survey

Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future Directions

Authors:

Mohammad Shojafar,

Rajkumar BuyyaAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 11s

Article No.: 233, Pages 1 - 38

https://doi.org/10.1145/3513002

Published: 09 September 2022 Publication History

Abstract

The Internet of Everything paradigm is being rapidly adopted in developing applications for different domains like smart agriculture, smart city, big data streaming, and so on. These IoE applications are leveraging cloud computing resources for execution. Fog computing, which emerged as an extension of cloud computing, supports mobility, heterogeneity, geographical distribution, context awareness, and services such as storage, processing, networking, and analytics on nearby fog nodes. The resource-limited, heterogeneous, dynamic, and uncertain fog environment makes task scheduling a great challenge that needs to be investigated. The article is motivated by this consideration and presents a systematic, comprehensive, and detailed comparative study by discussing the merits and demerits of different scheduling algorithms, focused optimization metrics, and evaluation tools in the fog computing and IoE environment. The goal of this survey article is fivefold. First, we review the fog computing and IoE paradigms. Second, we delineate the optimization metric engaged with fog computing and IoE environment. Third, we review, classify, and compare existing scheduling algorithms dealing with fog computing and IoE environment paradigms by leveraging some examples. Fourth, we rationalize the scheduling algorithms and point out the lesson learned from the survey. Fifth, we discuss the open issues and future research directions to improve scheduling in fog computing and the IoE environment.

References

[1]

Rajkumar Buyya and Amir Vahid Dastjerdi. 2016. Internet of Things: Principles and Paradigms. Elsevier.

[2]

Enzo Baccarelli, Paola G. Vinueza Naranjo, Michele Scarpiniti, Mohammad Shojafar, and Jemal H. Abawajy. 2017. Fog of everything: Energy-efficient networked computing architectures, research challenges, and a case study. IEEE Access 5 (2017), 9882–9910.

[3]

Jayavardhana Gubbi, Rajkumar Buyya, Slaven Marusic, and Marimuthu Palaniswami. 2013. Internet of Things (IoT): A vision, architectural elements, and future directions. Fut. Gen. Comput. Syst. 29, 7 (2013), 1645–1660.

Digital Library

[4]

Ala Al-Fuqaha, Mohsen Guizani, Mehdi Mohammadi, Mohammed Aledhari, and Moussa Ayyash. 2015. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17, 4 (2015), 2347–2376.

Digital Library

[5]

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. Fut. Gen. Comput. Syst. 25, 6 (2009), 599–616.

Digital Library

[6]

Qi Zhang, Lu Cheng, and Raouf Boutaba. 2010. Cloud computing: State-of-the-art and research challenges. J. Internet Serv. Applic. 1, 1 (2010), 7–18.

[7]

Dongsik Jo and Gerard Jounghyun Kim. 2019. IoT+ AR: Pervasive and augmented environments for “Digi-log” shopping experience. Hum.-centric Comput. Inf. Sci. 9, 1 (2019), 1–17.

Digital Library

[8]

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.

[9]

Redowan Mahmud, Fernando Luiz Koch, and Rajkumar Buyya. 2018. Cloud-fog interoperability in IoT-enabled healthcare solutions. In Proceedings of the 19th International Conference on Distributed Computing and Networking. 1–10.

Digital Library

[10]

Swati Malik and Kamali Gupta. 2019. Resource scheduling in fog: Taxonomy and related aspects. J. Comput. Theoret. Nanosci. 16, 10 (2019), 4313–4319.

[11]

Flavio Bonomi, Rodolfo Milito, Jiang Zhu, and Sateesh 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, 13–16.

Digital Library

[12]

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. J. Syst. Archit. 98 (2019), 289–330.

Digital Library

[13]

Redowan Mahmud, Kotagiri Ramamohanarao, and Rajkumar Buyya. 2020. Application management in fog computing environments: A taxonomy, review and future directions. Comput. Surv. 53, 4 (2020), 1–43.

Digital Library

[14]

Paola G. Vinueza Naranjo, Zahra Pooranian, Mohammad Shojafar, Mauro Conti, and Rajkumar Buyya. 2019. FOCAN: A fog-supported smart city network architecture for management of applications in the Internet of Everything environments. J. Parallel Distrib. Comput. 132 (2019), 274–283.

Digital Library

[15]

Xu Chen, Lei Jiao, Wenzhong Li, and Xiaoming Fu. 2015. Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Netw. 24, 5 (2015), 2795–2808.

Digital Library

[16]

Mostafa Ghobaei-Arani, Alireza Souri, and Ali A. Rahmanian. 2020. Resource management approaches in fog computing: A comprehensive review. J. Grid Comput. 18, 1 (2020), 1–42.

[17]

Cheol-Ho Hong and Blesson Varghese. 2019. Resource management in fog/edge computing: A survey on architectures, infrastructure, and algorithms. ACM Comput. Surv. 52, 5 (2019), 1–37.

Digital Library

[18]

Teena Mathew, K. Chandra Sekaran, and John Jose. 2014. Study and analysis of various task scheduling algorithms in the cloud computing environment. In Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 658–664.

[19]

Lina Ni, Jinquan Zhang, Changjun Jiang, Chungang Yan, and Kan Yu. 2017. Resource allocation strategy in fog computing based on priced timed petri nets. IEEE Internet Things J. 4, 5 (2017), 1216–1228.

[20]

Kallia Chronaki, Alejandro Rico, Marc Casas, Miquel Moretó, Rosa M. Badia, Eduard Ayguadé, Jesus Labarta, and Mateo Valero. 2017. Task scheduling techniques for asymmetric multi-core systems. IEEE Trans. Parallel Distrib. Syst. 28, 7 (2017), 2074–2087.

Digital Library

[21]

Alexander A. Visheratin, Mikhail Melnik, and Denis Nasonov. 2016. Workflow scheduling algorithms for hard-deadline constrained cloud environments. Procedia Comput. Sci. 80 (2016), 2098–2106.

Digital Library

[22]

Mohammed Al-Khafajiy, Thar Baker, Hilal Al-Libawy, Atif Waraich, Carl Chalmers, and Omar Alfandi. 2018. Fog computing framework for internet of things applications. In Proceedings of the 11th International Conference on Developments in eSystems Engineering (DeSE). IEEE, 71–77.

[23]

Muhammad Waqas, Yong Niu, Manzoor Ahmed, Yong Li, Depeng Jin, and Zhu Han. 2018. Mobility-aware fog computing in dynamic environments: Understandings and implementation. IEEE Access 7 (2018), 38867–38879.

[24]

Shreya Ghosh, Anwesha Mukherjee, Soumya K. Ghosh, and Rajkumar Buyya. 2019. Mobi-iost: Mobility-aware cloud-fog-edge-IoT collaborative framework for time-critical applications. IEEE Trans. Netw. Sci. Eng. 7, 4 (2019), 2271–2285.

[25]

Pejman Hosseinioun, Maryam Kheirabadi, Seyed Reza Kamel Tabbakh, and Reza Ghaemi. 2022. aTask scheduling approaches in fog computing: A survey. Trans. Emerg. Telecommun. Technol. 33, 3 (2022), e3792.

Digital Library

[26]

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.

[27]

Mithun Mukherjee, Lei Shu, and Di Wang. 2018. Survey of fog computing: Fundamental, network applications, and research challenges. IEEE Commun. Surv. Tutor. 20, 3 (2018), 1826–1857.

[28]

Xin Yang and Nazanin Rahmani. 2020. Task scheduling mechanisms in fog computing: Review, trends, and perspectives. 50, 1 (2020), 22–38.

[29]

Mohammad Reza Alizadeh, Vahid Khajehvand, Amir Masoud Rahmani, and Ebrahim Akbari. 2020. Task scheduling approaches in fog computing: A systematic review. Int. J. Commun. Syst. 33, 16 (2020), e4583.

[30]

Mir Salim Ul Islam, Ashok Kumar, and Yu-Chen Hu. 2021. Context-aware scheduling in Fog computing: A survey, taxonomy, challenges and future directions. J. Netw. Comput. Applic. 180 (2021), 103008.

[31]

Khaled Matrouk and Kholoud Alatoun. 2021. Scheduling algorithms in fog computing: A survey. Int. J. Netw. Distrib. Comput. 9, 1 (2021), 59–74.

[32]

Xinyi Zhao, Qun Zong, Bailing Tian, Boyuan Zhang, and Ming You. 2019. Fast task allocation for heterogeneous unmanned aerial vehicles through reinforcement learning. Aerosp. Sci. Technol. 92 (2019), 588–594.

[33]

Amir Vahid Dastjerdi, Harshit Gupta, Rodrigo N. Calheiros, Soumya K. Ghosh, and Rajkumar Buyya. 2016. Fog computing: Principles, architectures, and applications. In Internet of Things. Elsevier, 61–75.

[34]

Andrew S. Tanenbaum and Maarten Van Steen. 2007. Distributed Systems: Principles and Paradigms. Prentice-Hall.

Digital Library

[35]

Poonam Singh, Maitreyee Dutta, and Naveen Aggarwal. 2017. A review of task scheduling based on meta-heuristics approach in cloud computing. Knowl. Inf. Syst. 52, 1 (2017), 1–51.

Digital Library

[36]

Mala Kalra and Sarbjeet Singh. 2015. A review of metaheuristic scheduling techniques in cloud computing. Egypt. Inform. J. 16, 3 (2015), 275–295.

[37]

Syed Arshad Ali and Mansaf Alam. 2016. A relative study of task scheduling algorithms in cloud computing environment. In Proceedings of the 2nd International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 105–111.

[38]

Mohammed Alodib. 2016. QoS-Aware approach to monitor violations of SLAs in the IoT. J. Innov. Dig. Ecosyst. 3, 2 (2016), 197–207.

[39]

Elhossiny Ibrahim, Nirmeen A. El-Bahnasawy, and Fatma A. Omara. 2016. Task scheduling algorithm in cloud computing environment based on cloud pricing models. In Proceedings of the World Symposium on Computer Applications & Research (WSCAR). IEEE, 65–71.

[40]

Samir Elmougy, Shahenda Sarhan, and Manar Joundy. 2017. A novel hybrid of shortest job first and round robin with dynamic variable quantum time task scheduling technique. J. Cloud Comput. 6, 1 (2017), 1–12.

Digital Library

[41]

Pinal Salot. 2013. A survey of various scheduling algorithm in cloud computing environment. Int. J. Res. Eng. Technol. 2, 2 (2013), 131–135.

[42]

Syed Hamid Hussain Madni, Muhammad Shafie Abd Latiff, Mohammed Abdullahi, Shafi’i Muhammad Abdulhamid, and Mohammed Joda Usman. 2017. Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment. PloS One 12, 5 (2017), e0176321.

[43]

Abraham Silberschatz, Peter B. Galvin, and Greg Gagne. 2003. Operating System Concepts. John Wiley & Sons.

[44]

Harshit Gupta, Amir Vahid Dastjerdi, Soumya K. Ghosh, and Rajkumar Buyya. 2017. iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, edge and fog computing environments. Softw.: Pract. Exper. 47, 9 (2017), 1275–1296.

[45]

Tejaswini Choudhari, Melody Moh, and Teng-Sheng Moh. 2018. Prioritized task scheduling in fog computing. In Proceedings of the ACMSE’18.

[46]

Mxolisi Mtshali, Hlabishi Kobo, Sabelo Dlamini, Matthew Adigun, and Pragasen 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 (icABCD). IEEE, 1–6.

[47]

Ahmad Mohammad Alsmadi, Roba Mahmoud Ali Aloglah, Nisrein Jamal Sanad Abu-darwish, Ahmad Al Smadi, Muneerah Alshabanah, Daniah Alrajhi, Hanouf Alkhaldi, and Mutasem K. Alsmadi. 2021. Fog computing scheduling algorithm for smart city. Int. J. Electric. Comput. Eng. 11, 3 (2021), 2219–2228.

[48]

Luiz F. Bittencourt, Javier Diaz-Montes, Rajkumar Buyya, Omer F. Rana, and Manish Parashar. 2017. Mobility-aware application scheduling in fog computing. IEEE Cloud Comput. 4, 2 (2017), 26–35.

[49]

Alexander Schrijver. 1998. Theory of Linear and Integer Programming. John Wiley & Sons.

Digital Library

[50]

Christodoulos A. Floudas and Xiaoxia Lin. 2005. Mixed integer linear programming in process scheduling: Modeling, algorithms, and applications. Ann. Oper. Res. 139, 1 (2005), 131–162.

[51]

Olena Skarlat, Matteo Nardelli, Stefan Schulte, and Schahram Dustdar. 2017. Towards QoS-aware fog service placement. In Proceedings of the IEEE 1st International Conference on Fog and Edge Computing (ICFEC). IEEE, 89–96.

[52]

Farooq Hoseiny, Sadoon Azizi, Mohammad Shojafar, and Rahim Tafazolli. 2021. Joint QoS-aware and cost-efficient task scheduling for fog-cloud resources in a volunteer computing system. ACM Trans. Internet Technol. 21, 4 (2021), 1–21.

Digital Library

[53]

Raafat O. Aburukba, Mazin AliKarrar, Taha Landolsi, and Khaled El-Fakih. 2020. Scheduling Internet of Things requests to minimize latency in hybrid Fog–Cloud computing. Fut. Gen. Comput. Syst. 111 (2020), 539–551.

[54]

Ismael Martinez, Abdallah Jarray, and Abdelhakim Senhaji Hafid. 2020. Scalable design and dimensioning of fog-computing infrastructure to support latency-sensitive IoT applications. IEEE Internet Things J. 7, 6 (2020), 5504–5520.

[55]

José Santos, Tim Wauters, Bruno Volckaert, and Filip De Turck. 2021. Towards end-to-end resource provisioning in fog computing over low power wide area networks. J. Netw. Comput. Applic. 175 (2021), 102915.

[56]

Judy C. Guevara and Nelson L. S. da Fonseca. 2021. Task scheduling in cloud-fog computing systems. Peer-to-Peer Netw. Applic. 14, 2 (2021), 962–977.

[57]

Nasim Soltani, Behzad Soleimani, and Behrang Barekatain. 2017. Heuristic algorithms for task scheduling in cloud computing: A survey. Int. J. Comput. Netw. Inf. Secur. 9, 8 (2017), 16.

[58]

Bushra Jamil, Mohammad Shojafar, Israr Ahmed, Atta Ullah, Kashif Munir, and Humaira Ijaz. 2020. A job scheduling algorithm for delay and performance optimization in fog computing. Concurr. Comput.: Pract. Exper. 32, 7 (2020), e5581.

[59]

Doan Hoang and Thanh Dat Dang. 2017. FBRC: Optimization of task scheduling in fog-based region and cloud. In Proceedings of the Trustcom/BigDataSE/ICESS Conference. IEEE, 1109–1114.

[60]

Yang Yang, Kunlun Wang, Guowei Zhang, Xu Chen, Xiliang Luo, and Ming-Tuo Zhou. 2018. MEETS: Maximal energy efficient task scheduling in homogeneous fog networks. IEEE Internet Things J. 5, 5 (2018), 4076–4087.

[61]

Georgios L. Stavrinides and Helen D. Karatza. 2019. A hybrid approach to scheduling real-time IoT workflows in fog and cloud environments. Multimedia Tools Applic. 78, 17 (2019), 24639–24655.

Digital Library

[62]

Nitin Auluck, Akramul Azim, and Kaneez Fizza. 2019. Improving the schedulability of real-time tasks using fog computing. IEEE Trans. Serv. Comput. 15, 1 (2019), 372–385.

[63]

Randa M. Abdelmoneem, Abderrahim Benslimane, and Eman Shaaban. 2020. Mobility-aware task scheduling in cloud-fog IoT-based healthcare architectures. Comput. Netw. 179 (2020), 107348.

[64]

Deze Zeng, Lin Gu, Song Guo, Zixue Cheng, and Shui Yu. 2016. Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Trans. Comput. 65, 12 (2016), 3702–3712.

Digital Library

[65]

Zahra Pooranian, Mohammad Shojafar, Paola G. Vinueza Naranjo, Luca Chiaraviglio, and Mauro Conti. 2017. A novel distributed fog-based networked architecture to preserve energy in fog data centers. In Proceedings of the IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 604–609.

[66]

Guowei Zhang, Fei Shen, Nanxi Chen, Pengcheng Zhu, Xuewu Dai, and Yang Yang. 2018. DOTS: Delay-optimal task scheduling among voluntary nodes in fog networks. IEEE Internet Things J. 6, 2 (2018), 3533–3544.

[67]

Yang Yang, Shuang Zhao, Wuxiong Zhang, Yu Chen, Xiliang Luo, and Jun Wang. 2018. DEBTS: Delay energy balanced task scheduling in homogeneous fog networks. IEEE Internet Things J. 5, 3 (2018), 2094–2106.

[68]

Michael J. Neely. 2010. Stochastic network optimization with application to communication and queueing systems. Synth. Lect. Commun. Netw. 3, 1 (2010), 1–211.

[69]

Xuan-Qui Pham and Eui-Nam Huh. 2016. Towards task scheduling in a cloud-fog computing system. In Proceedings of the 18th Asia-Pacific Network Operations and Management Symposium (APNOMS). IEEE, 1–4.

[70]

John H. Drake, Ahmed Kheiri, Ender Özcan, and Edmund K. Burke. 2020. Recent advances in selection hyper-heuristics. Eur. J. Oper. Res. 285, 2 (2020), 405–428.

[71]

Lindong Liu, Deyu Qi, Naqin Zhou, and Yilin Wu. 2018. A task scheduling algorithm based on classification mining in fog computing environment. Wirel. Commun. Mob. Comput. 2018 (2018).

Digital Library

[72]

Sabihe Kabirzadeh, Dadmehr Rahbari, and Mohsen Nickray. 2017. A hyper heuristic algorithm for scheduling of fog networks. In Proceedings of the 21st Conference of Open Innovations Association (FRUCT). IEEE, 148–155.

Digital Library

[73]

Chun-Wei Tsai, Wei-Cheng Huang, Meng-Hsiu Chiang, Ming-Chao Chiang, and Chu-Sing Yang. 2014. A hyper-heuristic scheduling algorithm for cloud. IEEE Trans. Cloud Comput. 2, 2 (2014), 236–250.

[74]

Juan Wang and Di Li. 2019. Task scheduling based on a hybrid heuristic algorithm for smart production line with fog computing. Sensors 19, 5 (2019), 1023.

[75]

Ashish Mohan Yadav, Kuldeep Narayan Tripathi, and S. C. Sharma. 2022. A bi-objective task scheduling approach in fog computing using hybrid fireworks algorithm. Journal Supercomput. 78, 3 (2022), 4236–4260.

Digital Library

[76]

Essam H. Houssein, Ahmed G. Gad, Yaser M. Wazery, and Ponnuthurai Nagaratnam Suganthan. 2021. Task scheduling in cloud computing based on meta-heuristics: Review, taxonomy, open challenges, and future trends. Swarm Evolut. Comput. 62 (2021), 100841.

[77]

Mohamed Abdel-Basset, Laila Abdel-Fatah, and Arun Kumar Sangaiah. 2018. Metaheuristic algorithms: A comprehensive review. In Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications. Elsevier, 185–231.

[78]

Chun-Wei Tsai and Joel J. P. C. Rodrigues. 2013. Metaheuristic scheduling for cloud: A survey. IEEE Syst. J. 8, 1 (2013), 279–291.

[79]

Xin-She Yang, Su Fong Chien, and Tiew On Ting. 2014. Computational intelligence and metaheuristic algorithms with applications. The Scientific World Journal 2014 (2014).

[80]

Qianyu Liu, Yunkai Wei, Supeng Leng, and Yijin Chen. 2017. Task scheduling in fog enabled internet of things for smart cities. In Proceedings of the IEEE 17th International Conference on Communication Technology (ICCT). IEEE, 975–980.

[81]

Yabin Wang, Chenghao Guo, and Jin Yu. 2018. Immune scheduling network based method for task scheduling in decentralized fog computing. Wirel. Commun. Mob. Comput. 2018 (2018).

Digital Library

[82]

Binh Minh Nguyen, Huynh Thi Thanh Binh, and Bao Do Son2019. Evolutionary algorithms to optimize task scheduling problem for the IoT based bag-of-tasks application in cloud–fog computing environment. Appl. Sci. 9, 9 (2019), 1730.

[83]

Salim Bitam, Sherali Zeadally, and Abdelhamid Mellouk. 2018. Fog computing job scheduling optimization based on bees swarm. Enterp. Inf. Syst. 12, 4 (2018), 373–397.

[84]

Dadmehr Rahbari and Mohsen Nickray. 2019. Low-latency and energy-efficient scheduling in fog-based IoT applications. Turk. J. Electric. Eng. Comput. Sci. 27, 2 (2019), 1406–1427.

[85]

K. P. N. Jayasena and B. S. Thisarasinghe. 2019. Optimized task scheduling on fog computing environment using meta heuristic algorithms. In Proceedings of the IEEE International Conference on Smart Cloud (SmartCloud). IEEE, 53–58.

[86]

Seyedali Mirjalili and Andrew Lewis. 2016. The whale optimization algorithm. Adv. Eng. Softw. 95 (2016), 51–67.

Digital Library

[87]

Jiuyun Xu, Zhuangyuan Hao, Ruru Zhang, and Xiaoting Sun. 2019. A method based on the combination of laxity and ant colony system for cloud-fog task scheduling. IEEE Access 7 (2019), 116218–116226.

[88]

Haluk Topcuoglu, Salim Hariri, and Min-you Wu. 2002. Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13, 3 (2002), 260–274.

Digital Library

[89]

Yan Sun, Fuhong Lin, and Haitao Xu. 2018. Multi-objective optimization of resource scheduling in Fog computing using an improved NSGA-II. Wirel. Person. Commun. 102, 2 (2018), 1369–1385.

Digital Library

[90]

Mostafa Ghobaei-Arani, Alireza Souri, Fatemeh Safara, and Monire Norouzi. 2020. An efficient task scheduling approach using moth-flame optimization algorithm for cyber-physical system applications in fog computing. Trans. Emerg. Telecommun. Technol. 31, 2 (2020), e3770.

Digital Library

[91]

Seyedali Mirjalili. 2015. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowle.-based Syst. 89 (2015), 228–249.

Digital Library

[92]

Shudong Wang, Tianyu Zhao, and Shanchen Pang. 2020. Task scheduling algorithm based on improved firework algorithm in fog computing. IEEE Access 8 (2020), 32385–32394.

[93]

Ming Yang, Hao Ma, Shuang Wei, You Zeng, Yefeng Chen, and Yuemei Hu. 2020. A multi-objective task scheduling method for fog computing in cyber-physical-social services. IEEE Access 8 (2020), 65085–65095.

[94]

Farooq Hoseiny, Sadoon Azizi, Mohammad Shojafar, Fardin Ahmadizar, and Rahim Tafazolli. 2021. PGA: A priority-aware genetic algorithm for task scheduling in heterogeneous fog-cloud computing. In Proceedings of the IEEE IEEE Conference on Computer Communications Workshops. 1–6.

[95]

Narayana Potu, Chandrashekar Jatoth, and Premchand Parvataneni. 2021. Optimizing resource scheduling based on extended particle swarm optimization in fog computing environments. Concurr. Comput.: Pract. Exper. 33, 23 (2021), e6163.

[96]

Rongbin Xu, Yeguo Wang, Yongliang Cheng, Yuanwei Zhu, Ying Xie, Abubakar Sadiq Sani, and Dong Yuan. 2018. Improved particle swarm optimization based workflow scheduling in cloud-fog environment. In Proceedings of the International Conference on Business Process Management. Springer, 337–347.

[97]

Lotfi A. Zadeh. 1996. Soft computing and fuzzy logic. In Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi a Zadeh. World Scientific, 796–804.

[98]

Muhammad Imran Tariq, Shahzadi Tayyaba, Muhammad Waseem Ashraf, Muhammad Imran, Emil Pricop, Otilia Cangea, Nicolae Paraschiv, and Natash Ali Mian. 2020. An analysis of the application of fuzzy logic in cloud computing. J. Intell. Fuzzy Syst.Preprint 38, 5 (2020), 5933–5947.

[99]

Mohammed Anis Benblidia, Bouziane Brik, Leila Merghem-Boulahia, and Moez Esseghir. 2019. Ranking fog nodes for tasks scheduling in fog-cloud environments: A fuzzy logic approach. In Proceedings of the 15th International Wireless Communications & Mobile Computing Conference (IWCMC). IEEE, 1451–1457.

[100]

Guangshun Li, Yuncui Liu, Junhua Wu, Dandan Lin, and Shuaishuai Zhao. 2019. Methods of resource scheduling based on optimized fuzzy clustering in fog computing. Sensors 19, 9 (2019), 2122.

[101]

Saeed Javanmardi, Mohammad Shojafar, Valerio Persico, and Antonio Pescapè. 2021. FPFTS: A joint fuzzy particle swarm optimization mobility-aware approach to fog task scheduling algorithm for Internet of Things devices. Softw.: Pract. Exper. 51, 12 (2021), 2519–2539.

[102]

Chu-Ge Wu, Wei Li, Ling Wang, and Albert Y. Zomaya. 2021. An evolutionary fuzzy scheduler for multi-objective resource allocation in fog computing. Fut. Gen. Comput. Syst. 117 (2021), 498–509.

[103]

Hala S. Ali, Rashmi Ranjan Rout, Priyanka Parimi, and Sajal K. Das. 2021. Real-time task scheduling in fog-cloud computing framework for IoT applications: A fuzzy logic based approach. In Proceedings of the International Conference on COMmunication Systems & NETworkS (COMSNETS). IEEE, 556–564.

[104]

Yailen Martínez Jiménez. 2012. A Generic Multi-agent Reinforcement Learning Approach for Scheduling Problems. PhD. Vrije Universiteit Brussel.

[105]

Richard S. Sutton and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. The MIT Press.

Digital Library

[106]

Paulo Henrique Valente Klaine. 2019. Self-organization for 5G and Beyond Mobile Networks Using Reinforcement learning. Ph.D. Dissertation. University of Glasgow.

[107]

Yunior César Fonseca Reyna, Yailen Martínez Jiménez, Juan Manuel Bermúdez Cabrera, and Beatriz M. Méndez Hernández. 2015. A reinforcement learning approach for scheduling problems. Investigac. Operac. 36, 3 (2015), 225–231.

[108]

Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath. 2017. Deep reinforcement learning: A brief survey. IEEE Sig. Process. Mag. 34, 6 (2017), 26–38.

[109]

Christopher J. C. H. Watkins and Peter Dayan. 1992. Q-learning. Mach. Learn. 8, 3–4 (1992), 279–292.

Digital Library

[110]

Alexandru Iulian Orhean, Florin Pop, and Ioan Raicu. 2018. New scheduling approach using reinforcement learning for heterogeneous distributed systems. J. Parallel Distrib. Comput. 117 (2018), 292–302.

Digital Library

[111]

Xiaolan Liu, Zhijin Qin, and Yue Gao. 2019. Resource allocation for edge computing in IoT networks via reinforcement learning. arXiv preprint arXiv:1903.01856 (2019).

[112]

Qing Wu, Zhiwei Wu, Yuehui Zhuang, and Yuxia Cheng. 2018. Adaptive DAG tasks scheduling with deep reinforcement learning. In Proceedings of the International Conference on Algorithms and Architectures for Parallel Processing. Springer, 477–490.

[113]

Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio. 2016. Deep Learning. Vol. 1. The MIT Press, Cambridge.

Digital Library

[114]

Oliver Faust, Yuki Hagiwara, Tan Jen Hong, Oh Shu Lih, and U. Rajendra Acharya. 2018. Deep learning for healthcare applications based on physiological signals: A review. Comput. Meth. Prog. Biomed. 161 (2018), 1–13.

[115]

Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436–444.

[116]

S. Amudha and M. Murali. 2020. Deep learning based energy efficient novel scheduling algorithms for body-fog-cloud in smart hospital. J. Amb. Intell. Human. Comput. (2020), 1–20.

[117]

Fredrik Osterlind, Adam Dunkels, Joakim Eriksson, Niclas Finne, and Thiemo Voigt. 2006. Cross-level sensor network simulation with COOJA. In Proceedings of the 31st IEEE Conference on Local Computer Networks. IEEE, 641–648.

[118]

Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath. 2015. A brief survey of deep reinforcement learning. Nature 518, 7540 (2015), 529–533.

[119]

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski et al. 2015. Human-level control through deep reinforcement learning. Nature 518, 7540 (2015), 529–533.

[120]

Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, and Nando Freitas. 2016. Dueling network architectures for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning. PMLR, 1995–2003.

Digital Library

[121]

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. Fut. Gen. Comput. Syst. (2019).

[122]

Vijay R. Konda and John N. Tsitsiklis. 2000. Actor-critic algorithms. In Proceedings of the Conference on Advances in Neural Information Processing Systems. 1008–1014.

[123]

Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971 (2015).

[124]

Hongzi Mao, Mohammad Alizadeh, Ishai Menache, and Srikanth Kandula. 2016. Resource management with deep reinforcement learning. In Proceedings of the 15th ACM Workshop on Hot Topics in Networks. ACM, 50–56.

Digital Library

[125]

Weijia Chen, Yuedong Xu, and Xiaofeng Wu. 2017. Deep reinforcement learning for multi-resource multi-machine job scheduling. arXiv preprint arXiv:1711.07440 (2017).

[126]

Yufei Ye, Xiaoqin Ren, Jin Wang, Lingxiao Xu, Wenxia Guo, Wenqiang Huang, and Wenhong Tian. 2018. A new approach for resource scheduling with deep reinforcement learning. arXiv preprint arXiv:1806.08122 (2018).

[127]

Simeng Bian, Xi Huang, and Ziyu Shao. 2019. Online task scheduling for fog computing with multi-resource fairness. In Proceedings of the IEEE 90th Vehicular Technology Conference (VTC’19-Fall). IEEE, 1–5.

[128]

Ali Ghodsi, Matei Zaharia, Benjamin Hindman, Andy Konwinski, Scott Shenker, and Ion Stoica. 2011. Dominant resource fairness: Fair allocation of multiple resource types. In Proceedings of the Conference on Networked Systems Design & Implementation. 24–24.

[129]

John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017).

[130]

John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. 2015. High-dimensional continuous control using generalized advantage estimation. arXiv preprint arXiv:1506.02438 (2015).

[131]

Shreshth Tuli, Shashikant Ilager, Kotagiri Ramamohanarao, and Rajkumar Buyya. 2020. Dynamic scheduling for stochastic edge-cloud computing environments using A3C learning and residual recurrent neural networks. IEEE Trans. Mob. Comput. (2020).

[132]

Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016. Asynchronous methods for deep reinforcement learning. In Proceedings of the International Conference on Machine Learning. 1928–1937.

Digital Library

[133]

Boxuan Yue, Junwei Fu, and Jun Liang. 2018. Residual recurrent neural networks for learning sequential representations. Information 9, 3 (2018), 56.

[134]

Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, César A. F. De Rose, and Rajkumar Buyya. 2011. CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw.: Pract. Exper. 41, 1 (2011), 23–50.

Digital Library

[135]

Bhathiya Wickremasinghe, Rodrigo N. Calheiros, and Rajkumar Buyya. 2010. CloudAnalyst: A CloudSim-based visual modeller for analysing cloud computing environments and applications. In Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications. IEEE, 446–452.

Digital Library

[136]

Henri Casanova, Arnaud Legrand, and Martin Quinson. 2008. SimGrid: A generic framework for large-scale distributed experiments. In Proceedings of the 10th International Conference on Computer Modeling and Simulation (UKSIM’08). IEEE, 126–131.

Digital Library

[137]

Antonio Gulli and Sujit Pal. 2017. Deep Learning with Keras. Packt Publishing Ltd.

Digital Library

[138]

Redowan Mahmud and Rajkumar Buyya. 2019. Modelling and simulation of fog and edge computing environments using iFogSim toolkit. Fog Edge Comput.: Princ. Parad. (2019), 1–35.

[139]

David Perez Abreu, Karima Velasquez, Marilia Curado, and Edmundo Monteiro. 2020. A comparative analysis of simulators for the cloud to fog continuum. Simul. Model. Pract. Theor. 101 (2020), 102029.

[140]

B. Barney. 2015. Introduction to Parallel Computing. Lawrence Livermore National Laboratory, USA.

[141]

Asif Ali Laghari, Awais Khan Jumani, and Rashid Ali Laghari. 2021. Review and state of art of fog computing. Arch. Comput. Meth. Eng. (2021), 1–13.

[142]

Assad Abbas, Samee U. Khan, and Albert Y. Zomaya. 2020. Fog Computing: Theory and Practice. John Wiley & Sons.

[143]

Daewoo Kim, Sangwoo Moon, David Hostallero, Wan Ju Kang, Taeyoung Lee, Kyunghwan Son, and Yung Yi. 2019. Learning to schedule communication in multi-agent reinforcement learning. arXiv preprint arXiv:1902.01554 (2019).

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Chen XCai ZZhang HMa RBuyya R(2025)FasDL: An Efficient Serverless-Based Training Architecture With Communication Optimization and Resource ConfigurationIEEE Transactions on Computers10.1109/TC.2024.348520274:2(468-482)Online publication date: Feb-2025
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Index Terms

Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future Directions
1. Networks
  1. Network algorithms
    1. Control path algorithms
      1. Network resources allocation
  2. Network services
    1. Cloud computing

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 11s

January 2022

785 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3551650

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 09 September 2022

Online AM: 07 February 2022

Accepted: 01 January 2022

Revised: 01 January 2022

Received: 01 June 2021

Published in CSUR Volume 54, Issue 11s

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Cited By

Nimmy Francis N. V. Balaji (2025)Techniques for load balancing throughout the cloud: a comprehensive literature analysisInternational Journal of Computational and Experimental Science and Engineering10.22399/ijcesen.79611:1Online publication date: 12-Jan-2025
https://doi.org/10.22399/ijcesen.796
Chen XCai ZZhang HMa RBuyya R(2025)FasDL: An Efficient Serverless-Based Training Architecture With Communication Optimization and Resource ConfigurationIEEE Transactions on Computers10.1109/TC.2024.348520274:2(468-482)Online publication date: Feb-2025
https://doi.org/10.1109/TC.2024.3485202
Wen DCao QFeng SZhang ZZhou P(2025)A Chaotic Elite Cloning Artificial Jellyfish Algorithm for Efficient Task Allocation in IOTWSNsIEEE Sensors Journal10.1109/JSEN.2024.352371025:4(6905-6919)Online publication date: 15-Feb-2025
https://doi.org/10.1109/JSEN.2024.3523710
Chia Chuan WUl Arfeen Laghari SManickam SAshraf EKaruppayah S(2025)Challenges and Opportunities in Fog Computing Scheduling: A Literature ReviewIEEE Access10.1109/ACCESS.2024.352526113(14702-14726)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2024.3525261
Sun MDing JZhao ZChen JHuang GWang L(2025)Out-of-order execution enabled deep reinforcement learning for dynamic additive manufacturing schedulingRobotics and Computer-Integrated Manufacturing10.1016/j.rcim.2024.10284191:COnline publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1016/j.rcim.2024.102841
Alsharif MJahid AKannadasan RSingla MGupta JNisar KAbdel-Aty AKim M(2025)Survey of energy-efficient fog computing: Techniques and recent advancesEnergy Reports10.1016/j.egyr.2025.01.03913(1739-1763)Online publication date: Jun-2025
https://doi.org/10.1016/j.egyr.2025.01.039
Al-Qadhi ALatip RChiong RAthauda RHussin M(2025)Independent task scheduling algorithms in fog environments from users’ and service providers’ perspectives: a systematic reviewCluster Computing10.1007/s10586-024-04771-228:3Online publication date: 1-Jun-2025
https://dl.acm.org/doi/10.1007/s10586-024-04771-2
Alsadie D(2024)Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospectsPeerJ Computer Science10.7717/peerj-cs.212810(e2128)Online publication date: 17-Jun-2024
https://doi.org/10.7717/peerj-cs.2128
Sharma NKumar LDwivedi NKaur AKaur G(2024)Resource Allocation and Security Threat in Cloud Computing: A SurveyCGC International Journal of Contemporary Technology and Research10.46860/cgcijctr.2024.06.10.3816:2(381-387)Online publication date: 17-Sep-2024
https://doi.org/10.46860/cgcijctr.2024.06.10.381
Manikandan NVinod DAnto Arockia Rosaline RNancy PPremalatha G(2024)Green Computing-Based Digital Waste Management and Resource Allocation for Distributed Fog Data CentersComputational Intelligence for Green Cloud Computing and Digital Waste Management10.4018/979-8-3693-1552-1.ch011(209-226)Online publication date: 27-Feb-2024
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