises in shape of contests\nExamination -> csc diploma\nown presentations\nexcursion to oxford and bletchley park, dinner at a college\ndinner at cruiser in the thames\nsports activities everyday\n\n\n
- Motivation: hands-on exercises in shape of contests\nExamination -> csc diploma\nown presentations\nexcursion to oxford and bletchley park, dinner at a college\ndinner at cruiser in the thames\nsports activities everyday\n\n\n
- Motivation: hands-on exercises in shape of contests\nExamination -> csc diploma\nown presentations\nexcursion to oxford and bletchley park, dinner at a college\ndinner at cruiser in the thames\nsports activities everyday\n\n\n
- Motivation: hands-on exercises in shape of contests\nExamination -> csc diploma\nown presentations\nexcursion to oxford and bletchley park, dinner at a college\ndinner at cruiser in the thames\nsports activities everyday\n\n\n
- Motivation: hands-on exercises in shape of contests\nExamination -> csc diploma\nown presentations\nexcursion to oxford and bletchley park, dinner at a college\ndinner at cruiser in the thames\nsports activities everyday\n\n\n
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