The Digital Ocean

N. M. Patrikalakis1

nmp@mit.edu

S. L. Abrams2

stephen abrams@harvard.edu

J. G. Bellingham1

belling@mit.edu

W. Cho1

wjcho@mit.edu

K. P. Mihanetzis1

kmihanet@alum.mit.edu

A. R. Robinson2

robinson@pacific.harvard.edu

H. Schmidt1

henrik@keel.mit.edu

P. C. H. Wariyapola1

gitano@mit.edu

1Massachusetts Institute of TechnologyCambridge, MA 02139, USA

2 Harvard UniversityCambridge, MA 02138, USA

Abstract

The ocean is fundamentally important to many ar-eas of modern society and thus improved knowledge ofthe ocean is essential. Ocean scientists have made re-markable progress in observation technology, modelingand assimilation in physical oceanography, acoustics,and biology. To some extent, such advances have beenconfined to each discipline. Therefore a great demandhas arisen for a modern distributed computing and net-working infrastructure within which we bring togetheradvanced modeling, observation tools and field estima-tion methods. This paper describes a knowledge net-work of distributed heterogeneous data and software re-sources for multidisciplinary ocean research.

1 Introduction

The ocean is central to many areas of modern so-ciety. Our food supply, transportation, national secu-rity, and recreational activities depend critically uponthe state of the ocean. Improved knowledge of theocean is thus essential in order to forecast possible en-vironmental disasters, control the impact of human ac-tivity in climate changes, or respond to sea-borne ag-gressions. Over the last decades, ocean scientists havemade remarkable progress both in the ability to ob-serve the ocean and in the methodology of field es-timation. Sophisticated techniques have been devel-oped by physical oceanographers in order to dynami-cally combine computations and data through assim-ilation, and to model the interaction of phenomenaon different scales through nested computational do-mains. Sampling strategies and biological modelingare also undergoing constant improvement through ad-equate and adaptive coupling with the above models.

To some extent, the advances that have been made inobservation technology, modeling and assimilation inphysical oceanography, acoustics, biology, have beenconfined to each discipline. Therefore a great demandhas arisen for a modern distributed computing and net-working infrastructure within which we bring togetheradvanced modeling, observation tools and field estima-tion methods in the above disciplines. We are currentlydeveloping a knowledge network (named Poseidon:http://czms.mit.edu/poseidon/) of distributed het-erogeneous data and software resources, which will al-low seamless search, exchange, analysis, and visualiza-tion of resources, and enable widely distributed com-puting such as efficient forecasting and adaptive sam-pling of the ocean. The distributed computing in-frastructure will remove many obstacles to multidis-ciplinary ocean research, improve the timely distribu-tion and dissemination of results, in turn increasingscientific productivity and benefit both the scientificcommunity and society.

This paper briefly describes our recent work on thePoseidon system development, which includes: de-velopment of a Littoral Ocean Observing and Predict-ing System (LOOPS); construction of a network-basedarchitecture; executive system for dependency graph-based workflow management; and ontology and meta-data creation for distributed geophysical resources.

2 Review of the State-of-the-Art

The wide availability of high-performance networks,tools and interfaces for access, has made possible theconstruction of distributed information systems. Asystem of this type uses individual data and soft-ware servers, located on diverse computer hosts, as thebuilding-blocks for a unified application. A number ofprojects are underway to research various aspects of

0-7695-0643-7/00 $10.00 � 2000 IEEE

network communications for distributed systems, pro-ducing experimental systems focusing on high perfor-mance parallelism (Legion) [1], client/server architec-tures for service libraries (Ninf) [2], and vertically in-tegrated virtual metacomputers (Globus) [3]. Muchrecent research has been undertaken towards the de-velopment of software-based middleware, which man-ages network communications efficiently between dis-tributed clients and servers. One example, which weuse for the Poseidon system, is the Common ObjectRequest Broker Architecture (CORBA) [4]. CORBAis centered on the Object Request Broker (ORB),which manages all communications between CORBA-compliant objects, regardless of implementation lan-guage, operating system, or hardware platform, usingInterface Definition Language (IDL), and derived ser-vices.

Middleware products merely provide communica-tion services across networks, making no assumptionsconcerning the problem domain of the clients andservers using them. Integrated domain-specific infor-mation systems have been developed in the areas ofnumerical processing, such as NetSolve, which pro-vides transparent access to a pool of distributed com-putational servers [5]. To specifically manage meteo-rological and other geophysical data, several Internet-based search and retrieval systems have been devel-oped. Examples are a central clearing-house for NASAreal-time satellite data (EOSDIS) [6]; a system for thedissemination of meteorological data, data standards,and processing software (Unidata) [7]; the DistributedOceanographic Data System (DODS) for dissemina-tion of oceanographic data with appropriate translators[8]; collaborative study of geo-scientific data (OASIS)[9]; providing a Web-based catalog of environmentaldatasets (MEL) [10]; and DIENST-based [11] data inte-gration and visualization for coastal zone management(Thetis) [12].

3 Littoral Ocean Observing and Pre-dicting System (LOOPS)

With the advent of new sensors, storage technolo-gies, and especially widespread access to the Internet,the potential exists for a new era of ocean science in-vestigation where scientists, students, and governmentofficials have frictionless access to oceanographic data,simulation results, and software. Realistic field esti-mates – including real-time nowcasts and forecasts aswell as simulations – are now feasible due to the adventof Ocean Observing and Prediction Systems (OOPS),in which a set of coupled interdisciplinary models arelinked to an observational network, consisting of vari-

ous sensors mounted on a variety of platforms, via dataassimilation schemes – see Figure 1.

OceanMeasurement

Database

Sensor Modalities

Physical, geological, biological,chemical, acoustical, and opticalRemote or in situ

Ocean Simulations

Data assimilationDynamical models

EstimatedOcean

Database

NaturalOcean

Oceanographic Applications

Control and sampling strategies

Observations

Measurement models

Figure 1: High-level OOPS architecture

A major project in this area is LOOPS: LittoralOcean Observing and Prediction System [13]. Recentreal-time experiences of HOPS (Harvard Ocean Pre-diction System) in the LOOPS illustrate the diversityof situations and applications of the system. For ex-ample, the distribution of real-time products via theWWW is shown in Figures 3-(a),(b). Figure 3-(a):This is from the operational Web page of Rapid Re-sponse 97, a NATO real-time exercise. The figureshows a forecast of surface temperature for 18 Septem-ber 1997 with overlying velocity vectors for the IonianBasin of the Mediterranean Sea. The same quantitiesare shown for Massachusetts Bay in Figure 3-(b) forthe LOOPS/AFMIS (Advanced Fisheries ManagementInformation System) Massachusetts Bay Sea Trial ofAugust–October 1998 (MBST-98). This figure wasavailable in real-time from the MBST-98Web site. Fig-ure 4-(a): A whale was found dead in Cape Cod Bayon 21 April 1999 at the position noted by the star.The whale was known to be alive off Race Point on15 April. This figure was created to present to theNational Marine Fisheries Service Criminal Investiga-tion Division the possible locations of ship strikes of a

2

0-7695-0643-7/00 $10.00 � 2000 IEEE

Figure 2: AOSN: Autonomous Ocean Sampling Networks

whale given an unknown time of impact. The ellipsesmark the possible locations for a ship strike at an in-dividual date and time which would lead to transportof the whale carcass by the hindcast currents to within2.5 km of the location at which the whale was found.The gray lines indicate the shipping lanes to Bostonand the Atlantic Ocean from the Cape Cod Canal (notshown). Figure 4-(b): The predicted drift of floatingdebris from the crash of Egypt Air Flight 990 is illus-trated here. Given an estimated position of impact, thepath of floating debris within an initial locus of 10 kmwas predicted forward in time. This information wasmade available to the National Transportation SafetyBoard (NTSB) during the search period.

The Massachusetts Bay Sea Trial (MBST-98, seealso Figure 3-(b)) utilized interactive multi-scale adap-tive sampling for three research vessels and two fleetsof Autonomous Underwater Vehicles (AUVs) and wasperformed in collaboration with AFMIS-NASA and theAutonomous Ocean Sampling Network (AOSN-ONR)programs. The objective of AOSN [14] is to createand demonstrate the next-generation robotic oceano-graphic survey system - see also Figure 2. This isbeing accomplished by: (1) Creating small, high per-

formance mobile platforms capable of several monthdeployments. Both propeller-driven, fast survey ve-hicles, and buoyancy-driven glider vehicles have beendeveloped. (2) Creating an infrastructure that sup-ports controlling, recovering data from, and manag-ing the energy of, remotely deployed mobile platforms.Elements include moorings, docking stations, acous-tic communications, two-way satellite communications,and the Internet. (3) Demonstrate these capabilities inscience-driven field experiments. (4) Develop opera-tional techniques that make most effective use of thesenew assets, including adaptive sampling strategies. Si-multaneous synoptic physical and biological data setswere obtained over a range of scales. The multi-scalesampling strategies were based on: (1) ocean field fore-casts with the HOPS assimilating all prior data (re-gions of most active or interesting dynamics) and (2)forecasts of error variances and of dominant eigende-compositions of error covariances, using Error Sub-space Statistical Estimation (ESSE) [15]. All of thesampling patterns of the platforms and sensors weredesigned and made available in real-time, assimilat-ing yesterday’s data today for tomorrow’s forecast andsampling. This data was assimilated using standard

3

0-7695-0643-7/00 $10.00 � 2000 IEEE

(a) Ionian Basin of Mediterranean Sea - Rapid Response97

(b) Massachusetts Bay - LOOPS/AFMIS Mass. BaySea Trial 1998

Figure 3: HOPS predictions

(a) Cape Cod Bay - drift of dead whale

(b) New England Bight - path of floating debris

Figure 4: HOPS predictions

4

0-7695-0643-7/00 $10.00 � 2000 IEEE

optimal interpolation (OI) and ESSE. Real time fore-casts of fields and error covariance eigendecompositionswere provided. Several dynamical interactions amongthe circulation, productivity and ecosystem systemswere found. These accomplishments have resulted ina combined and compatible physical, biological, andchemical multi-scale data set applicable to interactiveprocess studies and data assimilation, adaptive sam-pling, and predictive skill Observational System Simu-lation Experiments (OSSEs).

4 Poseidon Architecture

documents(text, image,

audio, video) user

CORBAsearch, retrieval, data translation,... authentication, accounting,...

rawmarinedata

dataacquisitionmodalities

encapsulatedobject resources

simulation/analysissoftware

objectwrapper

Metadata

marineontology

metadatatemplates

legacy resources

nativedistributed

data search/retrieval and

softwareexecution

Poseidon services

www browser

ORB

ORB

ORB

Poseidon server

Javaapplets

resourceregistry

scientificresults

@@@@@@@@@@

@@@@@@@@@@@@@@@

ORB

Poseidonuser interface

model mgmt. system,metadata creation,

standard visualization,statistical analysis, etc.

@@@@@@@@@@@@@@@

Figure 5: Poseidon system architecture

The software infrastructure for LOOPS will beprovided by the Poseidon system, which is con-ceived as a network of distributed data and soft-ware resources, communicating with one anotheracross a Common Object Request Broker Architecture(CORBA: http://www.org.omg/) backplane, see Fig-ure 5. The standard user’s environment will be pro-vided by means of a standard Web browser, to whichthe Poseidon User Interface and Model ManagementSystem will be automatically downloaded as JavaTM

(http://www.javasoft.com/) applets from a Posei-don server. Initially, a single centralized server is en-visioned. As the system utilization grows, however, ge-ographically dispersed multiple instances of the server

may be established, with the necessary provisions forthe concomitant problems of data consistency. ThePoseidon server also maintains a resource registry,which identifies the resources that are available formetadata searches and for data access. Resources in-clude marine ontology, metadata templates, measuredocean data (and in fact the platforms or robots collect-ing such data), documents (text, image, audio, video),scientific simulation/analysis software and simulationdata (scientific results). Newly developed resources canbe constructed directly as CORBA-compliant objects.Legacy resources, on the other hand, will require anobject wrapper, which is composed of a CORBA front-end and a back-end that supports the resource-specificcommunication protocol.

5 Model Management System

The heart of the Poseidon architecture is theModel Management System (MMS) [16], which is re-sponsible for: (1) creation of a Graphical User Inter-face (GUI) that allows a user to build an informationworkflow; (2) validation of the workflow; and (3) man-agement of the execution of the workflow. In the earlystages of the Poseidon system development, a num-ber of tests were conducted to test the linking of re-mote/heterogeneous applications. In order to producea full-scale example of the application linking, two dif-ferent applications were wrapped with CORBA layersto become Poseidon objects: (1) an application fromHarvard University’s HOPS for physical prediction ofthe ocean; (2) an application from MIT, Department ofOcean Engineering for acoustic tomography. These ap-plications were invoked from a remote applet (Figure 6)that supplied them with a set of initialization param-eters and asked for a two dimensional, color contourrepresentation of the acoustic field intensity in a ver-tical cross-section of the ocean in Massachusetts Bay.In this way, linking of heterogeneous codes written indifferent programming languages, executed on remoteplatforms running under different operating systems,and invoked by remote users, was demonstrated.

A Poseidon workflow is constructed as a dataflowdependency graph. An example of a dependency graphis shown in Figure 7 for the case of the Haro Strait ex-periment sound speed and current inversion [17]. Thesolid line corresponds to the actual inversion that wasimplemented. Had the addition of a local circulationmodel been possible (dashed line), a fully multidisci-plinary inversion combining the large sampling cover-age of the acoustic data set with the physics of a cir-culation model would have been achieved.Poseidon architecture involves a paradigm shift in

5

0-7695-0643-7/00 $10.00 � 2000 IEEE

Figure 6: Poseidon’s applet with received acoustic field

large ocean simulations. Currently, an expert scien-tist downloads legacy software and data to his/her owncomputer and largely manually navigates through theworkflow of program executions, based on the expertknowledge and assistance from the authors of legacysoftware and owners of measured data. Poseidon, onthe other hand, will allow for efficient and largely auto-mated workflow creation for certain problem domainsbased on the concept of metadata for scientific soft-ware. While the example workflow we have created inFigure 6 is completely static (i.e., the sequence of op-erations and the data exchange mechanisms are hard-coded, and is transparent to the user who sees onlyone interface to a black box), we have experimentedwith dynamic, near run-time, workflow creation, to al-low the user to combine a series of software and dataobjects into a workflow and execute it through a GUI.The components of the workflow are represented graph-ically as objects with input and output parameters thatcan be combined with other objects on the canvas andexecuted as a combined workflow. The user sees thegraphical objects, but not the transparent back-end ex-ecution of the workflow. Ultimately, we plan to developa system that will be able to automatically create work-flows to generate user-specified output parameters from

combinations of existing data and software objects, us-ing metadata, and workflow graphs as well as heuristicand AI methods.

In addition, Poseidon will create a service-orientedprototype for ocean simulations without the currentdownloading of software to a single environment. TheWorld Wide Web (http://www.w3.org/) and CORBAprovide the basic underlying technologies for service-style oceanographic simulations from hosts able to runsimulation resources and models. This service-orientedarchitecture could have provisions for network security,user authentication, and accounting.

6 Ontology and Metadata Creation

The Poseidon project involves the developmentof sharable ontologies (or common vocabulary) andmetadata (or data about data) for actual mea-sured/simulated data [18, 19] and for modeling soft-ware. The use of metadata to describe the propertiesand characteristics of data is beneficial for the con-struction of automated scientific information retrievalsystems. However, current metadata systems wouldrequire duplication of large portions of the metadata,which makes the construction of detailed metadata

6

0-7695-0643-7/00 $10.00 � 2000 IEEE

Figure 8: Poseidon Metadata Creator user interface

Sound speedestimate

Current estimate

Acoustic travel times

Array shape

Current-meter

Local sound speed

Current model

Sound speed model

Figure 7: Dataflow dependency graph correspondingto the Haro Strait oceanographic experiment (1996)inversion [17]

cumbersome and time consuming. Our solution tothis problem can come from borrowing concepts fromobject-oriented programming systems, which providehierarchical taxonomic structure for scientific data andsoftware.

We have implemented a method that incorporatestwo existing metadata standards (Dublin Core for doc-uments, FGDC standard for geospatial metadata) inan expandable object-oriented structure known as theWarwick Framework. Furthermore, since metadata isexpensive and tedious to produce, a Web-based soft-ware tool has been developed that simplifies the processand reduces the storage of redundant information - seeFigure 8. While we can search the metadata for the in-formation we need, we still need an ontology to ensurethat we can identify data unambiguously. Since no ex-isting vocabulary encapsulates all aspects of the oceansciences and ocean systems management, we have fa-cilitated the production of such a resource by creatinga Web-based tool (Figure 9) that will allow specialistswith the requisite domain knowledge to populate theontology independently. The tool handles all the logis-tical issues of storage, maintenance, and distribution.

To date, we have focused our efforts on metadata fordata. Construction of complex oceanographic work-

7

0-7695-0643-7/00 $10.00 � 2000 IEEE

Figure 9: Poseidon Ontology Creator user interface

flows will require resource discovery of both data andsoftware. Metadata for software is an important re-search issue [20] that would need to involve formalcharacterization of the input and output structure,range of modeling validity for input parameters, andparametrization in terms of cost, accuracy and method-ology of solution. A promising approach involves se-mantic networks and rule-based inference engine [21].

7 Conclusions

We have presented ongoing work on a moderndistributed computing and networking infrastructurefor multidisciplinary ocean research. The adventof Littoral Ocean Observing and Predicting System(LOOPS) enables realistic field estimates, includingreal-time nowcasts and forecasts as well as simulations,in which a set of coupled interdisciplinary models arelinked to an observational network, consisting of var-ious sensors mounted on a variety of platforms, viadata assimilation schemes. Poseidon is a knowledgenetwork which will allow seamless search, exchange,analysis, and visualization of resources, and enablewidely distributed computing such as efficient forecast-ing and adaptive sampling of the ocean. The Model

Management System (MMS) of the Poseidon archi-tecture is responsible for: (1) creation of a GraphicalUser Interface (GUI) that allows a user to build aninformation workflow; (2) validation of the workflow;and (3) management of the execution of the workflow.The Poseidon project also involves the developmentof sharable ontologies and metadata for actual mea-sured/simulated data and for modeling software.

Acknowledgments: Department of Commerce(NOAA, Sea Grant) under grant NA86RG0074,National Ocean Partnership Program via ONRgrant N00014-97-1-1018. The authors also thankProf. S. Lalis for his comments on this work.

References

[1] A. S. Grimshaw and W. A. Wulf. Legion: The nextlogical step toward the world-wide virtual com-puter. Communications of ACM, 40(1), January1997.

[2] M. Sato, H. Nakada, S. Sekiguchi, S. Matsuoka,U. Nagashima, and H. Takagi. Ninf: A network-based information library for global world-widecomputing infrastructure. InHPCN ’97, pages 491–502, 1997.

[3] I. Foster and C. Kesselman. Globus: A metacom-puting infrastructure toolkit. In IPPS/SPDP ’98,Heterogeneous Computing Workshop, 1998.

[4] S. Vinoski. CORBA: Integrating diverse appli-cations within distributed heterogeneous environ-ments. IEEE Communications Magazine, 14(2),February 1997.

[5] H. Casanova and J. Dongarra. NetSolve: A net-work server for solving computational science prob-lems. Journal of Supercomputer Applications andHigh Performance Computing, 111(3):212–223, Fall1997.

[6] NASA: Earth observing sys-tem data and information system.(http://spsosun.gsfc.nasa.gov/New EOSDIS.html)

[7] R. C. Dengel and J. T. Young. The Unidata re-covery system. In Proceedings, Ninth InternationalConference on Interactive Information and Pro-cessing Systems for Meteorology, Hydrology, andOceanography, Anaheim, CA, January 1993.

[8] J. Gallagher and G. Milkowski. Data trans-port within the distributed oceanographic data

8

0-7695-0643-7/00 $10.00 � 2000 IEEE

system. In Fourth International World WideWeb Conference, Boston, MA, December 1995.(http://www.w3.org/Conferences/WWW4/Overview.html)

[9] E. Mesrobian, R. Muntz, and E. Shek. OASIS:An EOSDIS science computing facility. In Interna-tional Symposium on Optical Science, Engineering,and Instrumentation, Conference on Earth Observ-ing System, August 1996.

[10] Master Environmental Library (MEL):(http://mel.dmso.mil/)

[11] C. Lagoze and J. Davis. Dienst: an architecturefor distributed document libraries. Communica-tions of the ACM, 38(4):45, April 1995.

[12] C. Houstis, C. Nikolaou, M. Marazakis, N. M.Patrikalakis, J. Sairamesh, and A. Thomasic.THETIS: Design of a data management and datavisualization system for coastal zone managementof the Mediterranean sea. D-lib Magazine, Novem-ber 1997. (http://www.dlib.org/)

[13] A. R. Robinson et al. Realtime Forecast-ing of the Multiscale, Interdisciplinary CoastalOcean with the Littoral Ocean Observing and Pre-dicting System (LOOPS). Abstract of Lectureat AGU Ocean Sciences meeting, January 2000.(http://czms.mit.edu/poseidon/publication/)

[14] T. Curtin, J. G. Bellingham, J. Catipovic, andD. Webb. Autonomous Ocean Sampling Networks.Oceanography, 6(3):86–94, 1993.

[15] A. R. Robinson, P. F. J. Lermusiaux andN. Q. Sloan, III. Data Assimilation. in The Sea:The Global Coastal Ocean I, Processes andMethods(K. H. Brink and A. R. Robinson, editors), Volume10, John Wiley and Sons, New York, NY, 1998.

[16] K. P. Mihanetzis. Towards a Distributed In-formation System for Coastal Zone Manage-ment. MIT, Joint Ocean Engineer and M.S. inOcean Systems Management Thesis, May 1999.(http://czms.mit.edu/poseidon/publication/)

[17] P. Elisseeff, H. Schmidt, M. Johnson, D. Herold,N. R. Chapman, and M. M. McDonald. Acous-tic tomography of a coastal front in Haro Strait,British Columbia. Journal of the Acoustical Soci-ety of America, 1999, in press.

[18] P. C. H. Wariyapola, N. M. Patrikalakis,S. L. Abrams, P. Elisseeff, A. R. Robinson,

H. Schmidt, and K. Streitlien. Ontology and Meta-data Creation for the Poseidon Distributed CoastalZone Management System. Proceedings of IEEEForum on Research and Technology Advances inDigital Libraries, IEEE ADL’99, pp. 180–189. May1999, Los Alamitos, CA:IEEE, 1999.

[19] P. C. H. Wariyapola. Towards an Ontology andMetadata Structure for a Distributed InformationSystem for Coastal Zone Management. MIT, M.S.in Ocean Engineering Thesis. September 1999.(http://czms.mit.edu/poseidon/publication/)

[20] N. M. Patrikalakis, P. J. Fortier, Y. Ioannidis,C. N. Nikolaou, A. R. Robinson, J. R. Rossignac,A. Vinacua, and S. L. Abrams. Distributed In-formation, Computation, and Process Managementfor Scientific and Engineering Environments. D-libMagazine, April 1999. (http://www.dlib.org/)

[21] V. Christophides, C. Houstis, S. Lalis, andH. Tsalapata. Ontology-Driven Integration of Sci-entific Repositories. NGITS ’99, New GenerationInformation Technologies, Lecture Notes in Com-puter Science, Elsevier, Hobart, Israel, July 1999.

9

0-7695-0643-7/00 $10.00 � 2000 IEEE