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Background and Motivations

Storage and processing of raw data take place at geographically distributed computing facilities; sharing data across the globe is realized through transfers over high-speed networks. However, the default behavior of networks is to treat all data flows equally, thus, data flows of higher priority and/or urgency may be adversely impacted by competing data flows of lower priority. In distributed data-intensive environments, this can be a major problem that significantly degrades the effective "goodput" of the overall system. The policies and priorities of user communities are not able to be effectively expressed or implemented, except by very labor- intensive and error-prone human intervention. In such network environments, the capability to prioritize, protect, and regulate the various data flows becomes of high importance, because such a capability can be used for deterministically scheduling network resources to support user community priorities and, furthermore, co-scheduling of associated resources like storage systems. However, in order to assure the success of high bandwidth flow from a storage system at a source site to a storage system at a target site, it is not sufficient to provision the network capacity. It is also necessary to ensure that the source and target storage systems have the bandwidth and storage allocation that can take advantage of the network speed. For this reason, we are proposing coordinating between components that can dynamically manage and reserve storage on demand, and provision network capacity on demand. Furthermore, because such transfers can take long, transient failures can are likely to occur. The components managing the end-to-end transfers need to be monitor, and recover from such transient failures.

We plan to take advantage of existing components already used in productions to achieve this goal by having them work cooperatively. The existing components are a storage resource manager from LBNL, called the Berkeley Storage Manager (BeStMan), the TeraPaths component from BNL, and OSCARS provisioning supported by ESnet and Internet2. Our vision of the end-to-end capability is having a client at the target end make a request for the data it wants to get by providing a source directory where the data resides. The target SRM contacts the source SRM to find out the total volume requested as well as the directory structure. It then allocates enough space from a common pool for the data volume requested. Next, it checks with the source site for its transfer bandwidth and compares to its own bandwidth. It then coordinates with TeraPaths to provide the requested bandwidth in the earliest possible time. It can also request a lesser bandwidth from TeraPaths if that is available earlier. According to the available bandwidth, it then schedules multiple file transfers to take advantage of the allocated bandwidth. The file transfer protocol used is determined from what is supported at both ends through a negotiation protocol that SRMs support. Note that each end may have multiple machines that can transfer data, and the scheduling of these machines is a task that both ends need to manage. Finally, the target site monitors the success of transfers, continuously manages the scheduling and streaming of files, and recovers from transient failures, by reissuing transfers that failed, or restoring partial transfers that were in progress. An example of such a need already emerging is the next generation Earth Systems Grid (ESG) [ESG-web] supported by DOE. This project that started about eight years ago, has developed the technology of sharing climate simulation data to researchers world-wide already has served over 425 Terabytes to 1000's of users. So far, data requests are relatively small, averaging about 500 GBs each, but expectations for next generation simulation data is at least an order of magnitude growth over the next few year. Much of the storage management is provided by BeStMan for space allocation for users, for scheduling concurrent transfers, and for staging files from mass storage archive to disk cache in order to serve clients.

A particularly challenging future task is now planned for the next generation ESG, where large volumes of data need to be mirrored to other sites around the world. The design decision of mirroring sites comes from the need to share data world-wide, and important (new desired) data, called “core data” would be more efficiently served to clients from sites in their own continents. The core data, once assembled, is expected to amount to 300-500 TBs. The challenge is to manage mirroring of such a volume to multiple sites around the world. Given that end-to-end provisioning of 1Gb/s can support a transfer of about 10 TBs a day, it will take 30 days sustained transfer to move 300 TBs. Future networks are likely to become faster, but still this illustrates the need for sustained provisioning end-to-end, as well as monitoring and recovery.

High Speed Data on Demand

Data-intensive applications communities, including high energy and nuclear physics, astrophysics, climate modeling, nanoscale materials science, and genomics, are expected to generate exabytes of data over the next 5 years, which must be transferred, visualized, and analyzed by geographically distributed teams of scientists. In the area of experimental science, there are several extremely valuable experimental facilities, such as the Large Synoptic Survey Telescope (LSST) [LSST-web], the Large Hadron Collider (LHC) [LHC- web], the Spallation Neutron Source (SNS) [SNS-web], the Advanced Photon Source (APS) [APS-web], and the Relativistic Heavy Ion Collider (RHIC) [RHIC-web]. The common requirements among these applications are: (i) intensive data transfers; (ii) remote visualizations of datasets and on-going computations; (iii) computational monitoring and steering; and (iv) remote experimentation and control. High-performance network capabilities must be available to scientists at the application level in a transparent, virtualized manner. Scientists must have the capability to move large datasets from remote experiment locations and across federated network environments to their home institutions.

Heterogeneous Data Storage Mechanism and Environment

Today’s science applications include a wide variety of platforms, hardware, network, storage media, and software components to deliver the critical data storage functionality: flat file servers (NFS and AFS), various FTP file servers, mass storage systems, relational databases, and web servers for serving files and on-line streaming video. Among these science applications, the LSST produces close to a petabyte of archive images every month, and utilizes hybrid approaches to store image contents into flat files and related catalogs into relational databases. LHC experiments deploy a similar mechanism to store raw event and derived data objects into flat files, and to store detector conditions data, and thumbnail data for each event, into a high-performance Oracle database cluster. LHC application users must use the GridFTP protocol and its associated data transfer software stack to retrieve data. Genomics science commonly use relational database to store microarray data. The National Climate Data Center (DCDC), the world's largest active archive of weather and meteorology data, allows users to download data from its Web server clusters. Many computer scientists look forward to a single, data storage-agnostic tool to monitor, manage, and provide data access to application users. An existing storage resource manager (SRM-BeStMan) is used by several physical science communities to manage disk space and provision storage space for large dataset transfers requested by thousands of users.

Research Objectives

This proposal presents a plan to design and develop a versatile, end-to-end, performance guaranteed data transfer system based on an existing storage resource management system (SRM), and a tool for providing virtual paths with bandwidth guarantees (TeraPaths). In particular, the proposed framework provides a transformative functionality of bridging the current and emerging advanced network and storage management research with science applications in a transparent way, and integrating and optimizing network resource provisioning and storage space reservation together, to provide data transfer applications with a guaranteed and predictable quality of service. The basic research will focus on a polymorphous protocol of scheduling and reserving hybrid network paths that consist of multiple layer segments for transfers, as well as approaches to providing users and applications resilience to not only failures within a network (e.g., broken connections) but also failures of storage sites (due to malfunctioning hardware/software).