Traffic Optimization for ExaScale Science Applications

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Last updated 2017-03-13
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ALTO WG                                                         Q. Xiang
Internet-Draft                                    Tongji/Yale University
Intended status: Informational                                 H. Newman
Expires: September 14, 2017           California Institute of Technology
                                                            G. Bernstein
                                                       Grotto Networking
                                                               A. Mughal
                                                               J. Balcas
                                      California Institute of Technology
                                                                J. Zhang
                                                       Tongji University
                                                                   H. Du
                                                                 Y. Yang
                                                  Tongji/Yale University
                                                          March 13, 2017

         Traffic Optimization for ExaScale Science Applications


   Massive datasets continue to be acquired, simulated, processed and
   analyzed by globally distributed scientific collaborations, and the
   volume of this data is growing exponentially.  These datasets need to
   be exchanged through a global network infrastructure.  Applications
   that manage and analyze such massive data volumes can benefit
   substantially from the information about networking, computing and
   storage resources from each member sites, and more directly from
   network-resident services that optimize and load balance resource
   usage among multiple data transfer and analytic requests, and achieve
   a better utilization of multi-resources in clusters.

   The Application-Layer Traffic Optimization (ALTO) protocol can
   provide via extensions the network information about different
   clusters/sites, to both users and proactive network management
   services where applicable, with the goal of improving both
   application performance and network resource utilization.  However,
   it has been verified in both science networks and commercial data
   center networks that network resource in many cases is not the
   bottleneck preventing the efficiency of large dataset transfer and
   data-intensive analytics.  To achieve a greater overall efficiency of
   the science programs' workflows information about different
   resources, such as computing, storage and networking, should be
   provided to data intensive applications simultaneously.

   In this document, we propose that it is feasible to use existing ALTO
   services to provides not only network information, but also

Xiang, et al.          Expires September 14, 2017               [Page 1]
Internet-Draft        ExaScale Network Optimization           March 2017

   information about other resources in science networks including
   computing and storage.  We introduce an Exascale Science Application
   Orchestrator (ExaO), which achieves an efficient multi-resource
   allocation to support low-latency dataset transfer and data intensive
   analytics in exascale science networks.  ExaO provides simple APIs
   for users to submit and manage dataset transfer and analytic requests
   and to monitor the status of each request, along with fine-grained
   local and global network and site state information in real-time.  It
   collects cluster information from multiple ALTO services utilizing
   topology extensions and leverages emerging SDN control capabilities
   to orchestrate the resource allocation for dataset transfers and
   analytic tasks, leading to improved transfer and analytic latency as
   well as more efficient utilization of multi-resources in clusters/

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