A Review of Workload Challenges in Fog Computing Environment

Year 2023, Volume: 7 Issue: 1, 1564 - 1578, 30.06.2023

Omar Anwer Nafea , Turkan Ahmed Khaleel

https://doi.org/10.56554/jtom.1273276

Abstract

Users nowadays in environments with Fog computing require applications that respond quickly to their requests for everything they want to access and work quickly and require to increase in the Quality of Service (QoS) metrics such as minimum energy consumption, bandwidth efficiency, and reduction latency in a Fog network, resulting in an improvement in the system's performance, that is done by getting to know the workload on the network and how to deal with it. In this paper, the various Fog computing workloads are described, along with where each one should be executed, in addition, discuss the load-balancing techniques and strategies count as a very important issue and one of the important challenges in the Fog computing environment, that play a significant role in resource management like resource provisioning, task offloading, resource scheduling, and resource allocation this will be done based on reviewing previous research and discussing the most important concepts in it.

Keywords

Fog Computing, workload, Cloud, Qos, Load-balancing

References

• Aazam, M., Zeadally, S., & Harras, K. A. (2018). Deploying Fog Computing in Industrial Internet of Things andIndustry 4.0. IEEE Transactions on Industrial Informatics, 14(10), 4674–4682.doi https://doi.org/10.1109/TII.2018.2855198.
• Adhianto, L., Banerjee, S., Fagan, M., Krentel, M., Marin, G., Mellor-Crummey, J., & Tallent, N. R. (2017). A survey on load balancing algorithms for virtual machines placement in cloud computing. Concurrency and Computation: Practice and Experience, 22(6), 685–701.doi https://doi.org/10.1002/cpe.4123.
• Alqahtani, F., & Amoon, M. (2021). Reliable scheduling and load balancing for requests in cloud-fog computing. Peer-to-Peer Networking and Applications, 14, No. 4. doi https://doi.org/10.1007/s12083-021-01125-2.
• Baek, J., Kaddoum, G., Garg, S., Kaur, K., & Gravel, V. (2019). Managing Fog Networks using Reinforcement Learning Based Load Balancing Algorithm. April, 15–18. https://ieeexplore.ieee.org/document/8885745
• Bigelow, S. J. (n.d.). Workload. Retrieved November 6, 2022, from https://www.techtarget.com/searchdatacenter/definition/workload
• Calzarossa, Maria Carla, Luisa Massari, D. T. (2016). Workload Characterization: A Survey Revisited. ACM Computing Surveys (CSUR)., Vol. 48, N(3), pp 1–43. https://doi.org/10.1145/2856127.
• Chiang, M., & Zhang, T. (2016). Fog and IoT: An Overview of Research Opportunities. IEEE Internet of Things Journal, 3(6), 854–864.doi https://doi.org/10.1109/JIOT.2016.2584538.
• Chowdhary, G., & Rathod, D. (2019). Load Balancing of Fog Computing Centers : Minimizing Response Time of High Priority Requests. Int. J. Innov. Technol. Explor. Eng., 8,(October), no. 11, pp. 2713–2716. doi https://doi.org/10.35940/ijitee.K2171.0981119.
• Cinzia Cappiello, P. P. and M. V. (2018). A Data Utility Model for Data-Intensive Applications in Fog Computing Environments. In Fog Computing: Concepts, Frameworks and Technologies. Springer International Publishing. doi https://doi.org/10.1007/978-3-319-94890-4.
• Costa, B., Bachiega, J., De Carvalho, L. R., & Araujo, A. P. F. (2022). Orchestration in Fog Computing: A Comprehensive Survey. ACM Computing Surveys, 55(2).doi https://doi.org/10.1145/3486221 .
• Dastjerdi, A. V., & Buyya, R. (2016). Fog Computing: Helping the Internet of Things Realize Its Potential. Computer, 49(8), 112–116.doi https://doi.org/10.1109/MC.2016.245 .
• Datta, S. K., Bonnet, C., & Haerri, J. (2015). Fog Computing architecture to enable consumer centric Internet of Things services. Proceedings of the International Symposium on Consumer Electronics, ISCE, 1–2. doi https://doi.org/10.1109/ISCE.2015.7177778
• Fan, Q., Member, S., & Ansari, N. (2018). Towards Workload Balancing in Fog Computing Empowered IoT. IEEE Transactions on Network Science and Engineering, PP(X), 1. doi https://doi.org/10.1109/TNSE.2018.2852762.
• Habibi, P., Member, S., Farhoudi, M., Leon-garcia, A., & Fellow, L. (2020). Fog Computing : A Comprehensive Architectural Survey. 69105–69133. https://ieeexplore.ieee.org/abstract/document/9046806.
• Hamrioui, S., Lorenz, P., & Grtc, M. (2017). Load Balancing Algorithm for Efficient and Reliable IoT Communications within E-health Environment. IEEE Global Communications Conference, 1–6. https://doi.org/10.1109/GLOCOM.2017.8254435.
• Hao, Z., Novak, E., Yi, S., & Li, Q. (2017). Challenges and Software Architecture for Fog Computing. IEEE Internet Computing, 21(2), 44–53.doi%20https:/doi.org/10.1109/MIC.2017.26.
• Hassan, K., B, N. J., Zahid, M., & Ansar, K. (2019). Hill Climbing Load Balancing Algorithm on Fog Computing: Vol. vol 24. Springer International Publishing. doi https://doi.org/10.1007/978-3-030-02607-3.
• He, S., Cheng, B., Wang, H., Xiao, X., Cao, Y., & Chen, J. (2018). Data security storage model for fog computing in large-scale IoT application. INFOCOM 2018 - IEEE Conference on Computer Communications Workshops, 39–44.doi https://doi.org/10.1109/INFCOMW.2018.8406927.
• Hu, P., Member, S., Ning, H., Member, S., Qiu, T., & Member, S. (2016). Fog Computing-Based Face Identification and Resolution Scheme in Internet of Things. 3203(c), 1–11. doi https://doi.org/10.1109/TII.2016.2607178.
• Jimeno, M., Téllez, N., Salazar, A., & Nino-ruiz, E. D. (2018). A Tabu Search Method for Load Balancing in Fog Computing. Int. J. Artif. Intell., 16,(September), no. 2, pp. 106–135. https://www.researchgate.net/publication/327752530_A_Tabu_Search_Method_for_Load_Balancing_in_Fog_Computing.
• Kaur, M., & Aron, R. (2021). A systematic study of load balancing approaches in the fog computing environment. In Journal of Supercomputing (Vol. 77, Issue 8). Springer US.doi https://doi.org/10.1007/s11227-020-03600-8.
• Kumari, S., Singh, S., & April, M. (2017). Fog Computing : Characteristics and Challenges. Vol.6(2), 113–117. https://www.researchgate.net/publication/340272352_Fog_Computing_Characteristics_and_challenges. Lin, C. C., & Yang, J. W. (2018). Cost-Efficient Deployment of Fog Computing Systems at Logistics Centers in Industry 4.0. IEEE Transactions on Industrial Informatics, 14(10), 4603–4611. doi https://doi.org/10.1109/TII.2018.2827920.
• Lyu, X., Ren, C., Ni, W., Tian, H., & Liu, R. P. (2018). Distributed Optimization of Collaborative Regions in Large-Scale Inhomogeneous Fog Computing. IEEE Journal on Selected Areas in Communications, 36(3), 574–586.doi https://doi.org/10.1109/JSAC.2018.2815359 .
• M.Kaur and Aron, R. (2021). FOCALB : Fog Computing Architecture of Load Balancing for Scientific FOCALB : Fog Computing Architecture of Load Balancing for Scientific Workflow Applications. Journal of Grid Computing, Vol 19, No(January 2022), 1-22.doi https://doi.org/10.1007/s10723-021-09584-w.
• Mahmud, R., Srirama, S. N., Ramamohanarao, K., & Buyya, R. (2019). Quality of Experience (QoE)-aware placement of applications in Fog computing environments. Journal of Parallel and Distributed Computing, 132(August 2019), 190–203.doi https://doi.org/10.1016/j.jpdc.2018.03.004.
• Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017). A Survey on Mobile Edge Computing: The Communication Perspective. IEEE Communications Surveys and Tutorials, 19(4), 2322–2358. doi https://doi.org/10.1109/COMST.2017.2745201.
• Milani, A. S., & Navimipour, N. J. (2016). Load balancing mechanisms and techniques in the cloud environments: Systematic literature review and future trends. Journal of Network and Computer Applications, 71, 86–98. doi https:/doi.org/10.1016/j.jnca.2016.06.003.
• Mishra, S. K., Sahoo, B., & Parida, P. P. (2020). Load balancing in cloud computing: A big picture. Journal of King Saud University - Computer and Information Sciences, 32(2), 149–158. doi https://doi.org/10.1016/j.jksuci.2018.01.003.
• Mohd, M., Maswood, S., & Alharbi, A. G. (2020). A Novel Strategy to Achieve Bandwidth Cost Reduction and Load Balancing in A Cooperative Three-Layer Fog-Cloud Computing Environment. IEEE Access, 8, 113737–113750.doi https://doi.org/10.1109/ACCESS.2020.3003263.
• Negash, B., Rahmani, A. M., Liljeberg, P., & Jantsch, A. (2018). Fog Computing Fundamentals in the Internet-of-Things. In Fog Computing in the Internet of Things (pp. 3–13). Springer International Publishing. doi https://doi.org/10.1007/978-3-319-57639-8_1.
• Neghabi, A. A., Navimipour, N. J., Hosseinzadeh, M., & Rezaee, A. (2018). Load Balancing Mechanisms in the Software Defined Networks: A Systematic and Comprehensive Review of the Literature. IEEE Access, 6, 14159–14178.doi https://doi.org/10.1109/ACCESS.2018.2805842.
• Puthal, D., Obaidat, M. S., Nanda, P., Prasad, M., Mohanty, S. P., & Zomaya, A. Y. (2018). Secure and Sustainable Load Balancing of Edge Data Centers in Fog Computing. May, 60–65. https://ieeexplore.ieee.org/abstract/document/8360851.
• Qiao, G., Leng, S., Zhang, K., & He, Y. (2018). Collaborative task offloading in vehicular edge multi-access networks. IEEE Communications Magazine, 56(8), 48–54. doi https://doi.org/10.1109/MCOM.2018.1701130.
• Rahimi, M., Songhorabadi, M., & Kashani, M. H. (2020). Fog-based smart homes: A systematic review. Journal of Network and Computer Applications, 153(March).doi https://doi.org/10.1016/j.jnca.2020.102531
• Rahmani, A. M., Gia, T. N., Negash, B., Anzanpour, A., Azimi, I., Jiang, M., & Liljeberg, P. (2017). Exploiting Smart E-Health Gateways at the Edge of Healthcare Internet-of-Things : A Fog Computing Approach. Future Generation Computer Systems.doi https://doi.org/10.1016/j.future.2017.02.014.
• Rahul, S., & Aron, R. (2021). Fog computing architecture, application and resource allocation: a review. In CEUR Workshops (Vol. 4638, pp. 0-2).
• Raza, Z., & Jangu, N. (2022). Workload Classification For Better Resource Management in Fog-Cloud Environments. International Journal of Systems and Service-Oriented Engineering, 12(1), 1–14. doi https://doi.org/10.4018/ijssoe.297135.
• Sheng, Z., Yang, S., Yu, Y., Vasilakos, A., McCann, J., & Leung, K. (2013). A Survey on The Ietf Protocol Suite for The Internet of Things: Standards, challenges, and Opportunities. IEEE Wireless Communications, 20(6), 91–98.doi https://doi.org/10.1109/MWC.2013.6704479.
• Shi, C., Ren, Z., Yang, K., Chen, C., Zhang, H., Xiao, Y., & Hou, X. (2018). Ultra-low latency cloud-fog computing for industrial Internet of Things. IEEE Wireless Communications and Networking Conference, WCNC, 2018-April, 1–6.doi https://doi.org/10.1109/WCNC.2018.8377192.
• Singh, S., & Chana, I. (2016). Cloud resource provisioning: survey, status and future research directions. Knowledge and Information Systems, 49(3), 1005–1069. doi https:/doi.org/10.1007/s10115-016-0922-3.
• Singh, S. P., Kumar, R., Sharma, A., & Nayyar, A. (2020). Leveraging energy-efficient load balancing algorithms in fog computing. March, 1–16.doi https:/doi.org/10.1002/cpe.5913.
• Singh, S. P., Nayyar, A., Kumar, R., & Sharma, A. (2019). Fog computing: from architecture to edge computing and big data processing. Journal of Supercomputing, 75(4), 2070–2105.doi https://doi.org/10.1007/s11227-018-2701-2.
• Stephanie Vozza. (2022). Redefining Workloads in Cloud Environments. https://www.nutanix.com/theforecastbynutanix/technology/rethinking-cloud-workloads.
• Sultan, O. H., & Khaleel, T. (2022). Challenges of Load Balancing Techniques in Cloud Environment: A Review. Al-Rafidain Engineering Journal (AREJ), 27(2), 227–235. doi https://doi.org/10.33899/rengj.2022.134056.1179
• Sumathy, S., & Manju, A. B. (2019). Efficient load balancing algorithm for task preprocessing in fog computing environment. In Smart Innovation, Systems and Technologies (Vol. 105). Springer Singapore. doi https://doi.org/10.1007/978-981-13-1927-3_31.
• Talaat, F. M., Saraya, M. S., Saleh, A. I., Ali, H. A., & Ali, S. H. (2020). A load balancing and optimization strategy (LBOS) using reinforcement learning in fog computing environment. Journal of Ambient Intelligence and Humanized Computing, 11(11), 4951–4966.doi https:/doi.org/10.1007/s12652-020-01768-8. Téllez, N., Jimeno, M., Salazar, A., & Nino-Ruiz, E. D. (2018). A Tabu search method for load balancing in fog computing. International Journal of Artificial Intelligence, 16(2), 106–135. https://www.researchgate.net/publication/327752530_A_Tabu_Search_Method_for_Load_Balancing_in_Fog_Computing.
• Tim Mell, P. G. (2009). Draft NIST Working Definition of Cloud Computing. National Institute of Standards and Technology, 53(March), 50. http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf
• Vailshery, L. S. (2022). Number of Internet of Things (IoT) connected devices worldwide from 2019 to 2021, with forecasts from 2022 to 2030. Statista. https://www.statista.com/statistics/1183457/iot-connected-devices-worldwide/.
• Velde, V., & Rama, B. (2017). An advanced algorithm for load balancing in cloud computing using fuzzy technique. Proceedings of the 2017 International Conference on Intelligent Computing and Control Systems, ICICCS 2017, 2018-Janua, 1042–1047. doi https://doi.org/10.1109/ICCONS.2017.8250624.
• Verma, M. (2016). Real Time Efficient Scheduling Algorithm for Load Balancing in Fog Computing Environment. .." Int. J. Inf. Technol. Comput. Sci, Vol 8, No.(April), 1–10. doi https://doi.org/10.5815/ijitcs.2016.04.01.
• Verma, M., Bhardawaj, N., & Yadav, A. K. (2015). An architecture for Load Balancing Techniques for Fog Computing Environment. 269–274.doi https://doi.org/10.090592/IJCSC.2015.627.
• Yi, S., Hao, Z., Qin, Z., & Li, Q. (2016). Fog computing: Platform and applications. Proceedings - 3rd Workshop on Hot Topics in Web Systems and Technologies, HotWeb 2015, November 2015, 73–78. doi https://doi.org/10.1109/HotWeb.2015.22.
• Zhang, G., Shen, F., Yang, Y., Qian, H., & Yao, W. (2018). Fair task offloading among fog nodes in fog computing networks. IEEE International Conference on Communications, 2018-May, 1–6. doi https://doi.org/10.1109/ICC.2018.8422316.
• Zhang, P., Liu, J. K., Richard Yu, F., Sookhak, M., Au, M. H., & Luo, X. (2018). A Survey on Access Control in Fog Computing. IEEE Communications Magazine, 56(2), 144–149. doi https://doi.org/10.1109/MCOM.2018.1700333.
• Zhuang, H., Li, C., Wang, Q., & Zhou, X. (2018). SSLB Self-Similarity-Based Load Balancing for Large-Scale Fog Computing. Arabian Journal for Science and Engineering, 43, no. 12, pp. 7487–7498. doi https://doi.org/10.1007/s13369-018-3169-3.