[alto] Fwd: New Version Notification for draft-xiang-alto-exascale-network-optimization-03.txt

[Qiao Xiang <[e-mail]>]

Dear All,

We have submitted a new version of our resource orchestration framework for
multi-domain data analytics. Would you please share your thoughts and
comments with us? Thank you very much!


Best
Qiao


---------- Forwarded message ----------
From: [e-mail] <[e-mail]>
Date: Tue, Jul 4, 2017 at 2:14 AM
Subject: New Version Notification for
draft-xiang-alto-exascale-network-optimization-03.txt
To: Justas Balcas <[e-mail]>, Greg Bernstein <
[e-mail]>, Jingxuan Jensen Zhang <
[e-mail]>, Azher Mughal <[e-mail]>, "
[e-mail]" <[e-mail]>, "Y. Richard Yang" <
[e-mail]>, Harvey Newman <[e-mail]>, Qiao Xiang <
[e-mail]>, Kai Gao <[e-mail]>



A new version of I-D, draft-xiang-alto-exascale-network-optimization-03.txt
has been successfully submitted by Qiao Xiang and posted to the
IETF repository.

Name:           draft-xiang-alto-exascale-network-optimization
Revision:       03
Title:          Resource Orchestration for Multi-Domain Data Analytics
Document date:  2017-07-03
Group:          Individual Submission
Pages:          25
URL:            https://urldefense.proofpoint.com/v2/url?u=https-3A__www.
ietf.org_internet-2Ddrafts_draft-2Dxiang-2Dalto-2Dexascale-2Dnetwork-
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Abstract:
   Data-intensive analytics is entering the era of multi-domain,
   geographically-distributed, collaborative computing, where different
   organizations contribute various resources to collaboratively
   collect, share and analyze extremely large amounts of data.  Examples
   of this paradigm include the Compact Muon Solenoid (CMS) and A
   Toroidal LHC ApparatuS (ATLAS) experiments of the Large Hadron
   Collider (LHC) program.  Massive datasets continue to be acquired,
   simulated, processed and analyzed by globally distributed science
   networks in these collaborations.  Applications that manage and
   analyze such massive data volumes can benefit substantially from the
   information about networking, computing and storage resources from
   each member's site, and more directly from network-resident services
   that optimize and load balance resource usage among multiple data
   transfers and analytics requests, and achieve a better utilization of
   multiple 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.  In this
   document, we propose that it is feasible to use existing ALTO
   services to provides not only network information, but also
   information about computation and storage resources in data analytics
   networks.  We introduce a uniform resource orchestration framework
   (Unicorn), which achieves an efficient multi-resource allocation to
   support low-latency dataset transfer and data intensive analytics for
   collaborative computing.  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 analytics tasks, leading to
   improved transfer and analytics latency as well as more efficient
   utilization of multi-resources in sites.




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-- 
Qiao Xiang
Postdoctoral Fellow,
Department of Computer Science,
Yale University