Internet DRAFT - draft-chong-t2trg-llwnc
draft-chong-t2trg-llwnc
T2TRG Chong, Song
Internet-Draft KAIST
Intended status: Standards Track Jang, Hyeonjoon
Expires: December 15, 2021 KAIST
October 2020
Low End-to-End Latency Content Caching
in Wireless Network Clouds
draft-chong-t2trg-llwnc-00
Abstract
In this document, we consider the content caching design without
requiring historical content access information or content popularity
profiles in a hierarchical cellular network architecture.
Our design aims to dynamically select caching locations for
different contents where caching locations can be content servers,
cloud units (CUs), and base stations (BSs). Our design objective
is to support as high content request rates as possible while
maintaining the finite service time.
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This Internet-Draft will expire on December 15, 2021.
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Internet-Draft Wireless Cloud Content Caching October 2020
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . .. . . . . . 2
2. Main Idea . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1. System Model . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2. Hybrid Content Caching Design . . . . . . . . . . . .. . . . . 4
3. IANA Considerations . . . . . . .. . . . . . . . . . . . . . . 5
4. Security Considerations . . . . . . . . . . . . . . . . . . . 5
5. References . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
5.1. Normative References . . . . . . . . . . . . . . . . . . . . . 5
5.2. Informative References . . . . . .. . . . . . . . . . . . . . 5
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . . 6
1. Introduction
With the rapidly increasing mobile video traffic,
both backhaul and fronthaul networks connecting the internet
with the mobile core and edge networks such as base stations
(BSs) (see Fig. 1) become more and more congested. Since
popular contents are more frequently requested by end users,
we can reduce the end-to-end latency of video content services
and backhaul/fronthaul traffic loads by placing popular
contents at locations closer to the end users. Content
popularity profile, which captures its average access frequency
from end users, can be spatio-temporally varying in wireless
networks due to user mobility or social interaction between
mobile users. Moreover, the massive deployment of small
cells such as femto-cells in the cellular networks increases
the spatial granularity which renders the real-time estimation
of the content popularity in each BS more challenging.
Meanwhile, edge-centric technologies (e.g., edge computing/
caching and distributed Self-Organizing Network (SON))
and cloud-centric technologies (e.g., cloud computing/caching
and Cloud Radio Access Network (C-RAN)) have been devised
to support the latency-critical applications and to address
the huge amount of workloads in the cloud servers, respectively.
Recently, hybrid network architecture and operations
which adaptively exploit the edge-centric and cloud-centric
natures of the wireless network environments, has been proposed.
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2. Main Idea
In this document we consider the caching design in a general wireless
network architecture where each group of a small number of BSs is
connected to a cloud unit(CU) where individual BSs and the CU have
content caching repositories (see Fig. 1). The caching design must
adaptively determine the content caching locations at BSs and CUs
depending on the network dynamics to support as high average content
request rates as possible with finite content service time. To address
this design while not requiring information on historical content access
or content popularity profile, we employ the Lyapunov optimization
method [a] for which the short-term max-weight problem derived from the
Lyapunov drift must be optimized in the slot-by-slot basis. Because the
max-weight problem is NP-hard and difficult to tackle [b] due to the
coupling between CU and BS caching decisions, we propose an
approximation algorithm which achieves the constant approximation ratio
to the optimal weight by exploiting the submodularity of the
slot-by-slot objective function and the structure of hierarchical
content caching networks.
+------------------+ +-------+ +--+ End-to-End +------+
| Original Content |---------| Cloud |--------|BS| Path(case1) | End |
| Servers | Backhaul| Unit | Frount +--+ <==========>| |
+------------------+ +-------+ haul | users|
| <=================================================>+------+
Backhaul | End-to-End Path(case3)
|
+-------+ Frouthaul +--+ +------+
| Cloud |----------- |BS| ------------| End |
| | +--+ | users|
| Unit |<===========================>+------+
+-------+ End-to-End Path(case2)
Figure 1: Network Architecture
2.1 System Model
Fig. 1 illustrates the hierarchical network architecture considered in
the dynamic caching design. We consider a video content set where
the file size of different contents is assumed to be the same and equal
to s (in bits). Each video content file is assumed to be split into
multiple chunks, and we assume that a file can be recovered at
the destination even if not all of the chunks for the file are
successfully delivered [c]. We assume there are E cloud units (CUs) and
N base stations (BSs) in the system. All contents are saved in their
own original content servers distributed throughout the internet.
We consider a time-slotted system with equal-size time slots of
duration Δt and the time slots are indexed as t = 0, 1, ...2.
Moreover, caching control decisions are made in the slot-by slot basis.
In each time slot t, the amount of data from content requested by end
users associated with BSs and CUs is independent and identically
distributed over time slots.
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When content f is requested from an end user, the requested content is
transmitted from its closest caching location among the associated BS,
CU and original content server which has the corresponding content
placed by a deployed caching strategy. Hereafter, we call this closest
network node as a source node of content f. Hence, the end-to-end path
of content f could vary as the choice of the source node changes.
(Figure 1.)
To capture the dynamics of content requests, services, and the
corresponding content service time, we introduce virtual queues for
each BS where the virtual queue backlog for content f,
i.e., Q_f(t), evolves over time as follows:
Q_f(t+1) = [Q_f(t) - r_f(t) + A_f(t)]^+ ,
for every CU and associated BS, content(f), where [x]^+ = max(x,0).
In the above, we define A_f(t) and r_f(t) as the amount of data
from content f requested by end users associated with BSs and the
amount of served data of content f at BSs, respectively.
The amount of served data r_f(t) from each virtual queue during time slot
t depends on the caching decision, i.e., to cache content f at CU or BS.
The average virtual queue backlog of content f indirectly captures the
average end-to-end latency of content f.
2.2. Hybrid Content Caching Design
The hybrid content caching algorithm should (i) achieve low average
end-to-end latency by stabilizing virtual queues if the content request
rate vector is within the capacity region and (ii) support as high
average content request rates as possible. For any content request rate
vector inside the capacity region,all virtual queues must be stable,
i.e., supremum of the following value
t-1
(1/r)* Σ {expectation of sum of Q_f(r) w.r.t every f, BS and CU}
r=0
should be bounded as t goes to infinity.
To develop such a caching algorithm, we could employ the Lyapunov
optimization method [a]. Toward this end, we define Lyapunov function
and Lyapunov drift function w.r.t. Q_f(t) and derive an upper bound of
the Lyapunov drift function using the queueing dynamics of the virtual
queues. Then the content caching algorithm can be developed by
minimizing the upper bound of the Lyapunov drift function in each time
slot.
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3. IANA Considerations
There are no IANA considerations related to this document.
4. Security Considerations
There are no security considerations related to this document.
5. References
5.1. Normative References
[a] M. Neely, “Stochastic network optimization with application to
communication and queueing systems,” Synthesis Lectures on
Communication Networks, pp. 1–211, 2010.
[b] R. Hemmecke, M. Koppe, J. Lee, and R. Weismantel, 50 Years of
Integer Programming 1958-2008. Springer, 2009.
[c] F. Pantisano, M. Bennis, W. Saad, and M. Debbah, “Match to Cache
:Joint user association and backhaul allocation in cache-aware
small cell networks,” in Proc. of IEEE ICC, London, UK, Jun.
2015, pp. 3082–3087.
5.2. Informative References
Acknowledgements
This work was supported by Institute for Information & communications
Technology Promotion(IITP) grant funded by the Korea government(MSIT)
(No.2015-0-00557, Resilient/Fault-Tolerant Autonomic Networking Based
on Physicality, Relationship and Service Semantic of IoT Devices)
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Authors' Addresses
Song Chong
The Graduate School of Artificial Intelligence,
Korea Advanced Institute of Science and Technology(KAIST)
Daejeon, South Korea
Phone: +82 (0)42 350 3473
Email: songchong@kaist.edu
Hyeonjoon Jang
Electrical Engineering Department,
Korea Advanced Institute of Science and Technology(KAIST)
Daejeon, South Korea
Phone: +82 (0)42 350 5473
Email: thefelix@kaist.ac.kr
Sewoong Lee
Electrical Engineering Department,
Korea Advanced Institute of Science and Technology(KAIST)
Daejeon, South Korea
Phone: +82 (0)42 350 5473
Email: dltpdnd21@kaist.ac.kr
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