EUA Datasets

本仓库维护一组从真实世界数据源收集的EUA数据集，公开发布以促进边缘计算研究。数据集包括边缘服务器位置和用户位置数据。所有数据位于澳大利亚地区。

This repository maintains a collection of EUA datasets gathered from real-world data sources, publicly released to facilitate research in edge computing. The datasets include edge server locations and user location data, all of which are situated within the Australian region.

原始信息汇总

数据集概述

数据集名称

• EUA Datasets

数据集内容

• edge-servers 文件夹：包含边缘服务器位置的数据集。
• users 文件夹：包含用户位置的数据集。

数据集来源

• 数据集来源于澳大利亚地区的真实世界数据源。

数据集用途

• 该数据集旨在促进边缘计算领域的研究。

数据集相关出版物

1. Guangming Cui, Qiang He, Xiaoyu Xia, Feifei Chen, Fang Dong, Hai Jin, Yun Yang, OL-EUA: Online User Allocation for NOMA-based Mobile Edge Computing, IEEE Transactions on Mobile Computing (TMC, CORE A*, CCF A), 2021. DOI: 10.1109/TMC.2021.3112941
2. Guangming Cui, Qiang He, Feifei Chen, Hai Jin, Yang Xiang, Yun Yang, Location Privacy Protection via Delocalization in 5G Mobile Edge Computing Environment, IEEE Transactions on Services Computing (TSC, CORE A*, CCF B), 2021. DOI: 10.1109/TSC.2021.3112659
3. Guangming Cui, Qiang He*, Xiaoyu Xia, Feifei Chen, Tao Gu, Hai Jin and Yun Yang, Demand Response in NOMA-based Mobile Edge Computing: A Two-phase Game-theoretical Approach, IEEE Transactions on Mobile Computing (TMC, CORE A*, CCF A), 2021. DOI: 10.1109/TMC.2021.3108581
4. Xiaoyu Xia, Feifei Chen, Qiang He*, Guangming Cui, John Grundy, Mohamed Abdelrazek, Athman Bouguettaya, Hai Jin, OL-MEDC: An Online Approach for Cost-effective Data Caching in Mobile Edge Computing Systems, IEEE Transactions on Mobile Computing (TMC, CORE A*, CCF A), 2021. DOI: 10.1109/TMC.2021.3107918
5. Xiaoyu Xia, Feifei Chen, Qiang He*, Guangming Cui, John Grundy, Mohamed, Abdelrazek, Xiaolong Xu, Hai Jin, Data, User and Power Allocations for Caching in Multi-Access Edge Computing, IEEE Transactions on Parallel and Distributed Systems (TPDS, CORE A*, CCF A), 2021. DOI: 10.1109/TPDS.2021.3104241
6. Xiaoyu Xia, Feifei Chen, Qiang He, John Grundy, Mohamed Abdelrazek, Hai Jin, Online Collaborative Data Caching in Edge Computing, IEEE Transactions on Parallel and Distributed Systems (TPDS, CORE A*, CCF A, 中科院1区, Q1), DOI: 10.1109/TPDS.2020.3016344, 2020.
7. Bo Li, Qiang He*, Guangming Cui, Xiaoyu Xia, Feifei Chen, Hai Jin and Yun Yang, READ: Robustness-oriented Edge Application Deployment in Edge Computing Environment, IEEE Transactions on Services Computing (TSC, CORE A*, CCF B, 中科院1区, Q1), DOI: 10.1109/TSC.2020.3015316, 2020.
8. Xiaoyu Xia, Feifei Chen, Qiang He*, John Grundy, Mohamed Abdelrazek, Hai Jin, Cost-Effective App Data Distribution in Edge Computing, IEEE Transactions on Parallel and Distributed Systems (TPDS, CORE A*, CCF A, 中科院1区, Q1), Vol. 32(1), pp. 31-44, DOI: 10.1109/TPDS.2020.3010521, 2020.
9. Xiaoyu Xia, Feifei Chen, Qiang He*, Guangming Cui, Phu Lai, Mohamed Abdelrazek, John Grundy, Hai Jin, Graph-based Data Caching Optimization for Edge Computing, Future Generation Computer Systems (FGCS, CORE A, CCF C, 中科院2区, Q1), Vol. 133, pp. 228-239, 2020. DOI: 10.1016/j.future.2020.07.016
10. Guangming Cui, Qiang He*, Feifei Chen, Hai Jin, Yun Yang, Trading off between User Coverage and Network Robustness for Edge Server Placement, IEEE Transactions on Cloud Computing (TCC, 中科院1区, Q1), DOI: 10.1109/TCC.2020.3008440, 2020.
11. Phu Lai, Qiang He*, John Grundy, Feifei Chen, Mohamed Abdelrazek, John Hosking, Yun Yang, Cost-Effective App User Allocation in an Edge Computing Environment, IEEE Transactions on Cloud Computing (TCC, 中科院1区, Q1), DOI: 10.1109/TCC.2020.3001570, 2020.
12. Phu Lai, Qiang He*, Guangming Cui, Xiaoyu Xia, Mohamed Abdelrazek, Feifei Chen, John Hosking, John Grundy, Yun Yang, QoE-aware User Allocation in Edge Computing Systems with Dynamic QoS, Future Generation Computer Systems (FGCS, CORE A, CCF C, 中科院2区, Q1), Vol 112, pp. 684-694, 2020. DOI: 10.1016/j.future.2020.06.029
13. Guangming Cui, Qiang He*, Xiaoyu Xia, Phu Lai, Feifei Chen, Tao Gu, Yun Yang, Interference-aware SaaS User Allocation Game for Edge Computing, IEEE Transactions on Cloud Computing (TCC, 中科院1区, Q1), DOI: 10.1109/TCC.2020.3008440, 2020.
14. Qinglan Peng, Yunni Xia, Yan Wang, Chunrong Wu, Jia Lee, A Decentralized Collaborative Approach to Online Edge User Allocation in Edge Computing Environments, 18th International Conference on Service-Oriented Computing (ICSOC2020, CORE A, CCF B), Dubai, UAE, 2020.
15. Guobing Zou, Ya Liu, Zhen Qin, Jin Chen, Zhiwei Xu, Yanglan Gan, Bofeng Zhang, Qiang He, TD-EUA: Task-decomposable Edge User Allocation with QoE Optimization, 18th International Conference on Service-Oriented Computing (ICSOC2020, CORE A, CCF B), Dubai, UAE, 2020.
16. Wei Du, Xiran Zhang, Qiang He, Wei Liu, Guangming Cui, Feifei Chen, Yuan Ji, Chenran Cai, Yanchao Yang, Fault-tolerating Edge Computing with Server Redundancy based on a Variant of Group Degree Centrality, 18th International Conference on Service-Oriented Computing (ICSOC2020, CORE A, CCF B), Dubai, UAE, 2020.
17. Zhiwei Xu, Guobing Zou, Xiaoyu Xia, Ya Liu, Yanglan Gan, Bofeng Zhang, Qiang He*, Distance-aware Edge User Allocation with QoE Optimization, 27th IEEE International on Web Services (ICWS2020 CORE A, CCF B), Beijing, China, 2020.
18. Xiaoyu Xia, Feifei Chen, Guangming Cui, Mohamed Abdelrazek, John Grundy, Hai Jin, Qiang He*, Budgeted Data Caching based on k-Median in Mobile Edge Computing, 27th IEEE International on Web Services (ICWS2020 CORE A, CCF B), Beijing, China, 2020.
19. Ying Liu, Ao Zhang, Xiaoyu Xia, Feifei Chen, Bin Zhang and Qiang He*, Proactive Data Cache and Replacement in the Edge Computing Environment, 13th IEEE Conference on Cloud Computing (CLOUD2020, CORE B, CCF C), Beijing, China, 2020.
20. Feifei Chen, Jingwen Zhou, Xiaoyu Xia, Hai Jin and Qiang He*, Optimal Application Deployment in Mobile Edge Computing Environment, 13th IEEE Conference on Cloud Computing (CLOUD2020, CORE B, CCF C), Beijing, China, 2020.
21. Phu Lai, Qiang He*, Guangming Cui, Feifei Chen, Mohamed Abdelrazek, John Grundy, John Hosking and Yun Yang, Quality of Experience-Aware User Allocation in Edge Computing Systems: A Potential Game, 40th IEEE International Conference on Distributed Computing Systems (ICDCS2020, CORE A, CCF B), Singapore, 2020.
22. Guangming Cui, Qiang He*, Xiaoyu Xia, Feifei Chen, Hai Jin and Yun Yang, Robustness-oriented k Edge Server Placement, 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid2020, CORE A, CCF C), Melbourne, Australia, 2020. DOI: 10.1109/CCGrid49817.2020.00-85
23. Qiang He, Guangming Cui, Xuyun Zhang, Feifei Chen, Shuiguang Deng, Hai Jin, Yun Yang, A Game-Theoretical Approach for User Allocation in Edge Computing Environment, IEEE Transactions on Parallel and Distributed Systems (TPDS, CORE A*), accepted in August 2019.
24. Phu Lai, Qiang He, Guangming Cui, Xiaoyu Xia, Mohamed Abdelrazek, Feifei Chen, John Hosking, John Grundy, and Yun Yang, Edge User Allocation with Dynamic Quality of Service, 17th International Conference on Service-Oriented Computing (ICSOC2019, CORE A), Toulouse, France, 2019.
25. Xiaoyu Xia, Feifei Chen, Qiang He, Guangming Cui, Phu Lai, Mohamed Abdelrazek, John Grundy, and Hai Jin, Graph-based Optimal Data Caching in Edge Computing, 17th International Conference on Service-Oriented Computing (ICSOC2019, CORE A), Toulouse, France, 2019.
26. Qinglan Peng, Yunni Xia, Zeng Feng, Jia Lee, Chunrong Wu, Xin Luo, Wanbo Zheng, Hui Liu, Yidan Qin, Peng Chen, Mobility-Aware and Migration-Enabled Online Edge User Allocation in Mobile Edge Computing, 26th IEEE International Conference on Web Services (ICWS2019, CORE A), Milan, Italy, 2019.
27. Hailiang Zhao, Cheng Zhang, Wei Du, Qiang He and Shuiguang Deng, A Mobility-Aware Cross-edge Computation Offloading Framework for Partitionable Applications, 26th IEEE International Conference on Web Services (ICWS2019, CORE A), Milan, Italy, 2019.
28. Wei Du, Qiwang Lei, Qiang He, Wei Liu, Feifei Chen, Lei Pan, Tao Lei and Hailiang Zhao, Multiple Energy Harvesting Devices Enabled Joint Computation Offloading and Dynamic Resource Allocation for Mobile-Edge Computing Systems, 26th IEEE International Conference on Web Services (ICWS2019, CORE A), Milan, Italy, 2019.
29. Ying Liu, Qiang He, Dequan Zheng and Bin Zhang, Data Caching Optimization in the Edge Computing Environment, 26th IEEE International Conference on Web Services (ICWS2019, CORE A), Milan, Italy, 2019.
30. Phu Lai, Qiang He, Mohamed Abdelrazek, Feifei Chen, John Hosking, John Grundy, and Yun Yang, Optimal Edge User Allocation in Edge Computing with Variable Sized Vector Bin Packing, 16th International Conference on Service-Oriented Computing (ICSOC2018, CORE A), pp. 230-245, Hangzhou, China, 2018.

搜集汇总

数据集介绍

构建方式

EUA Datasets的构建基于澳大利亚地区的真实数据源，涵盖了边缘服务器和用户的地理位置信息。数据集的收集过程涉及从澳大利亚通信和媒体管理局获取的无线电基站数据，以及通过IP-API将IP地址转换为地理坐标。这些数据经过整合和处理，形成了包含边缘服务器覆盖范围和用户分布的详细数据集，旨在为边缘计算领域的研究提供支持。

特点

EUA Datasets的显著特点在于其真实性和地域针对性，所有数据均来源于澳大利亚地区，确保了数据的准确性和实用性。此外，数据集结构清晰，分为边缘服务器和用户两个主要文件夹，便于研究人员快速定位和分析所需信息。通过Python和Google Maps API生成的可视化地图，进一步增强了数据集的可解释性和应用价值。

使用方法

研究人员可以通过访问EUA Datasets的GitHub仓库下载所需数据，数据集以文件夹形式组织，分别包含边缘服务器和用户的位置信息。使用时，建议参考提供的示例地图和相关文献，以更好地理解数据结构和应用场景。为确保学术诚信，使用该数据集的研究成果应引用相关文献，具体引用信息可在数据集的README文件中找到。

背景与挑战

背景概述

EUA Datasets，由澳大利亚地区的研究人员和机构共同创建，旨在推动边缘计算领域的研究。该数据集的核心研究问题集中在边缘服务器与用户位置的优化分配，以提升边缘计算环境中的服务质量和效率。自2018年以来，主要研究人员如Qiang He、Feifei Chen等，通过多次国际会议和期刊发表相关研究成果，显著推动了边缘计算领域的发展。这些研究不仅在理论上提出了多种优化算法，还在实际应用中验证了其有效性，对边缘计算技术的实际部署和应用产生了深远影响。

当前挑战

尽管EUA Datasets在边缘计算领域取得了显著进展，但仍面临若干挑战。首先，数据集的构建过程中，如何确保边缘服务器和用户位置数据的准确性和实时性是一个重大挑战。其次，随着边缘计算环境的动态变化，如何实现用户与服务器的动态分配，以适应不断变化的需求和网络条件，是当前研究的一个难点。此外，数据集的隐私保护和安全性问题也不容忽视，如何在保证数据可用性的同时，确保用户隐私不被侵犯，是未来研究的重要方向。

常用场景

经典使用场景

在边缘计算领域，EUA Datasets数据集的经典使用场景主要集中在边缘服务器与用户位置的优化分配上。该数据集通过提供澳大利亚地区的边缘服务器位置和用户分布数据，支持研究者进行边缘计算环境下的用户分配、资源优化和服务质量提升等研究。例如，研究者可以利用这些数据进行边缘服务器的部署策略优化，以实现更高效的资源利用和更低的延迟。

解决学术问题

EUA Datasets数据集解决了边缘计算领域中多个关键的学术研究问题。首先，它为研究者提供了真实世界的数据，使得边缘计算环境下的用户分配和资源管理研究更加贴近实际应用。其次，该数据集支持研究者探索边缘计算中的动态资源分配、服务质量保障以及网络鲁棒性等问题，推动了边缘计算理论与实践的结合。通过这些研究，不仅提升了边缘计算系统的性能，还为未来的智能城市和物联网应用提供了技术支持。

衍生相关工作

基于EUA Datasets数据集，研究者们开展了一系列经典工作。例如，Guangming Cui等人提出了OL-EUA算法，用于基于NOMA的移动边缘计算中的在线用户分配，显著提升了系统的资源利用率和用户服务质量。此外，Qiang He等人通过游戏理论方法研究了边缘计算环境中的用户分配问题，为动态资源管理提供了新的思路。这些研究不仅丰富了边缘计算的理论体系，还为实际应用提供了有力的技术支持。