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GPPD

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  1. GPPD Activitieson SLD “Sistemas Largamente Distribuídos”

  2. Areas • UbiquitousComputing and Sensor Networks • MassivelyMultiplayer Online Games • Grid andCloudComputing • MapReduce

  3. MW for Ubicomp / Wireless Sensor Networks

  4. MW for Ubicomp / Wireless Sensor Networks • Current team • Anubis Rossetto – PhD student • Dependability; health care • Carlos OberdanRolim – PhD student • Context aware; health care • JoãoLadislao – PhD student • Context aware; agricultural

  5. MW for Ubicomp / Wireless Sensor Networks • Current team • Gisele Souza – Master student • SW engineering • Rodrigo Souza – PhD student • Wireless sensor networks; agricultural • ValderiLeithardt– PhD student • Resourcediscoveryon WSN

  6. MW for Ubicomp / Wireless Sensor Networks • Recent team • Cristiano Costa - Former phd student • architecture model; context service • Diego Midon Pereira – master student • Probabilistic difusion • Luciano C. da Silva - PhD student • Context adaptation • Several others • PhD and master students • Since 2000

  7. MW for Ubicomp / Wireless Sensor Networks • Mobile team • TG students • Intelligent Systems for Urban Transport • Other applications • HumbertoFelizzola • Luciano Goulart • Renan Drabach • SébastienSkorupski • Polytech, Grenoble, France

  8. MW for Ubicomp / Wireless Sensor Networks • Ubicomp (UC) → delivering more meaningful services which are ubiquitously available • higher integration of systems, • improved mobilityand scalability, • context-awareness, self-adaptation, etc.

  9. MW for Ubicomp / Wireless Sensor Networks • Research interests → development of middleware and frameworks to foster development of UC systems • Current focus: • support for mobile context-aware systems • support for autonomous control of adaptations • communication protocols for ad-hoc networks • Agricultural systems • Health care systems

  10. MW for Ubicomp / Wireless Sensor Networks • Research interests → … • Previous grants from Fapergs, CNPq and RNP • Since 2000

  11. MW for Ubicomp / Wireless Sensor Networks • Research interests → … • Outcomes: so far, produced 4 generations of Ubicomp middleware (ISAM, ContextS, GRADEp and Continuum) • Partners include UNISINOS, UFSM, UCPel and UFPEL

  12. MW for Ubicomp / Wireless Sensor Networks • MW4G Project • Definition • International partnership project • between UFRGS and University of Coimbra (Portugal) • financed by CAPES-Grice • to work on Wireless Sensor Networks (WSNs) • Main objective → proposal and evaluation of new content and mechanisms of middleware for WSNs

  13. MW for Ubicomp / Wireless Sensor Networks • Other Projects • INF Smart Cities • Large INF project

  14. MW for Ubicomp / Wireless Sensor Networks • Grouppages • Currentactivities • https://saloon.inf.ufrgs.br/twiki/view/Projetos/UbicompOverview • Isamproject: • https://saloon.inf.ufrgs.br/twiki/view/Projetos/ISAM/WebHome • MW4R: • https://saloon.inf.ufrgs.br/twiki/view/Projetos/MW4R/WebHome

  15. MassivelyMultiplayer Online Games

  16. MassivelyMultiplayer Online Games • Currentteam • Eduardo Bezerra • Phdstudent • Formermasterstudent • InterestalgorithmsandLoadBalancing • Fabio Cecin • Formerphdandmasterstudent • Architecturemodels (p2p) • Statecheating • Felipe Severino • Masterstudent; cheating

  17. MassivelyMultiplayer Online Games • Objective: decentralizethe network support • Client-server (traditionalandexpensive) • Fullydecentralized (P2P) • Hybrid (client-server + p2p)

  18. Massively Multiplayer Online Games • Issuesimpliedbydecentralization • Game stateconsistency management • Saturationofthepeers’ network link • Firewall/NAT betweenpeers • Cheatingfacilitatedbythelackof central arbiter

  19. MassivelyMultiplayer Online Games • Projects/Works: • FreeMMG • mmogmiddlewarewith a hybridarchitecture • eachcell - portionofthe virtual environment - is managedby a P2P group • andtheinteractionbetweenthecells is mediatedby a central server • Fabio Cecinmasterthesis

  20. Massively Multiplayer Online Games • Projects/Works: • P2PSE – hybridmiddleware, which divides the game into: • Actionspaces: • fast-pacedandnetwork-demanding game interactions, such as fighting; • consistsofsmall-scalespacesdisjointfromtherestofthe game world, • wheretheinteraction is in a P2P manner, with a limitednumberof players

  21. MassivelyMultiplayer Online Games • Projects/Works: • P2PSE – (cont.): • Social space: • uniqueandlargespacemanagedbythe central server, • whereonly social interactions are allowed, such as chatting, trading etc., • betweenanunlimitednumberof players • FreeMMG 2: PhD Thesisof Fabio Cecin

  22. MassivelyMultiplayer Online Games • Projects/Works: • Cosmmus: doctorateplanof Eduardo Bezerra • Loadbalancingalgorithms • Interestalgorithms (communication) • Optimisticmulticastalgorithms • New work, atLugano • Cheatingtreatment: masterplanof Felipe Severino

  23. MassivelyMultiplayer Online Games • GroupPages • Games • https://saloon.inf.ufrgs.br/twiki/view/Projetos/Jogos/WebHome

  24. Activities on • Grid and Volunteer Computing • MapReduce

  25. Grid andCloudComputing • CurrentTeam • Alexandre Miyazaki – IC student • Bruno Donassolo - Master student • Eduardo Martins da Rocha – TG student • Eder Fontoura - Master student • Julio Anjos – Master student

  26. Grid andCloudComputing • CurrentTeam • MarkoPetek - Phdstudent • Otávio KrelingZabaleta – TG student • Pedro de Botelho Marcos – Master Student • Wagner Kolberg - Master student

  27. Grid andCloudComputing • RecentTeam • Diego Gomes - Formermasterstudent • Rafael dalZotto – Formermasterstudent

  28. Grid andCloudComputing • Grid: whatis? • Grid is a type of parallel and distributed system that enables the sharing, selection, and aggregation of geographically distributed "autonomous" resources dynamically at runtime depending on their availability, capability, performance, cost, and users' quality-of-service requirements • http://www.cs.mu.oz.au/~raj/GridInfoware/gridfaq.html

  29. Grid andCloudComputing • Grid: whatis • Grids are persistent environments that enable software applications to integrate instruments, displays, computational and information resources that are managed by diverse organizations in widespread locations • GGF 2002

  30. Grid andCloudComputing • Some software for grid • Globus Project • Most (?) widelyused grid sw • http://www.globus.org/ • GSI – Security • MDS – InformationService • GRAM – Execution Management • Data Management • Intensive use of Web Services • Not in lastversion

  31. Grid andCloudComputing • Some software for grid • Boinc Project • Desktop gridorVolunteer Computing • http://boinc.berkeley.edu/ • Master/slavearchitecture • Several applications (projects) • Seti@home • http://setiathome.berkeley.edu/ • Client (anonymous) machineaskstasks to server machine • Client machines are notreliable • Verycomplexclientscheduler

  32. Grid andCloudComputing • Projects: • Profile-basedschedulingalgorithmstaking in accountthe use oftheresources. • ModeledonXtremWebarchitecture (Eder Fontoura); • A proposal for fastdiskless checkpoint withobjectprevalence for volunteer computing environments, focusedon small devices (Rafael DalZotto);

  33. Grid andCloudComputing • Projects: • Evaluationandcomparisonbetween • theXtremWebscheduler (FCFS) andthemodelproposedby Fontoura, • throughsimulationsontheSimGridandexperimentsonthe Grid5000; • To runsimulationsontheSimGrid • in a distributedwayon a real gridarchitecture; • Modelingthe BOINC scheduleronSimGrid • Bruno, Julio, Wagner andEduardo

  34. Grid andCloudComputing • Projects: • EvaluationofBoincSchedulerwhen • classical throughput-oriented projects X new burst projects • With a game theoretic modeling • Nash Equilibrium • Usingthe BOINC scheduleronSimGrid • Bruno Donassoloand Eduardo Rocha

  35. Grid andCloudComputing • Projects: • In thecontextoftheCern-CMSexperiment: • Developmentof a PhdThesisandof a MscDissertationonthecreationof a Files and Replicas System to use ontheGrid • Currently: • onePhdStudentand • a formerMScStudent • BothdoingresearchonCern-Geneve • MarkoPetekand Diego Gomes

  36. Grid andCloudComputing • Implementations: • XtremWebdeploymentwith • the original XW architecturescheduler (FCFS) • andthemodelproposedby Eder Fontoura • onthe Grid5000 (Julio Anjos); • XtremWebsimulationontheSimGrid • Desktop Computing scenario • Bothschedulers

  37. MapReduceon • Desktop Grid and Cloud Environments

  38. MapReduce Team • CurrentTeam • Alexandre Miyazaki – IC student • JulioAnjos – Master student • Otávio KrelingZabaleta – TG student • Pedro de Botelho Marcos – Master Student • Wagner Kolberg - Master student

  39. The MapReduce Model • MapReduceis a programmingmodel for large-scaleparallelcomputing, and for processinglarge data sets • It abstracts thecomplexityofdistributingparallelprogrammingapplications • The modelisinspiredonthemapandreduceprimitivespresent in manyfunctionallanguageslikeLispandHaskell • The HadoopimplementationoftheMapReducemodelisoneofthemostused in production systems (e.g. Yahoo, Facebook, Amazon)

  40. The MapReduce Model

  41. MapReduce State of Art

  42. Implementations on other platforms • GPGPU • MARS • http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5557865&tag=1 • Three versions: • GPU • GPU/CPU • GPU + Hadoop - Utiliza o streaming • Problem: • GPGPUS have no dynamic memory allocation • Necessary to introduce additional steps to calculate the output size of Map and Reduce phases

  43. Implementations on other platforms • Multicore • Phoenix • http://dl.acm.org/citation.cfm?id=1317533.1318097 • Number of maps is the number of cores • Also for reduces • Main problem • Input size limited to memory computer capacity

  44. Implementations on other platforms • Desktop Grid • BitDew • http://dl.acm.org/citation.cfm?id=1918097 • Funcioning similar to Hadoop. • Main problem • Environment volatility

  45. Implementations on other platforms • Cloud computing • Cloud MapReduce - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5948637 • Decentralized • Deployed on the Amazon cloud OS • Only 3000 lines of code (Hadoop has 300k) • There is a task queue • Each VM searches a task and executes it • Results are written in queues to be consumed in other stages • Problem: • It uses proprietary OS-specific services • Which prevents its use elsewhere

  46. Implementations on other platforms • Cloud computing • Azure MapReduce - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5708501 • Same as above • However using MS cloud

  47. Problems • Heterogeneity • LATE • http://dl.acm.org/citation.cfm?id=1855744 • Predicting the tasks that will take longer and launching them speculatively, shortening the job execution time • Problem: it can launch tasks unnecessarily

  48. Problems • Volatility • MOON • http://dl.acm.org/citation.cfm?id=1851476.1851489 • Stable machines were used to prevent data loss and ensure that a job finishes execution • Problem: • It uses task re-execution to tolerate failures • Cost could be very high on volatile environment

  49. Problems • Fault tolerant • Single points of failure • Master • Master replication • http://dl.acm.org/citation.cfm?id=1651263.1651271 • HDFS NameNode • fault tolerant library • http://dl.acm.org/citation.cfm?id=1629602

  50. Problems • Fault tolerant • Byzantines • Result verification • Centralized • http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6008723 • Distributed • http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6009055