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'3' -- Possible downref: Non-RFC (?) normative reference: ref. '4' -- Possible downref: Non-RFC (?) normative reference: ref. '5' Summary: 1 error (**), 0 flaws (~~), 7 warnings (==), 6 comments (--). Run idnits with the --verbose option for more detailed information about the items above. -------------------------------------------------------------------------------- 1 T2TRG Hong, Choong Seon 2 Internet-Draft Kyung Hee University 3 Intended status: Standards Track Chit Wutyee Zaw 4 Expires: August 09, 2022 Kyung Hee University 5 Kang, Seok Won 6 Kyung Hee University 7 October 2020 9 User Centric Assignment and Partial Task Offloading for Mobile Edge 10 Computing in Ultra-Dense Networks 11 draft-hongcs-t2trg-ucapto-00 13 Abstract 15 By collocating servers at base stations, Mobile Edge Computing (MEC) 16 provides low latency to users for real time applications such as 17 Virtual Reality and Augmented Reality. To satisfy the growing demand 18 of users, base stations are deployed densely in highly populated 19 areas. Coordinated Multipoint Transmission (CoMP) allows users to 20 connect to multiple base stations simultaneously. In ultra-dense 21 networks, by offloading the partials of tasks to different base 22 stations, users can achieve lower latency and utilize the computation 23 ability of the surrounding base stations. To control the signaling 24 overhead, the number of base stations that can be connected should be 25 limited. In this paper, we propose a user-centric base station 26 assignment algorithm by considering the possible load of base 27 stations. Moreover, a partial task offloading algorithm is proposed 28 to utilize the computation of under-loaded base stations. Resource 29 allocation is then solved by convex optimization. 31 Status of this Memo 33 This Internet-Draft is submitted in full conformance with the 34 provisions of BCP 78 and BCP 79. 36 Internet-Drafts are working documents of the Internet Engineering 37 Task Force (IETF). Note that other groups may also distribute 38 working documents as Internet-Drafts. The list of current Internet- 39 Drafts is at http://datatracker.ietf.org/drafts/current/. 41 Internet-Drafts are draft documents valid for a maximum of six 42 months and may be updated, replaced, or obsoleted by other 43 documents at any time. It is inappropriate to use Internet-Drafts 44 as reference material or to cite them other than as 45 "work in progress." 46 This Internet-Draft will expire on August 09, 2020. 48 Copyright Notice 50 Copyright (c) 2018 IETF Trust and the persons identified as the 51 document authors. All rights reserved. 53 This document is subject to BCP 78 and the IETF Trust's Legal 54 Provisions Relating to IETF Documents 55 (http://trustee.ietf.org/license-info) in effect on the date of 56 publication of this document. Please review these documents 57 carefully, as they describe your rights and restrictions with respect 58 to this document. Code Components extracted from this document must 59 include Simplified BSD License text as described in Section 4.e of 60 the Trust Legal Provisions and are provided without warranty as 61 described in the Simplified BSD License. 63 Table of Contents 65 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 1 66 1.1. Terminology and Requirements Language . . . . . . . . . . 2 67 2. System Model . . . . . . . . . . . . . . . . . . . . . . . . . . 2 68 3. Problem Formulation. . . . . . . . . . . . . . . . . . . . . . . 3 69 4. User-centric Assignment and Partial Offloading . . . . . . . . . 3 70 4.1. User-centric Assignment. . . . . . . . . . . . . . . . . 3 71 4.2. Partial Offloading . . . . . . . . . . . . . . . . . . . 4 72 4.3. Radio Resource Allocation. . . . . . . . . . . . . . . . 4 73 5. Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 74 6. IANA Considerations. . . . . . . . . . . . . . . . . . . . . . . 5 75 7. Security Considerations . . . . . . . . . . . . . . . . . . . . 5 76 8. References . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 77 8.1. Normative References . . . . . . . . . . . . . . . . . . . . . 5 78 8.2. Informative References . . . . . . . . . . . . . . . . . . . . 6 79 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . . 6 81 1. Introduction 83 Mobile Edge Computing (MEC) has been an interesting topic in both 84 academia and industry for its ability to provide low latency and high 85 computation to users by setting up severs near to users. Computation 86 and latency intensive applications requires users to offload their tasks 87 to servers to achieve the minimum delay and maintain the energy of 88 users’ devices. In densely deployed networks, users can utilize the 89 resources of nearby base stations (BS) by offloading partials of their 90 tasks with the technology provided by Coordinated Multipoint 91 Transmission (CoMP). 92 Despite the advantages that MEC brings, there are many challenges to 93 tackle in MEC which are pointed out in [1]. The communication aspect is 94 surveyed in [2] where authors considered joint management of radio and 95 computation resources. Authors also introduced standards and application 96 scenarios. 98 Authors in [3] developed a distributed approach for the offloading of 99 computation tasks, caching of content and allocation of resources by 100 using an alternating direction method of multipliers. Task offloading 101 for ultra-dense network was considered in [4] where authors divided the 102 task placement and resource allocation problems and proposed an 103 efficient offloading approach. But, authors considered to offload to one 104 BS. In this paper, we consider partial offloading in ultra-dense 105 networks. To avoid the overloading at BSs, we take the number of 106 possible users who can connect to BSs into account and propose a 107 heuristic algorithm for user-centric assignment. In addition, a partial 108 offloading algorithm is proposed to utilize the resources of under- 109 loaded BSs by offloading the larger portion of tasks to those BSs. Then, 110 resource allocation is solved with the help of convex optimization. 112 1.1. Terminology and Requirements Language 114 The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", 115 "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this 116 document are to be interpreted as described in RFC 2119 [RFC2119]. 118 2. System Model 120 A network with densely deployed BSs is considered where users can 121 offload their tasks to multiple BSs simultaneously. 122 We consider the Orthogonal Frequency Division Multiple Access in both 123 uplink and downlink transmission. We also consider that MEC server are 124 equipped with multi-core technology that they can compute offloaded 125 tasks simultaneously. The user’s task has three parameters, b_i, o_i and 126 c_i which are size of input file, output result and task in CPU cycles. 128 +---------------+ +--------+ +--------+ +--------+ 129 | Mobile Device | | SBS 1 | | SBS 2 | | SBS 3 | 130 | | | | | | | | 131 +---------------+ +--------+ +--------+ +--------+ 132 | | 133 | +-----------------+ | 134 | | Offload partial | | 135 | | portion of task | | 136 | +-----------------+ | 137 | | 138 | +-----------------+ 139 | | Compute the | 140 | | offloaded task | 141 | +-----------------+ 142 | | 143 | +-----------------+ | 144 | | Return task | | 145 | | result | | 146 | +-----------------+ | 147 | | 148 --------------------------------------------------------------- 149 | | 150 | +-----------------+ | 151 | | Offload partial | | 152 | | portion of task | | 153 | +-----------------+ | 154 | | 155 | +-----------------+ 156 | | Compute the | 157 | | offloaded task | 158 | +-----------------+ 159 | | 160 | +-----------------+ | 161 | | Return task | | 162 | | result | | 163 | +-----------------+ | 164 | | 165 --------------------------------------------------------------- 166 | | 167 | +-----------------+ | 168 | | Offload partial | | 169 | | portion of task | | 170 | +-----------------+ | 171 | | 172 | +-----------------+ 173 | | Compute the | 174 | | offloaded task | 175 | +-----------------+ 176 | | 177 | +-----------------+ | 178 | | Return task | | 179 | | result | | 180 | +-----------------+ | 181 | | 182 --------------------------------------------------------------- 184 Figure 1: Partial offloading with Coordinated Transmission in 185 an Ultra-Dense Network 187 3. Problem Formulation 189 The objective of the partial offloading and resource allocation problem 190 is to minimize the latency of all mobile users where the task must be 191 computed fully. The maximum number of SBSs that a user can associate to 192 is limited. The uplink bandwidth for task offloading and downlink 193 bandwidth for result transmission are limited. In addition, the 194 computing resource at MEC servers and local computing resource are also 195 restricted. 197 4. User-centric Assignment and Partial Offloading 199 4.1. User-centric Assignment to SBSs 200 First, we need to determine the user assignment to the BSs by 201 considering the overloading possibility. The score from a user to a SBS 202 is calculated in which the uplink, downlink singal-to-noise ratios and 203 the inverse proportion of the number of users who are likely to 204 associate to a SBS is considered. 206 +---------------+ +--------+ +--------+ +--------+ 207 | Mobile Device | | SBS 1 | | SBS 2 | | SBS 3 | 208 | | | | | | | | 209 +---------------+ +--------+ +--------+ +--------+ 210 | 211 +-----------------+ 212 | Calculate score | 213 | for all SBSs | 214 +-----------------+ 215 | 216 +-----------------+ 217 | Choose 3 SBSs | 218 | with highest | 219 | scores | 220 +-----------------+ 221 | 222 --------------------------------------------------------------- 223 | +-----------------+ | 224 | | Send the signal | | 225 | | for assignment | | 226 | +-----------------+ | 227 | | 228 --------------------------------------------------------------- 229 | | 230 | +-----------------+ | 231 | | Send the signal | | 232 | | for assignment | | 233 | +-----------------+ | 234 | | 235 --------------------------------------------------------------- 236 | | 237 | +-----------------+ | 238 | | Send the signal | | 239 | | for assignment | | 240 | +-----------------+ | 241 --------------------------------------------------------------- 243 Figure 2: User-centric Assignment 245 4.2 Partial Task Offloading 247 After the assignment is done, the fractions of the task allocated to 248 BSs are resolved by utilizing the resources of under-loaded BSs. The 249 higher portion of a task is offloaded to a SBS with a lower total 250 computing load of all the assigned users. SBSs are sorted according 251 to the increasing computing loads of the users. The portion of the 252 task is offloaded to SBSs in the order. 254 +------------------+---------------+------------+------------+ 255 User's| Portion | Portion | Portion | Portion | 256 task | offloaded to | offloaded to |offloaded to| computed at| 257 | SBS 1 | SBS 2 | SBS 3 | the user | 258 +------------------+---------------+------------+------------+ 259 \ / \ / \ / \ / 260 \ / \ / \ / \ / 261 \ / \ / \ / \ / 262 \ / \ / \ / \ / 263 \ / \ / \ / \ / 264 \ / \ / \/ \/ 265 +------------+ +-----------+ +-----------+ +-----------+ 266 | SBS 1 | | SBS 2 | | SBS 3 | | Local | 267 +------------+ +-----------+ +-----------+ +-----------+ 269 Figure 3: Partial Task Offloading 271 4.3. Radio Resource Allocation 273 After obtaining the partial task offloading, we need to solve the 274 resource allocation problem. The resource allocation problem is convex 275 which can easily be solved. In this paper, we use cvxpy [5] to solve 276 this problem. For the local CPU cycles assignment, the maximum 277 available CPU cycle is assigned since the objective is minimizing the 278 latency. 280 5. Results 282 Poisson Point Process is used to model the deployment of BSs and users 283 where their densities are 0.6/m2 6/m2 respectively. For power density 284 thermal noise, -174dBm/Hz is used. Fig. 2 shows the simulation setup 285 used in the paper. Transmit power of pico BSs and users are 23dbm and 286 20dbm respectively. CPU speed is 4GHz at MEC server and 0.3GHz at user. 287 The total uplink and downlink bandwidth are 20MHz each. The size of 288 input file follows a uniform distribution between [300, 800] KB. The 289 uniform distribution is also used to model the size of tasks and output 290 files which are [0.5, 1] GHz and [0.2, 2.5] MB respectively. 291 The latency obtained at SBSs are different but most of the SBSs have the 292 similar latency results due to the different user task requirements. 293 In the highly dense networks, the proposed approach can keep most of the 294 BSs to achieve comparable results. The proposed approach obtains lower 295 latency compared to the baseline approach where the loads of SBSs are 296 not considered and task allocation is done uniformly. The difference 297 becomes significant as the number of users increases. 299 6. IANA Considerations 301 There are no IANA considerations related to this document. 303 7. Security Considerations 305 There are no security considerations related to this document. 307 8. References 309 8.1. Normative References 311 [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate 312 Requirement Levels", BCP 14, RFC 2119, March 1997. 314 [1] P. Mach and Z. Becvar, "Mobile Edge Computing: A Survey on 315 Architecture and Computation Offloading," IEEE Communications 316 Surveys & Tutorials, vol. 19, no. 3, pp. 1628-1656, 2017. 317 [2] Y. Mao, C. You, J. Zhang, K. Huang and K. B. Letaief, "A 318 Survey on Mobile Edge Computing: The Communication Perspective," 319 IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 320 2322-2358, 2017. 321 [3] C. Wang, C. Liang, F. R. Yu, Q. Chen and L. Tang, "Computation 322 Offloading and Resource Allocation in Wireless Cellular Networks 323 With Mobile Edge Computing," IEEE Transactions on Wireless 324 Communications, vol. 16, no. 8, pp. 4924-4938, 2017. 325 [4] M. Chen and Y. Hao, "Task Offloading for Mobile Edge Computing 326 in Software Defined Ultra-Dense Network," IEEE Journal on 327 Selected Areas in Communications, vol. 36, no. 3, pp. 587-597, 328 2018. 329 [5] S. Diamond and S. Boyd, "CVXPY: A Python-Embedded Modeling 330 Language for Convex Optimization," Journal of Machine Learning 331 Research, vol. 17, no. 83, pp. 1-5, 2016. 333 8.2. Informative References 334 Authors' Addresses 336 Choong Seon Hong 337 Computer Science and Engineering Department, Kyung Hee University 338 Yongin, South Korea 339 Phone: +82 (0)31 201 2532 340 Email: cshong@khu.ac.kr 342 Chit Wutyee Zaw 343 Computer Science and Engineering Department, Kyung Hee University 344 Yongin, South Korea 345 Phone: +82 (0)31 201 2987 346 Email: cwyzaw@khu.ac.kr 348 Seok Won Kang 349 Computer Science and Engineering Department, Kyung Hee University 350 Yongin, South Korea 351 Phone: +82 (0)31 201 2987 352 Email: dudtntdud@khu.ac.kr