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Resource Scheduling Techniques for Optimal Quality of Service in Fog Computing Environment: A Review

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Abstract

in present-day Internet of Things (IoT) is a trending technology and playing a vital role in human life to build up a quality life. With the advancement in IoT devices, Fog computing is turn-out solution to handle IoT applications efficiently. Many IoT applications are run in Fog environments with central Fog nodes, and by servers in Cloud. Because of the heterogeneous and distributed environment of the Fog system, the management of an increasing number of IoT applications within available resources for optimal QoS (Quality-of-service) is a necessity. In this work, a review of resource scheduling techniques for optimal QoS has been done. The provided taxonomy for approaches of QoS management in the Fog environment is classified into Resource Scheduling, Energy Efficiency, and Security. In this work issues and approaches of four resource scheduling techniques like task scheduling, resource allocation, task offloading, and application placement are discussed in detail. A comparative analysis of these four techniques on performance metrics, advantages and disadvantages, and implementation tools are discussed.

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References

  1. Aburukba, R. O., AliKarrar, M., Landolsi, T., & El-Fakih, K. (2020). Scheduling internet of things requests to minimize latency in hybrid fog cloud computing. Future Generation Computer Systems, 111, 539–551.

    Google Scholar 

  2. Ghobaei-Arani, M., & Shahidinejad, A. (2022). A cost-efficient IoT service placement approach using whale optimization algorithm in fog computing environment. Expert Systems with Applications, 200, 117012.

    Google Scholar 

  3. Shingare, H., & Kumar, M. (2023). Whale optimization-based task offloading technique in integrated cloud-fog environment. In Soft Computing for Problem Solving: Proceedings of the SocProS 2022 (pp. 459–469). Springer.

  4. Kumar, S., & Tiwari, R. (2021). Dynamic popularity window and distance-based efficient caching for fast content delivery applications in CCN, Engineering Science and Technology, an. International Journal, 24, 829–837.

    Google Scholar 

  5. Bansal, M., & Malik, S. K. (2020). A multi-faceted optimization scheduling framework based on the particle swarm optimization algorithm in cloud computing. Sustainable Computing: Informatics and Systems, 28, 100429.

    Google Scholar 

  6. Thangavel, S., & Saravanakumar, S. (2022). Certain investigations in data migration and security by sequence cover cat and cover particle swarm optimization in fog computing. Available at SSRN 4199645.

  7. Mahmud, R., Kotagiri, R., & Buyya, R. (2018). Fog computing: A taxonomy, survey and future directions (pp. 103–130).

  8. Goel, G., Tiwari, R., Koundal, D., & Upadhyay, S. (2021). Analysis of resource scheduling algorithms for optimization in IoT-fog-cloud system.

  9. Tiwari, R., Mittal, M., Garg, S., & Kumar, S. (2022). Energy-aware resource scheduling in fog environment for IoI-based applications, Energy conservation solutions for fog-edge computing paradigms (pp. 1–19).

  10. Saif, F.A., Latip, R., Hanapi, Z., & Shafinah, K. (2023). Multi-objective grey wolf optimizer algorithm for task scheduling in cloud-fog computing. IEEE Access.

  11. Tiwari, R., Sille, R., Salankar, N., & Singh, P. (2021). Utilization and energy consumption optimization for cloud computing environment. In Cyber Security and Digital Forensics: Proceedings of ICCSDF (pp. 609–619). Springer.

  12. Bharathi, R., Abirami, T., Dhanasekaran, S., Gupta, D., Khanna, A., Elhoseny, M., & Shankar, K. (2020). Energy efficient clustering with disease diagnosis model for IoT based sustainable healthcare systems. Sustainable Computing: Informatics and Systems, 28, 100453.

    Google Scholar 

  13. Salimian, M., Ghobaei-Arani, M., & Shahidinejad, A. (2021). Toward an autonomic approach for internet of things service placement using gray wolf optimization in the fog computing environment. Software: Practice and Experience, 51, 1745–1772.

    Google Scholar 

  14. Abdelmoneem, R. M., Benslimane, A., & Shaaban, E. (2020). Mobility-aware task scheduling in cloud-Fog IoT-based healthcare architectures. Computer Networks, 179, 107348.

    Google Scholar 

  15. Goel, G., & Tiwari, R. (2022). Dynamic resource allocation in fog computing environment. Advancements in Interdisciplinary Research: First International Conference, AIR 2022, Prayagraj, India, May 6–7 (pp. 85–93). Springer: Revised Selected Papers.

    Google Scholar 

  16. Goel, G., Tiwari, R., Anand, A., & Kumar, S. (2021). Workflow scheduling using optimization algorithm in fog computing. In International Conference on Innovative Computing and Communications: Proceedings of ICICC (Vol. 2, pp. 379–390). Springer.

  17. Sharma, V., & Bala, M. (2020). An improved task allocation strategy in cloud using modified k-means clustering technique. Egyptian Informatics Journal.

  18. Lal, G., Goel, T., Tanwar, V., & Tiwari, R. (2016). Performance tuning approach for cloud environment. In Intelligent systems technologies and applications (pp. 317–326). Springer.

  19. Wang, T., Liang, Y., Jia, W., Arif, M., Liu, A., & Xie, M. (2019). Coupling resource management based on fog computing in smart city systems. Journal of Network and Computer Applications, 135, 11–19.

    Google Scholar 

  20. Hashemi, S. M., Sahafi, A., Rahmani, A. M., & Bohlouli, M. (2022). Gwo-sa: Gray wolf optimization algorithm for service activation management in fog computing. IEEE Access, 10, 107846–107863.

    Google Scholar 

  21. Tiwari, R., Mittal, M., & Goyal, L. M. (2022). Energy conservation solutions for Fog-edge computing paradigms. Springer.

  22. Al Ahmad, M., Patra, S. S., & Barik, R. K. (2020). Energy-efficient resource scheduling in Fog computing using SDN framework (pp. 567–578).

  23. Abedi, S., Ghobaei-Arani, M., Khorami, E., & Mojarad, M. (2022). Dynamic resource allocation using improved firefly optimization algorithm in cloud environment. Applied Artificial Intelligence, 36, 2055394.

    Google Scholar 

  24. Abdel-Basset, M., Mohamed, R., Chakrabortty, R. K., & Ryan, M. J. (2021). Iega: An improved elitism-based genetic algorithm for task scheduling problem in fog computing. International Journal of Intelligent Systems, 36, 4592–4631.

    Google Scholar 

  25. Ahmed, O. H., Lu, J., Xu, Q., Ahmed, A. M., Rahmani, A. M., & Hosseinzadeh, M. (2021). Using differential evolution and moth-flame optimization for scientific workflow scheduling in fog computing. Applied Soft Computing, 112, 107744.

    Google Scholar 

  26. Subbaraj, S., Thiyagarajan, R., & Rengaraj, M. (2021). A smart fog computing based real-time secure resource allocation and scheduling strategy using multi-objective crow search algorithm. Journal of Ambient Intelligence and Humanized Computing, 1, 1–13.

    Google Scholar 

  27. Goel, G., & Tiwari, R. (2022). Resource scheduling in fog environment using optimization algorithms for 6g networks. International Journal of Software Science and Computational Intelligence (IJSSCI), 14, 1–24.

    Google Scholar 

  28. Mani, S. K., & Meenakshisundaram, I. (2020). Improving quality-of-service in fog computing through efficient resource allocation. Computational Intelligence.

  29. Murtaza, F., Akhunzada, A., & ul Islam, S., Boudjadar, J., & Buyya, R. (2020). Qos-aware service provisioning in fog computing. Journal of Network and Computer Applications, 1, 102674.

  30. Vatanparvar, K., & Al Faruque, M. A. (2018). Control-as-a-service in cyber-physical energy systems over fog computing. In Fog Computing in the Internet of Things (pp. 123–144). Springer.

  31. Hsieh, S.-Y., Liu, C.-S., Buyya, R., & Zomaya, A. Y. (2020). Utilization-prediction-aware virtual machine consolidation approach for energy-efficient cloud data centers. Journal of Parallel and Distributed Computing, 139, 99–109.

    Google Scholar 

  32. Haghi Kashani, M., Rahmani, A. M., & Jafari Navimipour, N. (2020). Quality of service-aware approaches in fog computing. International Journal of Communication Systems, 33, e4340.

    Google Scholar 

  33. Bellendorf, J., & Mann, Z. Á. (2020). Classification of optimization problems in fog computing. Future Generation Computer Systems, 107, 158–176.

    Google Scholar 

  34. 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, e4583.

    Google Scholar 

  35. Aslanpour, M. S., Gill, S. S., & Toosi, A. N. (2020). Performance evaluation metrics for cloud, fog and edge computing: A review, taxonomy, benchmarks and standards for future research. Internet of Things, 1, 100273.

    Google Scholar 

  36. Aazam, M., Zeadally, S., & Harras, K. A. (2018). Offloading in fog computing for IoT: Review, enabling technologies, and research opportunities. Future Generation Computer Systems, 87, 278–289.

    Google Scholar 

  37. Hong, C.-H., & Varghese, B. (2018). Resource management in fog/edge computing: A survey. arXiv preprint arXiv:1810.00305.

  38. Grover, J., & Garimella, R. M. (2019). Optimization in edge computing and small-cell networks. In Edge Computing (pp. 17–31). Springer.

  39. Vambe, W. T., Chang, C., & Sibanda, K. (2020). A review of quality of service in fog computing for the internet of things. International Journal of Fog Computing (IJFC), 3, 22–40.

    Google Scholar 

  40. Abd Elaziz, M., Abualigah, L., & Attiya, I. (2021). Advanced optimization technique for scheduling IoT tasks in cloud-fog computing environments. Future Generation Computer Systems, 124, 142–154.

    Google Scholar 

  41. Hussain, M., Azar, A. T., Ahmed, R., Umar Amin, S., Qureshi, B., Dinesh Reddy, V., Alam, I., Khan, Z. I., et al. (2023). SONG: A multi-objective evolutionary algorithm for delay and energy aware facility location in vehicular fog networks. Sensors, 23, 667.

    Google Scholar 

  42. Gupta, H., Vahid Dastjerdi, A., Ghosh, S. K., & Buyya, R. (2017). IFOGSIM: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience, 47, 1275–1296.

    Google Scholar 

  43. Yin, L., Luo, J., & Luo, H. (2018). Tasks scheduling and resource allocation in fog computing based on containers for smart manufacturing. IEEE Transactions on Industrial Informatics, 14, 4712–4721.

    Google Scholar 

  44. Battula, S. K., Garg, S., Montgomery, J., & Kang, B. H. (2019). An efficient resource monitoring service for fog computing environments. IEEE Transactions on Services Computing.

  45. Mutlag, A. A., Abd Ghani, M. K., Arunkumar, N., Mohammed, M. A., & Mohd, O. (2019). Enabling technologies for fog computing in healthcare IoT systems. Future Generation Computer Systems, 90, 62–78.

  46. Al-Tarawneh, M. A. (2022). Bi-objective optimization of application placement in fog computing environments. Journal of Ambient Intelligence and Humanized Computing, 13, 445–468.

    Google Scholar 

  47. Bandopadhaya, S., Dey, R., & Suhag, A. (2020). Integrated healthcare monitoring solutions for soldier using the internet of things with distributed computing. Sustainable Computing: Informatics and Systems, 26, 100378.

    Google Scholar 

  48. Lv, Z., Chen, D., Lou, R., & Wang, Q. (2020). Intelligent edge computing based on machine learning for smart city. Future Generation Computer Systems, 115, 90–99.

    Google Scholar 

  49. Bitam, S., Zeadally, S., & Mellouk, A. (2018). Fog computing job scheduling optimization based on bees swarm. Enterprise Information Systems, 12, 373–397.

    Google Scholar 

  50. Ghobaei-Arani, M., Souri, A., Safara, F., & Norouzi, M. (2020). An efficient task scheduling approach using moth-flame optimization algorithm for cyber-physical system applications in fog computing. Transactions on Emerging Telecommunications Technologies, 31, e3770.

    Google Scholar 

  51. Malleswaran, S. K. A., & Kasireddi, B. (2019). An efficient task scheduling method in a cloud computing environment using firefly crow search algorithm (FF-CSA).

  52. Nazir, S., Shafiq, S., Iqbal, Z., Zeeshan, M., Tariq, S., & Javaid, N. (2018). Cuckoo optimization algorithm based job scheduling using cloud and fog computing in smart grid (pp. 34–46).

  53. Moh, T. C. M., & Moh, T. (2018). Prioritized task scheduling in fog computing.

  54. Naha, R. K., Garg, S., Chan, A., & Battula, S. K. (2020). Deadline-based dynamic resource allocation and provisioning algorithms in fog-cloud environment. Future Generation Computer Systems, 104, 131–141.

    Google Scholar 

  55. Li, G., Liu, Y., Wu, J., Lin, D., & Zhao, S. (2019). Methods of resource scheduling based on optimized fuzzy clustering in fog computing. Sensors, 19, 2122.

    Google Scholar 

  56. Sun, Y., Lin, F., & Xu, H. (2018). Multi-objective optimization of resource scheduling in fog computing using an improved NSGA-II. Wireless Personal Communications, 102, 1369–1385.

    Google Scholar 

  57. Rehman, S., Javaid, N., Rasheed, S., Hassan, K., Zafar, F., & Naeem, M. (2018). Min-min scheduling algorithm for efficient resource distribution using cloud and fog in smart buildings (pp. 15–27).

  58. Hussein, M. K., & Mousa, M. H. (2020). Efficient task offloading for IoT-based applications in fog computing using ant colony optimization. IEEE Access, 8, 37191–37201.

    Google Scholar 

  59. Kaur, K., Garg, S., Kaddoum, G., Gagnon, F., & Jayakody, D. N. K. (2019). Enlob: Energy and load balancing-driven container placement strategy for data centers (pp. 1–6).

  60. Deng, R., Lu, R., Lai, C., Luan, T. H., & Liang, H. (2016). Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet of Things Journal, 3, 1171–1181.

    Google Scholar 

  61. Peralta, G., Garrido, P., Bilbao, J., Agüero, R., & Crespo, P. M. (2020). Fog to cloud and network coded based architecture: Minimizing data download time for smart mobility. Simulation Modelling Practice and Theory, 101, 102034.

    Google Scholar 

  62. Naranjo, P. G. V., Pooranian, Z., Shojafar, M., Conti, M., & Buyya, R. (2019). FOCAN: A fog-supported smart city network architecture for management of applications in the internet of everything environments. Journal of Parallel and Distributed Computing, 132, 274–283.

    Google Scholar 

  63. Gao, N., Xu, C., Peng, X., Luo, H., Wu, W., & Xie, G. (2020). Energy-efficient scheduling optimization for parallel applications on heterogeneous distributed systems. Journal of Circuits, Systems and Computers, 1, 2050203.

  64. Pereira, J., Ricardo, L., Luís, M., Senna, C., & Sargento, S. (2019). Assessing the reliability of fog computing for smart mobility applications in VANETs. Future Generation Computer Systems, 94, 317–332.

    Google Scholar 

  65. Wang, A., Yan, P., & Batiha, K. (2020). A comprehensive study on managing strategies in the fog environments. Transactions on Emerging Telecommunications Technologies, 31, e3833.

    Google Scholar 

  66. Khan, Z. A., Butt, A. A., Alghamdi, T. A., Fatima, A., Akbar, M., Ramzan, M., & Javaid, N. (2019). Energy management in smart sectors using fog based environment and meta-heuristic algorithms. IEEE Access, 7, 157254–157267.

    Google Scholar 

  67. Oma, R., Nakamura, S., Duolikun, D., Enokido, T., & Takizawa, M. (2018). An energy-efficient model for fog computing in the internet of things (iot). Internet of Things, 1, 14–26.

    Google Scholar 

  68. Butt, A. A., Khan, S., Ashfaq, T., Javaid, S., Sattar, N. A., & Javaid, N. (2019). A cloud and fog based architecture for energy management of smart city by using meta-heuristic techniques (pp. 1588–1593).

  69. Toor, A., ul Islam, S., Sohail, N., Akhunzada, A., Boudjadar, J., Khattak, H. A., Din, I. U., & Rodrigues, J. J. (2019). Energy and performance aware fog computing: A case of DVFs and green renewable energy. Future Generation Computer Systems, 101, 1112–1121.

  70. Arora, T., Dhir, R., & Soni, R. (2023). Innovations in multimedia information processing & retrieval.

  71. Mohammed, M. A., Mohammed, I. A., Hasan, R. A., Ţăpuş, N., Ali, A. H., & Hammood, O. A. (2019). Green energy sources: Issues and challenges (pp. 1–8).

  72. Luo, J., Yin, L., Hu, J., Wang, C., Liu, X., Fan, X., & Luo, H. (2019). Container-based fog computing architecture and energy-balancing scheduling algorithm for energy IoT. Future Generation Computer Systems, 97, 50–60.

    Google Scholar 

  73. Shahid, M. H., Hameed, A. R., ul Islam, S., Khattak, H. A., Din, I. U., & Rodrigues, J. J. (2020). Energy and delay efficient fog computing using caching mechanism. Computer Communications.

  74. Goel, G., & Tiwari, R. (2022). Task management in IoT-Fog-cloud environment employing static scheduling techniques. ENP Engineering Science Journal, 2, 13–20.

    Google Scholar 

  75. Tuli, S., Mahmud, R., Tuli, S., & Buyya, R. (2019). Fogbus: A blockchain-based lightweight framework for edge and fog computing. Journal of Systems and Software, 154, 22–36.

    Google Scholar 

  76. Chen, C.-M., Huang, Y., Wang, K.-H., Kumari, S., & Wu, M.-E. (2020). A secure authenticated and key exchange scheme for fog computing. Enterprise Information Systems 1–16.

  77. Huang, B., Cheng, X., Cao, Y., & Zhang, L. (2018). Lightweight hardware based secure authentication scheme for fog computing (pp. 433–439).

  78. Kumari, A., & Tanwar, S. (2020). Secure data analytics for smart grid systems in a sustainable smart city: Challenges, solutions, and future directions. Sustainable Computing: Informatics and Systems, 28, 100427.

    Google Scholar 

  79. de Souza, C. A., Westphall, C. B., Machado, R. B., Sobral, J. B. M., & dos Santos Vieira, G. (2020). Hybrid approach to intrusion detection in fog-based IoT environments. Computer Networks, 180, 107417.

    Google Scholar 

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  1. UPES, Dehradun, Uttarakhand, India

    Gaurav Goel & Rajeev Tiwari

  2. Chandigarh Engineering College-CGC, Landran, Mohali, India

    Gaurav Goel

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Goel, G., Tiwari, R. Resource Scheduling Techniques for Optimal Quality of Service in Fog Computing Environment: A Review. Wireless Pers Commun 131, 141–164 (2023). https://doi.org/10.1007/s11277-023-10421-4

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