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A profit-aware server deployment approach for edge computing

COMPUTING 2025年第1期107卷 1-30页

作者： Wang, Zhongmin Dong, Hanchen Jin, Xiaomin Chen, Yanping Xian Univ Posts & Telecommun Sch Comp Sci & Technol Changan West St Xian 710121 Shaanxi Peoples R China Shaanxi Key Lab Network Data Anal & Intelligent Pr Changan West St Xian 710121 Shaanxi Peoples R China Xian Key Lab Big Data & Intelligent Comp Changan West St Xian 710121 Shaanxi Peoples R China

In recent years, the demand for computing resources in daily life has risen due to the rapid development of smart terminal devices. Cloud computing facilities may not be able to handle the massive amounts of data that will be generated in the future. Edge computing, a new paradigm after cloud computing, places edge servers closer to the terminals, improving the efficiency of processing the massive data generated by the terminals. However, during actual use, the edge computing system is limited by factors such as population density and service demand, resulting in suboptimal data processing efficiency and operator profits. This paper studies the deployment of edge servers in a fixed area, focusing on operator profitability. We propose the Advanced K-Means method, an improvement of K-means for users with different needs in a fixed area, to maximize system profitability at various times. Experimental results based on subscriber and base station datasets from Melbourne's Central Business District (CBD) demonstrate that this deployment strategy improves profitability by at least 22%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, outperforming several other representative approaches.

关键词： Edge computing server deployment Profit optimization K-Means

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Joint server deployment and Task Scheduling for the Maximal Profit in Mobile-Edge Computing

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IEEE INTERNET OF THINGS JOURNAL 2023年第24期10卷 22501-22513页

作者： Gao, Yu Tao, Jun Wang, Haotian Wang, Zuyan Sun, Weice Song, Changping Southeast Univ Sch Cyber Sci & Engn Nanjing 210000 Peoples R China Southeast Univ Key Lab CNII MOE Nanjing 210000 Peoples R China Purple Mt Labs Network & Commun Secur Nanjing 210000 Peoples R China

Recently, adopting mobile-edge computing (MEC) to accommodate the compute-intensive and delay-sensitive tasks from mobile devices has gained increasing attention from the research community. In contrast to a cloud-centric scheme, deploying servers at the network edge offers the advantage of delivering faster and more efficient services. However, pioneering works primarily focus on a homogeneous server deployment strategy, which distributes the same quantity of servers among a specific number of selected locations. In this work, we aim to lay the theoretical foundation for budget-constrained profits maximization (BCPM) problem, which is a coupled problem of server deployment and task scheduling. Subsequently, a two-step optimization method is proposed. Through seeking the maximum matches in the constructed bipartite graph, a task scheduling algorithm is first designed to maximize the profits under the server deployment. Then, two approximation algorithms with provable approximation ratios are exploited to perform nearly optimal deployment of servers in a homogeneous and heterogeneous manner, respectively. Extensive simulations with real-world data set and system settings are conducted. The results show that the proposed algorithms can achieve at least a 10.54% increase in total profits and the average processing delay of tasks can be shortened by about 17%.

关键词： Approximation algorithm mobile-edge computing (MEC) profit maximization server deployment

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Latency-Aware server deployment in Internet of Vehicles Based on Multi-Agent Reinforcement Learning 25

Latency-Aware Server Deployment in Internet of Vehicles Base...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Liu, Xinran Liu, Yaqiong Mu, Junsheng Shou, Guochu Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Key Lab Network Syst Architecture & Conve Beijing 100876 Peoples R China

ISBN: (纸本)9798350303582;9798350303599

The performance of the servers directly affects the efficiency of the whole intelligent Internet of Vehicles (loV) system. A reasonable server deployment strategy can effectively reduce network latency and improve the efficiency of IoV. Therefore, it is necessary to study the problem of server deployment in IoV. In this paper, we formulate a latency-aware server deployment problem based on Multi-Agent Reinforcement Learning (MARL) and utilize an Actor-Critic network based on Long Short-Term Memory (LSTM) encoding network to solve it. Besides, four deployment strategies (DQN, DRQN, Random and No Movement) are used as the baseline methods for performance comparison with our proposed method on two real-world datasets. The simulation results show our solution has excellent performance in reducing network latency under different scenarios.

关键词： Internet of Vehicles (IoV) Multi-Agent Reinforcement Learning (MARL) Markov Decision Process (MDP) server deployment

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An Edge server deployment Strategy for Multi-Objective Optimization in the Internet of Vehicles 29

An Edge Server Deployment Strategy for Multi-Objective Optim...

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29th IEEE Symposium on Computers and Communications (IEEE ISCC)

作者： Zhou, Zhou Pooraniant, Zahra Shojafart, Mohammad Martinellit, Fabio Changsha Univ Sch Comp Sci & Engn Changsha Peoples R China Italian Natl Res Council CNR Pisa Italy Univ Surrey Inst Commun Syst ICS 5G 6GIC Guildford Surrey England

ISBN: (纸本)9798350354249;9798350354232

This paper proposes an edge server deployment strategy based on multi-agent reinforcement learning (CKM-MAPPO) to address the multi-objective optimization problem in a vehicle networking environment. CKM-MAPPO focuses on optimizing the load balancing among edge servers and minimizing edge servers' delay and energy consumption. Firstly, the Canopy and K-means algorithms determine the edge server deployment's number and initial location. Then, we utilize the multi-agent reinforcement learning algorithm to decide the optimal deployment location of the edge server. We performed a series of tests, and the experimental results show that CKM-MAPPO improves load balancing by 26.5% and reduces delay and energy consumption by 12.4% and 17.9%, respectively.

关键词： Edge Computing Internet of Vehicles Reinforcement Learning Resource Optimization server deployment

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A Joint Edge server and Service deployment Method in C-RAN With Multilayer MEC for Multicommunities

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IEEE INTERNET OF THINGS JOURNAL 2025年第1期12卷 904-918页

作者： Pan, Jeng-Shyang Yang, Qingyong Chu, Shu-Chuan Chao, Han-Chieh Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 211544 Peoples R China Chaoyang Univ Technol Dept Informat Management Taichung 413 Taiwan Tamkang Univ Dept Artificial Intelligence New Taipei 251 Taiwan Natl Dong Hwa Univ Dept Elect Engn Hualien 974 Taiwan UCSI Univ Inst Comp Sci & Innovat Kuala Lumpur 56000 Malaysia

Combining cloud radio access network (C-RAN) and mobile edge computing (MEC) can effectively reduce network service latency and improve network reliability. The locations of edge servers (ESs) in the architecture and the services deployed affect the network quality and the operator's revenue. However, few studies have focused on ES or service deployment in this architecture, and almost no research has addressed both joint deployments. Furthermore, previous studies rarely consider the collaborative deployment across multiple communities (regions) and the cooperation among ESs simultaneously. To fill this gap, this study first constructs a C-RAN network system model with multilayer MEC involving multicommunities and establishes a multiobjective mixed-integer programming (MOMIP) model aimed at maximizing the profit of service providers and the comprehensive average resource utilization of the ESs. Then, for the established model, this study proposes a three-step heuristic algorithm to solve. The algorithm has been used to determine the number of ESs to be deployed in each community, the deployment locations of ESs at each layer, and the services to be deployed on each ES. Finally, this study conducts a series of simulation experiments. Experimental results show that the proposed algorithm outperforms the baseline algorithms, and the obtained deployment scheme is more reasonable.

关键词： Heuristic algorithms Computer architecture Optimization servers Mathematical models Resource management Microservice architectures Computational modeling Clustering algorithms Artificial intelligence Cloud radio access network (C-RAN) heuristic algorithm mobile edge computing (MEC) server deployment service deployment

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Utility-Aware Edge server deployment in Mobile Edge Computing 19th

Utility-Aware Edge Server Deployment in Mobile Edge Computin...

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19th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Qiu, Jianjun Li, Xin Qin, Xiaolin Wang, Haiyan Cheng, Yongbo Nanjing Univ Aeronaut & Astronaut CCST Nanjing Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing Peoples R China Zhejiang Gongshang Univ Hangzhou Peoples R China Nanjing Univ Finance & Econ Nanjing Peoples R China

ISBN: (纸本)9783030389918;9783030389901

Traditional Mobile Cloud Computing (MCC) has gradually turned to Mobile Edge Computing (MEC) to meet the needs of low-latency scenarios. However, due to the unpredictability of user behaviors, how to arrange edge servers in suitable locations and rationally allocate the computing resources is not easy. Besides, the workload between the servers maybe unbalanced, which could lead to a shrinkage of system utility and waste of energy. So we analyze the workloads in a large MEC system and use one day to represent a workload cycle rotation. Combining the idea of differential workload changes with the local greedy method, we propose a new Gradient algorithm under the constraint of given limited computing capacity. We conduct extensive simulations and compared it with the algorithm based on the average workload as the Weight and the Greedy algorithm, which shows that the Gradient algorithm can reach the maximum utility compared with Weight and Greedy methods.

关键词： Edge computing MEC server deployment Delay-sensitive

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DEVELOPMENT OF THE server deployment BASED ON FORMAL CHANGE MANAGEMENT

DEVELOPMENT OF THE SERVER DEPLOYMENT BASED ON FORMAL CHANGE ...

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作者： Ekonoja, Henna Lapland University of Applied Sciences

学位级别：硕士

The purpose of this study was to find out how the deployment of the servers has developed due to formal change management at the commissioner's Finnish Change Management and server deployment services. The aim was to figure out the used process phases, which work and which phases need developed. The aim was to build the first version of an electronic server order form, which was integrated into the IT service management tool of the commissioner in use. The data of the study was collected using qualitative methods and service design thinking in developing the existing service. The Finnish change management team of the IT service provider was group interviewed with the help of business model canvas and four responsible persons of the client service were interviewed face-to-face with open questions. The 5-step structure of typical service design thinking was used for modelling the electronic server order form. The data collection was done in definition and research phases and in the planning phase the results of the group and individual interviews were analyzed to figure out the server deployment process now in use and the phases of the process to be improved. Based on the interviews, the model of electronic server order form was planned and during the piloting phase the simplified version of the electronic server order form was implemented and published for a few clients of the commissioner. Based on the usage of the simplified version of the electronic server order form the first MyIT self-service portal server order form was built and introduced for the usage of limited staff members of the commissioner. This version of the server order form was a more automated version than the earlier and it was better integrated into the IT service management tool of the commissioner. At same time, the server development process was analyzed using the LEAN thinking and the phases to be improved recognized and some of them were eliminated. Työn aiheena oli selvittää, miten palveli

关键词： Business Model Canvas change management service design server deployment

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ParallEdge: Exploiting Computing-Mobility Parallelism for Efficient 5G/6G Edge Computing

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第5期23卷 4025-4037页

作者： Cong, Rong Zhao, Zhiwei Zhang, Linyuanqi Min, Geyong Univ Elect Sci & Technol China Coll Comp Sci & Engn Chengdu 610051 Sichuan Peoples R China Univ Exeter Coll Engn Math & Phys Sci Exeter EX4 4PY England

With the emergence of the 5G/6G communications, edge computing has attracted increasing research interests in recent years. To provide pervasive 5G/6G edge computing services, numerous edge servers are required for service coverage, and the deployment cost can be >10000 times larger than the deployment cost of the 4 G infrastructure. To address this fundamental limit, we propose ParallEdge, a deployment scheme that employs mobile edge servers for cost-effective service coverage. ParallEdge is designed based on the observation that the processing delay and server moving delay become comparable in many computing-intensive applications in the 5G/6G edge computing. Unlike the traditional "move-then-process" frameworks, ParallEdge follows a "move-while-processing" paradigm, which exploits the parallelism between task processing and server movement to enable resource sharing among more users. Moreover, with the joint optimization of path planning and task scheduling for multiple mobile servers, the deployment cost for service coverage can be further reduced. We analyze the approximation gap of the proposed algorithm, and conduct extensive simulation experiments based on the real-world application data, and the results show that ParallEdge can significantly reduce deployment cost and improve resource utilization compared to the state-of-the-art schemes.

关键词： servers Task analysis Delays Costs Resource management Edge computing Parallel processing 5G/6G Edge computing server deployment path planning task scheduling mobile servers

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An optimal edge server placement approach for cost reduction and load balancing in intelligent manufacturing

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JOURNAL OF SUPERCOMPUTING 2022年第3期78卷 4032-4056页

作者： Wang, Zhongmin Zhang, Weiye Jin, Xiaomin Huang, Yihua Lu, Chen Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Shaanxi Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Network Data Anal & Intelligent P Xian 710121 Shaanxi Peoples R China ZTE Corp 55 Hitech Rd South Shenzhen 518057 Peoples R China

With the development of the intelligent manufacturing, a large amount of data will be generated at the edge of the network. Industrial data have unprecedented requirements for transmission speed and quality. Edge computing makes full use of the computing power of the terminal, which realizes real-time processing of data. As the first step of the edge computing, the deployment of edge servers is the foundation and key. However, unreasonable edge server deployment strategies cannot meet the requirements of data processing in intelligent manufacturing. In this paper, we establish an edge server deployment optimization model to optimize deployment cost and load balance. Reliability is an important point in intelligent manufacturing, so we propose a fault-tolerant server deployment scheme. When the edge server fails, the fault-tolerant server can replace the fault server in time to ensure the normal workflow. To solve the above model, we propose a binary-based gray wolf genetic strategy algorithm, which improves the global optimal solution of the algorithm. Simulations reveal that up to 10.97% of the total load can be saved by using the gray wolf genetic algorithm and an average of 16.15% time savings can be achieved using our proposed method during hours.

关键词： Intelligent manufacturing Edge computing server deployment Load balance Cost reduction

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Edge servers on Wheels: deployment and Route Planning of Mobile servers for Internet of Vehicles 19

Edge Servers on Wheels: Deployment and Route Planning of Mob...

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19th International Conference on Mobility, Sensing and Networking (MSN)

作者： Tang, Zhihai Huang, Aiwen Wang, Yonghua Chang, Le Wang, Tian Guangdong Univ Technol Sch Automat Guangzhou Peoples R China Beijing Normal Univ Inst Artificial Intelligence & Future Networks Zhuhai Peoples R China

ISBN: (纸本)9798350358261;9798350358278

The rapid development of the Internet of Vehicles (IoV) has boosted the prosperity of various on-vehicle applications, e.g., route guidance, speed advisory, and autonomous driving. These applications call for computation offloading due to limited resources of the users, where edge computing becomes a promising solution. However, it remains an open problem how the user dynamics can be handled by deploying mobile edge servers on service vehicles. To this end, this paper proposes a mobile edge computing deployment scheme to fulfill computation offloading using service vehicles. We first build a mathematical model to characterize the deployment cost of multiple cruising routes of the mobile servers and their bases. Next, we design heuristic algorithms to obtain the solutions based on Adelson-Velsky and Landis (AVL)-tree, Gaussian Mixture Model and Simulated Annealing (GMM-SA). The AVL algorithm is used to partition the map into regions and place bases, and the GMM-SA-based algorithm is designed to find the best routes within each region. Experimental results demonstrate that our proposed mobile server deployment scheme achieves the best performance compared with other popular methods in terms of computation resource utilization and travelling distance of the mobile servers.

关键词： Internet of Vehicles (IoV) Mobile edge computing server deployment Route planning

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