DOE/MICS Mid-Year Project Report: June 4, 2003

Project Title: Optimizing Performance and Enhancing Functionality of Distributed Applications Using Logistical Networking (DE-FC02-01ER25465)

Project Type: SciDAC

PI: Institution: Micah Beck (lead), University of Tennessee, Knoxville

Jack Dongarra, University of Tennessee, Knoxville

James S. Plank, University of Tennessee, Knoxville

Rich Wolski, University of California at Santa Barbara

1. Executive summary

The multi-threaded research project in Logistical Networking for the DOE SciDAC project aims to create advanced, storage-enabled network services that can provide reliable, fast, flexible, scalable, and efficient delivery of data to support distributed and high performance applications of all types. During the most recent period, meaningful progress toward this goal was made on all key fronts — research and development, publication and dissemination, and planning and interaction for productive collaboration with the SciDAC research community. Highlights include the following:

ù Released Logistical Runtime System Tools (LoRS Tools) v0.81, with improved end-to-end data compression, encryption, and checksum services. LoRS Tools v0.81 includes access to DataMover plug-in functionality for UDP point-to-point and multicast transfer mechanisms, and a new version of LoRS View visualization software with an improved graphical user interface.

ù Released IBP Depot v1.3 which offers improved reliability, dynamic thread control, and IPv6 compatibility to allow IBP depots to run on IPv4 only, IPv6 only, or dual IPv4/IPv6 machines.

ù Developed the Read-Only Logistical File System (ROLFS) and the more functionally advanced Logistical File System (LFS) to enable users to interact with Logistical Networking technologies using a familiar, recognizable file system.

ù Began collaboration with the Terascale Supernova Initiative (TSI). The Logistical File System (LFS) has been ported with the Hierarchical Data Format (HDF) to allow TSI researchers to take large data sets generated by advanced computer simulations and output directly to IBP storage.

ù Deployed the first of five new high-powered, dedicated IBP servers to be located at major TSI research sites around the country. These high performance machines will provide an additional 9.5 TB of IBP storage space for use by TSI and the entire SciDAC research community.

ù Published “An End-to-End Approach to Globally Scalable Network Storage” at SIGCOMM 2002 Conference. One of only two position papers accepted in an extremely competitive field, this paper explains the unique architectural vision behind Logistical Networking.

ù Gave several major public demonstrations of the performance and functionality of Logistical Networking technology. At the international iGrid2002 conference, multiple standard TCP streams were used to accomplish data transfers of 100 Mbps from the US to Amsterdam. At SC2002, a distributed computing application running on NetSolve servers around the world used Logistical Networking technology to seamlessly access blocks from distributed data replicas for enhanced application performance.

2. Current accomplishments

2.1 Currently Deliverable Logistical Networking Technologies

Logistical Networking technologies offer a flexible, highly scalable means to manage distributed content and data of all kinds using shared network storage. Currently deliverable software tools allow the user to deploy their own IBP storage “depot(s)” or utilize available public IBP storage deployed worldwide to easily accomplish long haul data transfers, temporary storage of large data sets (on the order of terabytes), prepositioning of data for fast on-demand delivery, and high performance content distribution such as streaming video.

Internet Backplane Protocol (IBP) is a highly scalable, low-level mechanism for managing remote storage as a sharable network resource through deployment and shared use of lightweight storage allocations called storage “depots.” IBP is the foundation of the network storage stack and essential to the Logistical Networking approach.

The External Node (exNode) is a generalized data structure, analogous to a UNIX inode, holds metadata necessary to manage distributed content on IBP depots. ExNodes are used to aggregate IBP storage allocations and allow file-like structuring.

The Logistical Backbone (L-Bone) directory and resource discovery service catalogues registered IBP storage depots for an international deployment of 204 depots that serve 15 TB of storage as a shared resource for the scientific community. A second private directory is being established to serve the ESnet and SciDAC communities exclusively. This private L-Bone implementation currently offers 1.5 TB of storage, and will grow to 8 TB as planned IBP deployments are accomplished over the next three months.

Logistical Runtime System Tools (LoRS Tools) software suite uses the underlying capabilities provided by IBP, the exNode, and the L-Bone to implement high-level file management capabilities with strong properties, including high-performance access, reliability, and end-to-end services such as data compression, checksums, and encryption. The LoRS View visualization tool, included with the LoRS Tools package, provides graphical representations of LoRS Tools file management capabilities, allowing the user to view upload, download, and inter-depot data transfers in real time.

DataMovers are auxiliary IBP depot modules that support all kinds of customized or special purpose depot-to depot-transfers, including point-to-point, point-to-multipoint, and multicast transmission. Since the movement of large data sets is of immediate interest to our SciDAC application collaborators, we are experimenting with depots equipped with high-performance data movers for massive, long-haul transfers among remote collaborators (e.g. ORNL and CERN).

2.2 Research and development

Our most recent research has expanded the effectiveness of Logistical Networking in both performance and functionality. The entire software tools suite is now available and in use by our collaborators. Below are some of our accomplishments:

ù Released Logistical Runtime System Tools (LoRS Tools) v0.81, with improved end-to-end support, including data compression, default DES encryption, and checksum conditioning of stored data. Includes support for the use of TCP DataMover and reliable UDP DataMover plug-in features.

ù Released LoRS View v0.80, including an improved graphical user interface with easier to use location dialogs, more customizable preference parameters, and a “Route” control panel for executing single-threaded, pipelined routed data augmentations. This new version supports all features available with the LoRS Tools command line interface.

ù Released IBP Software version 1.3, for IPv6 compliant depots. IBP v1.3 may be run on dual stacks, IPv4 only, or IPv6 only machines. New features also include dynamic thread control, new DataMover plug-ins, GNU compliant installation procedure, and greater stability and reliability due to recent fixes.

ù Two new DataMover plug-in features for increased data transfer performance are included with IBP v1.3. The UDP/IP multicast DataMover uses unreliable UDP/IP multicast to accomplish point to multipoint transfers. The SABUL (Simple Available Bandwidth Utilization Library) DataMover uses a reliable UDP transfer stream along with a TCP flow control channel to provide very high throughput over long-haul transfers. SABUL was developed by Robert Grossman and his group at the University of Chicago and the National Center for Data Mining, who also collaborated with us in the development of the SABUL DataMover.

ù Investigated the efficacy of using on-line forecasting to achieve high-throughput and reliability levels without the overhead of redundant, parallel TCP streams.

ù IBPvo, an experimental personal video recording application, is currently available for use via the internet. IBPvo technology can be generalized to provide content delivery services to the SciDAC community, for instance streaming of large video files.

ù Read-Only Logistical Files System (ROLFS) is currently being tested as a supporting technology for IBPvo. ROLFS provides file management services, including automatically refreshing the time-limited IBP storage allocations used to store file content.

ù Logistical File System (LFS), currently a baseline library/user-space file system, has been ported with NCSA’s Hierarchical Data Format (HDF) v4.1 scientific data management library to allow the Terascale Supernova Initiative’s complex modeling applications to release large output data sets directly to the logistical network.

ù The first of five new 1.9 TB high-performance dedicated IBP servers, to be deployed at principle TSI research sites (ORNL, SDSC, Stony Brook, LBL, NCSU) around the country, has been successfully installed at ORNL and is currently in use by TSI group members. (More detail in Section 4, below.)

ù Three IBP depots located topologically close to the Abilene backbone allow for significant overlay routing possibilities. Clusters of public depots, thirty-one in California and eight in North Carolina, serve as backup transfer paths for TSI data transfers.

2.3 Application-Driven Research

2.3.1 Terascale Supernova Initiative

The TSI project has adopted Logistical Networking as a key component of their data management strategy. TSI is currently using the LoRS command line tools to share large data sets and computational results between collaboration sites. Previously, using traditional FTP tools, TSI collaborators tolerated transfer rates of 8 Mbps at best. Using the LoRS tools, they can now transfer data at speeds up to 220 Mbps between key research sites at ORNL and NCSU. See Figure 1, below.

The LoRS Tools software package allows SciDAC users to store, manage, and retrieve data via the Logistical Network. The latest LoRS Tools package includes a graphical user interface to allow straightforward mastery of capabilities, as well as the LoRS View visualization tool for viewing data manipulations in real time. These additions to the LoRS Tools package enhance the usability of the tools by making them accessible to researchers and students at all levels of expertise. The complete LoRS Tools package is available for download from our website.

New features of the LoRS software include dynamic thread control for the download command. While downloading data, LoRS may now use the progress-driven redundancy algorithm to maximize download performance by making informed decisions about thread allocation.

2.3.2 Logistical File Systems

We are currently focusing our efforts to develop two new Logistical file systems to aid TSI and other SciDAC collaborators in managing and sharing large data sets. The Read Only Logistical File System (ROLFS) is intended to facilitate file sharing across organizational and geographical boundaries. The Logistical File System (LFS) is a more full-featured file system designed for use on workstations or file servers. Both of these approaches provide a familiar file system interface for our novel network storage infrastructure.

ROLFS version 1.0, to be released in early June, uses a standard client-server model to provide an exNode directory that will allow users to freely share data with the SciDAC community. Full deployment of a private ROLFS directory for the use of TSI collaborators, with completed performance upgrades, will be in place by the end of the summer. A dedicated ROLFS server will actively manage TSI data set exNodes and provide a central directory for exNode storage and retrieval.

A key feature of ROLFS v1.0 is active exNode management, which relieves the user of the burden of monitoring and refreshing stored data. ROLFS performs the scheduled renewal of time-limited IBP storage allocations, as well as maintaining preset fault-tolerance and performance levels through automatic striping and redundancy. ROLFS restores degraded allocations by automatically replicating stored data fragments in order to maintain a minimum number of redundant copies of the data.

When mature, LFS will be a complete file system implementation with full capabilities to manipulate files and directories, including create, open, read, and write functionalities. LFS provides a much more scalable and flexible foundation for distributed data management than a traditional file system. LFS stores data on the logistical network, i.e. on IBP depots, while traditional file systems store data on local disk or a file server attached to the local network. LFS transparently handles the tasks of finding an appropriate IBP storage depot, allocating IBP storage, and storing data on the Logistical Network.

The current implementation of LFS has been ported with NCSA’s Hierarchical Data Format (HDF) v4.1 scientific data management library, to allow complex modeling applications used by TSI and other SciDAC researchers to release output directly to the Logistical Network. As TSI scales up the size of its simulations, the capability to output directly to the network without waiting for the entire simulation to finish will be critical to storing and moving data effectively. HDF is widely used among SciDAC research groups, hence using Logistical Networking functionality to enhance HDF will be an important segue for reaching the broad SciDAC community.

We are also extending LFS to include single-writer consistency, automatic replication generation, and automatic replica scheduling. This new system will use a dynamic scheduler that forecasts future performance and availability levels for IBP depots to determine the degree of replication and placement of replicas necessary to insure a specified availability and performance goal. The system is being tested using GridSAT, a Grid enabled satisfiability solver used in curcuit design and verification. GridSAT has been able to achieve new satisfiability results using dynamically allocated resources. We plan to use our new file system capabilities, which provide a standard Unix interface for programming ease, to implement a checkpointing facility for GridSAT.

In the next three months, we intend to make LFS compatible with the Condor project’s Pluggable File System (PFS). This will allow LFS to be deployed to applications quickly and ease the burden of porting LFS to operating systems whose file system hooks are different from those provided by Linux. PFS capability will allow SciDAC collaborators to take advantage of LFS without encountering system incompatibilities.

In addition, we are presently involved in a number of smaller projects related to file systems. Of particular note is an experimental IBP device driver for Linux. The /dev/ibp device allows applications with no knowledge of IBP to take advantage of Logistical Networking by simply opening the device file and reading or writing to it.

2.3.3 Adaptive Forecasting as a Strategy for Robustness

We are investigating efficient networking methodologies, capable of withstanding intermittent network and host failures. The typical approach to maximizing both throughput and connection reliability using TCP is to use parallel and redundant communication streams. If one stream fails, or becomes slow (due to ambient network congestion) the missing data is fetched or stored over another, better performing stream. Unfortunately, when redundant data is moved as a precautionary measure and then discarded, this approach can result in wasted bandwidth—a valuable commodity in today’s networks. We have developed a novel approach to managing replicated communication streams that relies on the on-line performance forecasts generated by the Network Weather Service (NWS). By dynamically ranking the connectivity between data source and sink, and adaptively discovering appropriate time out values, our approach achieves higher performance than the redundant streams approach, with the same robustness characteristics, using a small fraction of the bandwidth. We have prepared a paper on the subject and submitted it for consideration to SC2003.

2.3.4 Integration with Distributed Computing Middleware

Support for distributed computing that facilitates the research efforts of collaboratories and other advanced applications is an important part of the SciDAC vision. Our experiments with the integration of Logistical Networking technology and NetSolve middleware are designed to support that vision. NetSolve allows remote users, working with familiar interfaces such as Matlab and Mathematica, to access distributed hardware and software resources in order to perform complex calculations. Integration with Logistical Networking enables users to store (and replicate) data objects in IBP depots near the locations of NetSolve servers, and then point NetSolve servers to these depots to find data to use in computations. The proximity of the IBP depots to the NetSolve servers, as well as the existence of multiple replicas that can supply the needed data, will improve the performance of NetSolve, especially across the wide area. The user can thus run computations on remote data and retrieve only the pertinent portion of the output at the client.

The ability to utilize IBP for remote storage has been incorporated into the current release of NetSolve (v1.4), but full integration with exNode and LoRS technology is not complete. A prototype version showing the power of this enhanced functionality was exhibited at SC2002 (see Section 2.4.2 below).

2.4 Major Public Demonstrations

2.4.1 High Performance Data Transfer — iGrid 2002

A demonstration showcasing high performance data transfers using Logistical Networking was given at iGrid 2002 in Amsterdam, the Netherlands. The presentation began with a brief introduction to the network storage stack, the hierarchical arrangement of Logistical Networking technologies analogous to the IP stack. After an explanation of the technologies involved, a demonstration was performed in which video content was streamed directly from IBP storage to a video player for immediate viewing. The stored video content had been fragmented and distributed over several IBP depots around the world. The LoRS Tools were used to retrieve the scattered data blocks from storage and reassemble them in the proper order as content was released to the video player. During the demonstration, the LoRS View visualization tool displayed the status of the downloading data blocks and the video stream. LoRS View, which provides a real-time visual representation of individual data manipulations, showed the downloading of each data block as it happened and tracked the overall progress of the content stream as it was released to the video player.

2.4.2 Logistical Networking in Distributed Computing — SC2002

The integration of Logistical Networking technology with NetSolve was demonstrated at Supercomputing 2002. NetSolve enables users to accomplish complex computations while taking advantage of distributed resources, by sending work out to a pool of servers scattered around the world. The SC2002 demonstration clearly showed how this process can benefit from Logistical Networking. Instead of sending a huge data set directly to NetSolve servers, Logistical Networking allows the user to send only an exNode, a pointer to data store on IBP depots. The NetSolve server then retrieves the data, operates on it, and stores the results into a new exNode that is returned to the user. If the results of the first operation are to be used as input for a second operation, then the necessary data will already be stored near the NetSolve server and may be retrieved by the server directly. Enabling this capability will be part of our future work.

In preparation for the SC2002 demonstration, multiple copies of various matrices were stored in IBP servers in the US, Europe and Australia; while the NetSolve team set up several servers in those three locations. The client then made calls to NetSolve servers in each of the three regions, passing the same exNode to each server. The servers read the exNode and downloaded the data. The exNode contained location information about copies of the data spread around the global network, allowing each NetSolve server to retrieve data from depots of close proximity. In other words, NetSolve servers in California retrieved matrices from servers in the western part of the US; servers in Europe requested data mostly from European depots; Australian servers got data mostly from Australia, and so on. During the live demo, the LoRS View visualization tool and a NetSolve Monitor were used to show the process in action.

2.5 Publications —

S.Y. Mironova, M.W. Berry, S. Atchley, M. Beck, T. Wu, L.E. Holzman, W.M. Pottenger, and D.J. Phelps, Advancements in Text Mining, In "Data Mining: Next Generation Challenges and Future Directions," H. Kargupta, A. Joshi, K. Sivakumar, and Y. Yesha (Eds.), AAAI/MIT Press, Menlo Park, CA, 2003.

M. Beck, T. Moore, and J. S. Plank, "An End-to-End Approach to Globally Scalable Programmable Networking," to be presented at Future Directions in Network Architecture (FDNA-03), an ACM SIGCOMM 2003 Workshop, Karlsruhe, Germany, August 27, 2003 (to appear).

M. Beck, Y. Ding, E. Fuentes and S. Kancherla, “An Exposed Approach to Reliable Multicast in Heterogeneous Logistical Networks,” the 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2003), Tokyo, Japan, May 12-15, 2003.

A. Bassi, M. Beck, T. Moore, and J. Plank, “The Logistical Backbone: Scalable Infrastructure for Global Data Grids,” Asian Computing Science Conference 2002, Hanoi, Vietnam, December, 2002. Springer Verlag.

S. Atchley, M. Beck, H. Hagewood, J. Millar, T. Moore, J. S. Plank, and S. Soltesz, “Next Generation Content Distribution Using the Logistical Networking Testbed,” Technical Report UT-CS-02-498, University of Tennessee, Department of Computer Science, December 30, 2002.

S. Atchley, M. Beck, J. Millar, T. Moore, J.S. Plank, and S. Soltesz, “The Logistical Networking Testbed,” Technical Report UT-CS-02-496, University of Tennessee, Computer Science Department, December 16, 2002.

S. Atchley, S. Soltesz, J. S. Plank, and M. Beck, “Video IBPster,” Technical Report UT-CS-02-490, University of Tennessee, Department of Computer Science, October 31, 2002.

J. S. Plank, S. Atchley, Y. Ding, and M. Beck, “Algorithms for High Performance, Wide-Area, Distributed File Downloads,” Technical Report UT-CS-02-485, University of Tennessee, Department of Computer Science, October 8, 2002.

K. Meyer-Patel and M. Beck, “A Logistical Networking Model for Video-On-Demand,” IEEE International Conference on Multimedia and Expo, Lausanne, Switzerland, August 26-29, 2002.

M. Beck, T. Moore, and J. S. Plank, “An End-to-End Approach to Globally Scalable Network Storage,” ACM Sigcomm 2002 Conference, Pittsburgh, PA, August 19-23, 2002.

A. Bassi, M. Beck, J. Gelas, and L. Lefevre, “Logistical Storage in Active Networking: a promising framework for network services,” the 3rd International Conference on Internet Computing (IC 2002), Las Vegas, NV, June 24-27, 2002.

A. Bassi, M. Beck, G. Fagg, T. Moore, J. Plank, M. Swany, and R. Wolski, “The Internet Backplane Protocol: A Study in Resource Sharing,” the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2002), Berlin, Germany, May 21-24, 2002.

M. Beck and T. Moore, “Logistical Networking: When Institutions Peer,” the 2nd International Workshop on Global and Peer-to-Peer Computing on Large Scale Distributed Systems, part of CCGrid 2002, Berlin, Germany, May 21-24, 2002.

A. Bassi, M. Beck, E. Fuentes, T. Moore, and J. S. Plank, “Logistical Storage Resources for the Grid,” in the Proceedings of the International Conference on Computational Science (ICCS 2002), Part II, vol. 2330, LNCS. Amsterdam, the Netherlands: Springer Verlag, 2002.

S. Atchley, S. Soltesz, J. S. Plank, M. Beck, and T. Moore, “Fault-Tolerance in the Network Storage Stack,” presented at the IEEE Annual Workshop on Fault-Tolerant Parallel and Distributed Systems (held in conjunction with the International Parallel & Distributed Processing Symposium), Ft. Lauderdale, FL, USA, April 15-19, 2002.

J. S. Plank, M. Beck and T. Moore, “Logistical Networking Research and the Network Storage Stack,” USENIX FAST 2002 Conference on File and Storage Technologies, work in progress report, January, 2002.

M. Beck, T. Moore, J. Plank, “Scalable Sharing of Wide Area Storage Resources,” Technical Report UT-CS-02-475, University of Tennessee, Department of Computer Science, January, 2002.

M. Beck, T. Moore, and J. S. Plank, “Exposed vs. Encapsulated Approaches to Grid Service Architecture,” presented at 2nd International Workshop on Grid Computing, Denver, CO, Nov. 12, 2001.

J. S. Plank, A. Bassi, M. Beck, T. Moore, M. Swany, and R. Wolski, “Managing Data Storage in the Network,” IEEE Internet Computing, vol. 5, no. 5, pp. 50-58, September/October, 2001.

2.6 Publications in Process

S. Atchley, S. Soltesz, J. S. Plank, and M. Beck, “Video IBPster,” accepted for publication in Future Generation Computer Systems.

S. Atchley, M. Beck, J. Millar, T. Moore, J.S. Plank, and S. Soltesz, “The Logistical Networking Testbed,” submitted for review by the ACM Sigcomm CCR.

3. Future Accomplishments

3.1  Next three months:

ù Read Only Logistical File System (ROLFS) version 1.0, to be released in early June, provides an exNode directory that will allow users to freely share data with their community. ROLFS v1.0 will use a standard client-server model and feature unix-like access controls.

ù Full deployment of a private ROLFS directory for the use of TSI collaborators, with performance upgrades completed. This dedicated ROLFS server will actively manage data set exNodes and provide a central directory for exNode storage and retrieval.

ù Deployment of four additional IBP servers at principle Terascale Supernova Initiative (TSI) research sites around the country (SDSC, Stony Brook, LBL, NCSU), thereby increasing the IBP storage capacity available to the TSI and other SciDAC researchers by an additional 7.6 TB.

ù Further development of Logistical File System (LFS) with full capabilities to manipulate files and directories, including create, open, read, and write functionalities.  

ù Make LFS compatible with the Condor project’s Pluggable File System (PFS). This will allow LFS to be deployed to applications quickly and ease the burden of porting LFS to operating systems whose file system hooks are different from those provided by Linux.

ù Multiple resource capabilities for IBP will allow an IBP server to control depot storage implemented on multiple device types, such as disk and RAM, within the same system.

ù Support for point to multipoint SABUL DataMover using UDP/IP multicast.

ù Improved XOR encryption library for high throughput, faster performance.

ù Stronger encryption with 128 bit AES key.

ù Performance improvements including elimination of synchronization points and better pipelining of tasks.

ù Improved fault-tolerance and performance of LoRS upload and augment commands.

ù Develop coding for exNode “intentions,” allowing the user to specify preferred fault-tolerance parameters for exNodes including expected lifetime, fragmentation, and redundancy.

ù New L-Bone version to be released early summer will provide additional metadata for improved proximity resolution.

ù Further development of an experimental IBP device driver for Linux. The /dev/ibp device allows applications with no knowledge of IBP to take advantage of Logistical Networking by simply reading or writing to the device file.

3.2 Next six months:

ù IBP Depot Software version 1.3 to be installed on the Italian arm of the European 6NET, an IPv6 testbed. IBP storage depots will be installed on three 140GB POP hubs in Rome, Milan, and Bologna. Local depots will also be installed at the twelve participating Italian universities and research institutions.

ù Develop ROLFS-layer exNode management tool, a wide area service that will determine exNode intent and then use that intent information to provide management services for the exNode.

ù Network Functional Unit (NFU) integrated with IBP server to provide ability to manipulate data and perform computations remotely.

ù Incorporate internal DataMovers into IBP software, providing support for internal DataMovers as well as DataMover plug-ins.

ù Full integration of NetSolve and Logistical Networking technologies, using the LoRS API.

ù Porting the LoRS Tools software components to JAVA.

ù Extend LFS to include single-writer consistency, automatic replication generation, and automatic replica scheduling. These new file capabilities will be used to implement a checkpointing facility for GridSAT.

3.3 Next twelve months:

ù Native Windows versions of the LoRS Tools software components.

ù Implement persistence connections between IBP client and IBP server to improve performance, also pipeline requests to the server. 

ù Add security features to IBP server, such as required authentication for allocation and secure connection for transmission of commands between client and server.

ù Conduct research into overlay routing to determine the optimal use of intermediate staging for transferred data.

ù Research long-term storage scenarios for critical data, under high usage loads.

4. Research interactions

4.1 Collaboration with the Terascale Supernova Initiative (TSI)

Our primary research drive has been interactions with the TSI group. Recent Logistical Networking research advances resulting from these interactions are detailed above, in Section 2.3.

We are working closely with TSI group members to build a new, private Logistical Networking infrastructure modeled on the public L-Bone deployment, but to be used specifically for the advancement of TSI and other SciDAC research endeavors. The first in a series of five high-performance, 1.9 TB dedicated IBP servers has already been deployed at ORNL, with plans to deploy four more high powered machines at principle TSI sites around the country (SDSC, SUNY-Stony Brook, LBL, NCSU) by mid-summer. These deployments will provide an additional 9.5 TB of IBP storage space for SciDAC researchers. Preliminary testing shows transfer speeds of up to 430 Mbps between depots at ORNL and the UT Knoxville campus. While test transfers between depots at ORNL and NCSU reached speeds of 220 Mbps (see Figure 1), this number is expected to improve with the upcoming depot installations.

Successful completion of the upcoming deployments will represent the culmination of several months work negotiating with collocation services and support personnel at the five main TSI sites. Securing collocation agreements with the five proposed sites required efficient communication to establish clear and accurate expectations on both sides, and our consistent attention to the needs, resources, and security standards of the various sites. Although construction of this private infrastructure has been steered by interaction with TSI, the hardware and technologies will be available for use by all members of the SciDAC community.

4.2 Integration with Hierarchical Resource Management (HRM)

We are exploring the integration of Logistical Networking with Hierarchical Resource Management (HRM) software, to allow HRM to take advantage of Logistical Networking overlay routing and point-to-multipoint transfer capabilities. The question of whether the functionality of systems like HRM can be usefully augmented by the addition of Logistical Networking resources and services "in the network" is a central research topic for us, and the answer is of potentially great value to TSI and other DOE science projects.

4.3 Participation in SciDAC-Sponsored Meetings and Workshops

ù Presentation and poster session at 2003 SciDAC PI Meeting, March 10-11, 2003, Napa, CA. The goal of this second PI meeting was to give researchers the opportunity to access progress, share first year results, and develop collaborative goals for the future.

ù Participated in DOE Workshop on Ultra High-Speed Transport Protocols and Network for Large-Science Applications, April 10-11, 2003, Argonne National Laboratory, Argonne, IL. The objective of this “working” workshop was to address all aspects of network provisioning, transport protocols, and application-level capability needed to craft ultra-speed networks necessary to support emerging DOE distributed large-scale science applications.

ù Presentation and poster session at TSI Collaboration Meeting, February 5-6, 2003, Miami, FL. This meeting allowed participants to interact, plan, and coordinate research efforts with TSI group members and collaborators from across the country.

5. Remarks

None