Internet DRAFT - draft-abhishek-coin-xr-edge-cloud

draft-abhishek-coin-xr-edge-cloud



coin                                                         R. Abhishek
Internet-Draft                                                   Tencent
Intended status: Informational                            April 27, 2021
Expires: October 29, 2021


        A collaborative Edge-Cloud framework for XR applications
                  draft-abhishek-coin-xr-edge-cloud-00

Abstract

   This document discusses a collaborative edge-cloud model and
   application of network slicing for Extended Reality (XR), including
   both Augmented Reality (AR) and Virtual Reality (VR), especially with
   respect to the architectural framework and "QoS" based optimal
   latency tolerant resource allocation.

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   This Internet-Draft will expire on October 29, 2021.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Architectural Framework . . . . . . . . . . . . . . . . . . .   3
     2.1.  A collaborative Edge-Cloud model  . . . . . . . . . . . .   4
     2.2.  QoS based Resource management  for Network Slicing  . . .   5
   3.  Example . . . . . . . . . . . . . . . . . . . . . . . . . . .   5
   4.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   6
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .   6
   6.  Acknowledgment  . . . . . . . . . . . . . . . . . . . . . . .   6
   7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   6
     7.1.  Normative References  . . . . . . . . . . . . . . . . . .   6
     7.2.  Informative References  . . . . . . . . . . . . . . . . .   6
   Author's Address  . . . . . . . . . . . . . . . . . . . . . . . .   7

1.  Introduction

   To realize the full capacity of Extended Reality (XR), including both
   Augmented Reality (AR) and Virtual Reality (VR), a high-end hardware
   device is required.  This requirement arises because XR applications
   are likely to require a huge amount of processing power and storage
   to give the user the feeling of being in a truly immersive
   environment.  With the increasing number of XR applications, the
   requirement for the devices' processing capacity has increased.  More
   importantly, these XR applications require real-time video stream
   processing to recognize specific objects, besides, some AR
   application requires generation of new video frames
   [draft-ietf-mops-ar-use-case-00].  Therefore, the current challenges
   in using XR have been the capacity, energy consumption, and weight of
   the device.  All of these are arising due to the massive processing
   requirement of the applications running on the device.  Heavy devices
   result in the user having an uncomfortable experience, and high
   processing capacity makes the device expensive.  Besides, with
   limited resource availability at the device, processing tasks that
   require more than available resources would add computational and
   processing latencies.  Therefore, there exists a gap between the
   capabilities of the current state of the art and the requirements for
   the future.

   One way to overcome this is by offloading processing to network-based
   resources in the edge and the cloud.  However, the challenge is to
   minimize the latency when the processing is offloaded to the upper
   layers (edge and cloud) [Figure 1].  There are lots of contributing
   factors to this latency, such as sampling delay, computation delay
   including image processing and frame rendering delay, networking
   delay comprising of queuing and transmission delay.  Therefore, an
   optimized architecture is required in order for the computational and
   communicational delay not to throttle the XR system.  This document



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   talks about splitting a task among the user, edge, and the cloud and
   applying network slicing to minimize the latency experienced by the
   user, and provide QoS-based traffic routing resource allocation on
   the latency bounds for different traffic classes.  Besides, using a
   latency-bound network may result in the user having motion sickness
   [Latency-Network-AR-VR].  In this regard, using network slicing would
   work to the user's advantage by dedicating a specific virtual slice
   for XR, thereby improving the Quality of Experience.

2.  Architectural Framework

   The concept of using edge computing and cloud computing for
   offloading the processing from the user device to the upper layers
   has garnered much interest from the industry and academia in recent
   years.  Edge computing brings real-time data processing near the
   user, thereby running the application in closer network proximity to
   the (XR) user devices.  Processing the applications as close to the
   user as possible compared to running them on a centralized cloud or
   data center helps reduce the transmission latency.  With their high
   computational capabilities, the cloud servers can handle resource-
   intensive tasks requiring CPU and GPU-like processing.  Therefore,
   using a collaborative model comprising the user (XR), the edge, and
   the cloud is more optimal for performance and latency reduction.

   Network slicing allows partitioning the physical network into
   logically isolated sub-networks for flexible and optimized resource
   provisioning.  Thereby, one or more network slices can be completely
   dedicated to the needs of XR.  Each slice can host one or more
   Network Slice Subnet Instance (NSSI)
   [I-D.draft-defoy-coms-subnet-interconnection-04] for different
   application needs.  These network slices may have slice priority
   linked to it, which may help in resource allocation during stressed
   situations [Spartacus].  This slice priority may be helpful in
   resource allocation based on the traffic class.

   The architectural framework is shown in the figure below.  It can be
   partitioned into three layers: the user, the edge, and the cloud
   layers.  The edge and the cloud will have different network slices
   for the different traffic classes.  A task may be divided among the
   user, edge, and cloud layers.  The processing split among the user
   layer, edge layer, and the cloud layer further adds to optimization
   and reduced delay.  A task module is present at the user and the edge
   layer to split the task.  The user layer's task module decides which
   tasks are to be processed at the user's device and which tasks to be
   sent to the upper layers for further processing.






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    __________________________________________________________
   |  __________________________________________             |
   | |         ___________   _______   ________ |            |
   | | Cloud  | XR Slice  | |Slice 2|  |Slice 3||            |
   | | Layer   -----------   --------  -------- |            |
   | |__________________________________________|            |
   |  _____________________________________________________  |
   | |       ___________   _______   ________   |          | |
   | | Edge | XR Slice  | |Slice 2|  |Slice 3|  |  Task    | |
   | | Layer -----------   --------  --------   |  Module  | |
   | |__________________________________________|__________| |
   |                       ________________________________  |
   |                       |                     |  Task   | |
   |                       |   User Layer        |  Module | |
   |                       |_____________________|_________| |
   |_________________________________________________________|

                     Figure 1: Architectural Framework

2.1.  A collaborative Edge-Cloud model

   An effective collaboration among user, edge, and cloud layers is
   important for optimal performance and latency improvement.
   Processing at the network edge helps overcome cloud offloading
   shortcomings, such as long latencies and network congestion
   [Collaborative-Cloud-Edge-Computing].  However, the ability of edge
   computing is limited by its processing power to perform resource
   intensive tasks.  Thereby, a joint hierarchical architecture
   consisting of collaborative design involving user, edge, and the
   cloud is required to reduce the end-to-end latency and energy
   consumption and provide optimal computing performance.

   When the user's device cannot process any task on its own due to
   processing delay or computational limitations, it offloads the task
   to the edge.  The edge task module will decide if the task would be
   processed locally at the edge or processed collaboratively with the
   upper cloud.  Proportional resources are allocated for the network
   slices in the edge and the cloud.  Here, the split of the task
   between the cloud and the edge would be decided by the task module in
   the edge.  Several strategies for task offloading can be used, such
   as taking into account the service time for edge and the cloud and
   offloading the task to the upper layers in such a way to maximize
   parallelism among the user, edge, and cloud
   [Concurrent-tasks-offloading].  Besides, strategies based on energy
   consumption minimization and maximizing the throughput of the user
   [Offloading-services-for-mobile-devices] may be used for optimal
   resource allocation as well.




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   When a task requiring low computational processing is offloaded to
   the upper layers, the edge node can process it locally for the lower
   end-to-end delay and higher energy efficiency.  However, when a
   computational-intensive task is relayed to the upper layers, instead
   of offloading the complete task to the cloud, the task module in the
   edge may split the task between the edge and the cloud.  The split of
   tasks between the edge and cloud node may be based on the
   computational delay of the edge node, computational delay of the
   cloud node, and transmission delay of the cloud server.

   The task module will track the resource utilization and the running
   applications and their performances.  The resource management is done
   so that the QoS of the delay-sensitive traffic and resource
   utilization is maintained.  It may have different sub-modules for
   task placement and scheduling by tracking the state of different
   tasks [iFogSim].

2.2.  QoS based Resource management for Network Slicing

   Using a Software-Defined Networking[SDN] based architecture can help
   manage the network slices centrally with optimized resource
   utilization and cost-efficiency.  An efficient network slicing
   resource management is vital for latency-bound traffics.  Dynamically
   allocating resources based on the traffic needs and priority would
   help manage the network in a more efficient and optimized manner.
   Therefore, implying that the VNs would be mapped based on the slice
   traffic and required QoS.  For delay-sensitive traffic, the QoS can
   be based on the latency requirements, such as prioritizing delay-
   sensitive traffic as compared to delay-tolerant traffic such as live
   video vs. stored ones.

3.  Example

   One of the most time-sensitive XR applications includes healthcare,
   where a surgeon can utilize XR to perform surgery, even remotely.  A
   collaborative edge and cloud model is highly desirable for both
   benefits of low-latency and high throughput.  For such use-cases, the
   network needs to deliver data with low latency and high reliability.
   The application can sense and deliver the correct field of view while
   minimizing the motion-to-photon latency.  In this regard, having
   network slicing priority can help prioritize the traffic for such
   cases, whereas having a collaborative edge-cloud model can reduce the
   latency since processing the task at the central cloud is not advised
   as this would increase the motion-to-photon latency.  For real-time
   video processing such a remote surgery, applications require
   combining and synchronizing real-world data with the user's motion,
   thereby requiring a massive rendering process.  Since these graphics
   require heavy rending, the task is augmented by the upper edge and



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   cloud layers.  When the user device is not able to process the
   incoming video frames due to its limited processing capabilities, it
   offloads the task to the edge, the edge, instead of offloading the
   whole frame to the cloud, offloads only difference of the frame
   compared to the previous frame [Collaborative-Edge-and-Cloud] for
   processing.  Thereby sending only the frame difference instead of the
   whole frame, hence minimizing the latency and saving bandwidth.

4.  IANA Considerations

   This document has no actions for IANA.

5.  Security Considerations

   Security aspects relative to network slices (e.g., for transport
   slices, in [I-D.liu-teas-transport-network-slice-yang]) are
   applicable.

6.  Acknowledgment

   The author would like to thank Spencer Dawkins for reviewing the
   draft.

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>.

7.2.  Informative References

   [Collaborative-Cloud-Edge-Computing]
              "Collaborative Cloud and Edge Computing for Latency
              Minimization", <https://rb.gy/sf2ctz>.

   [Collaborative-Edge-and-Cloud]
              "Collaborative Edge and Cloud Neural Networks for Real-
              Time Video Processing",
              <http://www.vldb.org/pvldb/vol11/p2046-grulich.pdf>.

   [Concurrent-tasks-offloading]
              "Heuristic offloading of concurrent tasks for computation-
              intensive applications in mobile cloud computing",
              <https://ieeexplore.ieee.org/document/6849257>.




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   [draft-ietf-mops-ar-use-case-00]
              Krishna, R. and A. Rahman, "Media Operations Use Case for
              an Augmented Reality Application on Edge Computing
              Infrastructure", draft-ietf-mops-ar-use-case-00 (work in
              progress), March 2021.

   [I-D.draft-defoy-coms-subnet-interconnection-04]
              de Foy, X., Rahman, A., Galis, A., Makhijani, K., Qiang,
              Li., Homma, S., and P. Martinez-Julia, "Interconnecting
              (or Stitching) Network Slice Subnets", draft-defoy-coms-
              subnet-interconnection-04 (work in progress), March 2020.

   [iFogSim]  "iFogSim: A Toolkit for Modeling and Simulation of
              Resource Management Techniques in Internet of Things, Edge
              and Fog Computing Environments",
              <https://arxiv.org/abs/1606.02007>.

   [Latency-Network-AR-VR]
              "Support Precise Latency for Network Based AR/VR
              Applications with New IP",
              <https://eprints.eudl.eu/id/eprint/856/1/
              eai.27-8-2020.2294291.pdf>.

   [Offloading-services-for-mobile-devices]
              "On effective offloading services for resource-constrained
              mobile devices running heavier mobile Internet
              application", <https://rb.gy/zdvwvv>.

   [SDN]      ""Software-defined networking." Communications of the ACM
              56.9 (2013): 16-19",
              <https://dl.acm.org/doi/fullHtml/10.1145/2500468.2500473>.

   [Spartacus]
              "Spartacus: Service priority adaptiveness for emergency
              traffic in smart cities using software-defined
              networking", <https://rb.gy/cw0pbc>.

Author's Address

   Rohit Abhishek
   Tencent
   2747 Park Blvd
   Palo Alto, California  94306
   United States

   Phone: 8165857500
   Email: rabhishek@rabhishek.com




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