1 Google Workshop at TU Delft, 2010 – Online Games and Clouds Cloudifying Games: Rain for the Thirsty Alexandru Iosup Parallel and Distributed Systems Group Delft University of Technology Our team: Undergrad Adrian Lascateu, Alexandru Dimitriu (UPB, Romania), …, Grad Vlad Nae (U. Innsbruck, Austria), Siqi Shen (TU Delft, the Netherlands), … Staff Dick Epema, Henk Sips (TU Delft), Thomas Fahringer, Radu Prodan (U. Innsbruck), Nicolae Tapus, Mihaela Balint, Vlad Posea (UPB), etc.

Google Workshop at TU Delft, 2010 – Online Games and Clouds 2 What’s in a name? MSG, MMOG, MMO, … 1.Virtual world Explore, do, learn, socialize, compete + 2.Content Graphics, maps, puzzles, quests, culture + 3.Game data Player stats and relationships Romeo and Juliet Massively Social Gaming = (online) games with massive numbers of players (100K+), for which social interaction helps the gaming experience 250,000,000 active players

Google Workshop at TU Delft, 2010 – Online Games and Clouds 3 Cloud Futures Workshop 2010 – Cloud Computing Support for Massively Social Gaming 3 Background on Cloud Computing “The path to abundance” On-demand capacity Pay what you use Great for web apps (EIP, web crawl, DB ops, I/O) “The killer cyclone” Not so great performance for compute- or data- intensive applications 1 Long-term perf. variability 2 Tropical Cyclone Nargis (NASA, ISSS, 04/29/08) 1- Iosup et al., Performance Analysis of Cloud Computing Services for Many Tasks Scientific Computing, IEEE TPDS, Iosup et al., On the Performance Variability of Production Cloud Services, CCGrid VS

Google Workshop at TU Delft, 2010 – Online Games and Clouds 4 Cloudifying: PaaS for MSGs (Platform Challenge) Build MSG platform that uses (mostly) cloud resources Close to players No upfront costs, no maintenance Compute platforms: multi-cores, GPUs, clusters, all-in-one! Performance guarantees Code for various compute platforms—platform profiling Misprediction=$$$ What services? Vendor lock-in? My data Nae, Iosup, Prodan, Dynamic Resource Provisioning in Massively Multiplayer Online Games, IEEE TPDS, 2011.

Google Workshop at TU Delft, 2010 – Online Games and Clouds 5 Cloudifying: Content, Content, Content (Content Challenge) Produce and distribute content for 1BN people Game Analytics  Game statistic Crowdsourcing Storification Auto-generated game content Adaptive game content Content distribution/ Streaming content A. Iosup, POGGI: Puzzle-Based Online Games on Grid Infrastructures, EuroPar 2009 (Best Paper Award)

Google Workshop at TU Delft, 2010 – Online Games and Clouds 6 Cloudifying: 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) Build cloud-based layer to Improve gaming experience Ranking / Rating Matchmaking / Recommendations Play Style/Tutoring Organize Gaming Communities Player Behavior A. Iosup, CAMEO: Continuous Analytics for Massively Multiplayer Online Games on Cloud Resources. ROIA, Euro-Par 2009 Workshops.

Google Workshop at TU Delft, 2010 – Online Games and Clouds 7 Summary Current Technology The Future Happy players Happy cloud operators Million-user, multi-bn market Sim, Content, Analytics Massive Social Gaming Upfront payment Cost and scalability problems Makes players unhappy Our Vision Scalability & Automation Economy of scale with clouds Ongoing Work Content: POGGI Framework Platform: Analytics: CAMEO Framework Publications Gaming and Clouds 2008: ACM SC 2009: ROIA, CCGrid, NetGames, EuroPar (Best Paper Award), … 2010: IEEE TPDS, Elsevier CCPE,… 2011: Book Chapter, IEEE TPDS, IJAMC, … Graduation (Forecast) : 3PhD, 10+MSc, nBSc

Google Workshop at TU Delft, 2010 – Online Games and Clouds 8 Thank you for your attention! Questions? Suggestions? Observations? Alexandru Iosup (or google “iosup”) Parallel and Distributed Systems Group Delft University of Technology More Info: Do not hesitate to contact me…

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10 Cloud Computing [1/2] Low Performance for Sci.Comp. Evaluated the performance of resources from four production, commercial clouds. GrenchMark for evaluating the performance of cloud resources Four production, commercial IaaS clouds: Amazon Elastic Compute Cloud (EC2), Mosso, Elastic Hosts, and GoGrid. Finding: cloud performance low for sci.comp. S. Ostermann, A. Iosup, N. Yigitbasi, R. Prodan, T. Fahringer, and D. Epema, A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing, Cloudcomp 2009, LNICST 34, pp. 115–131, N. Yigitbasi, A. Iosup, D. Epema, S. Ostermann: C-Meter: A Framework for Performance Analysis of Computing Clouds. Proc. of CCGRID 2009:

Google Workshop at TU Delft, 2010 – Online Games and Clouds 11 Cloud Computing [2/2] Cloud Performance Variability Performance variability of production cloud services IaaS: Amazon Web Services PaaS: Google App Engine Year-long performance information for nine services Finding: about half of the cloud services investigated in this work exhibits yearly and daily patterns; impact of performance variability depends on application. A. Iosup, N. Yigitbasi, and D. Epema, On the Performance Variability of Production Cloud Services, CCGrid Amazon S3: GET US HI operations