AN ANALYSIS OF GRID TECHNOLOGIES FOR SUPPORT OF SPACE BASED OPERATIONS

Transcription

1 AN ANALYSIS OF GRID TECHNOLOGIES FOR SUPPORT OF SPACE BASED OPERATIONS Sam Chism* (LMSO) and Shirley Tseng** (IGI) *Lockheed Martin Space Operations and **Infinite Global Infrastructures, LLC & ABSTRACT This paper describes an initial investigation into the potential applications of grid or distributed computing technologies for NASA ground systems. Examples of existing grids include the NASA IPG (Information Power Grid: the Griphyn (Grid Physic Network: and others listed in appendix 1. A key benefit of implementing grid technologies would be lower operational costs resulting from more efficient and complete utilization of available resources. Examples include the capturing of unused computer resources to increase efficiency, the sharing of data resources, the ability to create virtual supercomputer centers at greatly reduced costs, and higher fault tolerance. The NASA ground architecture and its operational functions have been analyzed to identify potential benefits from the application of grid technologies. This paper concludes with a summary of the current status of grid technologies, forward plans, and the longterm outlook, with emphasis on their applicability to ground-based space operations at NASA. 1. INTRODUCTION The term grid came about in the mid-1990 s and referred to the concept of applying an advanced form of distributed computing to large engineering and science projects, (Anatomy, 2001). The development of the Internet, intranets, and web services played a large role in developing grid computing concepts to take advantage of large-scale sharing of distributed computational and data resources. A grid efficiently coordinating resource sharing and problem solving in dynamic, multi-institutional organizations. (Ref 1). The Grid is collaborative environment, where complexities of moving data, running programs and user management issues are incorporated in the system and invisible to the user. The Grid provide the infrastructure to connect people to their resources: data, computers, information systems, instruments, etc. Key to grids are security, resource management, and use of standards to ensure interoperability. The wider application of grid computing to areas outside of big science is driven by several factors, including the ever increasing computing power and bandwidth at lower cost and the rise of such compute intensive areas as the study of the human genome. Briefly, the advantages offered include: (1) greater use of available and otherwise unused computing resources (2) cost savings resulting from increased productivity from existing equipment without additional maintenance requirements. (3) greater fault tolerance; e.g. virtual supercomputers made up of hundreds or thousands of machines (4) flexibility to adjust to new computing needs in a timely fashion; e.g. a short term, unforeseen need for a large amount of computing power can be met without requiring physical changes (assuming no critical impacts to other applications) Grid technologies promise to provide scalable, secure, high-performance mechanisms for discovering and negotiating access to remote resources. The Grid concept promises to make it possible for scientific collaborations where resources are shared on an unprecedented scale, and for geographically distributed groups to work together in ways that were previously impossible. (Ref 14) 1

2 1.1 THE GRID ARCHITECTURE AND GRID MIDDLEWARE Figure 1-1 illustrates how a grid solution can be applied to a networked computing architecture. As indicated, the grid related layers are in the middleware layer. Some of these are purely grid services and others are grid related but shared in common with web services. Commercial web services standards are built into grid services. Grid middleware functions include collaborative tools, brokering, interoperability, grid management, security and optimized access to resources. The collaborative tools provide enhanced interaction between the grid nodes, with respect to sharing data sets and compute resources. This results in a more efficient and powerful collaborative environment.. The interoperability layer handles the translations necessary to communicate between sources using different protocols. The grid management layer manages schedules and optimizes the use of grid resources. The brokering function constantly looks for grid resources that are available and can be assigned grid computing activities The security layer ensures that application security constraints, including necessary access controls, can be enforced by the grid. The National Science Foundation (NSF) Middleware Initiative (NMI), which was launched in September 2001 has produced a first release (NMI-R1) which is essentially a recipe book for simplifying middleware and Grids (Gridcomputingplanet.com). This release bundles the Globus Toolkit, CondorG and other tools. Clients: X-Windows, Web Browsers, etc. http, https, etc. Common Grid Services: Data Management Monitoring Workflow Management Collaboration Tools Etc. Figure 1-1 Basic Grid Architecture Diagram Application Portals: Discipline Specific (e.g. SDSC for TeleScience) Problem Solving (PSE) (e.g. SciRun, Cactus) Environment Mgmt. (e.g. LaunchPad, HotPage) Composition Frameworks (e.g. XCAT) Web Services: WSDL, UDDI, SOAP File Transfer Events Etc. Grid Services: Resource Management: Job Submission Grid Information Svc: Data Grid Credential Mgmt. Grid X.509 Cert. Auth. GridFTP XML / SOAP over Grid Security Infrastructure Grid Protocols & Security Infrastructure Resources: Computers, Storage, etc. Derived from GGF4 Arch WG Presentation 2

3 2. THE GLOBAL GRID FORUM (GGF) The Global Grid Forum (GGF) is an international community-initiated forum of individual researchers and practitioners working on distributed computing, or grid technologies. The GGF came about in the mid 1990 s and was incorporated in July It is a mechanism for the coordination and collaboration of personnel around the world who are working grid computing applications, especially in large-scale science programs. The GGF has a number of working groups working to establish standards and best practices for grid technologies. The goals of the GGF are to: Facilitate/support the creation of regional and global grids Address architecture, infrastructure, standards and other technical requirements Educate the scientific community, industry, government and public Facilitate the application of grid technologies Provide a forum for grid technologies, applications and opportunities Exercise powers conferred under its incorporation The GGF membership is wide and growing. The following listing of select GGF members demonstrates the extent of its world-wide government, university and industry membership. The commercial enterprise membership has been the fastest growing component, a clear indication of the success and growing interest in the real application of grid technologies and the growing availability of grid COTS products. This membership includes large technology companies, companies with commercial interests in grid usage, and smaller companies focused on developing and selling grid products and services. Table 2-1 Overview of Selected Global Grid Forum (GGF) Membership GOVERNMENT UNIVERSITY INDUSTRY Argonne National Laboratory Boston University Cisco Brookhaven National Laboratory Caltech CSC DoD HPC Modernization Program Carnegie Mellon Ford Motor Fermi National Accelerator Lab. University of Chicago HP Labs/Compaq JPL Cornell University IBM Lawrence Berkeley National Lab. Dartmouth College Intel Los Alamos National Laboratory Illinois Institute of Technology Johnson & Johnson NASA Imperial College Motorola Laboratories Ames Research Center Mississippi State University Raytheon Nat'l Lab. for Applied Network Res. Ohio State University The Aerospace Corp. National Research Council Penn State University Platform Computing National Science Foundation Rutgers University Sun Microsystems NIST Texas Advanced Computing Center VA Linux Systems Oak Ridge National Laboratory University of California, San Diego Velocita Corp. US Department of Energy University of Ill, NCSA Viagenie Inc. US Science Foundation University of Michigan Zytec Telecom Ltd. University of Minnesota University of Tennessee University of Virginia 2.1 SPACE GRIDs NASA has developed a high performance computational grid named appropriately the Information Power Grid (IPG) [see ]. This is a collaborative effort between NASA s Ames, Glenn and Langley Centers, and the National Science Foundation (NSF), Partnerships for Advanced Computational Infrastructure (PACI), programs at the San Diego Supercomputer Center (SDSC), and the National Center for Supercomputer Applications (NCSA) at U of Illinois. 3

4 The IPG is funded by the Computing, Information and Communications Technology (CICT) program at the Ames Research Center. The IPG was designed to help support large engineering and science projects by facilitating the interaction between a diverse and geographically distributed array of people, computing resources and information systems. Figure 2-1 shows the IPG architecture. Network connectivity for the centers involved is provided through the National Research Engineering Network (NREN), with some centers also connected via the next generation Internet or Internet 2". The European Space Agency (ESA) SpaceGRID is targeting the exploitation of distributed data, applications and resources in support of space sciences. Planned for testing in 2003, this grid is targeting four space related disciplines: (1) Earth Observation (2) Space Research (space weather, plasma interactions, and radiation transport simulation) (3) Solar System Research (4) Spacecraft (Mechanical) Engineering ARC JPL Figure 2-1 NASA s Information Power Grid (IPG) SDSC MC AT/ SRB 1024 CPU, O3000 DMF MDS CA 300 node Condor pool NREN WAN Testbed Next Generation Internet NGIX Chicago NCSA MSFC Source: Dec 2001 workshop presentation: GRC CM U GSFC HQ LaRC 2.2 NATIONAL VIRTUAL OBSERVATORY The National Academy of Sciences and Astronomical Survey Committee recommended in its decadal survey (NAS99) the establishment of a National Virtual Observatory (NVO). NVO will utilize the latest computer and networking technologies to connect the archival and real-time resources of many earthbound and orbital astronomical observatories. The NVO is being funded with a five-year, $10 million grant from the NSF. Access to these many different data sets, covering a wide range of wavelengths will enable otherwise unlikely discoveries. Additionally, more resources can be directed in a timely fashion to these new discoveries, particularly if they are short-lived ones. 4

5 In addition to supporting professional astronomers, a major goal of the NVO is to make more information available to interested public, students and teachers, thereby enhancing public appreciation and support for large astronomy projects. See for more information. International NVOs are in various funding and design states around the world. 2.3 OTHER GRIDs AND GRID PROJECTS In addition to the space grids described above, there are others established to focus on range of applications (data grids, compute grids, science grids, access grids, knowledge grid, sensor grids, cluster grids, campus grids). Table 2-2 provides a summary of additional grid projects. The Condor, Globus and Legion projects were key in the early development of grid concepts. Condor was a project started in 1988 at the University of Wisconsin, Madison (Condor, 2002). This grid built on earlier work in the area of Distributed Resource Management (DRM) where the focus was on load balancing in a distributed system. Condor developed a high throughput computing (HTC) environment for distributively owned computing resources. HTC focuses on delivering large computation power over a long period of time, in contrast to high performance computing (HPC) which targets even more power over a shorter period of time. Condor takes unused computer resources, and makes them available to users by creating an environment of distributed ownership. Available for download on many UNIX platforms, an effort is underway to port Condor to Windows NT. The Globus Project, a collaboration between USC and Argonne National Laboratory, was established in 1996 to develop the fundamental technologies needed to build computational grids. The project s Globus Toolkit, adopted so far by Compaq, Cray, SGI, Sun, Veridian, Fujitsu, Hitachi and NEC, is seen as the emerging de-facto standard for grid computing. Legion is a wide-area operating system developed at the University of Virginia for use by government scientists. It utilizes principles similar to CORBA (Common Object Request Broker Architecture), however it is targeted for high performance. It is used to locate and schedule resources, to negotiate any issues between disparate operating systems, and to coordinate security implementations. Legion is designed for a system of millions of hosts and trillions of objects tied together with high-speed links (Legion, 2002). Legion is an integrated architecture, building its higher level structure on top of a single unified object model, unlike Globus which is a sum of services architecture, using sets of software components grouped into composite toolkits. Developed by Alexander Grimshaw, Legion protocols are now being marketed by AVAKI Corporation. AVAKI claims to have offered the first commercialized grid software solution which provides highly secure, virtualized access to data and compute resources. Many science communities have launched Grid projects. Many governments are funding R&D projects to develop core technologies, deploy production Grids, and apply Grid technologies to challenging problems. (See Appendix 1 for links to lists of grid projects) 5

6 Table 2-2 Overview of Selected Grids and Grid Projects GRID / PROJECT OWNER PURPOSE / GOALS Access Grid Department of Energy (DOE) Internet-based collaboration worldwide for large scale meetings Condor University of Wisconsin Grid project started in 1988 to develop High Throughput Computing (HTC) DOE DisCom*2 (Distance & Distributed Computing & Comm.) DOE Develop technologies for efficient use of high end platforms DOE Science Grid DOE Develop an advanced computing infrastructure for science missions EuroGrid European high performance Testbed focusing on a suite of apps, Laboratories including biomolecular sim., weather, etc. European DataGrid Grid Physics Network (GriPhyN) European Union (EU) National Science Foundation (NSF) High energy physics, earth observation, and biology Focuses: particle detection, gravity waves, and Sloan Digital Sky Survey IPG (Information Power Grid) NASA Development of ways to integrate distributed computing resources National Technology Grid NSF/National Computational Development of a collaborative Science Alliance (NCSA) computational environment ce/alliance/gridtech.html NEESgrid NSF Developing a national level virtual (Network for Earthquake laboratory for earthquake Engineering & Simulation) Engineering TeraGrid NSF funded through SDSC, Nationwide computing fabric to help (Prime Contractor: IBM) NCSA at Univ. of Ill., Caltech, molecular biologists study proteins and and Argonne National Lab. develop drugs to target diseases 6

7 3. GRID APPLICATIONS, PRODUCTS AND ISSUES/TRENDS 3.1 What Problems are Suitable for Grid Solutions? One method for determining how and when to apply a grid cpu utilization solution involves the use of a compute-to-data ratio. This c2d ratio can be defined in terms of gigaflop hours per gigabytes of data (GFH/GB). Note that one year of a 1 gigaflop computer = 3.6 trillion floating point operations. The grid computing company Parabon (Ref. 10) uses the following rule of thumb: If your problem has a c2d ratio lower than 1.5, you may be better off purchasing hardware than using an Internet service. Such problems require much data traffic, but gain little from a distributed environment because the computations are trivial. However, those problems with high c2d ratios are good candidates for Internet or grid computing. Examples of such problems include simulations, parameter studies, large searches, optimizations, Monte Carlo simulation, machine learning and data mining. Interestingly, since bandwidth speeds continue to outpace computing gains (Moore s law) and therefore get cheaper, the c2d ratio breakpoint will continue to decline. 3.2 Grid Products and Application Areas This section identifies and describes selected current and evolving areas recommended for the application of grid computing solutions beyond their big science origins. Pharmaceutical Companies GlaxoSmithKline is using grid computing for genomic research which requires protein folding analyses, the analysis of clinical data, and the screening of millions of potential compounds. Semiconductor Manufacturers Intel has utilized grid or distributed computing to increase the utilization of its computing resources from 35% to over 80% (Kawak, 2001). Intel CTO Patrick Gelsinger concludes that this greater efficiency and ability to accelerate validation processes has saved Intel more than $500 million! Manufacturing Nissan Motor Company replaced the central computer in their Vehicle Research Department with a distributed Compaq grid computer solution. This gave their users greater performance at reduced cost. Five years ago, it would take approximately months for the new car development period, but with the introduction of high speed computing systems, it takes only 18 months, said Dr. Himeno, senior researcher at the Vehicle Research Laboratory. Financial Companies J. P. Morgan & Company announced in July 2000 they d deployed Turbolinux s EnFuzion software the previous year to help power the firm s worldwide risk management system for fixed income derivatives. EnFuzion runs their existing customer applications without modification, and uses automated job scheduling during each node s idle CPU cycles. The initial system clustered more than 200 Dell Precision Workstations along with a backend system of more than 50 Sun Microsystems Enterprise Server CPUs. The initial savings was $ 7 million a year. 3.3 Grid Computing Issues The key concerns for grid computing systems are security, optimization, and interoperability. Security is of particular concern with networked computing resources, which can be both physically and organizationally dispersed. Grid computing systems must assure all parties that the access controls will protect their data from other participating parties, without impairing the performance. To optimize networked or grid systems policies must be enforced which govern the access and usage of resources by all parties. This includes priority of access and total time of usage, etc. Optimization is directly tied to the central underlying problem mentioned in the introduction, namely that of coordinating resource sharing and problem solving in dynamic, multi-institutional organizations. 7

8 The Global Grid Forum (GGF) Working Groups (see are actively working these and many other grid issues. Many grid technologies are covered under the OGSA (Open Grid Services Architecture) (Physiology, 2001). 4. APPLICATIONS FOR GRIDS IN GROUND/SPACE OPERATIONS A high level overview of the ground/space operations elements is given in Figure 4-1. The mission ground monitoring and control functions, at one or more locations, communicates with the space assets via one or more antenna sites, receiving telemetry from the spacecraft and sending messages/commands to them. Payload and systems data are distributed to support and scientific personnel at other locations. This section discusses how grid technologies could be applied to these functional areas. 4.1 Ground Station Virtualization A recent NASA initiative is considering the creation of a Federated Ground station Network (FGN) consisting of University owned ground stations (Cutler, 2002). Key to this concept is the creation of Virtual Ground Stations (VGSs) which would offermore power, flexibility and fault tolerance than a single station is currently capable of. Virtualization enables hardware and software distribution among independent ground stations. End users have transparent access to space systems and coordination is enhanced. This integration also enables automatic monitoring between component stations, inter-station testing and link optimization. Other advantages include the use of Internet and grid standards, the improvement of system status monitoring, faster and more cost-effective testing, and the minimization of link outages, all while maintaining better quality communications. 4.2 Mission Operations The mission computer processing resources for various subsystems used in operations control and monitoring, mission planning, and simulations could be used more efficiently, resulting in the reduction of hardware, maintenance and sustaining costs, while increasing fault tolerance. Taking advantage of unused computing power would also provide increased performance of computationally intensive applications. More cases could be evaluated in the same time-frame, and more optimal solutions could be identified. Finally, grid technologies could be used to provide enhanced collaborative environments for support room activities involving multiple sites. 4.3 Data Processing, Distribution and Collaborative Efforts The application of grid technology to improve the end-to-end processing of science data between between NASA Centers and remote users could: increase current performance aid in automation reduce costs introduce new analysis and collaboration capabilities not currently possible New capabilities include low-cost telescience grid tools such as: personalized scientist portals distributed collaboration tools visualization tools and large scale data analysis tools complex searching tools remote commanding Grid computing came from early efforts to apply distributed computing resources to large science projects. Grid computing continues to increase in application value as it evolves to meet the needs of current large scale science projects. Most obvious among space projects are the current earth resource projects involving specialized satellite platforms; e.g. Terra and Aqua, each of which produce very large amounts of data. 8

9 Grid Applications in a Generic Space Ops Architecture Data Acquisition Virtual Ground Station (VGS) User Community Ground Link Optimization Mission Monitoring and Control Virtualization for Distributed Operations Enhanced Collaborative Environments CSA, ESA, NASDA, NASA, RSA WAN Servers /WSs Virtual Computer and Storage System/s Figure 4-1 Functional Overview of Space Ops Grid Applications 5. CONCLUSIONS AND RECOMMENDATIONS Mission & Ops Data Storage Enhanced Data Sharing The development and application of grid systems represents a major step in the evolution of networked systems. These network computing architectures enable more efficient, complete usage of available resources. This results in greater information processing and/or performance, more flexibility and expandability, reduced costs, task-oriented resource assignment, and attendant maintenance. Grid systems originated with large scale government and university science projects. As a result of the very widespread and rapid increase in computer power and network bandwidth, grid technologies have made the transition to many other applications. These applications, such as biotech companies analyses of complex molecules for potential new drugs require more and more compute resources. An initial analysis indicates that grid techniques can offer solutions to key areas of the generalized space/ground ops architecture, including the following: (1) Gridding of ground stations into a Virtual Ground Network (VGN) (2) Virtual consolidation of ground operations such a project (e.g. Space Station) with widely dispersed resources and operations personnel can function as a single team, with all sharing the same, identical view of systems. (3) Grid computing and data storage in support of telemetry and science data processing (4) Grid based collaborative environments to support mission and science related operations. (5) Enhanced data distribution/sharing via a grid based data storage architecture NASA s IPG can be used to demonstrate grid based solutions. The grid infrastructure elements can be overlayed over private networks using standard protocols. REFERENCES 9

10 (3) Condor High Throughput Computing. (2002). (8) Cutler, J. W.; A. Fox and K. Bhasin. (2002). Applying the Lessons of Internet Services to Space Systems. To be presented at the GSAW and IEEE Aerospace conferences. (1) Foster, Ian; Carle Kesselman and Steven Tuecke. (2001) The Anatomy of the Grid, Enabling Scalable Virtual Organizations. To appear: Intl J. Supercomputer Applications, Available: (14) Foster, Ian. (2002). The Grid: A New Infrastructure for 21st Century Science. Physics Today Available: (2) Foster, Ian; Carl Kesselman, Jeffrey M. Nick and Steven Tuecke. (2002). The Physiology of the Grid, An Open Grid Services Architecture for Distributed Systems Integration. Available: (15) Ian Foster s 3 point checklist on What is the Grid. (2002). Available: (12) Information Power Grid: (7) An Introduction to Grid Computing Peer-To-Peer and Distributed Computing. (2001, February). Roberston Stephens Investment Bankers. Available: (9) Kawak, Chris and Robert Fagin. (2001, May). Internet Infrastructure & Services. Bear Streams. (4) Legion A Worldwide Virtual Computer. (2002). (11) Marchetti, Pier Giorgio and Hugh Evans. (2002, May 6-7). ESA "SpaceGrid" presentation by at Earth Observation Grid Workshop, Frascati, Italy. (5) Waldrop, M. Mitchell. (2002, May). Grid Computing A New Way of Linking Together Computers Large and Small Could Put the Planet s Information-Processing Power on Tap. MIT Technology Review (10) What Jobs Are Right for Internet Grid Computing Services? (2002) (6) Wladawsky-Berger, Irving. (2002). Advancing e-business into the Future. Appendix 1 : Links to Grid projects