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Yehia El-khatib and Chris Edwards. "A Survey-based Study of Grid Traffic". In Proceedings of the International Conference on Networks for Grid Applications (GridNets 2007), Lyon, France, October 17-19 ...

Yehia El-khatib and Chris Edwards. "A Survey-based Study of Grid Traffic". In Proceedings of the International Conference on Networks for Grid Applications (GridNets 2007), Lyon, France, October 17-19 2007.

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2007.10 grid nets-slides 2007.10 grid nets-slides Presentation Transcript

  • Diversity of Grid Traffic: A Survey-based StudyYehia El khatib, Christopher Edwards Computing Department Lancaster University
  • Outline Introduction Survey Goals Survey Process Survey Results Traffic Behaviour Future Work Conclusion
  • Introduction EC-GIN (Europe-China Grid InterNetworking) is a Framework 6 STREP project. EC-GIN aims at introduction a networking interface that provides programming abstractions to improve the performance of grid applications. The design of the interface requires an understanding of the network characteristics of grid applications.
  • Survey Goals The survey is to highlight some of the characteristics of current grid applications Scale and composition of the grid Dataset granularity Data delivery requirements (time restrictions, encryptions, one-to-many services) Others: transport layer protocol, middleware, etc. Special network services
  • Survey Process Questionnaire Structure 2 pages, also an online version 11 MCQs + 1 open-ended question. Level of Detail As simple as possible. Target Audience Developers, administrators, and advanced users. Dissemination Research projects that are employing or developing a grid application.
  • Survey Results [outline]1. Research Field2. Scale3. Composition4. Dataset Granularity5. Special Network Services
  • Survey Results [1/5] Research Field Software Visualization Particle Development 6% Physics 6% 18% Meteorology 6% Medicine 6% Astronomy Environmental 13% Sciences 6% Engineering 13% Social Sciences Mathematical 13% Analysis 13%
  • Survey Results [2/5] Scale 55 75 70 50% o f t h e su r v e y e d a p p lica t io n s 65 % o f s u r v e y e d a p p lic a t io n s 45 60 40 55 50 35 45 30 40 25 35 30 20 25 15 20 15 10 10 5 5 0 0 < = 10 10-100 100-400 400-1000 > 1000 3 – 10 10 – 100 100 – 1000 > = 1000 Num ber of nodes Number of domains
  • Survey Results [3/5] Composition Overall Grid Com posit ion Clusters Desk top Machines Em bedded Dev ices Mobile Dev ices
  • Survey Results [3/5] Composition Overall Grid Com posit ion 47% are deployed only on clusters Image analysis applications Simulation applications Clusters Desk top Machines Em bedded Dev ices Mobile 7% are deployed only on desktop Dev ices machines Data management applications
  • Survey Results [4/5] Dataset Granularity30 100 8020 60 4010 20 0 0 10 kB 100 kB 1 MB 10 MB 100 MB 1 GB 10 GB 100 GB 1 TB 10 kB 100 kB 1 MB 10 MB 100 MB 1 GB 10 GB 100 GB 1 TB
  • Survey Results [4/5] Dataset Granularity30 100 8020 60 4010 20 0 0 10 kB 100 kB 1 MB 10 MB 100 MB 1 GB 10 GB 100 GB 1 TB 10 kB 100 kB 1 MB 10 MB 100 MB 1 GB 10 GB 100 GB 1 TB Most common dataset size is 10 MB 23% of all datasets are ≤ 1 MB 12% of all datasets are 100 GB in size 50% of all datasets are ≤ 10 MB 25% of all datasets are ≥ 10 GB
  • Survey Results [5/5] Special Network Services 100% % of surveyed applicat ions 80% 60% Not Sure Unnecessary 40% Would Be Used Used 20% % Tran sfer Ad van ced Net work Delay Pre - Net work Top olog y d ict ion Reservat ion In form at ion
  • Traffic Behaviour [1/2] The results give an image of the traffic flow sizes that is different from common belief. We define five distinct classes of applications according to dataset sizes: Class A: less than 10 MB Class B: 0.5 – 100 MB Class C: 10 MB – 1 GB Class D: 100 kB – 100 GB Class E: 1 MB – 1 TB
  • Traffic Behaviour [2/2] E 20% The most common class is A, A where datasets are no larger 34% than 10 MB. Only 33% of all applications D 13% have datasets over 1 GB in size. Only 20% of all applications C B have datasets that stretch 13% 20% beyond 100 GB. All class C applications are deployed on mostly desktop machines. All class B applications are Astronomy and Meteorology applications, deployed over 100-300 nodes across 6-8 domains.
  • Future Work We intend to monitor the traffic created by a number of grid applications. We aim to present mathematical models of grid traffic that could be used to create artificial grid traffic (in simulators).
  • Conclusion We presented the outcome of a survey of grid application requirements and network behaviour. The results reflect a list of real demands of grid applications, which provides a solid starting point to the design of our interface. The suggested classification portrays the diversity in the traffic footprint of grid applications.