Cost-Effective Migration-Assisted User Reallocation in Edge Computing

被引:0
作者
Zhu, Jiahao [1 ]
Xiao, Fu [1 ]
Zhao, Lu [1 ]
Zhou, Jian [1 ]
Cai, Hui [1 ]
He, Xin [1 ]
机构
[1] Nanjing Univ Posts & Telecommun, Sch Comp Sci, Nanjing, Peoples R China
来源
IEEE CONFERENCE ON GLOBAL COMMUNICATIONS, GLOBECOM | 2023年
关键词
edge computing; edge server failures; user reallocation; user migration;
D O I
10.1109/GLOBECOM54140.2023.10437434
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Edge computing (EC) provides low-latency services by deploying edge servers close to users. However, these servers are prone to failures that can invalidate any predefined user allocation strategies. To ensure continuous services and maintain users' payments, affected users who are disconnected from the failed edge servers need to be reallocated. Unfortunately, due to the strict latency requirements of users and the limited resources on edge servers, many of them fail to be reallocated. Thus, we propose to migrate unaffected users from affected users' nearby edge servers to free up more resources for reallocation. In this paper, with the aim of maximizing the overall revenue and ensuring continuous service provisioning for users, we formulate the problem of Migration-Assisted User Reallocation (MUR) upon edge server failures and prove its NP-hardness. We then introduce an Integer Programming-based approach named MUR-O to find the optimal solution and a heuristic approach named MUR-H to efficiently find sub-optimal solutions. Experimental results on real-world datasets demonstrate that our approaches are superior to three representative approaches.
引用
收藏
页码:461 / 466
页数:6
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[41]   Low-cost Edge Computing devices and novel user interfaces for monitoring pivot irrigation systems based on Internet of Things and LoRaWAN technologies [J].
Matilla, Diego Mateos ;
Murciego, Alvaro Lozano ;
Jimenez-Bravo, Diego M. ;
Mendes, Andre Sales ;
Leithardt, Valderi R. Q. .
BIOSYSTEMS ENGINEERING, 2022, 223 :14-29
[42]   User Driven FPGA-Based Design Automated Framework of Deep Neural Networks for Low-Power Low-Cost Edge Computing [J].
Belabed, Tarek ;
Coutinho, Maria Gracielly F. ;
Fernandes, Marcelo A. C. ;
Sakuyama, Carlos Valderrama ;
Souani, Chokri .
IEEE ACCESS, 2021, 9 :89162-89180
[43]   Cost-Efficient and Quality-of-Experience-Aware Player Request Scheduling and Rendering Server Allocation for Edge-Computing-Assisted Multiplayer Cloud Gaming [J].
Gao, Yongqiang ;
Zhang, Chaoyu ;
Xie, Zhulong ;
Qi, Zhengwei ;
Zhou, Jiantao .
IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (14) :12029-12040