1.
g-Social Enhancing e-Science Tools with Social Networking FunctionalityAndriani Stylianou, Nicholas Loulloudes, Marios D. Dikaiakos

2.
Overview• Introduction• Motivation• Problem• Current Solutions• g-Social – Our Solution• Abstractions• Implementation• Conclusion - Questions 2

3.
Fourth Paradigm of Scientific Exploration (J. Gray)Source: J. Gray, talk to NRC/CSTB, “eScience - A Transformed ScientificMethod.” Mountain View CA, 11 January 2007. • 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: data exploration (eScience) – unify theory, experiment, and simulation – Data captured by instruments Or generated by simulator – Processed by software – Information/Knowledge stored in computer – Scientist analyzes database / files using data management and statistics – “Computational X” and “X-Informatics” 2009 3

4.
The disappearance of Tenacious (28/1/2007)FarallonIslands Jim Gray Manager of Microsoft Researchs eScience Group. 1998 ACM Turing Award 4

5.
The search for Tenacious (28/1/07 - 16/2/07)• Night of 28/1: the USCG launched an airborne and seaborne SAR operation for Tenacious – The SAR lasted for nearly two weeks - no signs found• 31/1: the scientific community mobilized to help the SAR mission using online tools – Computer scientists, oceanographers, engineers, volunteers, and Silicon Valley power players [NASA’s JPL, Amazon, Microsoft, Oracle, US Navy, Monterey Bay Aquarium Research Institute, SDSC, Cornell Theory Center, Purdue, UWisc, Singular, Canadian Space Agency, Digital Globe.]• A blog was setup to coordinate efforts and share ideas.Main foci of the effort were: – Map the trajectory that Tenacious might have followed, in case Jim Gray lost control of the boat - to help guide the SAR operation – Discover clues about Tenacious presence at sea – Map the trajectories of large vessels traveling in the area, that may have collided with Tenacious US/CG scoured 132,000 sq. miles of ocean 5

6.
Drift modeling 6

7.
The search for Tenacious: online version An exemplary e-Science application scenario• A multidisciplinary virtual organization of people with a common goal – Scientists, engineers, managers, officials, volunteers• A variety of algorithms and software tools: – Ocean-current models and simulators, image processing & recognition, cellphone signal tracking and triangulation, data-format transformation, data cleansing, satellite collection planning, data mining, image geo-referencing• A deluge of data (hundreds of GBs) retrieved over the net from varioussources, requiring processing and fusion to extract knowledge – Satellite orbits, satellite imagery at different resolutions, multispectral datasets, Web Databases, radio buoy and airborne sensors, HF radars, data about offshore currents, Web cameras• A federation of computing, networking and service infrastructures – Grids, clusters, storage devices, crowd-sourcing services 7

8.
Computing Grids• e-Science motivated the development of Grid technologies and Federated Computing Infrastructures during the last decade.• The Grid vision by Foster, Kesselman, Tuecke [Grid 1.0]: – Distributed computing infrastructures that enable flexible, secure, coordinated resource sharing among dynamic collections of individuals and institutions – Enable communities ( “ Virtual Organizations ” ) to share geographically distributed resources as they pursue common goals, in the absence of: Homogeneity, Central location, Central control, Existing trust relationships• The hype following the Grid: – One of the sources of the impact of scientific and technological changes on the economy and society [Jeremy Rifkin, “The European Dream,” Penguin 2004] – The Grid has been described as the Next Generation Internet, the implementation of the Global Computer etc. 8

9.
Grid Infrastructure development‣ Nowadays, Grid infrastructures comprise an impressive collection of computational and software resources ‣ drawing an increasing number of users from various disciplines 9

10.
Data-Intensive Scientific ProjectsMotivation Grid / Cloud Computing ScientistsResources Traditional Collaboration Tools 10

11.
Problem• Collaboration is done externally to scientific software environments (email, web, portals, IM, etc.).• Manual effort for transferring information from one tool to another.• Error prone and time consuming. Lack of a unified, user-friendly software and collaboration environment for scientists. 11

12.
Current Solutions Pros • Professional Networking • Minimal Collaboration FunctionalityGeneral-Purpose Cons OSN • External to existing scientific software environments – Web Based • Do not support resource* sharing Pros • More immersive collaboration environment than Generic OSN. • Resource sharing and ability to run experiments.Scientific OSN Cons • Application Domain Specific. • Proprietary infrastructures – High maintenance. • Introduce additional information sources -> User Information overload 13

13.
Our Solutiong-Eclipse (www.eclipse.org/geclipse)• Integrated workbench framework• Build on-top of Eclipse (Extensible and community support)• Toolset for users, operators & developers of Grid/Cloud infrastructures (gLite, GRIA, Amazon AWS) – Middleware agnostic• Rich functionality: • Development & Deployment • Benchmarking & Testing • Workflow ProgrammingOnline Social Networks• Easy establishment and management of groups• Automatic dissemination of notifications• Professional Networking• High Availability 14

14.
g-EclipseGrid Project View W o r k b e n c h Information View Authentication View JSDL Editor View 15

15.
g-SocialBuild on-top of the g-Eclipse FrameworkAims to enable collaboration among scientists that are/will utilize g-EclipseFeatures• Social Abstractions (Resources, Meta-data, Authentication).• Definition of structured and standardized social meta-data• Enrich social meta-data with links to project related resources.• Access resources easily .• Share project data and meta-data.• Retrieve shared information.• Seamless interaction with OSN. • Facebook • Twitter• Extensible for other OSNs g-Social Work Cycle 16

16.
g-Social AbstractionsEnable seamless sharing and retrieval (via an OSN) of all particulars of theresearch work performed in the context of a real scientific project.Abstract a Scientific Collaborative Environment which utilize Online SocialNetworks. 17

17.
Abstractions - ResourcesAny file(s) related to the execution ofa Grid task specific to a scientificproject• Input / Output Dataset• Executable• Source Code• Documentation• Publications• … 18

18.
Abstractions – Social Meta-dataDescriptive meta-data that provide tothe OSN and its users informationabout purpose and function of eachshared particular• Name• Function• Purpose• Version• Tags• License• …. 19

19.
Abstractions – Authentication ManagerEnforces security and privacy controlof users while interacting with theOSN• Authorization / Authentication against an OSN• Monitor life-cycle of authentication tokens 20

20.
Abstractions – Resource ManagerResource sharing• Interact with Authentication Manager• Social meta-data• Encapsulate the above in a form acceptable by and OSNResource Retrieval• Extraction of published meta-data• g-Eclipse Authentication Manager invocation• Resource access via g-Eclipse file system• Resource import in g-Eclipse workspace 21

21.
Abstractions – OSN Interface• OSN are by design web-based systems• OSN-gEclipse interface serves as an intermediate between the web- browser and g-Eclipse.• Invoking g-Eclipse when user clicks on an g-Social link inside an OSN. 22

22.
g-Social Implementation• The g-Eclipse Grid Project.• A placeholder for the organization offiles/information related to the execution ofGrid/Cloud tasks • Executables (local file system) • Input / Output dataset (g-Lite, AWS) • Documentation • Publication (IEEE, ACM, Elsevier) • Infrastructure Configurations 23

23.
Implementation (Social Meta-Data Editor) • Multi-Page GUI Editor • Easy Insertion of social meta-data • Specify Location of Resources• XML content meta-data• Extend Job Submission Definition Language (JSDL) schema to include social meta-data specification. 24

24.
g-Social View Collaborators Search for Shared Jobs OSN Authentication List of Shared Jobs Share Job View Job Details 25

25.
Implementation (g-Social View) Authorization • Authenticate / Authorize against OSN • Check auth of the underlying storage infrastructure when linking or retrieving a resource • Manage auth tokens life- cycle 26

26.
Implementation (g-Social View) Share Job to OSN • Share job details as defined in meta-data editor • Ask user to which OSN details should be posted • Parse social meta-data • Encapsulate them in OSN specific post formats. 27

27.
Implementation (g-Social View)View Share Job Details• Social Meta-data • Name • Description • Version• Resource Handles • Download Resource 28

28.
Conclusions & Future WorkConclusionsg-Social enhances integrated e-Science Tools (g-Eclipse) withSocial Networking functionality. Specifically it:• Enables the definition of social meta-data for sharing and retrieval of information among scientists.• Enriches meta-data with resource handles which might be scattered in heterogeneous storage infrastructures.• Provides mechanisms for sharing and retrieving scientific information with just a few clicks.Future Work• Standardize social meta-data definition• Support additional OSNs• Recommendation System• Release g-Social to Eclipse 29

29.
Questions – Contact InformationAndriani Stylianou (andriani.stylianou@epfl.ch)Nicholas Loulloudes (loulloudes.n@cs.ucy.ac.cy)Marios D. Dikaiakos (mdd@cs.ucy.ac.cy) http://grid.ucy.ac.cy 30