<?xml version="1.0" encoding="UTF-8"?>
<reference anchor="I-D.liu-dyncast-ps-usecases" target="https://datatracker.ietf.org/doc/html/draft-liu-dyncast-ps-usecases-04">
   <front>
      <title>Dynamic-Anycast (Dyncast) Problem Statement and Use Cases</title>
      <author initials="P." surname="Liu" fullname="Peng Liu">
         <organization>China Mobile</organization>
      </author>
      <author initials="P." surname="Eardley" fullname="Philip Eardley">
         <organization>BT</organization>
      </author>
      <author initials="D." surname="Trossen" fullname="Dirk Trossen">
         <organization>Huawei Technologies</organization>
      </author>
      <author initials="M." surname="Boucadair" fullname="Mohamed Boucadair">
         <organization>Orange</organization>
      </author>
      <author initials="L. M." surname="Contreras" fullname="Luis M. Contreras">
         <organization>Telefonica</organization>
      </author>
      <author initials="C." surname="Li" fullname="Cheng Li">
         <organization>Huawei Technologies</organization>
      </author>
      <author initials="Y." surname="Li" fullname="Yizhou Li">
         <organization>Huawei Technologies</organization>
      </author>
      <date month="July" day="8" year="2022" />
      <abstract>
	 <t>   Many service providers have been exploring distributed computing
   techniques to achieve better service response time and optimized
   energy consumption.  Such techniques rely upon the distribution of
   computing services and capabilities over many locations in the
   network, such as its edge, the metro region, virtualized central
   office, and other locations.  In such a distributed computing
   environment, providing services by utilizing computing resources
   hosted in various computing facilities (e.g., edges) is being
   considered, e.g., for computationally intensive and delay sensitive
   services.  Ideally, services should be computationally balanced using
   service-specific metrics instead of simply dispatching the service
   requests in a static way or optimizing solely connectivity metrics.
   For example, systematically directing end user-originated service
   requests to the geographically closest edge or some small computing
   units may lead to an unbalanced usage of computing resources, which
   may then degrade both the user experience and the overall service
   performance.  We have named this kind of network with dynamic sharing
   of edge compute resources &quot;Computing-Aware Networking&quot; (CAN).

   This document provides the problem statement and the typical
   scenarios of CAN, which is to provide the service equivalency by
   steering traffic dynamically to the appropriate service instance
   based on the basic edge computing deployment.

	 </t>
      </abstract>
   </front>
   <seriesInfo name="Internet-Draft" value="draft-liu-dyncast-ps-usecases-04" />
   
</reference>
