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Task Scheduling in Fog Computing - Classification, Review, Challenges and Future Directions

  • Received : 2022.04.05
  • Published : 2022.04.30

Abstract

With the advancement in the Internet of things Technology (IoT) cloud computing, billions of physical devices have been interconnected for sharing and collecting data in different applications. Despite many advancements, some latency - specific application in the real world is not feasible due to existing constraints of IoT devices and distance between cloud and IoT devices. In order to address issues of latency sensitive applications, fog computing has been developed that involves the availability of computing and storage resources at the edge of the network near the IoT devices. However, fog computing suffers from many limitations such as heterogeneity, storage capabilities, processing capability, memory limitations etc. Therefore, it requires an adequate task scheduling method for utilizing computing resources optimally at the fog layer. This work presents a comprehensive review of different task scheduling methods in fog computing. It analyses different task scheduling methods developed for a fog computing environment in multiple dimensions and compares them to highlight the advantages and disadvantages of methods. Finally, it presents promising research directions for fellow researchers in the fog computing environment.

Keywords

References

  1. Buyya, R., James, B. and Goscinski, A.M. (2010), Cloud Computing : Principles and Paradigms, Vol. 87, John Wiley and Sons.
  2. Dikaiakos, M.D., Katsaros, D., Mehra, P., Pallis, G. and Vakali, A. (2009), "Cloud computing : Distributed internet computing for IT and scientific research", IEEE Internet Computing, Vol. 13 No. 5, pp.10-13. https://doi.org/10.1109/MIC.2009.103
  3. de Carvalho, E.R., da Mota, A.E.A.S., de Souza Martins, G.M., Bastos, L. D S. L. and Melo, A.C.S. (2017), "The current context of lean and six sigma logistics applications in literature : a systematic review", Brazilian Journal of Operations and Production Management,Vol. 14 No.4, pp.586-602. https://doi.org/10.14488/BJOPM.2017.v14.n4.a14
  4. Yang, Xin, and Nazanin Rahmani. "Task scheduling mechanisms in fog computing : review, trends, and perspectives." Kybernetes (2020).
  5. Singh, R. M., Awasthi, L. K., & Sikka, G. (2022). Towards Metaheuristic Scheduling Techniques in Cloud and Fog : An Extensive Taxonomic Review. ACM Computing Surveys (CSUR), 55 (3), 1-43. https://doi.org/10.1145/3494520
  6. A. V. Dastjerdi, H. Gupta, R. N. Calheiros, S. K. Ghosh, and R. Buyya. 2016. Fog computing : Principles, Architectures, and Applications. Internet of Things. Morgan Kaufmann (2016), 61-75.
  7. A. Yousefpour, C. Fung, T. Nguyen, K. Kadiyala, F. Jalali, A. Niakanlahiji, J. Kong, and J. 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. https://doi.org/10.1016/j.sysarc.2019.02.009
  8. Kaur, N., Kumar, A., & Kumar, R. (2021). A systematic review on task scheduling in Fog computing : Taxonomy, tools, challenges, and future directions. Concurrency and Computation : Practice and Experience, 33 (21), e6432.
  9. Sharma S, Saini H. A novel four-tier architecture for delay aware scheduling and load balancing in fog environment. Sustain Comput Inform Syst. 2019 ; 24 : 100355.
  10. Luo, Juan, Luxiu Yin, Jinyu Hu, Chun Wang, Xuan Liu, Xin Fan, and Haibo Luo. "Container-based fog computing architecture and energy-balancing scheduling algorithm for energy IoT." Future Generation Computer Systems 97 (2019) : 50-60. https://doi.org/10.1016/j.future.2018.12.063
  11. Naha, Ranesh Kumar, Saurabh Garg, Andrew Chan, and Sudheer Kumar Battula. "Deadline-based dynamic resource allocation and provisioning algorithms in fog-cloud environment." Future Generation Computer Systems 104 (2020) : 131-141. https://doi.org/10.1016/j.future.2019.10.018
  12. Singh, A., Auluck, N., Rana, O., Jones, A., & Nepal, S. (2019). Scheduling Real-Time Security Aware Tasks in Fog Networks. IEEE Transactions on Services Computing, 14 (6), 1981-1994.
  13. Stavrinides, G. L., & Karatza, H. D. (2019). A hybrid approach to scheduling real-time IoT workflows in fog and cloud environments. Multimedia Tools and Applications, 78 (17), 24639-24655. https://doi.org/10.1007/s11042-018-7051-9
  14. Johnson, D. S., & Garey, M. R. (1979). A guide to the theory of NP completeness. computers and intractability.
  15. Perera, C., Qin, Y., Estrella, J.C., Reiff-Marganiec, S. and Vasilakos, A.V. (2017), "Fog computing for sustainable smart cities : a survey", ACM Computing Surveys, Vol. 50 No. 3, pp. 1-43.arXiv preprint arXiv : 1703.07079.
  16. Sharma, R. and Rani, S. (2019), "Resource scheduling in fog computing : a review", International Journal of Advanced Studies of Scientific Research, Vol. 4 No. 3.
  17. Chun-Wei Tsai and Joel J.P.C. Rodrigues. 2013. Metaheuristic scheduling for cloud : A survey. IEEE Systems Journal 8, 1 (2013), 279-291. https://doi.org/10.1109/JSYST.2013.2256731
  18. Z. H. Zhan, X. F. Liu, Y. J. Gong, J. Zhang, H. S. H. Chung, and Y. Li. 2015. Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Computing Surveys 47, 4 (2015), 1-33.
  19. Mala Kalra and Sarbjeet Singh. 2015. A review of metaheuristic scheduling techniques in cloud computing. Egyptian Informatics Journal 16, 3 (2015), 275-295. https://doi.org/10.1016/j.eij.2015.07.001
  20. S. H. H. Madni, M. S. A. Latiff, Y. Coulibaly, and S. M. Abdulhamid. 2016. An appraisal of meta-heuristic resource allocation techniques for IaaS cloud. Indian Journal of Science and Technology 9, 4 (2016), 1-14.
  21. A. R. Arunarani, D. Manjula, and V. Sugumaran. 2019. Task scheduling techniques in cloud computing : A literature survey. Future Generation Computer Systems 91 (2019), 407-415. https://doi.org/10.1016/j.future.2018.09.014
  22. P. Singh, M. Dutta, and N. Aggarwal. 2017. A review of task scheduling based on meta-heuristics approach in cloud computing. Knowledge and Information Systems 52, 1 (2017), 1-51. https://doi.org/10.1007/s10115-017-1044-2
  23. M. Kumar, S. C. Sharma, A. Goel, and S. P. Singh. 2019. A comprehensive survey for scheduling techniques in cloud computing. Journal of Network and Computer Applications 143 (2019), 1-33. https://doi.org/10.1016/j.jnca.2019.06.006
  24. Alizadeh MR, Khajehvand V, Rahmani AM, Akbari E. Task scheduling approaches in fog computing : a systematic review. Int J Commun Syst. 2020 ; 33 (16) : e4583. https://doi.org/10.1002/dac.4583
  25. Singh RM, Awasthi LK, Sikka G. Techniques for task scheduling in cloud and fog environment : a survey. Paper presented at : Proceedings of the International Conference on Futuristic Trends in Networks and Computing Technologies ; 2019 : 673-685.
  26. Hosseinioun, P., Kheirabadi, M., Kamel Tabbakh, S. R., & Ghaemi, R. (2020). aTask scheduling approaches in fog computing : a survey. Transactions on Emerging Telecommunications Technologies, e3792. https://doi.org/10.1002/ett.3792.
  27. Sindhu, V., & Prakash, M. (2019, November). A Survey on Task Scheduling and Resource Allocation Methods in Fog Based IoT Applications. In International Conference on Communication and Intelligent Systems (pp. 89-97). Springer, Singapore.
  28. Alizadeh, M. R., Khajehvand, V., Rahmani, A. M., & Akbari, E. (2020). Task scheduling approaches in fog computing : A systematic review. International Journal of Communication Systems, 33 (16), e4583. https://doi.org/10.1002/dac.4583
  29. Rajesh ME, Mahalakshmi MJ. Optimization of resource allocation using FCFS scheduling in cloud computing. Optimization. 2015 ; 5 (2) : 20-26.
  30. El Amrani C, Gibet Tani H. Smarter round robin scheduling algorithm for cloud computing and big data. J Data Mining Digital Humanit. 2018.
  31. Wu X, Huang D, Sun YE, Bu X, Xin Y, Huang H. An efficient allocation mechanism for crowdsourcing tasks with minimum execution time. In : International Conference on Intelligent Computing. Cham : Springer ; 2017, August : 156-167.
  32. Li Y, Niu J, Zhang J, Atiquzzaman M, Long X. Real-time scheduling for periodic tasks in homogeneous multi-core system with minimum execution time. In : International Conference on Collaborative Computing : Networking, Applications and Worksharing. Cham : Springer ; 2016 : 175-187.
  33. Srinath HMDM. Memory constrained load shared minimum execution time grid task scheduling algorithm in a heterogeneous environment. Indian J Sci Technol. 2015 ; 8 (15) : 15.
  34. Wu HY, Lee CR. Energy efficient scheduling for heterogeneous fog computing architectures. Paper presented at : Proceedings of the IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC),Tokyo, Japan ; Vol.1, 2018 : 555-560.
  35. Hoang, D., & Dang, T. D. (2017, August). FBRC : Optimization of task scheduling in fog-based region and cloud. In 2017 IEEE Trustcom/BigDataSE/ICESS (pp. 1109-1114). IEEE. https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.36.
  36. Ghobaei-Arani, Mostafa, Alireza Souri, Fatemeh Safara, and Monire Norouzi. "An efficient task scheduling approach using moth-flame optimization algorithm for cyber-physical system applications in fog computing." Transactions on Emerging Telecommunications Technologies 31, no. 2 (2020) : e3770.https://doi.org/10.1002/ett.3770.
  37. Sujana J, Angela J, Geethanjali M, Raj RV, Revathi T. Trust Model Based Scheduling of Stochastic Workflows in Cloud and Fog Computing. New York, NY : Springer ; 2019 : 29-54.
  38. Wang Y, Guo C, Yu J. Immune scheduling network based method for task scheduling in decentralized fog computing. Wirel Commun Mob Comput. 2018 ; 2018 : 1-9.https://doi.org/10.1155/2018/2734219.
  39. Wang J, Li D. Task scheduling based on a hybrid heuristic algorithm for smart production line with fog computing. Sensors. 2019 ; 19 (5) : 1023. https://doi.org/10.3390/s19051023
  40. Rahbari, Dadmehr, Sabihe Kabirzadeh, and Mohsen Nickray. "A security aware scheduling in fog computing by hyper heuristic algorithm." In 2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS), pp. 87 - 92. Ieee, 2017.
  41. Nazir, Saqib, Sundas Shafiq, Zafar Iqbal, Muhammad Zeeshan, Subhan Tariq, and Nadeem Javaid. "Cuckoo optimization algorithm based job scheduling using cloud and fog computing in smart grid." In International Conference on Intelligent Networking and Collaborative Systems, pp. 34-46. Springer, Cham, 2018.
  42. Boveiri HR, Khayami R, Elhoseny M, Gunasekaran M. An efficient swarm - intelligence approach for task scheduling in cloud-based internet of things applications.J Ambient Intell Humaniz Comput.2019 ; 10 (9) : 3469-3479. https://doi.org/10.1007/s12652-018-1071-1
  43. Sun Y, Lin F, Xu H. Multi-objective optimization of resource scheduling in fog computing using an improved NSGA-II. Wireless Personal Comm. 2018 ; 102 (2) : 1369-1385. https://doi.org/10.1007/s11277-017-5200-5
  44. Isard M, Prabhakaran V, Currey J, Wieder U, Talwar K, Goldberg A. Quincy : fair scheduling for distributed computing clusters. In : Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles. ACM ; 2009, October : 261-276.
  45. Hoang, Doan, and Thanh Dat Dang. "FBRC : Optimization of task scheduling in fog-based region and cloud." In 2017 IEEE Trustcom / Big Data SE / ICESS, pp. 1109-1114. IEEE, 2017.
  46. Rasheed S, Javaid N, Rehman S, Hassan K, Zafar F, Naeem M. A cloud-fog based smart grid model using max-min scheduling algorithm for efficient resource allocation. In : International Conference on Network-Based Information Systems. Cham : Springer ; 2018 : 273-285.
  47. Choudhari T, Moh M, Moh TS. Prioritized task scheduling in fog computing. In : Proceedings of the ACMSE 2018 Conference. ACM ; 2018 : 22.
  48. Kabirzadeh S, Rahbari D, Nickray M. A hyper heuristic algorithm for scheduling of fog networks. In : 2017 21st Conference of Open Innovations Association (FRUCT). IEEE ; 2017 : 148-155.
  49. Yin L, Luo J, Luo H. Tasks scheduling and resource allocation in fog computing based on containers for smart manufacturing. IEEE Trans Industrial Informatics. 2018 ; 14 (10) : 4712-4721. https://doi.org/10.1109/tii.2018.2851241
  50. Rahbari, D., & Nickray, M. (2017, November). Scheduling of fog networks with optimized knapsack by symbiotic organisms search. In 2017 21st Conference of Open Innovations Association (FRUCT) (pp. 278-283). IEEE.
  51. Liu Q, Wei Y, Leng S, Chen Y. Task scheduling in fog enabled Internet of Things for smart cities. In : 2017 IEEE 17th International Conference on Communication Technology (ICCT). IEEE ; 2017, October : 975-980.
  52. Wang Y, Guo C, Yu J. Immune scheduling network based method for task scheduling in decentralized fog computing. Wireless Communications and Mobile Computing. 2018 ; 2018.
  53. Bitam S, Zeadally S, Mellouk A. Fog computing job scheduling optimization based on bees swarm. Enterprise Info Syst. 2018 ; 12 (4) : 373-397. https://doi.org/10.1080/17517575.2017.1304579
  54. Oueis J, Strinati EC, Barbarossa S. The fog balancing : load distribution for small cell cloud computing. In : 2015 IEEE 81st Vehicular Technology Conference (VTC Spring). IEEE ; 2015 : 1-6.
  55. Intharawijitr K, Iida K, Koga H. Analysis of fog model considering computing and communication latency in 5G cellular networks. In : 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops). IEEE ; 2016 : 1-4.
  56. Deng R, Lu R, Lai C, Luan TH, Liang H. Optimal workload allocation in fog- cloud computing toward balanced delay and power consumption. IEEE Internet Things J. 2016 ; 3 (6) : 1171-1181. https://doi.org/10.1109/JIOT.2016.2565516
  57. Pham XQ, Huh EN. Towards task scheduling in a cloud-fog computing system. In : 2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS). IEEE ; 2016 : 1-4.
  58. Li G, Liu Y, Wu J, Lin D, Zhao S. Methods of resource scheduling based on optimized fuzzy clustering in fog computing. Sensors. 2019 ; 19 (9) : 21-22.
  59. Nguyen BM, Thi Thanh Binh H, Do Son B. Evolutionary algorithms to optimize task scheduling problem for the IoT based Bag-of-Tasks application in cloud - fog computing environment. Applied Sci. 2019 ; 9 (9) : 17-30.
  60. Wang J, Li D. Task scheduling based on a hybrid heuristic algorithm for smart production line with fog computing. Sensors. 2019 ; 19 (5) : 10-23.
  61. Benblidia MA, Brik B, Merghem-Boulahia L, Esseghir M. Ranking fog nodes for tasks scheduling in fog-cloud environments : a fuzzy logic approach. In : 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC). IEEE ; 2019, June : 1451-1457.
  62. Jamil B, Shojafar M, Ahmed I, Ullah A, Munir K, Ijaz H. A job scheduling algorithm for delay and performance optimization in fog computing. Concurr Comp : Practice Exp. 2020 ; 32 (7) : e5581. https://doi.org/10.1002/cpe.5581
  63. Zhao S, Yang Y, Shao Z, Yang X, Qian H, Wang CX. FEMOS : fog-enabled multitier operations scheduling in dynamic wireless networks. IEEE Internet Things J. 2018 ; 5 (2) : 1169-1183. https://doi.org/10.1109/jiot.2018.2808280
  64. Yang Y, Zhao S, Zhang W, Chen Y, Luo X, Wang J. DEBTS : delay energy balanced task scheduling in homogeneous fog networks. IEEE Internet Things J. 2018 ; 5 (3) : 2094-2106. https://doi.org/10.1109/jiot.2018.2823000
  65. Wan J, Chen B, Wang S, Xia M, Li D, Liu C. Fog computing for energy-aware load balancing and scheduling in smart factory. IEEE Trans Industrial Informatics. 2018 ; 14 (10) : 4548-4556. https://doi.org/10.1109/tii.2018.2818932
  66. Jie Y, Tang X, Choo KKR, Su S, Li M, Guo C. Online task scheduling for edge computing based on repeated Stackelberg game. J Parallel Distrib Comp. 2018 ; 122 : 159-172. https://doi.org/10.1016/j.jpdc.2018.07.019
  67. Cardellini V, Grassi V, Presti FL, Nardelli M. On QoS-aware scheduling of data stream applications over fog computing infrastructures. In : 2015 IEEE Symposium on Computers and Communication (ISCC). IEEE ; 2015, July : 271-276.
  68. Ningning S, Chao G, Xingshuo A, Qiang Z. Fog computing dynamic load balancing mechanism based on graph repartitioning. China Comm. 2016 ; 13 (3) : 156-164. https://doi.org/10.1109/CC.2016.7445510
  69. Zeng D, Gu L, Guo S, Cheng Z, Yu S. Joint optimization of task scheduling and image placement in fog computing supported software defined embedded system. IEEE Trans Comput. 2016 ; 65 (12) : 3702-3712. https://doi.org/10.1109/TC.2016.2536019
  70. Sharma S, Saini H. A novel four-tier architecture for delay aware scheduling and load balancing in fog environment. Sustain Comp : Info Syst. 2019 ; 24 : 100355.
  71. Gazori P, Rahbari D, Nickray M. Saving time and cost on the scheduling of fog - based IoT applications using deep reinforcement learning approach. Future Gen Comp Syst. 2019.1098-1115. https://doi.org/10.1016/j.future.2019.09.060
  72. Abdelmoneem RM, Benslimane A, Shaaban E. Mobility-aware task scheduling in cloud-fog IoT-based healthcare architectures. Comp Networks. 2020 ; 107348-107354. https://doi.org/10.1016/j.comnet.2020.107348
  73. Rahbari D, Kabirzadeh S, Nickray M. A security aware scheduling in fog computing by hyper heuristic algorithm. In : 2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS). IEEE ; 2017 : 87-92.
  74. Bittencourt LF, Diaz-Montes J, Buyya R, Rana OF, Parashar M. Mobility - aware application scheduling in fog computing. IEEE Cloud Comp. 2017 ; 4 (2) : 26-35. https://doi.org/10.1109/MCC.2017.27
  75. Verma M, Bhardwaj N, Yadav AK. Real time efficient scheduling algorithm for load balancing in fog computing environment. Int J Inf Technol Comput Sci. 2016 ; 8 (4) : 1-10. https://doi.org/10.5815/ijitcs.2016.04.01
  76. Gad-Elrab AA, Noaman AY. A two-tier bipartite graph task allocation approach based on fuzzy clustering in cloud-fog environment. Future Gen Comp Syst. 2020 ; 103 : 79-90. https://doi.org/10.1016/j.future.2019.10.003
  77. Ying Wah T, Gopal Raj R, Lakhan A. A novel cost-efficient framework for critical heartbeat task scheduling using the Internet of medical things in a fog cloud system. Sensors. 2020 ; 20 (2) : 441. https://doi.org/10.3390/s20020441
  78. Sun Z, Li C, Wei L, Li Z, Min Z, Zhao G. Intelligent sensor-cloud in fog computer : a novel hierarchical data job scheduling strategy. Sensors. 2019 ; 19 (23) : 50-83.
  79. Al Ahmad M, Patra SS, Barik RK. Energy-efficient resource scheduling in fog computing using SDN framework. In : Progress in Computing, Analytics and Networking. Singapore : Springer ; 2020 : 567-578.
  80. 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.
  81. JAMIL, B., IJAZ, H., SHOJAFAR, M., MUNIR, K., & BUYYA, R. (2022). Resource Allocation and Task Scheduling in Fog Computing and Internet of Everything Environments: A Taxonomy, Review, and Future Directions.
  82. Shakarami, A., Shakarami, H., Ghobaei-Arani, M., Nikougoftar, E., & Faraji-Mehmandar, M. (2022). Resource provisioning in edge/fog computing: A Comprehensive and Systematic Review. Journal of Systems Architecture, 122, 102362. https://doi.org/10.1016/j.sysarc.2021.102362
  83. AL-Amodi, S., Patra, S. S., Bhattacharya, S., Mohanty, J. R., Kumar, V., & Barik, R. K. (2022). Meta-heuristic Algorithm for Energy-Efficient Task Scheduling in Fog Computing. In Recent Trends in Electronics and Communication (pp. 915-925). Springer, Singapore.
  84. Singh, R. M., Awasthi, L. K., & Sikka, G. (2022). Towards Metaheuristic Scheduling Techniques in Cloud and Fog: An Extensive Taxonomic Review. ACM Computing Surveys (CSUR), 55(3), 1-43. https://doi.org/10.1145/3494520
  85. Hosseini, E., Nickray, M., & Ghanbari, S. (2022). Optimized task scheduling for cost-latency trade-off in mobile fog computing using fuzzy analytical hierarchy process. Computer Networks, 108752.
  86. Ometov, A., Molua, O. L., Komarov, M., & Nurmi, J. (2022). A Survey of Security in Cloud, Edge, and Fog Computing. Sensors, 22(3), 927. https://doi.org/10.3390/s22030927
  87. Hossam, H. S., Abdel-Galil, H., & Belal, M. A. A Survey of Fog Computing: Architecture and Research Challenges.
  88. Chandak, A. V., Ray, N. K., Barik, R. K., & Kumar, V. (2022). Performance Analysis of Task Scheduling Heuristics in Fog Environment. In Recent Trends in Electronics and Communication (pp. 857-863). Springer, Singapore.
  89. POTU, N., BHUKYA, S., JATOTH, C., & PARVATANENI, P. (2022). Quality-aware energy efficient scheduling model for fog computing comprised IoT network. Computers & Electrical Engineering, 97, 107603. https://doi.org/10.1016/j.compeleceng.2021.107603