T2TRG Hong, Choong Seon
Internet-Draft Kyung Hee University
Intended status: Standards Track Chit Wutyee Zaw
Expires: August 09, 2022 Kyung Hee University
Kang, Seok Won
Kyung Hee University
October 2020
User Centric Assignment and Partial Task Offloading for Mobile Edge
Computing in Ultra-Dense Networks
draft-hongcs-t2trg-ucapto-00
Abstract
By collocating servers at base stations, Mobile Edge Computing (MEC)
provides low latency to users for real time applications such as
Virtual Reality and Augmented Reality. To satisfy the growing demand
of users, base stations are deployed densely in highly populated
areas. Coordinated Multipoint Transmission (CoMP) allows users to
connect to multiple base stations simultaneously. In ultra-dense
networks, by offloading the partials of tasks to different base
stations, users can achieve lower latency and utilize the computation
ability of the surrounding base stations. To control the signaling
overhead, the number of base stations that can be connected should be
limited. In this paper, we propose a user-centric base station
assignment algorithm by considering the possible load of base
stations. Moreover, a partial task offloading algorithm is proposed
to utilize the computation of under-loaded base stations. Resource
allocation is then solved by convex optimization.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1. Terminology and Requirements Language . . . . . . . . . . 2
2. System Model . . . . . . . . . . . . . . . . . . . . . . . . . . 2
3. Problem Formulation. . . . . . . . . . . . . . . . . . . . . . . 3
4. User-centric Assignment and Partial Offloading . . . . . . . . . 3
4.1. User-centric Assignment. . . . . . . . . . . . . . . . . 3
4.2. Partial Offloading . . . . . . . . . . . . . . . . . . . 4
4.3. Radio Resource Allocation. . . . . . . . . . . . . . . . 4
5. Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
6. IANA Considerations. . . . . . . . . . . . . . . . . . . . . . . 5
7. Security Considerations . . . . . . . . . . . . . . . . . . . . 5
8. References . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
8.1. Normative References . . . . . . . . . . . . . . . . . . . . . 5
8.2. Informative References . . . . . . . . . . . . . . . . . . . . 6
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . . 6
1. Introduction
Mobile Edge Computing (MEC) has been an interesting topic in both
academia and industry for its ability to provide low latency and high
computation to users by setting up severs near to users. Computation
and latency intensive applications requires users to offload their tasks
to servers to achieve the minimum delay and maintain the energy of
users’ devices. In densely deployed networks, users can utilize the
resources of nearby base stations (BS) by offloading partials of their
tasks with the technology provided by Coordinated Multipoint
Transmission (CoMP).
Despite the advantages that MEC brings, there are many challenges to
tackle in MEC which are pointed out in [1]. The communication aspect is
surveyed in [2] where authors considered joint management of radio and
computation resources. Authors also introduced standards and application
scenarios.
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Authors in [3] developed a distributed approach for the offloading of
computation tasks, caching of content and allocation of resources by
using an alternating direction method of multipliers. Task offloading
for ultra-dense network was considered in [4] where authors divided the
task placement and resource allocation problems and proposed an
efficient offloading approach. But, authors considered to offload to one
BS. In this paper, we consider partial offloading in ultra-dense
networks. To avoid the overloading at BSs, we take the number of
possible users who can connect to BSs into account and propose a
heuristic algorithm for user-centric assignment. In addition, a partial
offloading algorithm is proposed to utilize the resources of under-
loaded BSs by offloading the larger portion of tasks to those BSs. Then,
resource allocation is solved with the help of convex optimization.
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
A network with densely deployed BSs is considered where users can
offload their tasks to multiple BSs simultaneously.
We consider the Orthogonal Frequency Division Multiple Access in both
uplink and downlink transmission. We also consider that MEC server are
equipped with multi-core technology that they can compute offloaded
tasks simultaneously. The user’s task has three parameters, b_i, o_i and
c_i which are size of input file, output result and task in CPU cycles.
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+---------------+ +--------+ +--------+ +--------+
| Mobile Device | | SBS 1 | | SBS 2 | | SBS 3 |
| | | | | | | |
+---------------+ +--------+ +--------+ +--------+
| |
| +-----------------+ |
| | Offload partial | |
| | portion of task | |
| +-----------------+ |
| |
| +-----------------+
| | Compute the |
| | offloaded task |
| +-----------------+
| |
| +-----------------+ |
| | Return task | |
| | result | |
| +-----------------+ |
| |
---------------------------------------------------------------
| |
| +-----------------+ |
| | Offload partial | |
| | portion of task | |
| +-----------------+ |
| |
| +-----------------+
| | Compute the |
| | offloaded task |
| +-----------------+
| |
| +-----------------+ |
| | Return task | |
| | result | |
| +-----------------+ |
| |
---------------------------------------------------------------
| |
| +-----------------+ |
| | Offload partial | |
| | portion of task | |
| +-----------------+ |
| |
| +-----------------+
| | Compute the |
| | offloaded task |
| +-----------------+
| |
| +-----------------+ |
| | Return task | |
| | result | |
| +-----------------+ |
| |
---------------------------------------------------------------
Figure 1: Partial offloading with Coordinated Transmission in
an Ultra-Dense Network
3. Problem Formulation
The objective of the partial offloading and resource allocation problem
is to minimize the latency of all mobile users where the task must be
computed fully. The maximum number of SBSs that a user can associate to
is limited. The uplink bandwidth for task offloading and downlink
bandwidth for result transmission are limited. In addition, the
computing resource at MEC servers and local computing resource are also
restricted.
4. User-centric Assignment and Partial Offloading
4.1. User-centric Assignment to SBSs
First, we need to determine the user assignment to the BSs by
considering the overloading possibility. The score from a user to a SBS
is calculated in which the uplink, downlink singal-to-noise ratios and
the inverse proportion of the number of users who are likely to
associate to a SBS is considered.
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+---------------+ +--------+ +--------+ +--------+
| Mobile Device | | SBS 1 | | SBS 2 | | SBS 3 |
| | | | | | | |
+---------------+ +--------+ +--------+ +--------+
|
+-----------------+
| Calculate score |
| for all SBSs |
+-----------------+
|
+-----------------+
| Choose 3 SBSs |
| with highest |
| scores |
+-----------------+
|
---------------------------------------------------------------
| +-----------------+ |
| | Send the signal | |
| | for assignment | |
| +-----------------+ |
| |
---------------------------------------------------------------
| |
| +-----------------+ |
| | Send the signal | |
| | for assignment | |
| +-----------------+ |
| |
---------------------------------------------------------------
| |
| +-----------------+ |
| | Send the signal | |
| | for assignment | |
| +-----------------+ |
---------------------------------------------------------------
Figure 2: User-centric Assignment
4.2 Partial Task Offloading
After the assignment is done, the fractions of the task allocated to
BSs are resolved by utilizing the resources of under-loaded BSs. The
higher portion of a task is offloaded to a SBS with a lower total
computing load of all the assigned users. SBSs are sorted according
to the increasing computing loads of the users. The portion of the
task is offloaded to SBSs in the order.
+------------------+---------------+------------+------------+
User's| Portion | Portion | Portion | Portion |
task | offloaded to | offloaded to |offloaded to| computed at|
| SBS 1 | SBS 2 | SBS 3 | the user |
+------------------+---------------+------------+------------+
\ / \ / \ / \ /
\ / \ / \ / \ /
\ / \ / \ / \ /
\ / \ / \ / \ /
\ / \ / \ / \ /
\ / \ / \/ \/
+------------+ +-----------+ +-----------+ +-----------+
| SBS 1 | | SBS 2 | | SBS 3 | | Local |
+------------+ +-----------+ +-----------+ +-----------+
Figure 3: Partial Task Offloading
4.3. Radio Resource Allocation
After obtaining the partial task offloading, we need to solve the
resource allocation problem. The resource allocation problem is convex
which can easily be solved. In this paper, we use cvxpy [5] to solve
this problem. For the local CPU cycles assignment, the maximum
available CPU cycle is assigned since the objective is minimizing the
latency.
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5. Results
Poisson Point Process is used to model the deployment of BSs and users
where their densities are 0.6/m2 6/m2 respectively. For power density
thermal noise, -174dBm/Hz is used. Fig. 2 shows the simulation setup
used in the paper. Transmit power of pico BSs and users are 23dbm and
20dbm respectively. CPU speed is 4GHz at MEC server and 0.3GHz at user.
The total uplink and downlink bandwidth are 20MHz each. The size of
input file follows a uniform distribution between [300, 800] KB. The
uniform distribution is also used to model the size of tasks and output
files which are [0.5, 1] GHz and [0.2, 2.5] MB respectively.
The latency obtained at SBSs are different but most of the SBSs have the
similar latency results due to the different user task requirements.
In the highly dense networks, the proposed approach can keep most of the
BSs to achieve comparable results. The proposed approach obtains lower
latency compared to the baseline approach where the loads of SBSs are
not considered and task allocation is done uniformly. The difference
becomes significant as the number of users increases.
6. IANA Considerations
There are no IANA considerations related to this document.
7. Security Considerations
There are no security considerations related to this document.
8. References
8.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997.
[1] P. Mach and Z. Becvar, "Mobile Edge Computing: A Survey on
Architecture and Computation Offloading," IEEE Communications
Surveys & Tutorials, vol. 19, no. 3, pp. 1628-1656, 2017.
[2] Y. Mao, C. You, J. Zhang, K. Huang and K. B. Letaief, "A
Survey on Mobile Edge Computing: The Communication Perspective,"
IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp.
2322-2358, 2017.
[3] C. Wang, C. Liang, F. R. Yu, Q. Chen and L. Tang, "Computation
Offloading and Resource Allocation in Wireless Cellular Networks
With Mobile Edge Computing," IEEE Transactions on Wireless
Communications, vol. 16, no. 8, pp. 4924-4938, 2017.
[4] M. Chen and Y. Hao, "Task Offloading for Mobile Edge Computing
in Software Defined Ultra-Dense Network," IEEE Journal on
Selected Areas in Communications, vol. 36, no. 3, pp. 587-597,
2018.
[5] S. Diamond and S. Boyd, "CVXPY: A Python-Embedded Modeling
Language for Convex Optimization," Journal of Machine Learning
Research, vol. 17, no. 83, pp. 1-5, 2016.
8.2. 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
Chit Wutyee Zaw
Computer Science and Engineering Department, Kyung Hee University
Yongin, South Korea
Phone: +82 (0)31 201 2987
Email: cwyzaw@khu.ac.kr
Seok Won Kang
Computer Science and Engineering Department, Kyung Hee University
Yongin, South Korea
Phone: +82 (0)31 201 2987
Email: dudtntdud@khu.ac.kr