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ALTO WG                                                         Q. Xiang
Internet-Draft                                           Yale University
Intended status: Informational                                     F. Le
Expires: September 12, 2019                                          IBM
                                                                 Y. Yang
                                                         Yale University
                                                          March 11, 2019


  ALTO for Multi-Domain Applications: A Review of Use Cases and Design
                              Requirements
              draft-xiang-alto-multidomain-usecases-00.txt

Abstract

   With the development of novel network technology, such as software
   defined networking and network function virtualization, many novel
   multi-domain applications, such as flexible interdomain routing,
   distributed, federated machine learning and multi-domain
   collaborative dataset transfer, have been deployed.  These
   applications can benefit substantially from the ALTO protocol
   [RFC7285], through which the information of multiple networks can be
   provided to applications.  This document first introduces several
   multi-domain applications and how they can benefit from ALTO.  It
   then describes a generic framework for multi-domain applications to
   use ALTO to improve the performance, followed by a discussion on new
   requirements and challenges for ALTO to better support these
   applications.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at http://datatracker.ietf.org/drafts/current/.

   Internet-Drafts are draft documents valid for a maximum of six months
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   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on September 12, 2019.






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Copyright Notice

   Copyright (c) 2019 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
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   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Requirements Language . . . . . . . . . . . . . . . . . . . .   3
   3.  Review of Multi-Domain Applications . . . . . . . . . . . . .   3
     3.1.  Flexible Interdomain Routing  . . . . . . . . . . . . . .   3
       3.1.1.  How flexible interdomain routing can benefit from
               ALTO? . . . . . . . . . . . . . . . . . . . . . . . .   3
       3.1.2.  Example . . . . . . . . . . . . . . . . . . . . . . .   4
     3.2.  Resource Orchestration for Collaborative Data Sciences  .   4
       3.2.1.  How multi-domain resource orchestration can benefit
               from ALTO . . . . . . . . . . . . . . . . . . . . . .   4
       3.2.2.  Example . . . . . . . . . . . . . . . . . . . . . . .   5
     3.3.  Federated Machine Learning  . . . . . . . . . . . . . . .   6
       3.3.1.  How federated machine learning can benefit from ALTO    6
       3.3.2.  Example . . . . . . . . . . . . . . . . . . . . . . .   6
   4.  A Generic Framework . . . . . . . . . . . . . . . . . . . . .   7
     4.1.  Workflow  . . . . . . . . . . . . . . . . . . . . . . . .   8
   5.  Requirements of ALTO in Multi-Domain Applications . . . . . .   9
     5.1.  Design Requirements . . . . . . . . . . . . . . . . . . .   9
     5.2.  Existing Efforts in the ALTO Working Group  . . . . . . .  10
   6.  Summary . . . . . . . . . . . . . . . . . . . . . . . . . . .  10
   7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  10
     7.1.  Normative References  . . . . . . . . . . . . . . . . . .  10
     7.2.  Informative References  . . . . . . . . . . . . . . . . .  11
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  12

1.  Introduction

   The ALTO protocol [RFC7285] provides network information to
   applications so that applications can make network informed decisions
   to improve the performance.  Not only traditional applications such
   peer-to-peer systems, many recent, novel multi-domain applications,



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   which orchestrate resources across multiple networks, can also
   benefit substantially from ALTO.

   The goal of this document is to explore how ALTO can help improve the
   performance of novel multi-domain applications, what ALTO extension
   services are needed, and what are the corresponding requirements and
   challenges for designing such extensions.  To this end, this document
   first give a case-by-case review of emerging multi-domain
   applications and how they can benefit from ALTO.  It then describes a
   generic framework for multi-domain applications to use ALTO to
   improve the performance, followed by a discussion on the need of new
   ALTO services and the corresponding requirements and challenges for
   these extensions to better support these applications.

2.  Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in [RFC2119].

3.  Review of Multi-Domain Applications

3.1.  Flexible Interdomain Routing

   Flexible interdomain routing can be a highly valuable service for
   network providers.  Specifically, an autonomous system (AS) providing
   such a service (the provider) allows other ASes (clients) to specify
   routing actions at the provider based on flexible matching conditions
   (e.g., match on TCP/IP 5-tuple).  In this way, a client AS using the
   flexible interdomain routing service can offload access and traffic
   control to provider ASes, leading to a simpler client network
   configuration while giving the provider ASes additional business
   opportunities.

3.1.1.  How flexible interdomain routing can benefit from ALTO?

   ALTO provides provider ASes a standardized approach to expose its
   routing capability to client ASes.  Traditional interdomain routing
   protocols such as BGP are not good options because they only expose
   the currently used routes, limiting client ASes' choices to specify
   flexible routes.  In contrast, ALTO and its extensions provide
   interfaces for provider ASes to expose not only currently used
   routes, but also available yet unused routes, to client ASes so that
   they can have the flexibility to specify different routes for
   different data traffic.






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3.1.2.  Example

   Consider the example in Figure 1.  AS A is compromised and being used
   to send DDoS traffic to AS E.  Without flexible interdomain routing,
   AS E can setup a firewall locally, but normal traffic from B to E
   will still be congested at C-D-E due to the existence of malicious
   traffic from A to E.  If AS C provides flexible interdomain routing
   service, AS E can specify such a firewall at AS C to block DDoS
   traffic from A, and at the same time avoid the congestion of normal
   traffic from B to E.


        +-----+ DDoS traffic
        | AS A|_
        |     | \   +--+---+         +---+--+        +------+
        +-----+  \  |      |         |      |        |      |
                  \_| AS C +---------+ AS D |--------| AS E |
        +-----+   / |      |         |      |        |      |
        | AS B|__/  +------+         +------+        +------+
        |     |
        +-----+ Normal traffic


        Figure 1: Flexible interdomain routing for DDoS mitigation.

3.2.  Resource Orchestration for Collaborative Data Sciences

   As the data volume increases exponentially over time, data analytics
   is transiting from a single-domain network to a multi-domain, geo-
   distributed network, where different member networks contribute
   various resources, e.g., computation, storage and networking
   resources, to collaboratively collect, share and analyze extremely
   large amounts of data.  Such a paradigm calls for a unified resource
   orchestration framework to manage a large set of distributively-
   owned, heterogeneous resources, with the objective of efficient
   resource utilization, following the autonomy and privacy of different
   domains.

3.2.1.  How multi-domain resource orchestration can benefit from ALTO

   One key design challenge for multi-domain resource orchestration is
   its resource information model.  Existing design options such as
   resource graph and ClassAds are inadequate because they cannot
   simultaneously (1) allow member networks to provide accurate
   information on different types of resource, (2) avoid the exposure of
   private information of member networks such as topology, and (3)
   allow data analytics jobs to accurately describe their requirements
   of different types of resources.  In contrast, the section 7.1 of



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   Figure 2 discusses the advantages of choosing ALTO as the resource
   information model for multi-domain resource orchestration, and how
   ALTO can simultaneously satisfy the aforementioned design
   requirements.

3.2.2.  Example

   Consider an example of three member networks in Figure 2, where s1
   and s2 are storage endpoints and d1 and d2 are computation endpoints.
   Assume a data analytics job is composed of two parallel tasks T1 and
   T2.  T1 needs dataset X as input and T2 needs dataset Y as input.

                                      .------------.
                                      | Network B  |
               .-------------.    ingB|            |
               | Network A   o--------|o-----d1    |
               |            /|        '------------'
               |    s1\    / |
               |       o--o  |        .------------.
               |    s2/    \ |        | Network C  |
               |            \|    ingC|            |
               |             o--------|o-----d2    |
               '-------------'        '------------'


              Figure 2: Multi-domain resource orchestration.

   Using the ALTO endpoint property service, an ALTO client in the
   resource orchestrator can discover that d1 satisfies the computing
   requirements of T1 and d2 satisfies the computing requirements of T2.
   Hence there are only two candidate endpoint pairs: (s1, d1) and (s2,
   d2).

   Afterwards, using the ALTO path vector extension, the ALTO client can
   retrieve the bandwidth sharing information of task T1 and T2, denoted
   as x1 and x2, respectively, as follows.

                         A: x1 + x2 <= 10Mbps
                         B: x1 <= 3Mbps
                         C: x2 <= 3Mbps

   With such information, the resource orchestrator can make the optimal
   resource orchestration decision to reserve 3 Mbps bandwidth for task
   T1, and 3 Mbps bandwidth for task T2.







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3.3.  Federated Machine Learning

   Instead of moving large-scale datasets from multiple devices /
   networks to a centralized location for training, federated learning,
   is a distributed machine learning approach which enables training on
   distributed datasets residing on different autonomous systems
   (devices or networks).  In this way, only updates on the training
   model need to be communicated between networks, leading to
   substantial reduction of networking resource consumption (e.g.,
   saving bandwidth).

3.3.1.  How federated machine learning can benefit from ALTO

   Federated machine learning requires efficient scheduling algorithms
   to decide how networking resources should be allocated to transmit
   training model updates between different ASes.  Similar as moving
   large-scale datasets between multiple ASes, moving updates of
   training model between ASes can also benefit from the availability of
   networking information, such as the AS-path and bandwidth sharing.
   ALTO provides a standardized approach for federated machine learning
   schedulers to retrieve such information from networks so that
   adaptive scheduling decisions can be made.

3.3.2.  Example

   Consider the example in Figure 3, where machine learning workers are
   located in AS A and D, while AS B and C are transit networks for data
   traffic transmitted between A and D.  When AS A has a large, critical
   training model update to send to D.  It first queries the ALTO
   servers and B and C for the endpoint cost (e.g., bandwidth) to
   transmit data from A to D.  Suppose the ALTO server at AS B returns
   an endpoint cost of 10Mbps, while the ALTO server at AS C returns an
   endpoint cost of 100 Mbps.  AS A can then use such information to
   make the optimal model update scheduling algorithm to send the
   training model update to AS D via AS C, instead of AS B.
















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                   +-----+
            -------| AS B|--
   +-----+ /       |     |  \  +--+---+
   | AS A|/        +-----+   \ |      |
   |     |\                   =| AS D +
   +-----+ \       +-----+   / |      |
            -------| AS C|--/  +------+
                   |     |
                   +-----+


                   Figure 3: Federated machine learning.

4.  A Generic Framework

   After reviewing several important, novel multi-domain applications
   that can benefit substantially from ALTO, this document describes a
   generic framework for such applications to use ALTO to retrieve
   information from networks to improve their performance.  The high-
   level architecture of this framework is given in Figure 4.































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 .---------------------------------------------------------------------.
 |Application Layer                                                    |
 |         .-------------.           .-------------.                   |
 |         |Application 1|    ...    |Application N|                   |
 |         |ALTO Client 1|           |ALTO Client N|                   |
 |         '-------------'           '-------------'                   |
 '-------------|---------\---------------/----|------------------------'
 .- - - - - - -| - - - - -\ - - - - - - /- - -|- - - - - - -- - - - - -.
 |Service      |           \           /      |                        |
 |Layer        |            \         /       |                        |
 |   .-----------------.                     .-----------------.       |
 |   |  Network 1      |                     |  Network N      |       |
 |   |                 |                     |                 |       |
 |   |ALTO Server 1    |          ...        |ALTO Server N    |       |
 |   |Execution Agent 1|                     |Execution Agent N|       |
 |   '-----------------'                     '-----------------'       |
 '- - - - - | - - - - - - - - - - - - - - - - - - - - -|- - - - - - - -'
 .----------|------------------------------------------|---------------.
 |Signaling |                                          |               |
 |Layer     |                                          |               |
 |  .----------------.                       .----------------.        |
 |  |   Network 1    |            ...        |    Network N   |        |
 |  '----------------'                       '----------------'        |
 '- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -'


         Figure 4: Generic framework of using ALTO in multi-domain
                               applications.

   The top layer of this framework is the application layer, in which
   each application deploy one or more ALTO clients to query for
   information provided by networks.  The middle layer is the service
   layer.  In this layer, each network deploys one more more ALTO
   servers to respond the queries sent by the ALTO clients from
   applications, and deploys one or more execution agents to respond to
   the applications' resource consumption actions.  The bottom layer is
   the signaling layer, in which each network deploys interdomain
   protocols / systems, such as routing protocol BGP and resource
   reservation system OSCARS.

4.1.  Workflow

   The basic workflow of this framework is as follows.

   o  An application identifies the networks whose resources (e.g.,
      networking, computation and storage) it may want to consume, and
      invokes its ALTO clients to query the ALTO servers deployed in
      those networks for detailed resource information using base ALTO



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      protocol and its extension services (e.g., path vector, cost
      calendar and so on);

   o  Upon receiving a query from an ALTO client, an ALTO server checks
      its local information, contacts the underlying signaling layer
      protocol / system of its residing network if local information is
      outdated, and returns the latest resource information to the
      querying ALTO client;

   o  The applications uses the resource information collected from ALTO
      servers to make resource allocation decisions (e.g., route
      selection, resource reservation, etc.), and send such decisions to
      corresponding execute agents in the corresponding networks (e.g.,
      the simple-reservation-interface of OSCARS).

5.  Requirements of ALTO in Multi-Domain Applications

   Using ALTO to improve the performance of recent novel multi-domain
   applications poses several new design requirements.  This section
   discusses these requirements and briefly review existing efforts in
   the ALTO working group aiming to satisfy them.

5.1.  Design Requirements

   o  Exposing information of alternative resources.  Current ALTO
      protocols and its extensions only provide information of currently
      used resources (e.g., currently used interdomain route).  However,
      exposing information of alternative resources (e.g., available but
      not used interdomain routes) may provide the users of new multi-
      domain applications (e.g., flexible interdomain routing) more
      flexibility on choosing different resources, giving networks that
      provide such applications additional business opportunities.

   o  Providing a unified, accurate representation of multiple types of
      resources.  Current ALTO protocols and its extensions mainly focus
      on providing network information to applications, with the
      exception of endpoint property service.  However, as new multi-
      domain applications often consume multiple types of resources
      across multiple networks, encoding such information accurately in
      a unified approach is crucial for deploying ALTO to improve such
      applications' performance.

   o  Providing interfaces for more flexible query.  Current ALTO
      protocol and its extensions allows applications to query resource
      information by specifying IP addresses of endpoints and simple
      filters.  However, with the emerging of new networking
      architecture (e.g., software defined networking and network
      function virtualization) and the fine-grained resource requirement



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      of applications (e.g., link-disjoint paths and endpoint
      precedence), applications need a more flexible interface to
      specify queries of resource information.

5.2.  Existing Efforts in the ALTO Working Group

   Several documents have been submitted to the ALTO working group, with
   the aim to satisfy one or more of the design requirements discussed
   above.  For example, [DRAFT-PV], [DRAFT-RSA], [DRAFT-UNICORN-INFO]
   and several other documents propose and apply the ALTO path vector
   extension to provide accurate networking resource information to
   support multi-domain resource orchestration.  [DRAFT-NFCHAIN]
   proposes to use ALTO to support resource orchestration for multi-
   domain service function chaining, and proposes a new ALTO extension
   to retrieve AS path of network functions across different networks.
   [DRAFT-CONTEXT] proposes proposes to extend cost information
   specified in RFC7285 by providing several possible cost values for
   the same cost metric where each value depends on qualitative criteria
   as opposed to quantitative criteria such as time.  [DRAFT-UR] makes a
   proposal to use mathematical programming constraint as a generic
   representation of multiple resources.  [DRAFT-FCS] proposes a
   flexible flow query extension service to allow applications to
   specify query entities based on flexible matching conditions (e.g.,
   TCP/IP 5-tuple) instead of IP addresses only.

6.  Summary

   This document reviews several emerging multi-domain applications and
   how they can benefit from ALTO.  It then describes a generic
   framework for multi-domain applications to use ALTO to improve the
   performance.  In addition, several design requirements are discussed.
   Though different drafts in the working group have been trying to
   address one or more these design requirements, a systematic
   investigation of these issues is still missing.  The authors of this
   document plan to perform such an investigation and make a unified
   design proposal in the next version of this document.

7.  References

7.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997, <https://www.rfc-
              editor.org/info/rfc2119>.






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7.2.  Informative References

   [DRAFT-CONTEXT]
              Randriamasy, S., "ALTO Contextual Cost Values", 2017,
              <https://datatracker.ietf.org/doc/draft-randriamasy-alto-
              cost-context/>.

   [DRAFT-FCS]
              Zhang, J., Gao, K., Wang, J., Xiang, Q., and Y. Yang,
              "ALTO Extension: Flow-based Cost Query", 2017,
              <https://datatracker.ietf.org/doc/draft-gao-alto-fcs/>.

   [DRAFT-NFCHAIN]
              Perez, D. and C. Rothenberg, "ALTO-based Broker-assisted
              Multi-domain Orchestration", 2018,
              <https://datatracker.ietf.org/doc/html/draft-
              lachosrothenberg-alto-brokermdo-01>.

   [DRAFT-PV]
              Bernstein, G., Lee, Y., Roome, W., Scharf, M., and Y.
              Yang, "ALTO Extension: Abstract Path Vector as a Cost
              Mode", 2015, <https://tools.ietf.org/html/draft-yang-alto-
              path-vector-01>.

   [DRAFT-RSA]
              Gao, K., Wang, X., Xiang, Q., Gu, C., Yang, Y., and G.
              Chen, "A Recommendation for Compressing ALTO Path
              Vectors", 2017, <https://datatracker.ietf.org/doc/draft-
              gao-alto-routing-state-abstraction/>.

   [DRAFT-UNICORN-INFO]
              Xiang, Q., Newman, H., Bernstein, G., Du, H., Gao, K.,
              Mughal, A., Balcas, J., Zhang, J., and Y. Yang,
              "Implementation and Deployment of A Resource Orchestration
              System for Multi-Domain Data Analytics", 2017,
              <https://datatracker.ietf.org/doc/draft-xiang-alto-
              exascale-network-optimization/>.

   [DRAFT-UP]
              Roome, W., Chen, S., Randriamasy, S., Yang, Y., and J.
              Zhang, "Unified Properties for the ALTO Protocol", 2015,
              <https://datatracker.ietf.org/doc/draft-ietf-alto-unified-
              props-new/>.








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   [DRAFT-UR]
              Xiang, Q., Le, F., and Y. Yang, "ALTO Extension: Unified
              Resource Representation", 2018,
              <https://datatracker.ietf.org/doc/draft-xiang-alto-
              exascale-network-optimization/>.

   [RFC7285]  Alimi, R., Ed., Penno, R., Ed., Yang, Y., Ed., Kiesel, S.,
              Previdi, S., Roome, W., Shalunov, S., and R. Woundy,
              "Application-Layer Traffic Optimization (ALTO) Protocol",
              RFC 7285, DOI 10.17487/RFC7285, September 2014,
              <https://www.rfc-editor.org/info/rfc7285>.

   [RFC8189]  Randriamasy, S., Roome, W., and N. Schwan, "Multi-Cost
              Application-Layer Traffic Optimization (ALTO)", RFC 8189,
              DOI 10.17487/RFC8189, October 2017, <https://www.rfc-
              editor.org/info/rfc8189>.

Authors' Addresses

   Qiao Xiang
   Yale University
   51 Prospect Street
   New Haven, CT
   USA

   Email: qiao.xiang@cs.yale.edu


   Franck Le
   IBM
   Thomas J. Watson Research Center
   Yorktown Heights, NY
   USA

   Email: fle@us.ibm.com


   Y. Richard Yang
   Yale University
   51 Prospect Street
   New Haven, CT
   USA

   Email: yry@cs.yale.edu







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