IJETMS LANDING PAGE

International Journal of Engineering Technology and Management Sciences

2023, Volume 7 Issue 1

TASK SCHEDULING IN FOG COMPUTING ENVIRONMENT: AN OVERVIEW

AUTHOR(S)

Nikita Sehgal, Savina Bansal and RK Bansal

DOI: https://doi.org/10.46647/ijetms.2023.v07i01.009

ABSTRACT
The number of Internet of Things (IoT) devices and sensors has significantly expanded in recent years. In general, fog computing increases cloud-based processing, storage, and networking capabilities by being located closer to IoT devices and sensors. Task scheduling is used to finish work in a set amount of time using a finite number of resources. The completion of tasks within the allotted time in fog computing is a key difficulty due to the increased amount of data that needs to be processed. Additionally, in order to identify current research gaps in the field of fog, we map the existing works to the taxonomy. This article offers a broad overview of various task and resource scheduling techniques used in fog computing. It examines and contrasts several techniques created for a fog computing environment to ascertain their contributions and limitations. Moreover, it offers encouraging study directions for other researchers working in this area.

Page No: 47 - 54

References:


[1] A.Dastjerdi, H. Gupta, R. Calheiros, S. Ghosh, and R. Buyya (2016), Chapter 4—fog computing: Principles, architectures, and applications, In Internet of Things: Principles and Paradigms, ed. R. Buyya, and A.V. Dastjerdi, New York: Morgan Kaufmann, pp. 61-75.
[2] Arwa Alrawais, Abdulrahman Alhothaily, Chunqiang Hu and Xiuzhen Cheng (2017),Fog Computing for the Internet of Things: Security and Privacy Issues, IEEE Internet Computing, 21(2), pp.34-42, DOI: 10.1109/MIC.2017.37
[3] Azizi, S., Shojafar, M., Abawajy, J., &Buyya, R. (2022). Deadline-aware and energy-efficient IoT task scheduling in fog computing systems: A semi-greedy approach. Journal of Network and Computer Applications, 103333.
[4] Bal, P. K., Mohapatra, S. K., Das, T. K., Srinivasan, K., & Hu, Y. C. (2022). A Joint Resource Allocation, Security with Efficient Task Scheduling in Cloud Computing Using Hybrid Machine Learning Techniques. Sensors, 22(3), 1242.
[5] Bonomi, F., Milito, R., Zhu, J., Addepalli, S.(2012), Fog computing and its role in the internet of things. Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing - MCC’12, p. 13. doi.org/10.1145/2342509.2342513.
[6] C.K.Chen, Y.H.Chang, Y.T.Chen ,C.C.Yang, J.K.Lee, (2007) Switching supports for stateful object remoting on network processors, Journal of Supercomputer 40, pp.281–298.
[7] Du J, Zhao L, Feng J, Chu X (2018) Computation offloading and resource allocation in mixed fog/cloud computing systems with min-max fairness guarantee, IEEE Transactions on Communications, 66(4), pp. 1594–1608.
[8] F Fellir, A El Attar, K Nafil,& L Chung,(2020). A multi-Agent based model for task scheduling in cloud-fog computing platform. In 2020 IEEE international conference on informatics, IoT, and enabling technologies (ICIoT), pp. 377-382.
[9] Fog computing and the internet of things: Extend the cloud to where the things are, cisco white paper. 2015. http://www.cisco.com/c/dam/en_us/solutions/trends/iot/ docs/computing-overview.pdf
[10] G.L. Stavrinides, H.D. Karatza (2019), A hybrid approach to scheduling real-time IoT workflows in fog and cloud environments, Multimedia Tools Applications 78, pp. 24639–24655.
[11] H. Topcuoglu, S. Hariri, M.y. Wu (2002), Performance-effective and low-complexity task scheduling for heterogeneous computing, IEEE Transactions Parallel Distributed Systems, 13, pp. 260–274.
[12] P Hosseinioun, M Kheirabadi, S. R. K. Tabbakh, & R Ghaemi, (2020), A new energy-aware tasks scheduling approach in fog computing using hybrid meta-heuristic algorithm, Journal of Parallel and Distributed Computing, 143, pp. 88-96.
[13] J. Xu, Z. Hao, R. Zhang, X. Sun (2019), A method based on the combination of laxity and ant colony system for cloud-fog task scheduling, IEEE Access, pp.116218–116226.
[14] Jasleen Kaur, Alka Agrawal and Raees Ahmad Khan (2020), Security Issues in Fog Environment: A Systematic Literature Review, Springer- International Journal of Wireless Information Networks, 27, pp. 467-483, DOI: https://doi.org/10.1007/s10776-020-00491-7.
[15] S.Javanmardi, M Shojafar, R Mohammadi, A Nazari, V Persico, & A Pescapè (2021), FUPE: A security driven task scheduling approach for SDN-based IoT–Fog networks, Journal of Information Security and Applications, 60, 102853.
[16] Jimoh Yakubu, Shafi Muhammad Abdulhamid, Haruna Atabo Christopher, Haruna Chiroma and Mohammed Abdullahi (2019), Security Challenges In Fog-Computing Environment: A Systematic Appraisal Of Current Developments, Springer- Journal of Reliable Intelligent Environments, 5, pp. 209-233, DOI:https://doi.org/10.1007/s40860-019-00081-2.
[17] Liu L, Chang Z, Guo X, Mao S, Ristaniemi T (2017), Multiobjective optimization for computation ofoading in fog computing, IEEE Internet Things Jounal, 5(1), 283–294.
[18] Medara, R., & Singh, R. S. (2021). Energy efficient and reliability aware workflow task scheduling in cloud environment. Wireless Personal Communications, 119(2), 1301-1320.
[19] Nirmal Kaur, Savina Bansal and Rakesh Bansal (2013), Energy aware scheduling strategies for distributed computing systems, International journal of advanced research in computer science and software engineering, 3, pp. 280-283
[20] N Kaur, S Bansal and RK Bansal (2016), Energy efficient duplication-based scheduling for precedence constrained tasks on heterogeneous computing cluster, Multiagent and Grid Systems 12 (3), pp. 239-252
[21] N Kaur, S Bansal, RK Bansal (2021), Survey on energy efficient scheduling techniques on cloud computing, Multiagent and Grid Systems, 17 (4), pp. 351-366
[22] Razaque, A., Jararweh, Y., Alotaibi, B., Alotaibi, M., Hariri, S., &Almiani, M. (2022), Energy-efficient and secure mobile fog-based cloud for the Internet of Things. Future Generation Computer Systems, 127, pp.1-13.
[23] S. Bitam, S. Zeadally, A. Mellouk (2018), Fog computing job scheduling - optimization based on bees swarm, Enterprise Information Systems ,12, pp. 373–397.
[24] S Bansal, P Kumar, K Singh (2002), Duplication-based scheduling algorithm for interconnection-constrained distributed memory machines, International Conference on High-Performance Computing, pp. 52-62
[25] S.Sarka, and S. Misra (2016), Theoretical modelling of fog computing: A green computing paradigm to support IoT applications, IET Networks,5(2), pp. 23-29
[26] Savina Bansal, Padam Kumar and Kuldip Singh (2003), An Improved Duplication Strategy for Scheduling Precedence Constrained Graphs in Multiprocessor Systems, IEEE Transactions on Parallel and Distributed Systems-TPDS, vol. 14, pp. 533-544.
[27] S. Sharma, H. Saini (2019), A novel four-tier architecture for delay aware scheduling and load balancing in fog environment, Sustainable Computing: Informatics and Systems, DOI:10.1016/j.suscom.2019.100355.
[28] S. Ningning, G. Chao, A. Xingshuo, Z. Qiang (2016), Fog computing dynamic load balancing mechanism based on graph repartioning,China Communications.13, pp.156–164.
[29] Savina Bansal, Rakesh Kumar Bansal, Kiran Arora (2020), Energy-cognizant scheduling for preference-oriented fixed-priority real-time tasks, Journal of Systems Architecture, vol. 108, doi.org/10.1016/j.sysarc.2020.101743
[30] M H Shahid, A R Hameed, S ul Islam, H A Khattak, I U Din, I. U., & J J Rodrigues, (2020), Energy and delay efficient fog computing using caching mechanism. Computer Communications, 154, 534-541.
[31] H Shah-Mansouri, VW Wong (2018), Hierarchical fog-cloud computing for IoT systems: A computation offloading game. IEEE Internet Things Journal, 5(4), pp. 3246-3257
[32] A Singh, N Auluck, O Rana, & S Nepal (2021), Scheduling Real Time Security Aware Tasks in Fog Networks, IEEE World Congress on Services.
[33] S Subbaraj & R Thiyagarajan, (2021), Performance oriented task-resource mapping and scheduling in fog computing environment, Cognitive Systems Research, 70, pp.40-50.
[34] T. Wang, Z. Liu, Y. Chen, Y. Xu, X. Dai (2014), Load balancing task scheduling based on genetic algorithm in cloud computing, Proceedings of the 2014 IEEE 12th International Conference on Dependable, Autonomic and Secure Computing, IEEE, Dalian,China,pp.146–152.
[35] Wang Q, Chen S (2020), Latency-minimum offloading decision and resource allocation for fog-enabled internet of things networks, Transactions on Emerging Telecommunications Technologies,31(12), pp 1-14.
[36] X. Xu, S. Fu, Q. Cai, W. Tian, W. Liu, W. Dou, (2018), Dynamic resource allocation for load balancing in fog environment, pp. 1-15.
[37] X. Zhang, H. Duan, J. Jin (2008), DEACO: hybrid ant colony optimization with differential evolution, Proceedings of the 2008 IEEE Congress on Evolutionary Computation, IEEE World Congresson Computational Intelligence, Hong Kong, China, pp.921–927.
[38] Xuan-Qui Pham, Nguyen Doan Man, Nguyen Dao Tan TriN.D.T.Tri, N.Q.Thai, E.N.Huh (2017), A cost-and performance-effective approach for task scheduling based on collaboration between cloud and fog computing, International Journal of Distributed Sensor Networks, 13(11), pp.1-16
[39] Y. Sun, F. Lin, H. Xu (2018), Multi-objective optimization of resource scheduling in Fog computing using an improved NSGA-II, Wireless Personal Communication,102, pp. 1369–1385.
[40] Y. Yang, S.Zhao, W.Zhang, Y.Chen, X.Luo, J.Wang (2018), DEBTS: delay energy balanced task scheduling in homogeneous fog networks , IEEE Journal of Internet of Things, 5, pp 2094-2106.

How to Cite This Article:
Nikita Sehgal, Savina Bansal and RK Bansal. TASK SCHEDULING IN FOG COMPUTING ENVIRONMENT: AN OVERVIEW. ijetms;7(1):47-54. DOI: 10.46647/ijetms.2023.v07i01.009