Redowan Mahmud
Skip Abstract Section
Abstract
The Internet of Things (IoT) paradigm is being rapidly adopted for the creation of smart environments in various domains. The IoT-enabled cyber-physical systems associated with smart city, healthcare, Industry 4.0 and Agtech handle a huge volume of data and require data processing services from different types of applications in real time. The Cloud-centric execution of IoT applications barely meets such requirements as the Cloud datacentres reside at a multi-hop distance from the IoT devices. Fog computing, an extension of Cloud at the edge network, can execute these applications closer to data sources. Thus, Fog computing can improve application service delivery time and resist network congestion. However, the Fog nodes are highly distributed and heterogeneous, and most of them are constrained in resources and spatial sharing. Therefore, efficient management of applications is necessary to fully exploit the capabilities of Fog nodes. In this work, we investigate the existing application management strategies in Fog computing and review them in terms of architecture, placement and maintenance. Additionally, we propose a comprehensive taxonomy and highlight the research gaps in Fog-based application management. We also discuss a perspective model and provide future research directions for further improvement of application management in Fog computing.
- M. Aazam and E. Huh. 2015. Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT. In Proceedings of the 2015 IEEE 29th International Conference on Advanced Information Networking and Applications. 687--694.Google Scholar
- Mohammad Aazam, Sherali Zeadally, and Khaled A. Harras. 2018. Offloading in fog computing for IoT: Review, enabling technologies, and research opportunities. Future Generation Computer Systems 87 (2018), 278--289.Google ScholarDigital Library
- Sadam Hussain Abbasi, Nadeem Javaid, Muhammad Hassaan Ashraf, Mubashar Mehmood, Maria Naeem, and Mubariz Rehman. 2019. Load stabilizing in fog computing environment using load balancing algorithm. In Advances on Broadband and Wireless Computing, Communication and Applications. Springer International Publishing, Cham, Switzerland, 737--750.Google Scholar
- Eman AbdElhalim, Marwa Obayya, and Sherif Kishk. 2019. Distributed fog-to-cloud computing system: A minority game approach. Concurrency and Computation: Practice and Experience 31, 15 (2019), e5162.Google ScholarCross Ref
- Mainak Adhikari and Hemant Gianey. 2019. Energy efficient offloading strategy in fog-cloud environment for IoT applications. Internet of Things 6 (2019), 100053.Google ScholarCross Ref
- M. Adhikari, M. Mukherjee, and S. N. Srirama. 2019. DPTO: A deadline and priority-aware task offloading in fog computing framework leveraging multi-level feedback queueing. IEEE Internet of Things Journal. Accepted. DOI:https://doi.org/10.1109/JIOT.2019.2946426Google Scholar
- Mahbuba Afrin, Jiong Jin, and Ashfaqur Rahman. 2018. Energy-delay co-optimization of resource allocation for robotic services in cloudlet infrastructure. In Proceedings of the International Conference on Service-Oriented Computing. 295--303.Google ScholarDigital Library
- 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. Future Generation Computer Systems 97 (2019), 119--130.Google ScholarDigital Library
- M. Afrin, M. R. Mahmud, and M. A. Razzaque. 2015. Real time detection of speed breakers and warning system for on-road drivers. In Proceedings of the 2015 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE’15). 495--498.Google Scholar
- Mahbuba Afrin, Md Razzaque, Iffat Anjum, Mohammad Mehedi Hassan, and Atif Alamri. 2017. Tradeoff between user quality-of-experience and service provider profit in 5G cloud radio access network. Sustainability 9, 11 (2017), 2127.Google ScholarCross Ref
- Surin Ahn, Maria Gorlatova, Parinaz Naghizadeh, Mung Chiang, and Prateek Mittal. 2018. Adaptive fog-based output security for augmented reality. In Proceedings of the 2018 Morning Workshop on Virtual Reality and Augmented Reality Network (VR/AR Network’18). ACM, New York, NY, 1--6.Google ScholarDigital Library
- M. Al-Khafajiy, T. Baker, H. Al-Libawy, Z. Maamar, M. Aloqaily, and Y. Jararweh. 2019. Improving fog computing performance via fog-2-fog collaboration. Future Generation Computer Systems 100 (2019), 260--280.Google ScholarCross Ref
- M. Ali, N. Riaz, M. I. Ashraf, S. Qaisar, and M. Naeem. 2018. Joint cloudlet selection and latency minimization in fog networks. IEEE Transactions on Industrial Informatics 14, 9 (Sept. 2018), 4055--4063.Google ScholarCross Ref
- Adam A. Alli and Muhammad Mahbub Alam. 2019. SecOFF-FCIoT: Machine learning based secure offloading in fog-cloud of things for smart city applications. Internet of Things 7 (2019), 100070.Google ScholarCross Ref
- A. Alnoman and A. Anpalagan. 2018. A dynamic priority service provision scheme for delay-sensitive applications in fog computing. In Proceedings of the 2018 29th Biennial Symposium on Communications (BSC’18). IEEE, Los Alamitos, CA, 1--5.Google Scholar
- A. Alrawais, A. Alhothaily, C. Hu, X. Xing, and X. Cheng. 2017. An attribute-based encryption scheme to secure fog communications. IEEE Access 5 (2017), 9131--9138.Google ScholarCross Ref
- Cosimo Anglano, Massimo Canonico, Paolo Castagno, Marco Guazzone, and Matteo Sereno. 2019b. Profit-aware coalition formation in fog computing providers: A game-theoretic approach. Concurrency and Computation: Practice and Experience. In Press.Google Scholar
- Cosimo Anglano, Massimo Canonico, and Marco Guazzone. 2019a. Online user-driven task scheduling for FemtoClouds. In Proceedings of the 2019 4th International Conference on Fog and Mobile Edge Computing (FMEC’19). 5--12.Google ScholarCross Ref
- Arman Anzanpour, Humayun Rashid, Amir M. Rahmani, Axel Jantsch, Nikil Dutt, and Pasi Liljeberg. 2019. Energy-efficient and reliable wearable Internet-of-Things through fog-assisted dynamic goal management. Procedia Computer Science 151 (2019), 493--500.Google ScholarDigital Library
- H. R. Arkian, A. Diyanat, and A. Pourkhalili. 2017. MIST: Fog-based data analytics scheme with cost-efficient resource provisioning for IoT crowdsensing applications. Journal of Network and Computer Applications 82 (2017), 152--165.Google ScholarDigital Library
- Deeksha Arya and Mayank Dave. 2017. Priority based service broker policy for fog computing environment. In Advanced Informatics for Computing Research. Springer Singapore, Singapore, 84--93.Google Scholar
- N. Auluck, A. Azim, and K. Fizza. 2019. Improving the schedulability of real-time tasks using fog computing. IEEE Transactions on Services Computing. Early Access. September 27, 2019. DOI:https://doi.org/10.1109/TSC.2019.2944360Google Scholar
- M. Avgeris, D. Dechouniotis, N. Athanasopoulos, and S. Papavassiliou. 2019. Adaptive resource allocation for computation offloading: A control-theoretic approach. ACM Transactions on Internet Technology 19, 2 (April 2019), Article 23, 20 pages.Google ScholarDigital Library
- E. Baccarelli, P. G. V. Naranjo, M. Scarpiniti, M. Shojafar, and J. H. Abawajy. 2017. Fog of everything: Energy-efficient networked computing architectures, research challenges, and a case study. IEEE Access 5 (2017), 9882--9910.Google ScholarCross Ref
- R. K. Barik, A. C. Dubey, A. Tripathi, T. Pratik, S. Sasane, R. K. Lenka, H. Dubey, K. Mankodiya, and V. Kumar. 2018. Mist data: Leveraging mist computing for secure and scalable architecture for smart and connected health. Procedia Computer Science 125 (2018), 647--653.Google ScholarCross Ref
- Sudheer Kumar Battula, Saurabh Garg, Ranesh Kumar Naha, Parimala Thulasiraman, and Ruppa Thulasiram. 2019. A micro-level compensation-based cost model for resource allocation in a fog environment. Sensors 19, 13 (2019), 2954.Google ScholarCross Ref
- Ranjit Kumar Behera, K. Hemant Kumar Reddy, and Diptendu Sinha Roy. 2020. A novel context migration model for fog-enabled cross-vertical IoT applications. In Proceedings of the International Conference on Innovative Computing and Communications. 287--295.Google Scholar
- Paolo Bellavista, Javier Berrocal, Antonio Corradi, Sajal K. Das, Luca Foschini, and Alessandro Zanni. 2019. A survey on fog computing for the Internet of Things. Pervasive and Mobile Computing 52 (2019), 71--99.Google ScholarCross Ref
- Paolo Bellavista, Antonio Corradi, Luca Foschini, and Domenico Scotece. 2019. Differentiated service/data migration for edge services leveraging container characteristics. IEEE Access 7 (2019), 139746--139758.Google ScholarCross Ref
- L. Belli, S. Cirani, L. Davoli, A. Gorrieri, M. Mancin, M. Picone, and G. Ferrari. 2015. Design and deployment of an IoT application-oriented testbed. Computer 48, 9 (Sept. 2015), 32--40.Google ScholarDigital Library
- Amira Rayane Benamer, Hana Teyeb, and Nejib Ben Hadj-Alouane. 2018. Latency-aware placement heuristic in fog computing environment. In On the Move to Meaningful Internet Systems. OTM 2018. Lecture Notes in Computer Science, Vol. 11230. Springer, 241--257.Google Scholar
- M. A. Benblidia, B. Brik, L. Merghem-Boulahia, and M. Esseghir. 2019. Ranking fog nodes for tasks scheduling in fog-cloud environments: A fuzzy logic approach. In Proceedings of the 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC’19). 1451--1457.Google Scholar
- Munish Bhatia, Sandeep K. Sood, and Simranpreet Kaur. 2019. Quantum-based predictive fog scheduler for IoT applications. Computers in Industry 111 (2019), 51--67.Google ScholarCross Ref
- Huynh Thi Thanh Binh, Tran The Anh, Do Bao Son, Pham Anh Duc, and Binh Minh Nguyen. 2018. An evolutionary algorithm for solving task scheduling problem in cloud-fog computing environment. In Proceedings of the 9th International Symposium on Information and Communication Technology (SoICT’18). ACM, New York, NY, 397--404.Google Scholar
- F. Bonomi, R. Milito, J. Zhu, and S. Addepalli. 2012. Fog computing and its role in the Internet of Things. In Proceedings of the 1st Edition of the MCC Workshop on Mobile Cloud Computing. ACM, New York, NY, 13--16.Google Scholar
- A. Brogi and S. Forti. 2017. QoS-aware deployment of IoT applications through the fog. IEEE Internet of Things Journal 4, 5 (Oct. 2017), 1185--1192.Google ScholarCross Ref
- Rajkumar Buyya, Chee Shin Yeo, Srikumar Venugopal, James Broberg, and Ivona Brandic. 2009. Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems 25, 6 (2009), 599--616.Google ScholarDigital Library
- Danilo Charântola, Alexandre C. Mestre, Rafael Zane, and Luiz F. Bittencourt. 2019. Component-based scheduling for fog computing. In Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC’19 Companion). ACM, New York, NY, 3--8.Google Scholar
- Min Chen, Wei Li, Giancarlo Fortino, Yixue Hao, Long Hu, and Iztok Humar. 2019. A dynamic service migration mechanism in edge cognitive computing. ACM Transactions on Internet Technology 19, 2 (April 2019), Article 30, 15 pages.Google ScholarDigital Library
- B. Cheng, G. Solmaz, F. Cirillo, E. Kovacs, K. Terasawa, and A. Kitazawa. 2018. FogFlow: Easy programming of IoT services over cloud and edges for smart cities. IEEE Internet of Things Journal 5, 2 (April 2018), 696--707.Google ScholarCross Ref
- Francesco Chiti, Romano Fantacci, and Benedetta Picano. 2019. A matching game for tasks offloading in integrated edge-fog computing systems. Transactions on Emerging Telecommunications Technologies 31, 2 (2019), e3718.Google Scholar
- Tejaswini Choudhari, Melody Moh, and Teng-Sheng Moh. 2018. Prioritized task scheduling in fog computing. In Proceedings of the ACMSE 2018 Conference (ACMSE’18). ACM, New York, NY, Article 22, 8 pages.Google ScholarDigital Library
- Abdullahi Chowdhury, Gour Karmakar, and Joarder Kamruzzaman. 2019. The co-evolution of cloud and IoT applications: Recent and future trends. In Handbook of Research on the IoT, Cloud Computing, and Wireless Network Optimization. IGI Global, 213--234.Google Scholar
- Federico Concone, Giuseppe Lo Re, and Marco Morana. 2019. A fog-based application for human activity recognition using personal smart devices. ACM Transactions on Internet Technology 19, 2 (March 2019), Article 20, 20 pages.Google ScholarDigital Library
- G. Cristescu, R. Dobrescu, O. Chenaru, and G. Florea. 2019. DEW: A new edge computing component for distributed dynamic networks. In Proceedings of the 2019 22nd International Conference on Control Systems and Computer Science (CSCS’19). 547--551.Google Scholar
- L. Dang, M. Dong, K. Ota, J. Wu, J. Li, and G. Li. 2018. Resource-efficient secure data sharing for information centric e-health system using fog computing. In Proceedings of the 2018 IEEE International Conference on Communications (ICC’18). 1--6.Google Scholar
- A. V. Dastjerdi, H. Gupta, R. N. Calheiros, S. K. Ghosh, and R. Buyya. 2016. Fog computing: Principles, architectures, and applications. In Internet of Things: Principles and Paradigms. Morgan Kaufmann, 61--75. DOI:https://doi.org/10.1016/B978-0-12-805395-9.00004-6Google Scholar
- Jean Lucas de Souza Toniolli and Brigitte Jaumard. 2019. Resource allocation for multiple workflows in cloud-fog computing systems. In Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC’19 Companion). ACM, New York, NY, 77--84.Google ScholarDigital Library
- S. Dehnavi, H. R. Faragardi, M. Kargahi, and T. Fahringer. 2019. A reliability-aware resource provisioning scheme for real-time industrial applications in a Fog-integrated smart factory. Microprocessors and Microsystems 70 (2019), 1--14.Google ScholarCross Ref
- R. Deng, R. Lu, C. Lai, T. H. Luan, and H. Liang. 2016. Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet of Things Journal 3, 6 (Dec. 2016), 1171--1181.Google Scholar
- Ruimiao Ding, Xuejun Li, Xiao Liu, and Jia Xu. 2019. A cost-effective time-constrained multi-workflow scheduling strategy in fog computing. In Service-Oriented Computing—ICSOC 2018 Workshops. Lecture Notes in Computer Science, Vol. 11434. Springer, 194--207.Google ScholarCross Ref
- T. Djemai, P. Stolf, T. Monteil, and J. Pierson. 2019. A discrete particle swarm optimization approach for energy-efficient IoT services placement over fog infrastructures. In Proceedings of the 2019 18th International Symposium on Parallel and Distributed Computing (ISPDC’19). 32--40.Google Scholar
- J. Du, L. Zhao, J. Feng, and X. Chu. 2018. Computation offloading and resource allocation in mixed fog/cloud computing systems with min-max fairness guarantee. IEEE Transactions on Communications 66, 4 (April 2018), 1594--1608.Google ScholarCross Ref
- Mohammed S. Elbamby, Mehdi Bennis, Walid Saad, Matti Latva-Aho, and Choong Seon Hong. 2018. Proactive edge computing in fog networks with latency and reliability guarantees. EURASIP Journal on Wireless Communications and Networking 2018, 1 (Aug. 2018), 209.Google ScholarCross Ref
- Olamilekan Fadahunsi and Muthucumaru Maheswaran. 2019. Locality sensitive request distribution for fog and cloud servers. Service Oriented Computing and Applications 13, 2 (June 2019), 127--140.Google ScholarDigital Library
- A. J. Fahs and G. Pierre. 2019. Proximity-aware traffic routing in distributed fog computing platforms. In Proceedings of the 2019 19th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (CCGRID’19). 478--487.Google Scholar
- J. Fan, X. Wei, T. Wang, T. Lan, and S. Subramaniam. 2017. Deadline-aware task scheduling in a tiered IoT infrastructure. In Proceedings of the 2017 IEEE Global Communications Conference (GLOBECOM’17). 1--7.Google Scholar
- Weidong Fang, Wuxiong Zhang, Wei Chen, Yang Liu, and Chaogang Tang. 2019. TMSRS: Trust management-based secure routing scheme in industrial wireless sensor network with fog computing. Wireless Networks 26 (Sept. 2019), 3169--3182.Google Scholar
- Peter Farhat, Hani Sami, and Azzam Mourad. 2019. Reinforcement R-learning model for time scheduling of on-demand fog placement. Journal of Supercomputing 76 (Oct. 2019), 388--410.Google Scholar
- C. Fiandrino, N. Allio, D. Kliazovich, P. Giaccone, and P. Bouvry. 2019. Profiling performance of application partitioning for wearable devices in mobile cloud and fog computing. IEEE Access 7 (2019), 12156--12166.Google ScholarCross Ref
- Sonja Filiposka, Anastas Mishev, and Katja Gilly. 2019. Mobile-aware dynamic resource management for edge computing. Transactions on Emerging Telecommunications Technologies 30, 6 (2019), e3626.Google ScholarDigital Library
- K. Fizza, N. Auluck, O. Rana, and L. Bittencourt. 2018. PASHE: Privacy aware scheduling in a heterogeneous fog environment. In Proceedings of the 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud’18). 333--340.Google Scholar
- Ahmed A. A. Gad-Elrab and Amin Y. Noaman. 2020. A two-tier bipartite graph task allocation approach based on fuzzy clustering in cloud--fog environment. Future Generation Computer Systems 103 (2020), 79--90.Google ScholarCross Ref
- Pegah Gazori, Dadmehr Rahbari, and Mohsen Nickray. 2019. Saving time and cost on the scheduling of fog-based IoT applications using deep reinforcement learning approach. Future Generation Computer Systems 110 (2019), 1098--1115. DOI:https://doi.org/10.1016/j.future.2019.09.060Google ScholarCross Ref
- Mostafa Ghobaei-Arani, Alireza Souri, and Ali A Rahmanian. 2019. Resource management approaches in fog computing: A comprehensive review. Journal of Grid Computing 18 (2019), 1--42.Google ScholarCross Ref
- S. Ghosh, A. Mukherjee, S. K. Ghosh, and R. Buyya. 2019. Mobi-IoST: Mobility-aware cloud-fog-edge-iot collaborative framework for time-critical applications. IEEE Transactions on Network Science and Engineering. Early Access. September 16, 2019. DOI:https://doi.org/10.1109/TNSE.2019.2941754Google Scholar
- Nam Ky Giang, Rodger Lea, and Victor C. M. Leung. 2020. Developing applications in large scale, dynamic fog computing: A case study. Software: Practice and Experience 50, 5 (2020), 519--532. DOI:https://doi.org/10.1002/spe.2695Google ScholarCross Ref
- Mohammad Goudarzi, Marimuthu Palaniswami, and Rajkumar Buyya. 2019. A fog-driven dynamic resource allocation technique in ultra dense femtocell networks. Journal of Network and Computer Applications 145, 1 (2019), 102407.Google ScholarCross Ref
- Jayavardhana Gubbi, Rajkumar Buyya, Slaven Marusic, and Marimuthu Palaniswami. 2013. Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems 29, 7 (2013), 1645--1660.Google ScholarDigital Library
- Carlos Guerrero, Isaac Lera, and Carlos Juiz. 2019. A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing 10, 6 (June 2019), 2435--2452.Google ScholarCross Ref
- M. A. Hassan, M. Xiao, Q. Wei, and S. Chen. 2015. Help your mobile applications with fog computing. In Proceedings of the 2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking Workshops (SECON Workshops’15). 1--6.Google Scholar
- J. He, J. Wei, K. Chen, Z. Tang, Y. Zhou, and Y. Zhang. 2018. Multitier fog computing with large-scale IoT data analytics for smart cities. IEEE Internet of Things Journal 5, 2 (April 2018), 677--686.Google ScholarCross Ref
- Xiang He, Zhiying Tu, Xiaofei Xu, and Zhongjie Wang. 2019. Re-deploying microservices in edge and cloud environment for the optimization of user-perceived service quality. In Service-Oriented Computing, S. Yangui, I. B. Rodriguez, K. Drira, and Z. Tari (Eds.). Springer International Publishing, Cham, Switzerland, 555--560.Google Scholar
- Cheol-Ho Hong and Blesson Varghese. 2018. Resource management in fog/edge computing: A survey. arXiv:1810.00305. http://arxiv.org/abs/1810.00305Google Scholar
- Pengfei Hu, Sahraoui Dhelim, Huansheng Ning, and Tie Qiu. 2017. Survey on fog computing: Architecture, key technologies, applications and open issues. Journal of Network and Computer Applications 98 (2017), 27--42.Google ScholarDigital Library
- C. Huang and K. Xu. 2016. Reliable realtime streaming in vehicular cloud-fog computing networks. In Proceedings of the 2016 IEEE/CIC International Conference on Communications in China (ICCC’16). 1--6.Google Scholar
- Tiansheng Huang, Weiwei Lin, Yin Li, LiGang He, and ShaoLiang Peng. 2019. A latency-aware multiple data replicas placement strategy for fog computing. Journal of Signal Processing Systems 91, 10 (Oct. 2019), 1191--1204.Google ScholarCross Ref
- S. Imai, C. A. Varela, and S. Patterson. 2018. A performance study of geo-distributed IoT data aggregation for fog computing. In Proceedings of the 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion. 278--283.Google Scholar
- IoT for All. 2018. The Big Three Make a Play for the Fog. Retrieved April 8, 2020 from https://www.iotforall.com/big-three-make-play-fog/.Google Scholar
- Bushra Jamil, Mohammad Shojafar, Israr Ahmed, Atta Ullah, Kashif Munir, and Humaira Ijaz. 2019. A job scheduling algorithm for delay and performance optimization in fog computing. Concurrency and Computation: Practice and Experience 32, 7 (2019), e5581. DOI:https://doi.org/10.1002/cpe.5581Google Scholar
- T. Jeong, J. Chung, J. W. Hong, and S. Ha. 2017. Towards a distributed computing framework for Fog. In Proceedings of the 2017 IEEE Fog World Congress (FWC’17). 1--6.Google Scholar
- Y. Jiang, Y. Chen, S. Yang, and C. Wu. 2019. Energy-efficient task offloading for time-sensitive applications in fog computing. IEEE Systems Journal 13, 3 (Sept. 2019), 2930--2941.Google ScholarCross Ref
- S. Josilo and G. Dan. 2019. Decentralized algorithm for randomized task allocation in fog computing systems. IEEE/ACM Transactions on Networking 27, 1 (Feb. 2019), 85--97.Google ScholarDigital Library
- Muhammad Babar Kamal, Nadeem Javaid, Syed Aon Ali Naqvi, Hanan Butt, Talha Saif, and Muhammad Daud Kamal. 2019. Heuristic min-conflicts optimizing technique for load balancing on fog computing. In Advances in Intelligent Networking and Collaborative Systems, F. Xhafa, L. Barolli, and M.Greguš (Eds.). Springer International Publishing, Cham, Switzerland, 207--219.Google Scholar
- A. Karamoozian, A. Hafid, and E. M. Aboulhamid. 2019. On the fog-cloud cooperation: How fog computing can address latency concerns of IoT applications. In Proceedings of the 4th International Conference on Fog and Mobile Edge Computing. 166--172.Google Scholar
- Firat Karatas and Ibrahim Korpeoglu. 2019. Fog-based data distribution service (F-DAD) for Internet of Things (IoT) applications. Future Generation Computer Systems 93 (2019), 156--169.Google ScholarDigital Library
- P. Kayal and J. Liebeherr. 2019. Autonomic service placement in fog computing. In Proceedings of the 2019 IEEE 20th International Symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM’19). 1--9.Google Scholar
- Bongjun Kim, Seonyeong Heo, Gyeongmin Lee, Seungbin Song, Jong Kim, and Hanjun Kim. 2019. Spinal code: Automatic code extraction for near-user computation in fogs. In Proceedings of the 28th International Conference on Compiler Construction (CC’19). ACM, New York, NY, 87--98.Google ScholarDigital Library
- Won-Suk Kim and Sang-Hwa Chung. 2018. User incentive model and its optimization scheme in user-participatory fog computing environment. Computer Networks 145 (2018), 76--88.Google ScholarCross Ref
- Guenter I. Klas. 2015. Fog computing and mobile edge cloud gain momentum Open Fog Consortium, ETSI MEC and cloudlets. Retrieved 8 April, 2020 from https://yucianga.info/?p=938.Google Scholar
- Frank Alexander Kraemer, Anders Eivind Braten, Nattachart Tamkittikhun, and David Palma. 2017. Fog computing in healthcare—A review and discussion. IEEE Access 5 (2017), 9206--9222.Google ScholarCross Ref
- G. Lee, W. Saad, and M. Bennis. 2019. An online optimization framework for distributed fog network formation with minimal latency. IEEE Transactions on Wireless Communications 18, 4 (April 2019), 2244--2258.Google ScholarCross Ref
- I. Lera, C. Guerrero, and C. Juiz. 2019. Availability-aware service placement policy in fog computing based on graph partitions. IEEE Internet of Things Journal 6, 2 (April 2019), 3641--3651.Google ScholarCross Ref
- Chao Li, Yushu Xue, Jing Wang, Weigong Zhang, and Tao Li. 2018a. Edge-oriented computing paradigms: A survey on architecture design and system management. ACM Computing Surveys 51, 2 (2018), 39.Google ScholarDigital Library
- Changlong Li, Hang Zhuang, Qingfeng Wang, and Xuehai Zhou. 2018b. SSLB: Self-similarity-based load balancing for large-scale fog computing. Arabian Journal for Science and Engineering 43, 12 (Dec. 2018), 7487--7498.Google ScholarCross Ref
- Guangshun Li, Jiahe Yan, Lu Chen, Junhua Wu, Qingyan Lin, and Ying Zhang. 2019c. Energy consumption optimization with a delay threshold in cloud-fog cooperation computing. IEEE Access 7 (2019), 159688--159697.Google ScholarCross Ref
- He Li, Kaoru Ota, and Mianxiong Dong. 2019b. Deep reinforcement scheduling for mobile crowdsensing in fog computing. ACM Transactions on Internet Technology 19, 2 (April 2019), Article 21, 18 pages.Google ScholarDigital Library
- Lei Li, Quansheng Guan, Lianwen Jin, and Mian Guo. 2019a. Resource allocation and task offloading for heterogeneous real-time tasks with uncertain duration time in a fog queueing system. IEEE Access 7 (2019), 9912--9925.Google ScholarCross Ref
- Songze Li, Mohammad Ali Maddah-Ali, and A. Salman Avestimehr. 2017. Coding for distributed fog computing. IEEE Communications Magazine 55, 4 (2017), 34--40.Google ScholarDigital Library
- Liqing Liu, Zheng Chang, Xijuan Guo, and T. Ristaniemi. 2017. Multi-objective optimization for computation offloading in mobile-edge computing. In Proceedings of the 2017 IEEE Symposium on Computers and Communications (ISCC’17). 832--837.Google Scholar
- Z. Liu, X. Yang, Y. Yang, K. Wang, and G. Mao. 2019. DATS: Dispersive stable task scheduling in heterogeneous fog networks. IEEE Internet of Things Journal 6, 2 (April 2019), 3423--3436.Google Scholar
- J. Luo, L. Yin, J. Hu, C. Wang, X. Liu, X. Fan, and H. Luo. 2019. Container-based fog computing architecture and energy-balancing scheduling algorithm for energy IoT. Future Generation Computer Systems 97 (2019), 50--60.Google ScholarDigital Library
- H. Madsen, B. Burtschy, G. Albeanu, and F. Popentiu-Vladicescu. 2013. Reliability in the utility computing era: Towards reliable Fog computing. In Proceedings of the 2013 20th International Conference on Systems, Signals, and Image Processing (IWSSIP’13). 43--46.Google Scholar
- Mukhtar M. E. Mahmoud, Joel J. P. C. Rodrigues, Kashif Saleem, Jalal Al-Muhtadi, Neeraj Kumar, and Valery Korotaev. 2018. Towards energy-aware fog-enabled cloud of things for healthcare. Computers 8 Electrical Engineering 67 (2018), 58--69.Google Scholar
- Md. Redowan Mahmud, Mahbuba Afrin, Md. Abdur Razzaque, Mohammad Mehedi Hassan, Abdulhameed Alelaiwi, and Majed Alrubaian. 2016. Maximizing quality of experience through context-aware mobile application scheduling in cloudlet infrastructure. Software: Practice and Experience 46, 11 (2016), 1525--1545.Google ScholarDigital Library
- Redowan Mahmud, Fernando Luiz Koch, and Rajkumar Buyya. 2018a. Cloud-fog interoperability in IoT-enabled healthcare solutions. In Proceedings of the 19th International Conference on Distributed Computing and Networking (ICDCN’18). ACM, New York, NY, Article 32, 10 pages.Google ScholarDigital Library
- Redowan Mahmud, Ramamohanarao Kotagiri, and Rajkumar Buyya. 2018b. Fog computing: A taxonomy, survey and future directions. In Internet of Everything. Springer, 103--130.Google Scholar
- Redowan Mahmud, Kotagiri Ramamohanarao, and Rajkumar Buyya. 2018c. Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology 19, 1 (Nov. 2018), Article 9, 21 pages.Google ScholarDigital Library
- Redowan Mahmud, Kotagiri Ramamohanarao, and Rajkumar Buyya. 2019a. Edge affinity-based management of applications in fog computing environments. In Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing (UCC’19). ACM, New York, NY, 1--10.Google ScholarDigital Library
- Redowan Mahmud, Satish Narayana Srirama, Kotagiri Ramamohanarao, and Rajkumar Buyya. 2019b. Quality of experience (QoE)-aware placement of applications in Fog computing environments. Journal of Parallel and Distributed Computing 132 (2019), 190--203.Google ScholarDigital Library
- Redowan Mahmud, Satish Narayana Srirama, Kotagiri Ramamohanarao, and Rajkumar Buyya. 2020. Profit-aware application placement for integrated Fog--Cloud computing environments. Journal of Parallel and Distributed Computing 135 (2020), 177--190.Google ScholarCross Ref
- R. Mahmud, A. N. Toosi, K. Rao, and R. Buyya. 2019. Context-aware placement of Industry 4.0 applications in fog computing environments. IEEE Transactions on Industrial Informatics. Early Access. November 8, 2019.Google Scholar
- Mirjana Maksimović. 2018. Implementation of fog computing in IoT-based healthcare system. JITA—Journal of Information Technology and Applications 14, 2 (2018), 100--107.Google Scholar
- B. Martinez, M. Monton, I. Vilajosana, and J. D. Prades. 2015. The power of models: Modeling power consumption for IoT devices. IEEE Sensors Journal 15, 10 (Oct. 2015), 5777--5789.Google ScholarCross Ref
- Jakob Mass, Chii Chang, and Satish Narayana Srirama. 2018. Context-aware edge process management for mobile thing-to-fog environment. In Proceedings of the 12th European Conference on Software Architecture: Companion Proceedings (ECSA’18). ACM, New York, NY, Article 44, 7 pages.Google ScholarDigital Library
- Houssemeddine Mazouzi, Nadjib Achir, and Khaled Boussetta. 2019. DM2-ECOP: An efficient computation offloading policy for multi-user multi-cloudlet mobile edge computing environment. ACM Transactions on Internet Technology 19, 2 (April 2019), Article 24, 24 pages.Google ScholarDigital Library
- S. Meixner, D. Schall, F. Li, V. Karagiannis, S. Schulte, and K. Plakidas. 2019. Automatic application placement and adaptation in cloud-edge environments. In Proceedings of the 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA’19). 1001--1008.Google Scholar
- Eduard Melnik, Anna Klimenko, and Vladislav Klimenko. 2019. A recovery technique for the fog-computing-based information and control systems. In Intelligent Systems in Cybernetics and Automation Control Theory, R. Silhavy, P. Silhavy, and Z. Prokopova (Eds.). Springer International Publishing, Cham, Switzerland, 216--227.Google Scholar
- Moumita Mishra, Sayan Kumar Roy, Anwesha Mukherjee, Debashis De, Soumya K. Ghosh, and Rajkumar Buyya. 2019. An energy-aware multi-sensor geo-fog paradigm for mission critical applications. Journal of Ambient Intelligence and Humanized Computing. Early Access. September 12, 2019.Google Scholar
- S. K. Mishra, D. Puthal, J. J. P. C. Rodrigues, B. Sahoo, and E. Dutkiewicz. 2018. Sustainable service allocation using a metaheuristic technique in a fog server for industrial applications. IEEE Transactions on Industrial Informatics 14, 10 (Oct. 2018), 4497--4506.Google ScholarCross Ref
- N. Mohamed, J. Al-Jaroodi, and I. Jawhar. 2019. Towards fault tolerant fog computing for IoT-based smart city applications. In Proceedings of the 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC’19). 0752--0757.Google Scholar
- C. Mouradian, S. Kianpisheh, M. Abu-Lebdeh, F. Ebrahimnezhad, N. T. Jahromi, and R. H. Glitho. 2019. Application component placement in NFV-based hybrid cloud/fog systems with mobile fog nodes. IEEE Journal on Selected Areas in Communications 37, 5 (May 2019), 1130--1143.Google ScholarCross Ref
- Carla Mouradian, Diala Naboulsi, Sami Yangui, Roch H. Glitho, Monique J. Morrow, and Paul A. Polakos. 2017. A comprehensive survey on fog computing: State-of-the-art and research challenges. IEEE Communications Surveys 8 Tutorials 20, 1 (2017), 416--464.Google Scholar
- M. Mtshali, H. Kobo, S. Dlamini, M. Adigun, and P. Mudali. 2019. Multi-objective optimization approach for task scheduling in fog computing. In Proceedings of the International Conference on Advances in Big Data, Computing, and Data Communication Systems. 1--6.Google Scholar
- Mithun Mukherjee, Lei Shu, and Di Wang. 2018. Survey of fog computing: Fundamental, network applications, and research challenges. IEEE Communications Surveys 8 Tutorials 20, 3 (2018), 1826--1857.Google Scholar
- Mohammed Islam NAAS, Laurent Lemarchand, Jalil Boukhobza, and Philippe Raipin. 2018. A graph partitioning-based heuristic for runtime IoT data placement strategies in a fog infrastructure. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing (SAC’18). ACM, New York, NY, 767--774.Google Scholar
- Ranesh Kumar Naha, Saurabh Garg, Dimitrios Georgakopoulos, Prem Prakash Jayaraman, Longxiang Gao, Yong Xiang, and Rajiv Ranjan. 2018. Fog computing: Survey of trends, architectures, requirements, and research directions. IEEE Access 6 (2018), 47980--48009.Google ScholarCross Ref
- Biji Nair and Mary Saira Bhanu Somasundaram. 2019. Overload prediction and avoidance for maintaining optimal working condition in a fog node. Computers 8 Electrical Engineering 77 (2019), 147--162.Google Scholar
- Y. Nan, W. Li, W. Bao, F. C. Delicato, P. F. Pires, Y. Dou, and A. Y. Zomaya. 2017. Adaptive energy-aware computation offloading for cloud of things systems. IEEE Access 5 (2017), 23947--23957.Google ScholarCross Ref
- Mansoor Nasir, Khan Muhammad, Jaime Lloret, Arun Kumar Sangaiah, and Muhammad Sajjad. 2019. Fog computing enabled cost-effective distributed summarization of surveillance videos for smart cities. Journal on Parallel and Distributed Computing 126 (2019), 161--170.Google ScholarCross Ref
- Shubha Brata Nath, Harshit Gupta, Sandip Chakraborty, and Soumya K. Ghosh. 2018. A survey of fog computing and communication: Current researches and future directions. arXiv:1804.04365. arxiv:1804.04365 http://arxiv.org/abs/1804.04365Google Scholar
- T. Nazar, N. Javaid, M. Waheed, A. Fatima, H. Bano, and N. Ahmed. 2019. Modified shortest job first for load balancing in cloud-fog computing. In Advances on Broadband and Wireless Computing, Communication and Applications, L. Barolli, F.-Y. Leu, T. Enokido, and H.-C. Chen (Eds.). Springer International Publishing, Cham, Switzerland, 63--76.Google Scholar
- Y. Niu, Y. Liu, Y. Li, Z. Zhong, B. Ai, and P. Hui. 2018. Mobility-aware caching scheduling for fog computing in mmWave band. IEEE Access 6 (2018), 69358--69370.Google ScholarCross Ref
- Hassan Noura, Ola Salman, Ali Chehab, and Raphael Couturier. 2019. Preserving data security in distributed fog computing. Ad Hoc Networks 94 (2019), 101937.Google ScholarDigital Library
- Ryuji Oma, Shigenari Nakamura, Dilawaer Duolikun, Tomoya Enokido, and Makoto Takizawa. 2019. Fault-tolerant fog computing models in the IoT. In Advances on P2P, Parallel, Grid, Cloud and Internet Computing, F. Xhafa, F.-Y. Leu, M. Ficco, and C.-T. Yang (Eds.). Springer International Publishing, Cham, Switzerland, 14--25.Google Scholar
- Opeyemi Osanaiye, Shuo Chen, Zheng Yan, Rongxing Lu, Kim-Kwang Raymond Choo, and Mqhele Dlodlo. 2017. From cloud to fog computing: A review and a conceptual live VM migration framework. IEEE Access 5 (2017), 8284--8300.Google ScholarCross Ref
- Umar Ozeer, Xavier Etchevers, Loïc Letondeur, François-Gaël Ottogalli, Gwen Salaün, and Jean-Marc Vincent. 2018. Resilience of stateful IoT applications in a dynamic fog environment. In Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services. ACM, New York, NY, 332--341.Google ScholarDigital Library
- Samodha Pallewatta, Vassilis Kostakos, and Rajkumar Buyya. 2019. Microservices-based IoT application placement within heterogeneous and resource constrained fog computing environments. In Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing. ACM, New York, NY, 71--81.Google ScholarDigital Library
- X. Pang, Z. Bie, and X. Lin. 2018. Access point decoding coded MapReduce for tree fog network. In Proceedings of the 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC’18). 384--388.Google Scholar
- Charith Perera, Yongrui Qin, Julio C. Estrella, Stephan Reiff-Marganiec, and Athanasios V. Vasilakos. 2017. Fog computing for sustainable smart cities: A survey. ACM Computing Surveys 50, 3 (2017), 32.Google ScholarDigital Library
- S. Prabavathy, K. Sundarakantham, S. Mercy Shalinie, and K. Narasimha Mallikarjunan. 2019. Fog computing-based autonomic security approach to Internet of Things applications. In Computational Intelligence: Theories, Applications and Future Directions—Volume II, N. K. Verma and A. K. Ghosh (Eds.). Springer Singapore, Singapore, 3--14.Google Scholar
- J. S. Preden, K. Tammemae, A. Jantsch, M. Leier, A. Riid, and E. Calis. 2015. The benefits of self-awareness and attention in fog and mist computing. Computer 48, 7 (July 2015), 37--45.Google ScholarDigital Library
- Carlo Puliafito, Enzo Mingozzi, Francesco Longo, Antonio Puliafito, and Omer Rana. 2019. Fog computing for the Internet of Things: A survey. ACM Transactions on Internet Technology 19, 2 (2019), 18.Google ScholarDigital Library
- C. Puliafito, E. Mingozzi, C. Vallati, F. Longo, and G. Merlino. 2018. Companion fog computing: Supporting things mobility through container migration at the edge. In Proceedings of the IEEE International Conference on Smart Computing. 97--105.Google Scholar
- Dadmehr Rahbari and Mohsen Nickray. 2019. Task offloading in mobile fog computing by classification and regression tree. Peer-to-Peer Networking and Applications 13 (Feb. 2019), 104--122.Google Scholar
- M. R. Ramli, P. T. Daely, J. Lee, and D. Kim. 2019. Bio-inspired service provisioning scheme for fog-based Industrial Internet of Things. In Proceedings of the 24th IEEE International Conference on Emerging Technologies and Factory Automation. 1661--1664.Google Scholar
- Rodrigo Roman, Javier Lopez, and Masahiro Mambo. 2018. Mobile edge computing, fog et al.: A survey and analysis of security threats and challenges. Future Generation Computer Systems 78 (2018), 680--698.Google ScholarCross Ref
- J. Santos, T. Wauters, B. Volckaert, and F. De Turck. 2019. Towards network-aware resource provisioning in Kubernetes for fog computing applications. In Proceedings of the 2019 IEEE Conference on Network Softwarization (NetSoft’19). 351--359.Google Scholar
- S. Sarkar, S. Chatterjee, and S. Misra. 2015. Assessment of the suitability of fog computing in the context of Internet of Things. IEEE Transactions on Cloud Computing PP, 99 (2015), 1.Google Scholar
- M. Satyanarayanan. 2017. The emergence of edge computing. Computer 50, 1 (Jan. 2017), 30--39.Google ScholarCross Ref
- Enrique Saurez, Kirak Hong, Dave Lillethun, Umakishore Ramachandran, and Beate Ottenwälder. 2016. Incremental deployment and migration of geo-distributed situation awareness applications in the fog. In Proceedings of the 10th ACM International Conference on Distributed and Event-Based Systems (DEBS’16). ACM, New York, NY, 258--269.Google ScholarDigital Library
- H. Shah-Mansouri and V. W. S. Wong. 2018. Hierarchical fog-cloud computing for IoT systems: A computation offloading game. IEEE Internet of Things Journal 5, 4 (Aug. 2018), 3246--3257.Google ScholarCross Ref
- Shivi Sharma and Hemraj Saini. 2019. A novel four-tier architecture for delay aware scheduling and load balancing in fog environment. Sustainable Computing: Informatics and Systems 24 (2019), 100355.Google ScholarCross Ref
- W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu. 2016. Edge computing: Vision and challenges. IEEE Internet of Things Journal 3, 5 (Oct. 2016), 637--646.Google ScholarCross Ref
- W. Shi and S. Dustdar. 2016. The promise of edge computing. Computer 49, 5 (May 2016), 78--81.Google ScholarDigital Library
- Syed Noorulhassan Shirazi, Antonios Gouglidis, Arsham Farshad, and David Hutchison. 2017. The extended cloud: Review and analysis of mobile edge computing and fog from a security and resilience perspective. IEEE Journal on Selected Areas in Communications 35, 11 (2017), 2586--2595.Google ScholarCross Ref
- Leila Shooshtarian, Dapeng Lan, and Amir Taherkordi. 2019. A clustering-based approach to efficient resource allocation in fog computing. In Pervasive Systems, Algorithms and Networks, C. Esposito, J. Hong, and K.-K. Raymond Choo (Eds.). Springer International Publishing, Cham, Switzerland, 207--224.Google Scholar
- Anil Singh and Nitin Auluck. 2019. Load balancing aware scheduling algorithms for fog networks. Software: Practice and Experience. Early Access. June 18, 2019. DOI:https://doi.org/10.1002/spe.2722Google Scholar
- Simar Preet Singh, Anju Sharma, and Rajesh Kumar. 2019. Design and exploration of load balancers for fog computing using fuzzy logic. Simulation Modelling Practice and Theory 101 (2019), 102017.Google ScholarCross Ref
- Olena Skarlat, Matteo Nardelli, Stefan Schulte, Michael Borkowski, and Philipp Leitner. 2017. Optimized IoT service placement in the fog. Service Oriented Computing and Applications 11, 4 (Dec. 2017), 427--443.Google ScholarDigital Library
- Z. Su, Q. Xu, J. Luo, H. Pu, Y. Peng, and R. Lu. 2018. A secure content caching scheme for disaster backup in fog computing enabled mobile social networks. IEEE Transactions on Industrial Informatics 14, 10 (Oct. 2018), 4579--4589.Google ScholarCross Ref
- Huaiying Sun, Huiqun Yu, Guisheng Fan, and Liqiong Chen. 2019. Energy and time efficient task offloading and resource allocation on the generic IoT-fog-cloud architecture. Peer-to-Peer Networking and Applications 13 (July 2019), 548--563.Google Scholar
- M. Suter, R. Eidenbenz, Y. Pignolet, and A. Singla. 2019. Fog application allocation for automation systems. In Proceedings of the 2019 IEEE International Conference on Fog Computing (ICFC’19). 97--106.Google Scholar
- Madiha H. Syed, Eduardo B. Fernandez, and Mohammad Ilyas. 2016. A pattern for fog computing. In Proceedings of the 10th Travelling Conference on Pattern Languages of Programs (VikingPLoP’16). ACM, New York, NY, Article 13, 10 pages.Google ScholarDigital Library
- Shreshth Tuli, Redowan Mahmud, Shikhar Tuli, and Rajkumar Buyya. 2019. FogBus: A blockchain-based lightweight framework for edge and fog computing. Journal of Systems and Software 154 (2019), 22--36.Google ScholarDigital Library
- Dimitrios Tychalas and Helen Karatza. 2020. A scheduling algorithm for a fog computing system with bag-of-tasks jobs: Simulation and performance evaluation. Simulation Modelling Practice and Theory 98 (2020), 101982.Google ScholarCross Ref
- Minoru Uehara. 2018. Mist Computing: Linking Cloudlet to Fogs. Springer International Publishing, Cham, Switzerland, 201--213.Google Scholar
- P. Varshney and Y. Simmhan. 2017. Demystifying fog computing: Characterizing architectures, applications and abstractions. In Proceedings of the 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC’17). 115--124.Google Scholar
- Salvatore Venticinque and Alba Amato. 2019. A methodology for deployment of IoT application in fog. Journal of Ambient Intelligence and Humanized Computing 10, 5 (May 2019), 1955--1976.Google ScholarCross Ref
- N. Verba, K. Chao, J. Lewandowski, N. Shah, A. James, and F. Tian. 2019. Modeling Industry 4.0 based fog computing environments for application analysis and deployment. Future Generation Computer Systems 91 (2019), 48--60.Google ScholarCross Ref
- Paola G. Vinueza Naranjo, Enzo Baccarelli, and Michele Scarpiniti. 2018. Design and energy-efficient resource management of virtualized networked fog architectures for the real-time support of IoT applications. Journal of Supercomputing 74, 6 (June 2018), 2470--2507.Google ScholarDigital Library
- Duc-Nghia Vu, Nhu-Ngoc Dao, Yongwoon Jang, Woongsoo Na, Young-Bin Kwon, Hyunchul Kang, Jason J. Jung, and Sungrae Cho. 2019. Joint energy and latency optimization for upstream IoT offloading services in fog radio access networks. Transactions on Emerging Telecommunications Technologies 30, 4 (2019), e3497.Google ScholarDigital Library
- D. Wang, Z. Liu, X. Wang, and Y. Lan. 2019a. Mobility-aware task offloading and migration schemes in fog computing networks. IEEE Access 7 (2019), 43356--43368.Google ScholarCross Ref
- T. Wang, J. Zhou, A. Liu, M. Z. A. Bhuiyan, G. Wang, and W. Jia. 2019. Fog-based computing and storage offloading for data synchronization in IoT. IEEE Internet of Things Journal 6, 3 (June 2019), 4272--4282.Google Scholar
- Wei Wang, Guanyu Wu, Zhe Guo, Liang Qian, Lianghui Ding, and Feng Yang. 2019. Data scheduling and resource optimization for fog computing architecture in Industrial IoT. In Distributed Computing and Internet Technology, G. Fahrnberger, S. Gopinathan, and L. Parida (Eds.). Springer International Publishing, Cham, Switzerland, 141--149.Google Scholar
- X. Wang, L. Wang, Y. Li, and K. Gai. 2018. Privacy-aware efficient fine-grained data access control in Internet of Medical Things based fog computing. IEEE Access 6 (2018), 47657--47665.Google ScholarCross Ref
- Y. Wang, K. Wang, H. Huang, T. Miyazaki, and S. Guo. 2019b. Traffic and computation co-offloading with reinforcement learning in fog computing for industrial applications. IEEE Transactions on Industrial Informatics 15, 2 (Feb. 2019), 976--986.Google Scholar
- Mohammad Wazid, Ashok Kumar Das, Neeraj Kumar, and Athanasios V. Vasilakos. 2019. Design of secure key management and user authentication scheme for fog computing services. Future Generation Computer Systems 91 (2019), 475--492.Google ScholarDigital Library
- P. Wiener, P. Zehnder, and D. Riemer. 2019. Towards context-aware and dynamic management of stream processing pipelines for fog computing. In Proceedings of the 2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC’19). 1--6.Google Scholar
- C. Wu and L. Wang. 2019. A deadline-aware estimation of distribution algorithm for resource scheduling in fog computing systems. In Proceedings of the 2019 IEEE Congress on Evolutionary Computation (CEC’19). 660--666.Google Scholar
- Y. Xiao and M. Krunz. 2018. Distributed optimization for energy-efficient fog computing in the Tactile Internet. IEEE Journal on Selected Areas in Communications 36, 11 (Nov. 2018), 2390--2400.Google ScholarCross Ref
- M. Yannuzzi, R. Milito, R. Serral-Gracia, D. Montero, and M. Nemirovsky. 2014. Key ingredients in an IoT recipe: Fog computing, cloud computing, and more fog computing. In Proceedings of the 2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD’14). 325--329.Google Scholar
- J. Yao and N. Ansari. 2019. QoS-aware fog resource provisioning and mobile device power control in IoT networks. IEEE Transactions on Network and Service Management 16, 1 (March 2019), 167--175.Google ScholarDigital Library
- Y. Yao, X. Chang, J. Misic, and V. Misic. 2019. Reliable and secure vehicular fog service provision. IEEE Internet of Things Journal 6, 1 (Feb. 2019), 734--743.Google ScholarCross Ref
- L. Yin, J. Luo, and H. Luo. 2018. Tasks scheduling and resource allocation in fog computing based on containers for smart manufacturing. IEEE Transactions on Industrial Informatics 14, 10 (Oct. 2018), 4712--4721.Google ScholarCross Ref
- Ashkan Yousefpour, Caleb Fung, Tam Nguyen, Krishna Kadiyala, Fatemeh Jalali, Amirreza Niakanlahiji, Jian Kong, and Jason P. Jue. 2019. All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture 98 (2019), 289--330.Google ScholarDigital Library
- A. Yousefpour, G. Ishigaki, R. Gour, and J. P. Jue. 2018. On reducing IoT service delay via fog offloading. IEEE Internet of Things Journal 5, 2 (April 2018), 998--1010.Google ScholarCross Ref
- L. Yu, T. Jiang, and Y. Zou. 2017. Fog-assisted operational cost reduction for cloud data centers. IEEE Access 5 (2017), 13578--13586.Google ScholarCross Ref
- J. Yue, M. Xiao, and Z. Pang. 2018. Distributed fog computing based on batched sparse codes for industrial control. IEEE Transactions on Industrial Informatics 14, 10 (Oct. 2018), 4683--4691.Google ScholarCross Ref
- W. Yanez, R. Mahmud, R. Bahsoon, Y. Zhang, and R. Buyya. 2020. Data allocation mechanism for Internet-of-Things systems with blockchain. IEEE Internet of Things Journal 7, 4 (2020), 3509--3522. DOI:https://doi.org/10.1109/JIOT.2020.2972776Google ScholarCross Ref
- Deze Zeng, Lin Gu, and Hong Yao. 2018. Towards energy efficient service composition in green energy powered cyber-physical fog systems. Future Generation Computer Systems.Google Scholar
- M. Zeng, Y. Li, K. Zhang, M. Waqas, and D. Jin. 2018. Incentive mechanism design for computation offloading in heterogeneous fog computing: A contract-based approach. In Proceedings of the IEEE International Conference on Communications (ICC’18). 1--6.Google Scholar
- G. Zhang, F. Shen, N. Chen, P. Zhu, X. Dai, and Y. Yang. 2019a. DOTS: Delay-optimal task scheduling among voluntary nodes in fog networks. IEEE Internet of Things Journal 6, 2 (April 2019), 3533--3544.Google Scholar
- G. Zhang, F. Shen, Z. Liu, Y. Yang, K. Wang, and M. Zhou. 2019b. FEMTO: Fair and energy-minimized task offloading for fog-enabled IoT networks. IEEE Internet of Things Journal 6, 3 (June 2019), 4388--4400.Google Scholar
- PeiYun Zhang, MengChu Zhou, and Giancarlo Fortino. 2018. Security and trust issues in fog computing: A survey. Future Generation Computer Systems 88 (2018), 16--27.Google ScholarDigital Library
- Dongcheng Zhao, Gang Sun, Dan Liao, Shizhong Xu, and Victor Chang. 2019. Mobile-aware service function chain migration in cloud--fog computing. Future Generation Computer Systems 96 (2019), 591--604.Google ScholarCross Ref
- S. Zhao, Y. Yang, Z. Shao, X. Yang, H. Qian, and C. Wang. 2018. FEMOS: Fog-enabled multitier operations scheduling in dynamic wireless networks. IEEE Internet of Things Journal 5, 2 (April 2018), 1169--1183.Google ScholarCross Ref
- H. Zheng, K. Xiong, P. Fan, Z. Zhong, and K. B. Letaief. 2019b. Fog-assisted multiuser SWIPT networks: Local computing or offloading. IEEE Internet of Things Journal 6, 3 (June 2019), 5246--5264.Google ScholarCross Ref
- Q. Zheng, J. Jin, T. Zhang, J. Li, L. Gao, and Y. Xiang. 2019a. Energy-sustainable fog system for mobile web services in infrastructure-less environments. IEEE Access 7 (2019), 161318--161328.Google ScholarCross Ref
- Jingya Zhou, Jianxi Fan, Jin Wang, and Juncheng Jia. 2019. Dynamic service deployment for budget-constrained mobile edge computing. Concurrency and Computation: Practice and Experience (2019), e5436. DOI:https://doi.org/10.1002/cpe.5436Google Scholar
- Z. Zhou, P. Liu, J. Feng, Y. Zhang, S. Mumtaz, and J. Rodriguez. 2019. Computation resource allocation and task assignment optimization in vehicular fog computing: A contract-matching approach. IEEE Transactions on Vehicular Technology 68, 4 (April 2019), 3113--3125.Google ScholarCross Ref
- C. Zhu, J. Tao, G. Pastor, Y. Xiao, Y. Ji, Q. Zhou, Y. Li, and A. Yia-Jaaski. 2019. Folo: Latency and quality optimized task allocation in vehicular fog computing. IEEE Internet of Things Journal 6, 3 (June 2019), 4150--4161.Google ScholarCross Ref
Index Terms
- Application Management in Fog Computing Environments: A Taxonomy, Review and Future Directions
Recommendations
Latency-Aware Application Module Management for Fog Computing Environments
Regular Papers, Special Issue on Service Management for IOT and Special Issue on Knowledge-Driven BPMThe fog computing paradigm has drawn significant research interest as it focuses on bringing cloud-based services closer to Internet of Things (IoT) users in an efficient and timely manner. Most of the physical devices in the fog computing environment, ...
A Pattern for Fog Computing
VikingPLoP '16: Proceedings of the 10th Travelling Conference on Pattern Languages of ProgramsFog Computing is a new variety of the cloud computing paradigm that brings virtualized cloud services to the edge of the network to control the devices in the IoT. We present a pattern for fog computing which describes its architecture, including its ...
Edge Affinity-based Management of Applications in Fog Computing Environments
UCC'19: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud ComputingFog computing overcomes the limitations of executing Internet of Things (IoT) applications in remote Cloud datacentres by extending the computation facilities closer to data sources. Since most of the Fog nodes are resource constrained, accommodation of ...
Comments