Internet DRAFT - draft-hongcs-t2trg-ras
draft-hongcs-t2trg-ras
T2TRG Hong, Choong Seon
Internet-Draft Kyung Hee University
Intended status: Standards Track Pandey, Shashi Raj
Expires: August 09, 2021 Kyung Hee University
Suhail, Sabah
Kyung Hee University
Tun, Yan Kyaw
Kyung Hee University
Kim, Kitae
Kyung Hee University
October 13, 2020
Resource Allocation Strategy for Latency Sensitive IoT Traffic
draft-hongcs-t2trg-ras-00
Abstract
An efficient resource allocation scheme directly affects the overall
system's network utilization, and notably, the wireless resource such
as bandwidth is itself an expensive commodity. In this regards, to
address the requirement of massively increased IoT traffic at the
edge, as a solution approach, a number of small cell base stations
(SBSs) have been deployed with certain computational capabilities.
However, it is still limited, and any inappropriate resource
allocation scheme for the associated nodes with traffic
characterized by ultra-reliability and low-latency (URLLC)
requirements can impact the system's resource utilization and
performance. Considering a reserve resource for such kind of traffic,
the resource allocation problem in each time slot behaves as a node
selection problem with contention amongst active nodes for the
resource. In this paper, we have formulated this as an index problem,
and with simulation results have shown that the cumulative reward in
terms of network utility is maximized following this approach.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . .. . . . . . 2
1.1. Terminology and Requirements Language. . . . . . . . . 2
2. System Model . . . . . . . . . . . . . . . . . . . . . . . . .2-3
3. Problem Formulation . . . . . . . . . . . . . . . . . . . . . .3-5
4. Results . . . . . . . . . . . . .. . . . . . . . . . . . . . . 6
5. IANA Considerations . . . . . . .. . . . . . . . . . . . . . . 6
6. Security Considerations . . . . . . . . . . . . . . . . . . . 6
7. References . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
7.1. Normative References . . . . . . . . . . . . . . . . . . . . . 8
7.2. Informative References . . . . . .. . . . . . . . . . . . . 8-9
Authors' Addresses . . . . . . . . . . . . . . . . . . . . .. . . . 10
1. Introduction
Radio resources such as bandwidth is considered scare, and
is therefore an expensive utility whose management has been
of a great challenge for the evolution of mobile networks.
On the top of that, traffic with strict latency and reliability
requirements in 5G networks requires an efficient resource
allocation scheme to make it a success [1]. On the other hand,
the massive growth of IoT networks has brought up numerous
challenges while handling such traffic in an effective way [2].
Wireless Network virtualization has emerged as an promising
alternative to manage the wireless resources amongst number
of participating users. It invoked the concept of network
slicing, and flexible allocation of the slices of network resources,
such as bandwidth [3], [4], [5] to the users. In [6], authors
have introduced an online network slice broker to facilitate the
network resources for improving the overall network utilization
ratio. The problem was formulated as a bandit problem
(MAB) corresponding to the mobile traffic forecasting scenario
[7]. As with the MAB problem, there exist the dilemma of
exploration and exploitation to attain the cumulative reward,
while minimizing the total regret in the system. The solution
for this can be found using Bellman's equation [10], however,
the solution cost is expensive for the larger systems.
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In case of IoT systems, with abrupt traffic response and latency
sensitive requirements, such approach may appear to be impractical.
Thus, there should be a mechanism to efficiently allocate the
network resources for addressing these stringent demands of
IoT traffic while improving the overall network utility. We
formulate this scenario by firstly employing the technique of
reserve resources for latency sensitive traffic of IoT nodes
in terms of mini-slots, as mentioned in 3GPP standard for
5G networks. We define the network scenario as a family of
bandit process, and implement the index theory approach [8]
for a family of semi-decision Markov Process to prioritize the
nodes that could maximize the overall network utilization upon
reserve resource allocation.
1.1. Terminology and Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC 2119 [RFC2119].
2. System Model
We consider n IoT nodes associated with a single small cell
base station (SBS) (see Figure 1). Each node can collect sensory
data, perform certain amount of computation upon it and
forward it to the SBS for diverse service oriented applications.
We further categorize the traffic generated by the IoT nodes in
terms of low-latency and ultra-reliability requirements. Here,
for each node i, we will define its state at time t, x_i(t) with
the fraction of reserve resource, bandwidth B demanded from
the SBS, by quantizing the reserved resource into N levels
denoted by a set N = {1, 2, . . . , N}, where x_i(t) belongs to
set N. If node i in state x_i(t) is chosen for the reserved
resource in the time slot t, the reward value obtained by the SBS
in terms of resource utilization is defined as r_i(x_i(t)).This
way a sequential node selection scenario exist for the SBS to
allocate reserve resource to one of the requesting node.
Because the reward distribution is unknown, the SBS can allocate
the reserve resource following the solution approach for the
multiarmed bandit problem, to maximize its cumulative reward over
the time. Alternatively, if we can prioritize a node i, given its
state x_i(t) and corresponding reward r_i(x_i(t)), the SBS can
sequentially resolve the node selection problem in an efficient
way.
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+---------------+ +------------+
| SBS | | IoT Nodes |
| | | |
+---------------+ +------------+
| |
| |
| +------------------+ |
| | Communication | |
| | link | |
| | | |
| +------------------+ |
| |
| <------------------------> |
Figure 1: System model
3. Problem Formulation
The defined problem can be represented as a n-arm bandit
and a single player (SBS) scenario, where at each time t, the
player (SBS) chooses one arm (IoT node) to play (allocate
its reserve resource as illustrated in Figure 2). The process can
be extended in reference with the sequence of time t_i and
states of the nodes, x_i(t), for all i, and be consider a family of
bandit process F = {F_1, F_2, . . . , F_n} as in [8]. Here, the
bandit process F_i, for all i is defined with the state x_i(t),
and reward at the state r_i(x_i(t)) > 0, and is considered to be
an exponentially discounted semi-Markov decision process with a
constant duration between the decision times. For convenience,
we keep it as 1.
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+-------------------+-------------------------------+
| Reserve Resource | |
B | | |
| | |
+-------------------+-------------------------------+
\ Mini slots /
\ /
\ /
\ /
\ /
\ /
\ /
\ /
\ /
\ /
\ /
\ /
\ /
\ /
\ /
\ /
\ /
\ /
\ /
+------------+-----------+-----------+-----------+
| | | | |
+------------+-----------+-----------+-----------+
Time slots (T)
---------------->
Figure 2: Resource reservation
We adapt the control set u = {0, 1}, where the control
0 freezes the process. That means, there is no change in
state and no reward is obtained from the process. Similarly,
control 1 is defined as the continuation control that returns
an immediate reward a_t r_i(x_i(t)) = exponential(-gamma t)
r_i(x_i(t)). Here, the parameters a(0 < a < 1) and gamma
(gamma > 0) are defined to be the discount factor and the
discount parameter respectively for obtaining a bounded reward
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value. This way, the presented problem resembles with a discounted-
reward Markov decision process which solution can be found using
dynamic programing equations [10]. However, the solution becomes
difficult to solve for a n-bandit process where the problem grows
exponentially. Therefore, using these definitions, we refer [9]
which states that for a discrete time Markov decision process,
there exists an optimal policy defined as index policy, which
is characterized by a real-valued index, v(F_i, x_i(t)), and it is
to continue the bandit process having greatest index.
+---------------------------+
| Input Bandit processes, |
| discount factor, |
| system parameters |
| |
+---------------------------+
|
|
v
+-----------------------------------+
|Calculate the index value v(F_i); |
| Removed the checked node i; | <-------------+
| | |
+-----------------------------------+ |
| |
v |
/ \ |
/ \ |
/ \ |
/ Are \ |
/ all \ No |
/ nodes i \ ----------------------------+
\ checked ?/
\ /
\ /
\ /
\ /
\ /
|
|Yes
v
+--------------------------------+
| Return maximum index value, |
| and its corresponding |
| index. |
+--------------------------------+
|
v
( End )
Figure 3: Algorithm 1: Index based node association
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This means, the return of reward with the choice of control u applied
for the bandit process will be improved by selecting continuation
control on the bandit with the greatest index. Therefore, for the node
selection problem at time t, we can evaluate the index values at the
bandit processes given state x_i(t), for all i. Then after, we can
choose to apply the continuation control to the bandit with highest
index value which guarantees for better discounted cumulative reward.
The detail implementation for this scenario is presented in
Algorithm 1 (see Figure 3).
4. Results
An index base resource allocation scheme considers latency sensitive
IoT traffic and improves the overall network utilization. The network
utilization can be defined in terms of cumulative reward while
effectively allocating reserve resource to the family of bandit
processes. We observe the improvement in cumulative reward while
implementing index based node selection approach for allocating the
reserved resource.
5. IANA Considerations
There are no IANA considerations related to this document.
6. Security Considerations
There are no security considerations related to this document.
the reserved resource.
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7. References
7.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997.
[1] Bennis, Mehdi and Debbah, Merouane and Poor, H Vincent, "Ultra-
Reliable and Low-Latency Wireless Communication: Tail, Risk and
Scale.", arXiv:1801.01270 , 2018.
[2] Chen, Shanzhi and Zhao, Jian, "The requirements, challenges, and
technologies for 5G of terrestrial mobile telecommunication.", IEEE
Communications Magazine, vol. 52, no. 5, pp. 36-43, 2014.
[3] Liang, Chengchao and Yu, F Richard, "Wireless network
virtualization: A survey, some research issues and challenges.",
IEEE Communications Surveys & Tutorials, vol. 17, no. 1, pp. 358-380,
2015.
[4] Nakao, Akihiro and Du, Ping and Kiriha, Yoshiaki and Granelli,
Fabrizio and Gebremariam, Anteneh Atumo and Taleb, Tarik and Bagaa,
Miloud,"End-to-end network slicing for 5g mobile networks.",
Journal of Information Processing, vol. 25, pp. 153-163, 2017.
[5] Zhang, Haijun and Liu, Na and Chu, Xiaoli and Long, Keping and
Aghvami, Abdol-Hamid and Leung, Victor CM, "Network slicing based
5G and future mobile networks: mobility, resource management, and
challenges.", IEEE Communications Magazine, vol. 55, no. 8, 138-145,
2017.
[6] Sciancalepore, Vincenzo and Zanzi, Lanfranco and Costa-Perez,
Xavier and Capone, Antonio, "ONETS: Online Network Slice Broker From
Theory to Practice", arXiv:1801.03484, 2018.
[7] Sciancalepore, Vincenzo and Samdanis, Konstantinos and Costa-Perez
,Xavier and Bega, Dario and Gramaglia, Marco and Banchs, Albert,
"Mobile traffic forecasting for maximizing 5G network slicing resource
utilization", In INFOCOM 2017-IEEE Conference on Computer
Communications, IEEE, pp. 1-9. IEEE, 2017
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[8] Gittins, John and Glazebrook, Kevin and Weber, Richard.,
2011 Multiarmed bandit allocation indices.
[9] Weber, Richard,1992 "On the Gittins index for multiarmed bandits"
The Annals of Applied Probability,1024-1033.
[10] Bellman, Richard, 2013 Dynamic programming.
7.2. Informative References
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Authors' Addresses
Choong Seon Hong
Computer Science and Engineering Department, Kyung Hee University
Yongin, South Korea
Phone: +82 (0)31 201 2532
Email: cshong@khu.ac.kr
Shashi Raj Pandey
Computer Science and Engineering Department, Kyung Hee University
Yongin, South Korea
Phone: +82 (0)10 3855 8816
Email: shashiraj@khu.ac.kr
Sabah Suhail
Computer Science and Engineering Department, Kyung Hee University
Yongin, South Korea
Phone:
Email: sabah@khu.ac.kr
Yan Kyaw Tun
Computer Science and Engineering Department, Kyung Hee University
Yongin, South Korea
Phone:
Email: ykyawtun7@khu.ac.kr
Kitae Kim
Computer Science and Engineering Department, Kyung Hee University
Yongin, South Korea
Phone:
Email: glideslope@khu.ac.kr
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