Load Balancing Algorithms in Cloud, Fog Computing and Convergence of Fog and Cloud – A Survey
Subject Areas : IT Strategy
Seyedeh Leili Mirtaheri
1
*
,
Mahya Azari Jafari
2
,
Sergio Greco
3
,
Ehsan Arianian
4
,
Reza Mansouri
5
1 - Department of Computer Engineering, Modeling, Electronics and Systems, University of Calabria, Italy
2 - Faculty of Mathematics and Computer Science, Kharazmi University, Tehran, Iran
3 - Department of Computer Engineering, Modeling, Electronics and Systems, University of Calabria, Italy
4 - Department of Information Technology, ICT Research Institute (ITRC), Tehran, Iran
5 - Computer Science Department, Georgia State University, Atlanta, GA, USA
Keywords: Fog Computing, Cloud Computing, Convergence of Fog and Cloud, Load Balancing,
Abstract :
Cloud computing and fog computing are deployed as computing storage and services for the end-users. Fog computing promotes task performance through storage, computing, and networking services. Instead of taking place in centralized cloud computing data centers, these services can be provided via near-edge devices. Efficient load balancing in distributed computing systems has been the main challenge. The load balancing algorithm has an important role in enhancing the Quality of Service (QoS), throughput, and resource utilization and diminishing the potential cost and its strategy and architecture completely depend on the centralized or distributed architecture of the system and the type of requests. Cloud computing and fog computing use centralized and distributed architectures, respectively. The load balancing algorithm in these two environments cannot be the same. Meanwhile, the demand for near real-time processing requests is drastically increasing; load balancing should be able to handle real-time requests. This paper reviews and investigates the modern and diverse load balancing aspects of fog and cloud computing systems. We also categorize the load balancing algorithms in cloud and fog computing: meta-heuristic algorithms, heuristic algorithms, learning algorithms, and customized algorithms. We propose different research classes about the algorithm's type, objectives, simulation tools, and so forth. This review demonstrates that the most prevalent categories of methods used in load balancing in fog and cloud computing are custom approaches and meta-heuristic algorithms, respectively. While the most renowned load balancing algorithms have not yet succeeded in fog environments, meta-heuristic algorithms have shown their competence in cloud environments impeccably.
[1] P. Hu, S. Dhelim, H. Ning, and T. Qiu, “Survey on fog computing: architecture, key technologies, applications and open issues,” Journal of Network and Computer Applications, vol. 98, pp. 27–42, Nov. 2017, doi: 10.1016/j.jnca.2017.09.002.
[2] M. Verma, N. Bhardwaj, and A. K. Yadav, “Real Time Efficient Scheduling Algorithm for Load Balancing in Fog Computing Environment,” International Journal of Information Technology and Computer Science, vol. 8, no. 4, pp. 1–10, Apr. 2016, doi: 10.5815/ijitcs.2016.04.01.
[3] S. Verma, A. K. Yadav, D. Motwani, R. S. Raw and H. K. Singh, "An efficient data replication and load balancing technique for fog computing environment," 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), 2016, pp. 2888-2895.
[4] A. Alharthi, F. Yahya, R. J. Walters, and G. B. Wills, “An Overview of Cloud Services Adoption Challenges in Higher Education Institutions,” Proceedings of the 2nd International Workshop on Emerging Software as a Service and Analytics, 2015, doi: 10.5220/0005529701020109.
[5] S. Mathew, “Implementation of Cloud Computing in Education - A Revolution,” International Journal of Computer Theory and Engineering, pp. 473–475, 2012, doi: 10.7763/ijcte.2012.v4.511.
[6] H. Atlam, R. Walters, and G. Wills, “Fog Computing and the Internet of Things: A Review,” Big Data and Cognitive Computing, vol. 2, no. 2, p. 10, Apr. 2018, doi: 10.3390/bdcc2020010.
[7] A. Ahmed, et al. "Fog Computing Applications: Taxonomy and Requirements," arXiv, 26 July 2019. doi: 10.48550/arXiv.1907.11621.
[8] E. Jafarnejad Ghomi, A. Masoud Rahmani, and N. Nasih Qader, “Load-balancing algorithms in cloud computing: A survey,” Journal of Network and Computer Applications, vol. 88, pp. 50–71, Jun. 2017, doi: 10.1016/j.jnca.2017.04.007.
[9] Z. M. Elngomi and K. Khanfar "A Comparative Study of Load Balancing Algorithms: A Review Paper," International Journal of Computer Science and Mobile Computing, vol. 5, no. 6, pp. 448-458, Jun. 2016.
[10] M. N. Arab, S. L. Mirtaheri, E. M. Khaneghah, M. Sharifi, and M. Mohammadkhani, “Improving Learning-Based Request Forwarding in Resource Discovery through Load-Awareness,” Lecture Notes in Computer Science, pp. 73–82, 2011, doi: 10.1007/978-3-642-22947-3_7.
[11] A. Chandak and N. K. Ray, “A Review of Load Balancing in Fog Computing,” 2019 International Conference on Information Technology (ICIT), Dec. 2019, doi: 10.1109/icit48102.2019.00087.
[12] X. He, Z. Ren, C. Shi, and J. Fang, “A novel load balancing strategy of software-defined cloud/fog networking in the Internet of Vehicles,” China Communications, vol. 13, no. 2, pp. 140–149, 2016, doi: 10.1109/cc.2016.7405730.
[13] J. Wan, B. Chen, S. Wang, M. Xia, D. Li, and C. Liu, “Fog Computing for Energy-Aware Load Balancing and Scheduling in Smart Factory,” IEEE Transactions on Industrial Informatics, vol. 14, no. 10, pp. 4548–4556, Oct. 2018, doi: 10.1109/TII.2018.2818932.
[14] R. Bukhsh, N. Javaid, Z. Ali Khan, F. Ishmanov, M. Afzal, and Z. Wadud, “Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid,” Energies, vol. 11, no. 12, p. 3345, Nov. 2018, doi: 10.3390/en11123345.
[15] S. H. Abbasi, N. Javaid, M. H. Ashraf, M. Mehmood, M. Naeem, and M. Rehman, “Load Stabilizing in Fog Computing Environment Using Load Balancing Algorithm,” Lecture Notes on Data Engineering and Communications Technologies, pp. 737–750, Oct. 2018, doi: 10.1007/978-3-030-02613-4_66.
[16] A. Kamalinia and A. Ghaffari, "Hybrid Task Scheduling Method for Cloud Computing by Genetic and PSO Algorithms," Journal of Information Systems and Telecommunication (JIST), vol 16, no. 4, pp. 1-10, Sep. 2016, doi: 10.7508/jist.2016.04.008.
[17] M. Verma and N. B. A. K. Yadav, “An architecture for load balancing techniques for fog computing environment,” International Journal of Computer Science and Communication, vol. 8, no. 2, pp. 43–49, 2015.
[18] S. Sharma and H. Saini, “Efficient Solution for Load Balancing in Fog Computing Utilizing Artificial Bee Colony,” International Journal of Ambient Computing and Intelligence, vol. 10, no. 4, pp. 60–77, Oct. 2019, doi: 10.4018/ijaci.2019100104.
[19] M. Zahid, N. Javaid, K. Ansar, K. Hassan, M. KaleemUllah Khan, and M. Waqas, “Hill Climbing Load Balancing Algorithm on Fog Computing,” Advances on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 238–251, Oct. 2018, doi: 10.1007/978-3-030-02607-3_22.
[20] N. Tellez, M. Jimeno, A. Salazar, and E. D. Nino-Ruiz, “A Tabu Search Method for Load Balancing in Fog Computing,” International journal of artificial intelligence, vol. 16, pp. 106–135, 2018. [21] M. K. Hussein and M. H. Mousa, “Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization,” IEEE Access, vol. 8, pp. 37191–37201, 2020, doi: 10.1109/access.2020.2975741.
[22] V. Arulkumar and N. Bhalaji, “Performance analysis of nature inspired load balancing algorithm in cloud environment,” Journal of Ambient Intelligence and Humanized Computing, Jan. 2020, doi: 10.1007/s12652-019-01655-x.
[23] A. Gupta and R. Garg, “Load Balancing Based Task Scheduling with ACO in Cloud Computing,” 2017 International Conference on Computer and Applications (ICCA), Sep. 2017, doi: 10.1109/comapp.2017.8079781.
[24] W. T. Wen, C. D. Wang, D. S. Wu, and Y. Y. Xie, “An ACO-based Scheduling Strategy on Load Balancing in Cloud Computing Environment,” 2015 Ninth International Conference on Frontier of Computer Science and Technology, Aug. 2015, doi: 10.1109/fcst.2015.41.
[25] P. Verma, S. Shrivastava, and R. K. Pateriya, “Enhancing load balancing in cloud computing by ant colony optimization method,” International Journal of Computer Engineering in Research Trends, vol. 4, no. 6, pp. 277–284, 2017.
[26] K. R. Remesh Babu and P. Samuel, “Enhanced Bee Colony Algorithm for Efficient Load Balancing and Scheduling in Cloud,” Advances in Intelligent Systems and Computing, pp. 67–78, Dec. 2015, doi: 10.1007/978-3-319-28031-8_6.
[27] S. Singhal, “Load Balancing in Cloud Computing using Mutative Bacterial Foraging Optimization,” Journal of Xidian University, vol. 14, no. 6, Jun. 2020, doi: 10.37896/jxu14.6/258.
[28] M. Yakhchi, S. M. Ghafari, S. Yakhchi, M. Fazeli, and A. Patooghi, “Proposing a load balancing method based on Cuckoo Optimization Algorithm for energy management in cloud computing infrastructures,” 2015 6th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO), May 2015, doi: 10.1109/icmsao.2015.7152209.
[29] B. Kruekaew and W. Kimpan, “Enhancing of Artificial Bee Colony Algorithm for Virtual Machine Scheduling and Load Balancing Problem in Cloud Computing,” International Journal of Computational Intelligence Systems, vol. 13, no. 1, p. 496, 2020, doi: 10.2991/ijcis.d.200410.002.
[30] S. Sefati, M. Mousavinasab, and R. Zareh Farkhady, “Load balancing in cloud computing environment using the Grey wolf optimization algorithm based on the reliability: performance evaluation,” The Journal of Supercomputing, May 2021, doi: 10.1007/s11227-021-03810-8.
[31] A. V and N. Bhalaji, “Load balancing in cloud computing using water wave algorithm,” Concurrency and Computation: Practice and Experience, vol. 34, no. 8, Sep. 2019, doi: 10.1002/cpe.5492.
[32] A. Pradhan and S. K. Bisoy, “A novel load balancing technique for cloud computing platform based on PSO,” Journal of King Saud University - Computer and Information Sciences, Oct. 2020, doi: 10.1016/j.jksuci.2020.10.016.
[33] R. M. Alguliyev, Y. N. Imamverdiyev, and F. J. Abdullayeva, “PSO-based Load Balancing Method in Cloud Computing,” Automatic Control and Computer Sciences, vol. 53, no. 1, pp. 45–55, Jan. 2019, doi: 10.3103/s0146411619010024.
[34] H. Sharma and G. Sekhon, "Load Balancing in Cloud Using Enhanced Genetic Algorithm," International Journal of Innovations and & Advancement in Computer Science, vol. 6, no. 1, pp. 100–107, 2017.
[35] D. Puthal, M. S. Obaidat, P. Nanda, M. Prasad, S. P. Mohanty, and A. Y. Zomaya, “Secure and Sustainable Load Balancing of Edge Data Centers in Fog Computing,” IEEE Communications Magazine, vol. 56, no. 5, pp. 60–65, May 2018, doi: 10.1109/mcom.2018.1700795.
[36] X. Xu, Q. Liu, L. Qi, Y. Yuan, W. Dou, and A. X. Liu, “A Heuristic Virtual Machine Scheduling Method for Load Balancing in Fog-Cloud Computing,” 2018 IEEE 4th International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS), May 2018, doi: 10.1109/bds/hpsc/ids18.2018.00030.
[37] A. B. Manju and S. Sumathy, “Efficient Load Balancing Algorithm for Task Preprocessing in Fog Computing Environment,” Smart Intelligent Computing and Applications, pp. 291–298, Nov. 2018, doi: 10.1007/978-981-13-1927-3_31.
[38] A. Singh and N. Auluck, “Load balancing aware scheduling algorithms for fog networks,” Software: Practice and Experience, Jun. 2019, doi: 10.1002/spe.2722.
[39] P. P. G. Gopinath and S. K. Vasudevan, “An In-depth Analysis and Study of Load Balancing Techniques in the Cloud Computing Environment,” Procedia Computer Science, vol. 50, pp. 427–432, 2015, doi: 10.1016/j.procs.2015.04.009.
[40] A. Kazeem Moses, A. Joseph Bamidele, O. Roseline Oluwaseun, S. Misra, and A. Abidemi Emmanuel, “Applicability of MMRR load balancing algorithm in cloud computing,” International Journal of Computer Mathematics: Computer Systems Theory, vol. 6, no. 1, pp. 7–20, Dec. 2020, doi: 10.1080/23799927.2020.1854864.
[41] T. C. Hung, L. N. Hieu, P. T. Hy, and N. X. Phi, “MMSIA,” Proceedings of the 3rd International Conference on Machine Learning and Soft Computing - ICMLSC 2019, 2019, doi: 10.1145/3310986.3311017.
[42] A. Aghdashi and S. L. Mirtaheri, “Novel dynamic load balancing algorithm for cloud-based big data analytics,” The Journal of Supercomputing, Aug. 2021, doi: 10.1007/s11227-021-04024-8.
[43] J. Baek, G. Kaddoum, S. Garg, K. Kaur, and V. Gravel, “Managing Fog Networks using Reinforcement Learning Based Load Balancing Algorithm,” 2019 IEEE Wireless Communications and Networking Conference (WCNC), Apr. 2019, doi: 10.1109/wcnc.2019.8885745.
[44] S. Sharma and H. Saini, “A novel four-tier architecture for delay aware scheduling and load balancing in fog environment,” Sustainable Computing: Informatics and Systems, vol. 24, p. 100355, Dec. 2019, doi: 10.1016/j.suscom.2019.100355.
[45] J. Zhao, K. Yang, X. Wei, Y. Ding, L. Hu, and G. Xu, “A Heuristic Clustering-Based Task Deployment Approach for Load Balancing Using Bayes Theorem in Cloud Environment,” IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 2, pp. 305–316, Feb. 2016, doi: 10.1109/tpds.2015.2402655.
[46] S. Kapoor and C. Dabas, “Cluster based load balancing in cloud computing,” 2015 Eighth International Conference on Contemporary Computing (IC3), Aug. 2015, doi: 10.1109/ic3.2015.7346656.
[47] S. Parida and Bakul Panchal, "An Efficient Dynamic Load Balancing Algorithm Using Machine Learning Technique in Cloud Environment," International journal of scientific research in science, engineering and technology, vol. 4, pp. 1184–1186, 2018.
[48] M. Li et al., “Distributed machine learning load balancing strategy in cloud computing services,” Wireless Networks, Jul. 2019, doi: 10.1007/s11276-019-02042-2.
[49] X. Sui, D. Liu, L. Li, H. Wang, and H. Yang, “Virtual machine scheduling strategy based on machine learning algorithms for load balancing,” EURASIP Journal on Wireless Communications and Networking, vol. 2019, no. 1, Jun. 2019, doi: 10.1186/s13638-019-1454-9.
[50] B. Kruekaew and W. Kimpan, “Multi-Objective Task Scheduling Optimization for Load Balancing in Cloud Computing Environment Using Hybrid Artificial Bee Colony Algorithm With Reinforcement Learning,” IEEE Access, vol. 10, pp. 17803–17818, 2022, doi: 10.1109/access.2022.3149955.
[51] D. Rathod and G. Chowdhary, “Load Balancing of Fog Computing Centers: Minimizing Response Time of High Priority Requests,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 11, pp. 2713–2716, Sep. 2019, doi: 10.35940/ijitee.k2171.0981119.
[52] S. Sthapit, J. R. Hopgood, and J. Thompson, “Distributed computational load balancing for real-time applications,” 2017 25th European Signal Processing Conference (EUSIPCO), Aug. 2017, doi: 10.23919/eusipco.2017.8081436.
[53] E. C. Pinto Neto, G. Callou, and F. Aires, “An algorithm to optimise the load distribution of fog environments,” 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Oct. 2017, doi: 10.1109/smc.2017.8122791.
[54] S. Ningning, G. Chao, A. Xingshuo, and Z. Qiang, “Fog computing dynamic load balancing mechanism based on graph repartitioning,” China Communications, vol. 13, no. 3, pp. 156–164, Mar. 2016, doi: 10.1109/cc.2016.7445510.
[55] J. Jijin, B.-C. Seet, P. H. J. Chong, and H. Jarrah, “Service load balancing in fog-based 5G radio access networks,” 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Oct. 2017, doi: 10.1109/pimrc.2017.8292300.
[56] H. A. Khattak et al., “Utilization and load balancing in fog servers for health applications,” EURASIP Journal on Wireless Communications and Networking, vol. 2019, no. 1, Apr. 2019, doi: 10.1186/s13638-019-1395-3.
[57] S. Hamrioui and P. Lorenz, “Load Balancing Algorithm for Efficient and Reliable IoT Communications within E-Health Environment,” GLOBECOM 2017 - 2017 IEEE Global Communications Conference, Dec. 2017, doi: 10.1109/glocom.2017.8254435.
[58] J. L. Crespo-Mariño and E. Meneses-Rojas, Eds., High Performance Computing. Cham: Springer International Publishing, 2020. doi: 10.1007/978-3-030-41005-6.
[59] X. Xu et al., “Dynamic Resource Allocation for Load Balancing in Fog Environment,” Wireless Communications and Mobile Computing, vol. 2018, pp. 1–15, 2018, doi: 10.1155/2018/6421607.
[60] D. Baburao, T. Pavankumar, and C. S. R. Prabhu, “Load balancing in the fog nodes using particle swarm optimization-based enhanced dynamic resource allocation method,” Applied Nanoscience, Jul. 2021, doi: 10.1007/s13204-021-01970-w.
[61] R. Beraldi, C. Canali, R. Lancellotti, and G. P. Mattia, “A Random Walk based Load Balancing Algorithm for Fog Computing,” 2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC), Apr. 2020, doi: 10.1109/fmec49853.2020.9144962.
[62] E. Batista, G. Figueiredo, M. Peixoto, M. Serrano, and C. Prazeres, “Load Balancing in the Fog of Things Platforms Through Software-Defined Networking,” 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Jul. 2018, doi: 10.1109/cybermatics_2018.2018.00297.
[63] R. Beraldi and H. Alnuweiri, “Exploiting Power-of-Choices for Load Balancing in Fog Computing,” 2019 IEEE International Conference on Fog Computing (ICFC), Jun. 2019, doi: 10.1109/icfc.2019.00019. [64] V. R. Chandakanna and V. K. Vatsavayi, “A sliding window based Self-Learning and Adaptive Load Balancer,” Journal of Network and Computer Applications, vol. 56, pp. 188–205, Oct. 2015, doi: 10.1016/j.jnca.2015.07.001.
[65] Y. Liu, C. Zhang, B. Li, and J. Niu, “DeMS: A hybrid scheme of task scheduling and load balancing in computing clusters,” Journal of Network and Computer Applications, vol. 83, pp. 213–220, Apr. 2017, doi: 10.1016/j.jnca.2015.04.017.
[66] S. S. Patil and A. N. Gopal, "Dynamic Load Balancing Using Periodically Load Collection with Past Experience Policy on Linux Cluster System," American Journal of Mathematical and Computer Modelling, vol. 2, No. 2, pp. 60–75, 2017, doi: 10.11648/j.ajmcm.20170202.13.
[67] V. Priya, C. Sathiya Kumar, and R. Kannan, “Resource scheduling algorithm with load balancing for cloud service provisioning,” Applied Soft Computing, vol. 76, pp. 416–424, Mar. 2019, doi: 10.1016/j.asoc.2018.12.021.
[68] D. F. Altayeb and F. A. Mustafa, "Analysis on Load Balancing Algorithms Implementation on Cloud Computing," International Journal of Innovative Research in Advanced Engineering, vol. 6, no. 2, pp. 1-32, 2016.
[69] S. L. Chen, Y. Y. Chen, and S. H. Kuo, “CLB: A novel load balancing architecture and algorithm for cloud services,” Computers & Electrical Engineering, vol. 58, pp. 154–160, Feb. 2017, doi: 10.1016/j.compeleceng.2016.01.029.
[70] S. L. Mirtaheri, L. Grandinetti, "Optimized Dynamic Load Balancing in Distributed Exascale Computing Systems," Ph.D. Thesis, Dept. of Electronics, Informatics and Systems Engineering, Univ. of Calabria, Italy, 2016, doi: 10.13126/unical.it/dottorati/1370.
[71] A. Bhandari and K. Kaur, “An Enhanced Post-migration Algorithm for Dynamic Load Balancing in Cloud Computing Environment,” Advances in Intelligent Systems and Computing, pp. 59–73, Oct. 2018, doi: 10.1007/978-981-13-1544-2_6.
[72] H. Naseri, S. Azizi, and A. Abdollahpouri, "BSFS: A Bidirectional Search Algorithm for Flow Scheduling in Cloud Data Centers," Journal of Information Systems and Telecommunication (JIST), vol. 3, no. 27, p. 175, Mar. 2020, doi: https://doi.org/10.7508/jist.2019.03.002.
[73] M. Kaur and R. Aron, “Energy-aware load balancing in fog cloud computing,” Materials Today: Proceedings, Dec. 2020, doi: 10.1016/j.matpr.2020.11.121.
[74] R. O. Aburukba, M. AliKarrar, T. Landolsi, and K. El-Fakih, “Scheduling Internet of Things requests to minimize latency in hybrid Fog–Cloud computing,” Future Generation Computer Systems, vol. 111, pp. 539–551, Oct. 2020, doi: 10.1016/j.future.2019.09.039.
[75] J. Bisht and V. Subrahmanyam, “Survey on Load Balancing and Scheduling Algorithms in Cloud Integrated Fog Environment,” Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2020, 27-28 February 2020, Jamia Hamdard, New Delhi, India, 2021, doi: 10.4108/eai.27-2-2020.2303123.
[76] A. Alarifi, F. Abdelsamie, and M. Amoon, “A fault-tolerant aware scheduling method for fog-cloud environments,” PLOS ONE, vol. 14, no. 10, p. e0223902, Oct. 2019, doi: 10.1371/journal.pone.0223902.
[77] F. Alqahtani, M. Amoon, and A. A. Nasr, “Reliable scheduling and load balancing for requests in cloud-fog computing,” Peer-to-Peer Networking and Applications, vol. 14, no. 4, pp. 1905–1916, Mar. 2021, doi: 10.1007/s12083-021-01125-2.
[78] F. Fakhar, "Investigate Network Simulation Tools in Designing and Managing Intelligent Systems," Journal of Information Systems and Telecommunication (JIST), vol. 28, no. 7, pp. 278-293, Jun. 2020, doi: 10.7508/jist.2019.04.004.
[79] E. M. Khaneghah, S. L. Mirtaheri and M. Sharifi, "Evaluating the Effect of Inter Process Communication Efficiency on High Performance Distributed Scientific Computing," 2008 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, Shanghai, China, 2008, pp. 366-372, doi: 10.1109/EUC.2008.11.