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.
Status of This Memo
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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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