draft-netana-nmop-network-anomaly-lifecycle-03.xml~

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<rfc category="exp" docName="draft-netana-nmop-network-anomaly-lifecycle-04"
     ipr="trust200902">
  <front>
    <title abbrev="network-anomaly-lifecycle">Experiment: Network Anomaly
    Lifecycle</title>

    <author fullname="Vincenzo Riccobene" initials="V." surname="Riccobene">
      <organization>Huawei</organization>

      <address>
        <postal>
          <street/>

          <city>Dublin</city>

          <region/>

          <code/>

          <country>Ireland</country>
        </postal>

        <phone/>

        <facsimile/>

        <email>vincenzo.riccobene@huawei-partners.com</email>

        <uri/>
      </address>
    </author>

    <author fullname="Antonio Roberto" initials="A." surname="Roberto">
      <organization>Huawei</organization>

      <address>
        <postal>
          <street/>

          <city>Dublin</city>

          <region/>

          <code/>

          <country>Ireland</country>
        </postal>

        <phone/>

        <facsimile/>

        <email>antonio.roberto@huawei.com</email>

        <uri/>
      </address>
    </author>

    <author fullname="Thomas Graf" initials="T" surname="Graf">
      <organization>Swisscom</organization>

      <address>
        <postal>
          <street>Binzring 17</street>

          <city>Zurich</city>

          <code>8045</code>

          <country>Switzerland</country>
        </postal>

        <email>thomas.graf@swisscom.com</email>
      </address>
    </author>

    <author fullname="Wanting Du" initials="W" surname="Du">
      <organization>Swisscom</organization>

      <address>
        <postal>
          <street>Binzring 17</street>

          <city>Zurich</city>

          <code>8045</code>

          <country>Switzerland</country>
        </postal>

        <email>wanting.du@swisscom.com</email>
      </address>
    </author>

    <author fullname="Alex Huang Feng" initials="A." surname="Huang Feng">
      <organization>INSA-Lyon</organization>

      <address>
        <postal>
          <street/>

          <city>Lyon</city>

          <region/>

          <code/>

          <country>France</country>
        </postal>

        <phone/>

        <facsimile/>

        <email>alex.huang-feng@insa-lyon.fr</email>

        <uri/>
      </address>
    </author>

    <date day="08" month="July" year="2024"/>

    <area>Operations and Management</area>

    <workgroup>NMOP</workgroup>

    <abstract>
      <t>Network Anomaly Detection is the act of detecting problems in the
      network. Accurately detect problems is very challenging for network
      operators in production networks. Good results require a lot of
      expertise and knowledge around both the implied network technologies and
      the specific service provided to consumers, apart from a proper
      monitoring infrastructure. In order to facilitate network anomaly
      detection, novel techniques are being introduced, including
      programmatical, rule-based and AI-based, with the promise of improving
      scalability and the hope to keep a high detection accuracy. To guarantee
      acceptable results, the process needs to be properly designed, adopting
      well-defined stages to accurately collect evidence of anomalies,
      validate their relevancy and improve the detection systems over time,
      iteratively.</t>

      <t>This document describes the lifecycle process to iteratively improve
      network anomaly detection accurately. Three key stages are proposed,
      along with a YANG model specifying the required metadata for the network
      anomaly detection covering the exchange of information between different
      stages of the lifecycle.</t>
    </abstract>
  </front>

  <middle>
    <section anchor="discussion_venues" title="Discussion Venues">
      <t>This note is to be removed before publishing as an RFC.</t>

      <t>Discussion of this document takes place on the Operations and
      Management Area Working Group Working Group mailing list
      (nmop@ietf.org), which is archived at
      https://mailarchive.ietf.org/arch/browse/nmop/.</t>

      <t>Source for this draft and an issue tracker can be found at
      https://github.com/network-analytics/draft-netana-nmop-network-
      anomaly-lifecycle.</t>
    </section>

    <section anchor="status" title="Status of this document">
      <t>This document is experimental. The main goal of this document is to
      propose an iterative lifecycle process to network anomaly detection by
      proposing a data model for metadata to be addressed at different
      lifecycle stages.</t>

      <t>The experiment consists of verifying whether the approach is usable
      in real use case scenarios to support proper refinement and adjustments
      of network anomaly detection algorithms. The experiment can be deemed
      successful if validated at least with an open-source implementation
      sucessfully applied in real production networks.</t>
    </section>

    <section anchor="Introduction" title="Introduction">
      <t>In <xref target="I-D.netana-nmop-network-anomaly-architecture"/>
      network anomalies are defined as "Whatever would let an operator frown
      and investigate when looking at the collected forwarding plane, control
      plane and management plane network data relative to a customer" .</t>

      <t>In <xref target="I-D.netana-nmop-network-anomaly-semantics"/> a
      semantic for the annotation of network anomalies has been defined in
      order to support the exchange of related metadata between different
      actors, formalizing a semantically consistent representation of the
      behaviors worth investigating. In the same document, symptoms are
      defined as the essential piece of information to analyze network
      anomalies and problems.</t>

      <t>The intention is to enable operators detecting problems in the
      network timely. A network problem is defined as "A state regarded as
      undesirable and may require remedial action" (see <xref
      target="I-D.ietf-nmop-terminology"/>).</t>

      <t>With all this in mind, this document starts from the assumption that
      it is still remarkably difficult to gain a full understanding and a
      complete perspective of "if" and "how" the network is deviating from the
      desired state: on the one side, symptoms are not necessarily a guarantee
      of a problem happening (e.g. there might be false positives), on the
      other side, the lack of symptom is not a guarantee of the absence of an
      problem (e.g. there might be false negatives). The concept of network
      anomaly in this document plays the role of a bridge between symptoms and
      problem: a network anomaly is defined as a collection of symptoms, but
      without the guarantee that the observed symptoms are impacting existing
      services. This opens up to the necessity of further validating the
      network anomalies to understand if the detected symptoms are actually
      impacting services and it requires different actors (both human and
      algorithmic) to jump in during the process and refine their
      understanding across the network anomaly lifecycle.</t>

      <t>Performing network anomaly detection is a process that requires a
      continuous learning and continuous improvement. Network anomalies are
      detected by collecting and understanding symptoms, then validated by
      confirming that there actually were service impacting and eventually
      need to be further analyzed by performing postmortem analysis to
      identify any potential adjustment to improve the detection capability.
      Each of these stages is an opportunity to learn and refine the process,
      and since implementations of these stages might also be provided by
      different parties and/or products, this document also contributes a
      formal structure to capture and exchange symptom information across the
      lifecycle.</t>
    </section>

    <section anchor="notation" title="Terminology">
      <t>The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
      "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
      "OPTIONAL" in this document are to be interpreted as described in BCP 14
      <xref target="RFC2119"/> <xref target="RFC8174"/> when, and only when,
      they appear in all capitals, as shown here.</t>

      <t>This document makes use of the terms defined in <xref
      target="I-D.ietf-nmop-terminology"/>.</t>

      <t><list style="symbols">
          <t>State</t>

          <t>Problem</t>

          <t>Event</t>

          <t>Alarm</t>

          <t>Symptom</t>
        </list></t>

      <t>The following terms are used as defined in <xref
      target="RFC9417"/>.</t>

      <t><list style="symbols">
          <t>Metric</t>

          <t>Intent</t>
        </list></t>

      <t>The following terms are defined in this document.</t>

      <t><list style="symbols">
          <t>Annotator: Is a human or an algorithm which produces metadata by
          describing anomalies with symptoms.</t>

          <t>False Positive: Is a detected anomaly which has been identified
          during the postmortem to be not anomalous.</t>

          <t>False Negative: Is anomalous but has been not been identified by
          the anomaly detection system.</t>
        </list></t>
    </section>

    <section anchor="Defining_desired_states" title="Defining Desired States">
      <t>The above definitions of network problem provide the scope for what
      to be looking for when detecting network anomalies. Concepts like
      "desirable state" and "required state" are introduced. This poses the
      attention on a significant problem that network operators have to face:
      the definition of what is to be considered "desirable" or "undesirable".
      It is not always easy to detect if a network is operating in an
      undesired state at a given point in time. To approach this, network
      operators can rely on different methodologies, more or less
      deterministic and more or less sensitive: on the one side, the
      definition of intents (including Service Level Objectives and Service
      Level Agreements) which approaches the problem top-down; on the other
      side, the definition of symptoms, by mean of solutions like <xref
      target="RFC9417">SAIN</xref>, <xref target="RFC9418"/> and <xref
      target="I-D.netana-nmop-network-anomaly-architecture"/>, which
      approaches the problem bottom-up. At the center of these approaches,
      there are the so-called symptoms, defined as reasons explaining what is
      not working as expected in the network, sometimes also providing hints
      towards issues and their causes.</t>

      <t>One of the more deterministic approaches is to rely on symptoms based
      on measurable service-based KPIs, for example, by using Service Level
      Indicators, Objectives and Agreements:</t>

      <dl>
        <dt>Service Level Agreement (SLA)</dt>

        <dd>An SLA is an agreement between parties that a service provider
        makes to its customers on the behavior of the provided service. SLAs
        are a tool to define exactly what customers can expect out of the
        service provided to them. In many cases, SLA breaches also come with
        contractual penalties.</dd>

        <dt>Service Level Objectives (SLOs)</dt>

        <dd>An SLO is a threshold above which the service provider acts to
        prevent a breach of an SLA. SLOs are a tool for service providers to
        know when they should start becoming concerned about a service not
        behaving as expected. SLOs are rarely connected to penalties as they
        usually are internal metrics for the service providers.</dd>

        <dt>Service Level Indicators (SLIs)</dt>

        <dd>An SLI is an observable metric that describes the state of a
        monitored subsystem. SLIs are a tool to gain measurable visibility
        about the behavior of a subsystem in the network. SLIs usually differ
        from SLOs as SLOs are usually expressed as thresholds, while SLIs
        would often be expressed e.g. as percentages.</dd>
      </dl>

      <t>However, the definition of these KPIs turns out to be very
      challenging in some cases, as accurate KPIs could require
      computationally expensive techniques to be collected or substantial
      modifications to existing network protocols.</t>

      <t>Alternative methodologies rely on symptoms as the way to generate
      analytical data out of operational data. For instance:</t>

      <dl>
        <dt>SAIN</dt>

        <dd>introduces the definition and exposure of symptoms as a mechanism
        for detecting those concerning behaviors in more deterministic ways.
        Moreover, the concept of "impact score" has been introduced by SAIN,
        to indicate what is the expected degree of impact that a given symptom
        will have on the services relying on the related subservice to which
        the symptom is attached.</dd>
      </dl>

      <dl>
        <dt>Daisy</dt>

        <dd>introduces the concept of concern score to indicate what is the
        degree of concern that a given symptom could cause a degradation for a
        service.</dd>
      </dl>

      <t>In general, defining boundaries between desirable vs. undesirable in
      an accurate fashion requires continuous iterations and improvements
      coming from all the stages of the network anomaly detection lifecycle,
      by which network engineers can transfer what they learn through the
      process into new symptom definitions and, ultimately, into refinements
      of the detection algorithms.</t>
    </section>

    <section anchor="lifecycle_network_anomaly"
             title="Lifecycle of a Network Anomaly">
      <t>The lifecycle of a network anomaly can be articulated in three
      phases, structured as a loop: Detection, Validation, Refinement.</t>

      <figure anchor="simplified_lifecycle"
              title="Anomaly Detection Refinement Lifecycle">
        <artwork align="center"><![CDATA[
                       +-------------+
            +--------> |  Detection  | ---------+
Adjustments |          +-------------+          | Symptoms
            |                                   |
            |                                   v
    +------------+                       +------------+
    | Refinement |<--------------------- | Validation |
    +------------+        Problem        +------------+
                        Confirmation
          
        ]]></artwork>
      </figure>

      <t>Each of these phases can either be performed by a network expert or
      an algorithm or complementing each other.</t>

      <t>The network anomaly metadata is generated by an annotator, which can
      be either a human expert or an algorithm. The annotator can produce the
      metadata for a network anomaly, for each stage of the cycle and even
      multiple versions for the same stage. In each version of the network
      anomaly metadata, the annotator indicates the list of symptoms that are
      part of the network anomaly taken into account. The iterative process is
      about the identification of the right set of symptoms.</t>

      <section anchor="network_anomaly_detection"
               title="Network Anomaly Detection">
        <t>The Network Anomaly Detection stage is about the continuous
        monitoring of the network through Network Telemetry <xref
        target="RFC9232"/> and the identification of symptoms. One of the main
        requirements that operator have on network anomaly detection systems
        is the high accuracy. This means having a small number of false
        negatives, symptoms causing service impact are not missed, and false
        positives, symptoms that are actually innocuous are not picked up.</t>

        <t>As the detection stage is becoming more and more automated for
        production networks, the identified symptoms might point towards three
        potential kinds of behaviors:</t>

        <t>i. those that are surely corresponding to an impact on services,
        (e.g. the breach of an SLO),</t>

        <t>ii. those that will cause problems in the future (e.g. rising
        trends on a timeseries metric hitting towards saturation),</t>

        <t>iii. those or which the impact to services cannot be confirmed
        (e.g. sudden increase/decrease of timeseries metrics, anomalous
        amounts of log entries, etc.).</t>

        <t>The first category requires immediate intervention (a.k.a. the
        problem is "confirmed"), the second one provides pointers towards
        early signs of an problem potentially happening in the near future
        (a.k.a. the problem is "forecasted"), and the third one requires some
        analysis to confirm if the detected symptom requires any attention or
        immediate intervention (a.k.a. the problem is "potential"). As part of
        the iterative improvement required in this stage, one that is very
        relevant is the gradual conversion of the third category into one of
        the first two, which would make the network anomaly detection system
        more deterministic. The main objective is to reduce uncertainty around
        the raised alarms by refining the detection algorithms. This can be
        achieved by either generating new symptom definitions, adjusting the
        weights of automated algorithms or other similar approaches.</t>
      </section>

      <section anchor="network_anomaly_validation"
               title="Network Anomaly Validation">
        <t>The key objective for the validation stage is clearly to decide if
        the detected symptoms are signaling a real problem (a.k.a. requires
        action) or if they are to be treated as false positives (a.k.a.
        suppressing the alarm). For those symptoms surely having impact on
        services, 100% confidence on the fact that a network problem is
        happening can be assumed. For the other two categories, "forecasted"
        and "potential", further analysis and validation is required.</t>
      </section>

      <section anchor="network_anomaly_refinement"
               title="Network Anomaly Refinement">
        <t>After validation of a problem, the service provider performs
        troubleshooting and resolution of the problem. Although the network
        might be back in a desired state at this point, network operators can
        perform detailed postmortem analysis of network problems with the
        objective to identify useful adjustments to the prevention and
        detection mechanisms (for instance improving or extending the
        definition of SLIs and SLOs, refining concern/impact scores, etc.),
        and improving the accuracy of the validation stage (e.g. automating
        parts of the validation, implementing automated root cause analysis
        and automation for remediation actions). In this stage of the
        lifecycle it is assumed that the problem is under analysis.</t>

        <t>After the adjustments are performed to the network anomaly
        detection methods, the cycle starts again, by "replaying" the network
        anomaly and checking if there is any measurable improvement in the
        ability to detect problems by using the updated method.</t>
      </section>
    </section>

    <section anchor="network_anomaly_state_machine"
             title="Network Anomaly State Machine">
      <t>In the context of this document, from a network anomaly detection
      point of view a network problem is defined as a collection of
      interrelated symptoms, as specified in <xref
      target="I-D.netana-nmop-network-anomaly-semantics"/>.</t>

      <t>The understanding of a network problem can change over time.
      Moreover, multiple actors are involved in the process of refining this
      understanding in the different phases.</t>

      <t>From this perspective, a problem can be refined according to the
      following states (<xref target="state_machine"/>).</t>

      <t><figure anchor="state_machine" title="Network Anomaly State Machine">
          <artwork align="center"><![CDATA[
            
                                             +---------+
                                             | Initial |-----------------+
                                             +---------+                 |
                                                  |                      |
                                            +-----+---------+            |
                                   +--------|---------------|------+     |
                                   | +------v-----+  +------v----+ |     |
                                   | |  Problem   |  |  Problem  | |     |
                             +---->| | Forecasted |  | Potential | |     |
                             |     | +------------+  +-----------+ |     |
                             |     +--------|--Detection---|-------+     |
                             |              |              |             |
        +-------+            |              +------- ----- +             |
        | Final |            |                      |                    |
        +---^---+            |                      |                    |
            |                |                      |                    |
            |                |                      v                    |
            |                |     +-----------Validation------------+   |
+-----------------------+    |     |  +-----------+                  |   |
|           |           |    |     |  |  Problem  |   |  Problem  |  |   |
|  +-----------------+  |    |     |  | Discarded |   | Confirmed |<-|---+
|  |    Detection    |  |    |     |  +-----|-----+   +-----------+  |
|  |     Adjusted    |-------+     +---------------------------------+
|  +--------^--------+  |                   |               |
|           |           |                   |               |
|           |           |               +---v---+           |
|           |           |               | Final |           |
|           |           |               +-------+           |
| +---------|--------+  |                                   |
| |     Problem      |  |                                   |
| |     Analyzed     |<-|-----------------------------------+
| +------------------+  |
+-------Refinement------+
            
          ]]></artwork>
        </figure></t>

      <section anchor="network-anomaly-model-tree"
               title="Overview of the Model for the Network Anomaly Metadata">
        <figure anchor="ietf-network-anomaly-metadata-tree"
                title="YANG tree diagram for ietf-network-anomaly-metadata">
          <artwork align="center"><![CDATA[
module: ietf-network-anomaly-metadata
  +--rw network-anomalies
     +--rw network-anomaly* [id version]
        +--rw id             yang:uuid
        +--rw version        uint32
        +--rw description?   string
        +--rw annotator
        |  +--rw (annotator-type)
        |  |  +--:(human)
        |  |  |  +--rw human        empty
        |  |  +--:(algorithm)
        |  |     +--rw algorithm    empty
        |  +--rw name?              empty
        +--rw state          identityref
        +--rw symptoms* [symptom_id]
           +--rw symptom_id    yang:uuid
            
          ]]></artwork>
        </figure>

        <figure anchor="ietf-network-anomaly-metadata-module"
                title="YANG module for ietf-network-anomaly-metadata">
          <artwork><![CDATA[
<CODE BEGINS> file "ietf-network-anomaly-metadata@2024-07-01.yang"
module ietf-network-anomaly-metadata {
  yang-version 1.1;
  namespace "urn:ietf:params:xml:ns:yang:ietf-network-anomaly-metadata";
  prefix network_anomaly_metadata;

  import ietf-yang-types {
    prefix yang;
    reference "RFC 6991: Common YANG Data Types";
  }

  organization
    "IETF NMOP Working Group";
  contact
    "WG Web:   <https://datatracker.ietf.org/wg/nmop/>
     WG List:  <mailto:nmop@ietf.org>

     Authors:  Vincenzo Riccobene
               <mailto:vincenzo.riccobene@huawei-partners.com>
               Antonio Roberto
               <mailto:antonio.roberto@huawei.com>
               Thomas Graf
               <mailto:thomas.graf@swisscom.com>
               Wanting Du
               <mailto:wanting.du@swisscom.com>
               Alex Huang Feng
               <mailto:alex.huang-feng@insa-lyon.fr>";
  description
    "This module defines objects for the description of network anomalies.
      Network anomalies are a collection of symptoms observed on
      the network nodes.

      Copyright (c) 2024 IETF Trust and the persons identified as
      authors of the code.  All rights reserved.

      Redistribution and use in source and binary forms, with or
      without modification, is permitted pursuant to, and subject
      to the license terms contained in, the Revised BSD License
      set forth in Section 4.c of the IETF Trust's Legal Provisions
      Relating to IETF Documents
      (https://trustee.ietf.org/license-info).
      
      This version of this YANG module is part of RFC XXXX; see the RFC
      itself for full legal notices.";

  revision 2024-07-01 {
    description
      "Initial version";
    reference
      "RFCXXXX: Experiment: Network Anomaly Postmortem Lifecycle";
  }

  identity network-anomaly-state {
    description
      "Base identity for representing the state of the network anomaly";
  }
  identity problem-forecasted {
    base network-anomaly-state;
    description
      "A problem has been forecasted, as it is expected that
      the indicated list of symptoms will impact a service
      in the near future";
  }
  identity problem-potential {
    base network-anomaly-state;
    description
      "A problem has been detected with a confidence
      lower than 100%. In order to confirm that this set of
      symptoms are generating service impact, it requires further
      validation";
  }
  identity problem-confirmed {
    base network-anomaly-state;
    description
      "After validation, the problem has been confirmed";
  }
  identity discarded {
    base network-anomaly-state;
    description
      "After validation, the network anomaly has been
      discarded, as there is no evindence that it is causing an
      problem";
  }
  identity analysed {
    base network-anomaly-state;
    description
      "The anomaly detection went through analysis to identify
      potential ways to further improve the detection process in
      for future anomalies";
  }
  identity adjusted {
    base network-anomaly-state;
    description
      "The network anomaly has been solved and analysed.
      No further action is required.";
  }

  container network-anomalies {
    description "Container having the network anomalies";
    list network-anomaly {
      key "id version";
      description "A network anomaly identified by an id, version 
        and state.";
      leaf id {
        type yang:uuid;
        description
            "Unique ID of the network network anomaly";
      }
      leaf version {
        type uint8;
        description
          "Version of the problem metadata object.
          It allows multiple versions of the metadata to be
          generated in order to support the definition of
          multiple problem objects from the same source to
          facilitate improvements overtime";
      }
      leaf description {
        type string;
        mandatory "false";
        description
          "Textual description of the network anomaly";
      }
      container annotator {
        description "Container defining the type of the annotator and the
          version of the algorithm if it is an algorithm who reported the anomaly.";
        choice annotator-type {
          description "The type of annotator who reported the anomaly.";
          mandatory "true";
          case human {
            leaf human {
              mandatory "true";
              type empty
            }
          }
          case algorithm {
            leaf algorithm {
              mandatory "true";
              type empty
            }
          }
        }
        leaf name {
          description "Name of the user annotator or the algorithm";
          mandatory "false";
          type empty;
        }
      }
      leaf state {
        type identityref {
          base network-anomaly-state;
        }
        mandatory true;
        description "State of the anomaly.";
      }
      list symptoms {
        key "symptom_id";
        description "List of symptoms identified by the symptom_id.";
        leaf symptom_id {
          type yang:uuid;
          description "UUID of the symptom that is part of this problem";
        }
      }
    }
  }
}
<CODE ENDS>]]></artwork>
        </figure>
      </section>
    </section>

    <section anchor="Implementation" title="Implementation status">
      <t>This section provides pointers to existing open source
      implementations of this draft. Note to the RFC-editor: Please remove
      this before publishing.</t>

      <section anchor="Implementation-Antagonist" title="Antagonist">
        <t>An open source implementation for this draft is called AnTagOnIst
        (Anomaly Tagging On hIstorical data), and it has been implemented in
        order to validate the application of the YANG model defined in this
        draft. Antagonist provides visual support for two important use cases
        in the scope of this document: <ul>
            <li>the generation of a ground truth in relation to symptoms and
            problems in timeseries data</li>

            <li>the visual validation of results produced by automated network
            anomaly detection tools.</li>
          </ul> The open source code can be found here: <xref
        target="Antagonist"/></t>
      </section>
    </section>

    <section anchor="Security" title="Security Considerations">
      <t>The security considerations will have to be updated according to
      "https://wiki.ietf.org/group/ops/yang-security-guidelines".</t>
    </section>

    <section anchor="Acknowledgements" title="Acknowledgements">
      <t>The authors would like to thank xxx for their review and valuable
      comments.</t>
    </section>
  </middle>

  <back>
    <references title="Normative References">
      <?rfc include="https://xml.resource.org/public/rfc/bibxml/reference.RFC.2119.xml"?>

      <?rfc include="https://xml.resource.org/public/rfc/bibxml/reference.RFC.8174.xml"?>

      <?rfc include="https://xml.resource.org/public/rfc/bibxml/reference.RFC.8340.xml"?>

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      <?rfc include="https://bib.ietf.org/public/rfc/bibxml-ids/reference.I-D.netana-nmop-network-anomaly-semantics.xml"?>

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      <reference anchor="Antagonist"
                 target="https://github.com/vriccobene/antagonist">
        <front>
          <title>Antagonist: Anomaly tagging on historical data</title>

          <author fullname="Vincenzo Riccobene" initials="V."
                  surname="Riccobene"/>

          <author fullname="Antonio Roberto" initials="A." surname="Roberto"/>

          <author fullname="Wanting Du" initials="W." surname="Du"/>

          <author fullname="Thomas Graf" initials="T." surname="Graf"/>

          <author fullname="Alex Huang Feng" initials="H."
                  surname="Huang Feng"/>
        </front>
      </reference>
    </references>
  </back>
</rfc>