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Course Announcements

Course Announcements. Tomorrow, Jan 11, at 09:45 , Lecture Hall H : “GPU Programming: Tips and Tricks” Ana Varbanescu Exam-related questions: contact Hai Xiang Lin and Ana Varbanescu Lab-related questions: contact Jie Shen s.shenjie@tudelft.nl. PDS: MSc courses. October 31, 2012. 2.

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Course Announcements

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  1. Course Announcements • Tomorrow, Jan 11, at 09:45, Lecture Hall H: “GPU Programming: Tips and Tricks”Ana Varbanescu • Exam-related questions: contact Hai Xiang Lin and Ana Varbanescu • Lab-related questions:contact Jie Shen s.shenjie@tudelft.nl

  2. PDS: MSc courses October 31, 2012 2 • High-Performance Computing (now) (IN4049, Hai-Xiang Lin, Henk Sips, and Ana Varbanescu) • Cloud Computing (sep – nov 2012) (IN4392, Dick Epema and Alexandru Iosup) • Distributed Computing Systems (feb – apr 2013) (IN4391, Alexandru Iosup) • Parallel Algorithms and Parallel Computers (feb-jun 2013) (IN4026, Henk Sips and Cees Witteveen) • Seminar P2P Systems (feb-mar 2013) (IN4312, Dick Epema and Johan Pouwelse) • Distributed Algorithms (feb-mar 2013) (IN4150, Dick Epema)

  3. Today’s Menu • Starter: Introduction to Cloud Computing • Main: Cloud Programming Models • Desert: Online Gaming as an HPC Problem

  4. What is Cloud Computing?3. A Useful IT Service “Use only when you want! Pay only for what you use!”

  5. IaaS Cloud Computing Many tasks VENI – @larGe: Massivizing Online Games using Cloud Computing

  6. Which Applications NeedCloud Computing? A Simplistic View… Social Gaming TsunamiPrediction EpidemicSimulation Web Server Exp. Research High Space SurveyComet Detected SW Dev/Test Social Networking Analytics Demand Variability Online Gaming Pharma Research Taxes, @Home Sky Survey OfficeTools HP Engineering Low High Low Demand Volume After an idea by Helmut Krcmar

  7. Today’s Menu • Starter: Introduction to Cloud Computing • Main: Cloud Programming Models • Desert: Online Gaming as an HPC Problem

  8. Terms for Today’s Discussion Programming model = language + libraries + runtime system that create a model of computation (an abstract machine)= “an abstraction of a computer system” Wikipedia Examples: message-passing vs shared memory, data- vs task-parallelism, … Abstraction level = distance from physical machine Examples: Assembly low-level vs Java is high level Many design trade-offs: performance, ease-of-use, common-task optimization, programming paradigm, … Q: What is the best abstraction level?

  9. Today’s Challenges • eScience • The Fourth Paradigm • The Data Deluge and Big Data • Possibly others

  10. eScience (John Taylor, UK Sci.Tech., 1999) • A new scientific method • Combine science with IT • Full scientific process: control scientific instrument or produce data from simulations, gather and reduce data, analyze and model results, visualize results • Mostly compute-intensive, e.g., simulation of complex phenomena • IT support • Infrastructure: LHC Grid, Open Science Grid, DAS, NorduGrid, … • From programming models to infrastructure management tools • Examples • * physics, Bioinformatics, Material science, Engineering, CompSci Q: Why is CompSci an example here?

  11. The Fourth Paradigm: The What From Hypothesis to Data • Thousand years ago: science was empirical describing natural phenomena • Last few hundred years: theoretical branch using models, generalizations • Last few decades: a computational branch simulating complex phenomena • Today (the Fourth Paradigm):data exploration unify theory, experiment, and simulation • Data captured by instruments or generated by simulator • Processed by software • Information/Knowledge stored in computer • Scientist analyzes results using data management and statistics Q1: What is the Fourth Paradigm? Q2: What are the dangers of the Fourth Paradigm? Source: Jim Gray and “The Fourth Paradigm”, http://research.microsoft.com/en-us/collaboration/fourthparadigm/

  12. What is “Big Data”? • Very large, distributed aggregations of loosely structured data, often incomplete and inaccessible • Easily exceeds the processing capacity of conventional database systems • Principle of Big Data: “When you can, keep everything!” • Too big, too fast, and doesn’t comply with the traditional database architectures

  13. What is a Bag of Tasks (BoT)? A System View BoT = set of jobs sent by a user… • Why Bag of Tasks? From the perspective of the user, jobs in set are just tasks of a larger job • A single useful result from the complete BoT • Result can be combination of all tasks, or a selection of the results of most or even a single task …that start at most Δs after the first job Q: What is the user’s view? Time [units] Iosup et al., The Characteristics and Performance of Groups of Jobs in Grids, Euro-Par, LNCS, vol.4641, pp. 382-393, 2007.

  14. BoTs Became the Dominant Programming Model for Grid Computing Iosup and Epema: Grid Computing Workloads. IEEE Internet Computing 15(2): 19-26 (2011)

  15. Practical Applications of the BoT Programming ModelParameter Sweeps in Condor [1/4] • Sue the scientist wants to “Find the value of F(x,y,z) for 10 values for x and y, and 6 values for z” • Solution: Run a parameter sweep, with 10 x 10 x 6 = 600 parameter values • Problem of the solution: • Sue runs one job (a combination of x, y, and z) on her low-end machine. It takes 6 hours. • That’s 150 daysuninterrupted computation on Sue’s machine! Source: Condor Team, Condor User’s Tutorial. http://cs.uwisc.edu/condor

  16. Practical Applications of the BoT Programming ModelParameter Sweeps in Condor [2/4] Universe = vanilla Executable = sim.exe Input = input.txt Output = output.txt Error = error.txt Log = sim.log Requirements = OpSys == “WINNT61” && Arch == “INTEL” && (Disk >= DiskUsage) && ((Memory * 1024)>=ImageSize) InitialDir = run_$(Process) Queue 600 Complex SLAs can be specified easily Also passed as parameter to sim.exe Source: Condor Team, Condor User’s Tutorial. http://cs.uwisc.edu/condor

  17. Practical Applications of the BoT Programming ModelParameter Sweeps in Condor [3/4] % condor_submit sim.submit Submitting job(s) ............................................................................................................................................................................................................................................................... Logging submit event(s) ............................................................................................................................................................................................................................................................... 600 job(s) submitted to cluster 3. Source: Condor Team, Condor User’s Tutorial. http://cs.uwisc.edu/condor

  18. Practical Applications of the BoT Programming ModelParameter Sweeps in Condor [4/4] % condor_q -- Submitter: x.cs.wisc.edu : <128.105.121.53:510> : x.cs.wisc.edu ID OWNER SUBMITTED RUN_TIME ST PRI SIZE CMD 3.0 frieda 4/20 12:08 0+00:00:05 R 0 9.8 sim.exe 3.1 frieda 4/20 12:08 0+00:00:03 I 0 9.8 sim.exe 3.2 frieda 4/20 12:08 0+00:00:01 I 0 9.8 sim.exe 3.3 frieda 4/20 12:08 0+00:00:00 I 0 9.8 sim.exe ... 3.598 frieda 4/20 12:08 0+00:00:00 I 0 9.8 sim.exe 3.599 frieda 4/20 12:08 0+00:00:00 I 0 9.8 sim.exe 600 jobs; 599 idle, 1 running, 0 held Source: Condor Team, Condor User’s Tutorial. http://cs.uwisc.edu/condor

  19. Ecosystems of Big-Data Programming Models Q: Where does MR-on-demand fit? Q: Where does Pregel-on-GPUs fit? High-Level Language Flume BigQuery SQL Meteor JAQL Hive Pig Sawzall Scope DryadLINQ AQL Programming Model PACT MapReduce Model Pregel Dataflow Algebrix Execution Engine FlumeEngine DremelService Tree TeraDataEngine AzureEngine Nephele Haloop Hadoop/YARN Giraph MPI/Erlang Dryad Hyracks Storage Engine S3 GFS TeraDataStore AzureData Store HDFS Voldemort LFS CosmosFS Asterix B-tree * Plus Zookeeper, CDN, etc. Adapted from: Dagstuhl Seminar on Information Management in the Cloud,http://www.dagstuhl.de/program/calendar/partlist/?semnr=11321&SUOG

  20. Today’s Menu • Starter: Introduction to Cloud Computing • Main: Cloud Programming Models • Desert: Online Gaming as an HPC Problem

  21. Massivizing Online Games: High Performance Computing and High Quality Time Alexandru Iosup Parallel and Distributed Systems GroupDelft University of Technology Our team: Undergrad Adrian Lascateu, Alexandru Dimitriu (UPB, Romania), …, Grad Siqi Shen, Yong Guo (TU Delft, the Netherlands), …Staff Dick Epema, Johan Pouwelse, Henk Sips (TU Delft), Thomas Fahringer, Radu Prodan (U. Innsbruck), Nicolae Tapus, Vlad Posea (UPB), … HPDC-Trends, Amsterdam, Mar 2012

  22. @large: Massivizing Online Games as an HPC Problem Online Gaming used to be art, may now be computing Online Gaming used to be multimedia, is now HPC Online Gaming used to be networking, is now all HPC Online Gaming used to be v-worlds, is now many apps

  23. What’s in a name? MSG, MMOG, MMO, … Over 250,000,000 active players Massively Social Gaming = (online) games with massive numbers of players (100K+), for which social interaction helps the gaming experience • Virtual World SimExplore, do, learn, socialize, compete+ • Game DataPlayer stats and relationships, others+ • Game ContentGraphics, maps, puzzles, quests, culture

  24. Sources: MMOGChart, own research. Sources: ESA, MPAA, RIAA. MSGs are a Popular, Growing Market • 25,000,000+ subscribed players (from 250,000,000+ active) • Over 10,000 MSGs in operation • Subscription market size $7.5B+/year, Zynga $600M+/year

  25. Sources: CNN, Zynga. Source: InsideSocialGames.com Zynga, an Amazon WS User Selling in-game virtual goods: “Zynga made est. $270M in 2009 from.”http://techcrunch.com/2010/05/03/zynga-revenue/ “Zynga made more than $600M in 2010 from selling in-game virtual goods.”S. Greengard, CACM, Apr 2011

  26. World of Warcraft, a Traditional HPC User(since 2003) • 10 data centers • 13,250 server blades, 75,000+ cores • 1.3PB storage • 68 sysadmins (1/1,000 cores) http://www.datacenterknowledge.com/archives/2009/11/25/wows-back-end-10-data-centers-75000-cores/

  27. Bungie, Computing then Serving 1.4PB/yr. • Halo 3 is one of the many successful games • Halo 3 players get, in 1.4PB • Detailed player profiles • Detailed usage stats • Ranking • CERN produces ~15PB/year (10x larger)

  28. Agenda • What’s in a Name? • Three Current Challenges • Platform Scalability Challenge • Gaming Analytics Challenge • Content Generation Challenge • Conclusion

  29. @large Research Challenge: V-World Platform for MMOGs Scaling quickly to millions of players - 1M in 4 days, 10M in 2 months - Up-front and operational costs - Performance, Scalability, & Cost

  30. Impact on Game Experience Responsive game Unresponsive game September 13, 2014 [Source: Nae, Iosup, and Prodan, ACM SC 2008 and IEEE TPDS 2011]

  31. Proposed hosting model: dynamic • Using data centers for dynamic resource allocation Massive join Massive leave Massive join • Main advantages: • Significantly lower over-provisioning • Efficient coverage of the world is possible [Source: Nae, Iosup, and Prodan, ACM SC 2008]

  32. Resource Provisioning and AllocationStaticvs.DynamicProvisioning 250% 25% [Source: Nae, Iosup, and Prodan, ACM SC 2008]

  33. Resource Provisioning and AllocationCompound Metrics • Trade-off Utility-Cost still needs investigation • Performance and Cost are not both improved by the policies we have studied Villegas, Antoniou, Sadjadi, Iosup. An Analysis of Provisioning and Allocation Policies for Infrastructure-as-a-Service Clouds, CCGrid, 2012.

  34. (Variable) Blackbox Performance Engineering • Performance Evaluation of Four Commercial Clouds • Amazon EC2, GoGrid, Elastic Hosts, Mosso • Resource acquisition • Single- and Multi-Instance benchmarking • Low compute and networking performance1 • Performance variability over time2 1- Iosup et al., Performance Analysis of Cloud Computing Services for Many Tasks Scientific Computing, IEEE TPDS, 2011, http://www.st.ewi.tudelft.nl/~iosup/cloud-perf10tpds_in-print.pdf 2- Iosup et al., On the Performance Variability of Production Cloud Services, CCGrid 2011, pds.twi.tudelft.nl/reports/2010/PDS-2010-002.pdf

  35. SLAs Supporting Real EcosystemsMultivariate SLA Languages • Specification of SLAs • Multivariate • Include provisions for faults • Include detailed penalties:compensation for temporaryQoS violations • Use existing IaaS cloud SLA specifications • How would the MMOG ecosystem operate? [TPDS 2011] • How to specify, use, and optimize for SLAs? (upcoming) Nae, Prodan,Iosup. A Cloud-Based Operational SLA Negotiation Model for MMOGs, (upcoming).

  36. @large Research Challenge: Content Generation for MMOGs Generating content on time for millions of players - Player-customized: Balanced, Diverse, Fresh - Up-front and operational costs - Response time, Scalability, & Cost

  37. Derived Content NewsGen, Storification (Procedural) Game Content (Generation) Hendricks, Meijer, vd Velden, Iosup, Procedural Content Generation for Games: A Survey, ACM TOMCCAP, 2012 Game Design Rules, Mechanics, … Game Scenarios Puzzle, Quest/Story, … Game Systems Eco, Road Nets, Urban Envs, … Game Space Height Maps, Bodies of Water, Placement Maps, … Game Bits Texture, Sound, Vegetation, Buildings, Behavior, Fire/Water/Stone/Clouds

  38. The POGGI Content Generation Framework Only the puzzle concept, and the instance generation and solving algorithms, are produced at development time * A. Iosup, POGGI: Puzzle-Based Online Games on Grid Infrastructures, EuroPar 2009 (Best Paper Award)

  39. @large Research Challenge: Continuous Analytics for MMOGs Analyzing the behavior of millions of players, on-time - Data mining, data access rights, cost v. accuracy, … - Reduce upfront costs - Low response time & Scalable - Large-scale Graph Processing

  40. The CAMEO Framework • Address community needs • Can analyze skill level, experience points, rank • Can assess community size dynamically • Using on-demand technology: Cloud Comp. • Dynamic cloud resource allocation, Elastic IP • Data management and storage: Cloud Comp. • Crawl + Store data in the cloud (best performance) • Performance, scalability, robustness: Cloud Comp. A. Iosup, CAMEO: Continuous Analytics for Massively Multiplayer Online Games on Cloud Resources. ROIA, Euro-Par 2009 Workshops, LNCS 6043, (2010)

  41. @large: Sample Analytics ResultsSkill Level Distribution in RuneScape • Runescape: 135M active accounts, 7M active (2008) • High-scoring players: 1.8M (2007) / 3.5M (2010) • (largest MMOG msmt.) • Player skill:distribution changes over time A. Iosup, A. Lascateu, N. Tapus, CAMEO: Enabling Social Networks for Massively Multiplayer Online Games through Continuous Analytics and Cloud Computing, ACM NetGames 2010. * A. Iosup, POGGI: Puzzle-Based Online Games on Grid Infrastructures EuroPar 2009 (Best Paper Award)

  42. @large: Social Everything! • Social Network=undirected graph, relationship=edge • Community=sub-graph, density of edges between its nodes higher than density of edges outside sub-graph (Analytics Challenge) Improve gaming experience • Ranking / Rating • Matchmaking / Recommendations • Play Style/Tutoring Self-Organizing Gaming Communities • Player Behavior

  43. Interaction group-socnet Coordinated large-scale social group @large: Sample Analytics ResultsActivity and Social Network • Bridge Base Online (BBO): 1M+ players, top free site • Dataset: 100K players • 9K group • Social relationshipsfrom bridge pairing • Large (~10K) online social groups can coordinate • Identified player behaviorcommunity builder, community member, random player, faithful player M. Balint, V. Posea, A. Dimitriu, and A. Iosup, An Analysis of Social Gaming Networks in Online and Face to Face Bridge Communities, LSAP 2011.

  44. @large: Sample Analytics ResultsAnalysis of Meta-Gaming Network • “When you play a number of games, not as ends unto themselves but as parts of a larger game, you are participating in a metagame.” (Dr. Richard Garfield, 2000) • XFire: since 2008 (3+ years), 500K of 20M players PhD S. Shen, and A. Iosup, The XFire Online Meta-Gaming Network: Observation and High-Level Analysis, MMVE 2011 * A. Iosup, POGGI: Puzzle-Based Online Games on Grid Infrastructures EuroPar 2009 (Best Paper Award)

  45. Summary Massivizing Online Gaming • Million-user, multi-bn market • V-World, Content, Analytics Current Technology @large: Our Vision • Upfront payment • Cost and scalability problems • Makes players unhappy • HPC has to help • Economy of scale with clouds @large: Ongoing Work PublicationsGaming and Clouds2008: ACM SC2009: ROIA, CCGrid, NetGames, EuroPar (Best Paper Award), …2010: IEEE TPDS, Elsevier CCPE2011: Book Chapter CAMEO, IEEE TPDS, IJAMC2012: IPDPS, CCGrid, … Graduation (Forecast)2012—14: 3PhD, 6Msc, 6BSc • Content: POGGI Framework • Platform: edutain@grid • Analytics: CAMEO Framework @large: The Future • Happy players • Happy cloud operators

  46. Thank you for your attention! Questions? Suggestions? Observations? More Info: Alexandru IosupA.Iosup@tudelft.nlhttp://www.pds.ewi.tudelft.nl/~iosup/ (or google “iosup”)Parallel and Distributed Systems GroupDelft University of Technology • http://www.st.ewi.tudelft.nl/~iosup/research.html • http://www.st.ewi.tudelft.nl/~iosup/research_gaming.html • http://www.st.ewi.tudelft.nl/~iosup/research_cloud.html Do not hesitate to contact me…

  47. PDS: MSc courses October 31, 2012 49 • High-Performance Computing (now) (IN4049, Hai-Xiang Lin, Henk Sips, and Ana Varbanescu) • Cloud Computing (sep – nov 2012) (IN4392, Dick Epema and Alexandru Iosup) • Distributed Computing Systems (feb – apr 2013) (IN4391, Alexandru Iosup) • Parallel Algorithms and Parallel Computers (feb-jun 2013) (IN4026, Henk Sips and Cees Witteveen) • Seminar P2P Systems (feb-mar 2013) (IN4312, Dick Epema and Johan Pouwelse) • Distributed Algorithms (feb-mar 2013) (IN4150, Dick Epema)

  48. Continuous Analytics for MMOGs MMOG Data = raw and derivative information from the virtual world (millions of users) Continuous Analytics for MMOGs = Analysis of MMOG data s.t. important events are not lost • Data collection • Data storage • Data analysis • Data presentation • … at MMOG rate and scale

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