Abstract
Edge computing (EC) is a promising paradigm for providing ultra-low latency experience for IoT applications at the network edge, through pre-caching required services in fixed edge nodes. However, the supply-demand mismatch can arise while meeting the peak period of some specific service requests. The mismatch between capacity provision and user demands can be fatal to the delay-sensitive user requests of emerging IoT applications and will be further exacerbated due to the long service provisioning cycle. To tackle this problem, we propose the mobile-assisted edge computing framework to improve the QoS of fixed edge nodes by exploiting mobile edge nodes. Furthermore, we devise a CRI (Credible, Reciprocal, and Incentive) auction mechanism to stimulate mobile edge nodes to participate in the services for user requests. The advantages of our mobile-assisted edge computing framework include higher task completion rate, profit maximization, and computational efficiency. Meanwhile, the theoretical analysis and experimental results guarantee the desirable economic properties of our CRI auction mechanism.
- [n.d.]. Didi Chuxing GAIA Initiative. Retrieved from https://outreach.didichuxing.com/research/opendata/.Google Scholar
- Mahbuba Afrin, Jiong Jin, Ashfaqur Rahman, Yu-Chu Tian, and Ambarish Kulkarni. 2019. Multi-objective resource allocation for edge cloud based robotic workflow in smart factory. Fut. Gen. Comput. Syst. 97 (2019), 119–130.Google ScholarCross Ref
- Arif Ahmed and Guillaume Pierre. 2018. Docker container deployment in fog computing infrastructures. In Proceedings of the IEEE International Conference on Edge Computing. 1–8.Google ScholarCross Ref
- Ramiro Alvarez and Mehrdad Nojoumian. 2020. Comprehensive survey on privacy-preserving protocols for sealed-bid auctions. Comput. Secur. 88 (2020).Google Scholar
- Cosmin Avasalcai, Christos Tsigkanos, and Schahram Dustdar. 2019. Decentralized resource auctioning for latency-sensitive edge computing. In Proceedings of the IEEE International Conference on Edge Computing. 72–76.Google ScholarCross Ref
- Bin Cao, Jiaxing Wang, Jing Fan, Jianwei Yin, and Tianyang Dong. 2017. Querying similar process models based on the Hungarian algorithm. IEEE Trans. Serv. Comput. 10, 1 (2017), 121–135.Google ScholarCross Ref
- Jin Cao, Lei Yang, and Jiannong Cao. 2019. Revisiting computation partitioning in future 5G-based edge computing environments. IEEE Internet Things J. 6, 2 (2019), 2427–2438.Google ScholarCross Ref
- Xuanyu Cao, Junshan Zhang, and H. Vincent Poor. 2018. An optimal auction mechanism for mobile edge caching. In Proceedings of the IEEE International Conference on Distributed Computing Systems. 388–399.Google Scholar
- Lixing Chen and Jie Xu. 2019. Budget-constrained edge service provisioning with demand estimation via bandit learning. IEEE J. Select. Areas Commun. 37, 10 (2019), 2364–2376.Google ScholarCross Ref
- Shutong Chen, Lei Jiao, Lin Wang, and Fangming Liu. 2019. An online market mechanism for edge emergency demand response via cloudlet control. In Proceedings of the IEEE International Conference on Computer Communications. 2566–2574.Google ScholarCross Ref
- Xu Chen, Lei Jiao, Wenzhong Li, and Xiaoming Fu. 2016. Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Netw. 24, 5 (2016), 2795–2808. Google ScholarDigital Library
- Ying Chen, Yongchao Zhang, and Xin Chen. 2018. Dynamic service request scheduling for mobile edge computing systems. Wirel. Commun. Mob. Comput. 2018 (2018), 1324897:1–1324897:10.Google Scholar
- Tung V. Doan, Giang T. Nguyen, Hani Salah, Sreekrishna Pandi, Michael Jarschel, Rastin Pries, and Frank H. P. Fitzek. 2019. Containers vs virtual machines: Choosing the right virtualization technology for mobile edge cloud. In Proceedings of the IEEE 5G World Forum. 46–52.Google Scholar
- Vajiheh Farhadi, Fidan Mehmeti, Ting He, Tom La Porta, Hana Khamfroush, Shiqiang Wang, and Kevin S. Chan. 2019. Service placement and request scheduling for data-intensive applications in edge clouds. In Proceedings of the IEEE International Conference on Computer Communications. 1279–1287.Google Scholar
- Shaoyong Guo, Yao Dai, Song Guo, Xuesong Qiu, and Feng Qi. 2020. Blockchain meets edge computing: Stackelberg game and double auction based task offloading for mobile blockchain. IEEE Trans. Vehic. Technol. 69, 5 (2020), 5549–5561.Google ScholarCross Ref
- Ting He, Hana Khamfroush, Shiqiang Wang, Tom La Porta, and Sebastian Stein. 2018. It’s hard to share: Joint service placement and request scheduling in edge clouds with sharable and non-sharable resources. In Proceedings of the IEEE International Conference on Distributed Computing Systems. 365–375.Google ScholarCross Ref
- Yeongjin Kim, Hyang-Won Lee, and Song Chong. 2019. Mobile computation offloading for application throughput fairness and energy efficiency. IEEE Trans. Wirel. Commun. 18, 1 (2019), 3–19. Google ScholarDigital Library
- L. Liu, S. Lu, R. Zhong, B. Wu, Y. Yao, Q. Zhang, and W. Shi. 2021. Computing systems for autonomous driving: State-of-the-art and challenges. IEEE Internet Things J. 8, 8 (2021), 6469–6486.Google ScholarCross Ref
- Lailong Luo, Deke Guo, Wenxin Li, Tian Zhang, Junjie Xie, and Xiaolei Zhou. 2015. Compound graph based hybrid data center topologies. Front. Comput. Sci. 9, 6 (2015), 860–874. Google ScholarDigital Library
- Jiangbin Lyu, Yong Zeng, Rui Zhang, and Teng Joon Lim. 2017. Placement optimization of UAV-Mounted mobile base stations. IEEE Commun. Lett. 21, 3 (2017), 604–607.Google ScholarCross Ref
- Y. Narahari. 2014. Game Theory and Mechanism Design. World Scientific Pub. Google ScholarDigital Library
- Lingjun Pu, Xu Chen, Jingdong Xu, and Xiaoming Fu. 2016. D2D fogging: An Energy-Efficient and incentive-aware task offloading framework via network-assisted D2D collaboration. IEEE J. Select. Areas Commun. 34, 12 (2016), 3887–3901. Google ScholarDigital Library
- Guanhua Qiao, Supeng Leng, Sabita Maharjan, Yan Zhang, and Nirwan Ansari. 2020. Deep reinforcement learning for cooperative content caching in vehicular edge computing and networks. IEEE Internet Things J. 7, 1 (2020), 247–257.Google ScholarCross Ref
- Y. Qin, D. Guo, X. Lin, and G. Cheng. 2020. Design and optimization of VLC enabled data center network. Tsinghua Sci. Technol.1 (2020). Google ScholarDigital Library
- Ju Ren, Deyu Zhang, Shiwen He, Yaoxue Zhang, and Tao Li. 2020. A survey on End-Edge-Cloud orchestrated network computing paradigms: Transparent computing, mobile edge computing, fog computing, and cloudlet. Comput. Surv. 52, 6 (2020), 125:1–125:36. Google ScholarDigital Library
- Wen Sun, Jiajia Liu, Yanlin Yue, and Peng Wang. 2020. Joint resource allocation and incentive design for blockchain-based mobile edge computing. IEEE Trans. Wirel. Commun. 19, 9 (2020), 6050–6064.Google ScholarCross Ref
- Wen Sun, Jiajia Liu, Yanlin Yue, and Haibin Zhang. 2018. Double auction-based resource allocation for mobile edge computing in industrial internet of things. IEEE Trans. Industr. Inform. 14, 10 (2018), 4692–4701.Google ScholarCross Ref
- Klervie Toczé and Simin Nadjm-Tehrani. 2019. ORCH: Distributed orchestration framework using mobile edge devices. In Proceedings of the IEEE International Conference on Fog and Edge Computing. 1–10.Google ScholarCross Ref
- Shaohua Wan, Zonghua Gu, and Qiang Ni. 2020. Cognitive computing and wireless communications on the edge for healthcare service robots. Comput. Commun. 149 (2020), 99–106.Google ScholarDigital Library
- Lin Wang, Qingyang Zhang, Youhuizi Li, Hong Zhong, and Weisong Shi. 2019. MobileEdge: Enhancing On-Board vehicle computing units using mobile edges for CAVs. In Proceedings of the 25th IEEE International Conference on Parallel and Distributed Systems (ICPADS’19). 470–479.Google ScholarCross Ref
- Yifan Wang, Shaoshan Liu, Xiaopei Wu, and Weisong Shi. 2018. CAVBench: A benchmark suite for connected and autonomous vehicles. In Proceedings of the IEEE/ACM Symposium on Edge Computing. 30–42.Google ScholarCross Ref
- Dapeng Wu, Hang Shi, Honggang Wang, Ruyan Wang, and Hua Fang. 2019. A Feature-based learning system for internet of things applications. IEEE Internet Things J. 6, 2 (2019), 1928–1937.Google ScholarCross Ref
- Junxu Xia, Geyao Cheng, Siyuan Gu, and Deke Guo. 2020. Secure and trust-oriented edge storage for internet of things. IEEE Internet Things J. 7, 5 (2020), 4049–4060.Google ScholarCross Ref
- Junjie Xie, Chen Qian, Deke Guo, Xin Li, Shouqian Shi, and Honghui Chen. 2019. Efficient data placement and retrieval services in edge computing. In Proceedings of the 39th IEEE International Conference on Distributed Computing Systems (ICDCS’19). 1029–1039.Google ScholarCross Ref
- Yanlin Yue, Wen Sun, and Jiajia Liu. 2019. Multi-task cross-server double auction for resource allocation in mobile edge computing. In Proceedings of the IEEE International Conference on Communications. 1–6.Google ScholarCross Ref
- Qingyang Zhang, Yifan Wang, Xingzhou Zhang, Liangkai Liu, Xiaopei Wu, Weisong Shi, and Hong Zhong. 2018. OpenVDAP: An open vehicular data analytics platform for CAVs. In Proceedings of the IEEE International Conference on Distributed Computing Systems. 1310–1320.Google ScholarCross Ref
- Yuan Zhang, Lei Jiao, Jinyao Yan, and Xiaojun Lin. 2019. Dynamic service placement for virtual reality group gaming on mobile edge cloudlets. IEEE J. Select. Areas Commun. 37, 8 (2019), 1881–1897.Google ScholarDigital Library
- Yao Zhang, Changle Li, Tom Hao Luan4, Yuchuan Fu, Weisong Shi, and Lina Zhu. 2019. A Mobility-aware vehicular caching scheme in content centric networks: Model and optimization. IEEE Trans. Vehic. Technol. 68, 4 (2019), 3100–3112.Google ScholarCross Ref
- Yang Zhang, Qingyu Yang, Wei Yu, Dou An, Donghe Li, and Wei Zhao. 2019. An online continuous progressive second price auction for electric vehicle charging. IEEE Internet Things J. 6, 2 (2019), 2907–2921.Google ScholarCross Ref
- Haibin Zhu, Dongning Liu, Siqin Zhang, Yu Zhu, Luyao Teng, and Shaohua Teng. 2016. Solving the many to many assignment problem by improving the Kuhn-Munkres algorithm with backtracking. Theoret. Comput. Sci. 618 (2016), 30–41. Google ScholarDigital Library
- Shichao Zhu, Lin Gui, Nan Cheng, Fei Sun, and Qi Zhang. 2020. Joint design of access point selection and path planning for UAV-assisted cellular networks. IEEE Internet Things J. 7, 1 (2020), 220–233.Google ScholarCross Ref
- Ivan D. Zyrianoff, Alexandre Heideker, Dener Silva, João H. Kleinschmidt, Juha-Pekka Soininen, Tullio Salmon Cinotti, and Carlos Kamienski. 2020. Architecting and deploying IoT smart applications: A performance-oriented approach. Sensors 20, 1 (2020), 84.Google ScholarCross Ref
Index Terms
- A Mobile-assisted Edge Computing Framework for Emerging IoT Applications
Recommendations
Correcting vindictive bidding behaviors in sponsored search auctions
In this study, we aim to develop a pricing mechanism that reduces the effects resulted by vindictive advertisers who bid on sponsored search auctions run by search engine providers. In particular, we aim to ensure payment fairness and price stability in ...
Competing sellers in online markets: reserve prices, shill bidding, and auction fees
AAMAS '06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systemsIn this paper, we consider competition between sellers offering similar items in concurrent online auctions, where each seller must set its individual auction parameters (such as the reserve price) in such a way as to attract buyers. We show that there ...
Prior-free auctions for budgeted agents
EC '13: Proceedings of the fourteenth ACM conference on Electronic commerceWe consider prior-free auctions for revenue and welfare maximization when agents have a common budget. The abstract environments we consider are ones where there is a downward-closed and symmetric feasibility constraint on the probabilities of service ...
Comments