A systematic study of load balancing approaches in the fog computing environment


Internet of Things has been growing, due to which the number of user requests on fog computing layer has also increased. Fog works in a real-time environment and offers from connected devices need to be processed immediately. With the increase in users requests on fog layer, virtual machines (VMs) at fog layer become overloaded. Load balancing mechanism can distribute load among all the VMs in equal proportion. It has become a necessity in the fog layer to equally, and equitably distribute all the workload among the existing VMs in the segment. Till now, many load balancing techniques have been proposed for fog computing. An empirical study of existing methods in load balancing have been conducted, and taxonomy has been presented in a hierarchical form. Besides, the article contains the year-wise comprehensive review and summary of research articles published in the area of load balancing from 2013 to 2020. Furthermore, article also contains our proposed fog computing architecture to resolve load balancing problem. It also covers current issues and challenges that can be resolved in future research works. The paper concludes by providing future directions.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14


  1. 1.

    Aazam M, Huh EN (2014) Fog computing and smart gateway based communication for cloud of things. In: 2014 International Conference on Future Internet of Things and Cloud. IEEE, pp 464–470

  2. 2.

    Abbasi SH, Javaid N, Ashraf MH, Mehmood M, Naeem M, Rehman M (2018) Load stabilizing in fog computing environment using load balancing algorithm. In: International Conference on Broadband and Wireless Computing, Communication and Applications. Springer, pp 737–750

  3. 3.

    Alakeel AM et al (2010) A guide to dynamic load balancing in distributed computer systems. Int J Comput Sci Inf Secur 10(6):153–160

    Google Scholar 

  4. 4.

    Alam MGR, Tran NH, Do CT, Pham C, Abedin SF, Bairagi AK, Haw R, Hong CS (2014) Distributed reinforcement learning based code offloading in mobile fog, pp 285–287

  5. 5.

    Alam MGR, Tun YK, Hong CS (2016) Multi-agent and reinforcement learning based code offloading in mobile fog. In: 2016 International Conference on Information Networking (ICOIN). IEEE, pp 285–290

  6. 6.

    Ali MJ, Javaid N, Rehman M, Sharif MU, Khan MK, Khan HA (2018) State based load balancing algorithm for smart grid energy management in fog computing. In: International Conference on Intelligent Networking and Collaborative Systems. Springer, pp 220–232

  7. 7.

    Amin A, Riyaz S, Ali A, Paul Z (2017) Review of iot data analytics using big data, fog computing and data mining. Int J Comput Sci Mob Comput 6:33–39

    Google Scholar 

  8. 8.

    Anawar MR, Wang S, Azam Zia M, Jadoon AK, Akram U, Raza S (2018) Fog computing: an overview of big iot data analytics. Wirel Commun Mob Comput 2018:1–57

    Article  Google Scholar 

  9. 9.

    Arshad H (2019) Evaluation and analysis of bio-inspired techniques for resource management and load balancing of fog computing. Int J Adv Comput Sci Appl 9(7):1–22

    MathSciNet  Google Scholar 

  10. 10.

    Arunarani A, Manjula D, Sugumaran V (2019) Task scheduling techniques in cloud computing: a literature survey. Future Gen Comput Syst 91:407–415

    Article  Google Scholar 

  11. 11.

    Aslam S, Shah MA (2015) Load balancing algorithms in cloud computing: a survey of modern techniques. In: 2015 National Software Engineering Conference (NSEC). IEEE, pp 30–35

  12. 12.

    Atlam H, Walters R, Wills G (2018) Fog computing and the internet of things: a review. Big Data Cogn Comput 2(2):10

    Article  Google Scholar 

  13. 13.

    Babu KR, Samuel P (2016) Enhanced bee colony algorithm for efficient load balancing and scheduling in cloud. In: Innovations in Bio-Inspired Computing and Applications. Springer, pp 67–78

  14. 14.

    Baliga J, Ayre RW, Hinton K, Tucker RS (2011) Green cloud computing: balancing energy in processing, storage, and transport. Proc IEEE 99(1):149–167

    Article  Google Scholar 

  15. 15.

    Bano H, Javaid N, Tehreem K, Ansar K, Zahid M, Nazar T (2018) Cloud computing based resource allocation by random load balancing technique. In: International Conference on Broadband and Wireless Computing, Communication and Applications. Springer, pp 28–39

  16. 16.

    Beraldi R, Canali C, Lancellotti R, Mattia GP (2020) Distributed load balancing for heterogeneous fog computing infrastructures in smart cities. Pervas Mob Comput 67:101221

    Article  Google Scholar 

  17. 17.

    Beraldi R, Canali C, Lancellotti R, Mattia GP (2020) A random walk based load balancing algorithm for fog computing. In: 2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC). IEEE, pp 46–53

  18. 18.

    Beraldi R, Mtibaa A, Alnuweiri H (2017) Cooperative load balancing scheme for edge computing resources. In: 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC). IEEE, pp 94–100

  19. 19.

    Bhatia M, Sood SK, Kaur S (2020) Quantumized approach of load scheduling in fog computing environment for iot applications. Computing 102:1097–1115

    Article  Google Scholar 

  20. 20.

    Bhavani B, Guruprasad H (2014) Resource provisioning techniques in cloud computing environment: a survey. Int J Res Comput Commun Technol 3(3):395–401

    Google Scholar 

  21. 21.

    Bibri SE (2018) The iot for smart sustainable cities of the future: an analytical framework for sensor-based big data applications for environmental sustainability. Sustain Cities Soc 38:230–253

    Article  Google Scholar 

  22. 22.

    Bila N, de Lara E, Joshi K, Lagar-Cavilla HA, Hiltunen M, Satyanarayanan M (2012) Jettison: efficient idle desktop consolidation with partial vm migration. In: Proceedings of the 7th ACM European Conference on Computer Systems, pp 211–224

  23. 23.

    Bonomi F, Milito R, Natarajan P, Zhu J (2014) Fog computing: a platform for internet of things and analytics. In: Big Data and Internet of Things: A Roadmap for Smart Environments. Springer, pp 169–186

  24. 24.

    Buyya R, Venugopal S (2005) A gentle introduction to grid computing and technologies. Database 2:R3

    Google Scholar 

  25. 25.

    Chandak A, Ray NK (2019) A review of load balancing in fog computing. In: 2019 International Conference on Information Technology (ICIT). IEEE, pp 460–465

  26. 26.

    Chawla A, Ghumman NS (2018) Package-based approach for load balancing in cloud computing. In: Big Data Analytics. Springer, pp 71–77

  27. 27.

    Chen TC, Chen CT (2000) Method for configurable intelligent-agent-based wireless communication system. US Patent 6076099

  28. 28.

    Chiang M, Zhang T (2016) Fog and iot: an overview of research opportunities. IEEE Int Things J 3(6):854–864

    Article  Google Scholar 

  29. 29.

    Consortium O et al (2017) Openfog reference architecture for fog computing. Architecture Working Group 1–162

  30. 30.

    Dasgupta K, Mandal B, Dutta P, Mandal JK, Dam S (2013) A genetic algorithm (ga) based load balancing strategy for cloud computing. Procedia Technol 10:340–347

    Article  Google Scholar 

  31. 31.

    De Falco I, Laskowski E, Olejnik R, Scafuri U, Tarantino E, Tudruj M (2015) Extremal optimization applied to load balancing in execution of distributed programs. Appl Soft Comput 30:501–513

    Article  Google Scholar 

  32. 32.

    Desai T, Prajapati J (2013) A survey of various load balancing techniques and challenges in cloud computing. Int J Sci Technol Res 2(11):158–161

    Google Scholar 

  33. 33.

    Devi DC, Uthariaraj VR (2016) Load balancing in cloud computing environment using improved weighted round robin algorithm for nonpreemptive dependent tasks. Sci World J 2016:1–14

    Article  Google Scholar 

  34. 34.

    Dey NS, Gunasekhar T (2019) A comprehensive survey of load balancing strategies using hadoop queue scheduling and virtual machine migration. IEEE Access 7:92259–92284

    Article  Google Scholar 

  35. 35.

    Dou W, Xu X, Liu X, Yang LT, Wen Y (2018) A resource co-allocation method for load-balance scheduling over big data platforms. Future Gen Comput Syst 86:1064–1075

    Article  Google Scholar 

  36. 36.

    Dsouza C, Ahn GJ, Taguinod M (2014) Policy-driven security management for fog computing: Preliminary framework and a case study. In: Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014). IEEE, pp 16–23

  37. 37.

    Elsharkawey MA, Refaat HE (2018) Mlrts: multi-level real-time scheduling algorithm for load balancing in fog computing environment. Int J Mod Educ Comput Sci 10(2):1

    Article  Google Scholar 

  38. 38.

    Fahs A, Pierre G (2019) Proximity-aware traffic routing in distributed fog computing platforms. pp 478–487

  39. 39.

    Fan Q, Ansari N (2018) Towards workload balancing in fog computing empowered iot. IEEE Trans Netw Sci Eng 7:253–262

    MathSciNet  Article  Google Scholar 

  40. 40.

    Farahani B, Firouzi F, Chang V, Badaroglu M, Constant N, Mankodiya K (2018) Towards fog-driven iot ehealth: promises and challenges of iot in medicine and healthcare. Future Gen Comput Syst 78:659–676

    Article  Google Scholar 

  41. 41.

    G N (2020) How many iot devices are there in 2020? [all you need to know]. https://techjury.net/blog/how-many-iot-devices-are-there/#gref

  42. 42.

    Ghobaei-Arani M, Souri A, Rahmanian AA (2019) Resource management approaches in fog computing: a comprehensive review. J Grid Comput 18:1–42

    Article  Google Scholar 

  43. 43.

    Gill SS, Buyya R (2019) Resource provisioning based scheduling framework for execution of heterogeneous and clustered workloads in clouds: from fundamental to autonomic offering. J Grid Comput 17(3):385–417

    Article  Google Scholar 

  44. 44.

    Giordano A, Spezzano G, Vinci A (2016) Smart agents and fog computing for smart city applications. In: International Conference on Smart Cities. Springer, pp. 137–146

  45. 45.

    Hong CH, Varghese B (2019) Resource management in fog/edge computing: a survey on architectures, infrastructure, and algorithms. ACM Comput Surv 52(5):1–37

    Article  Google Scholar 

  46. 46.

    Hosseinpour F, Plosila J, Tenhunen H (2016) An approach for smart management of big data in the fog computing context. In: 2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom). IEEE, pp 468–471

  47. 47.

    Huang T, Xu B, Cai H, Du J, Chao KM, Huang C (2018) A fog computing based concept drift adaptive process mining framework for mobile apps. Future Gen Comput Syst 89:670–684

    Article  Google Scholar 

  48. 48.

    Hussein MK, Mousa MH (2020) Efficient task offloading for iot-based applications in fog computing using ant colony optimization. IEEE Access 8:37191–37201

    Article  Google Scholar 

  49. 49.

    Jalali F, Hinton K, Ayre R, Alpcan T, Tucker RS (2016) Fog computing may help to save energy in cloud computing. IEEE J Sel Areas Commun 34(5):1728–1739

    Article  Google Scholar 

  50. 50.

    Javaid N, Butt AA, Latif K, Rehman A (2019) Cloud and fog based integrated environment for load balancing using cuckoo levy distribution and flower pollination for smart homes. In: 2019 International Conference on Computer and Information Sciences (ICCIS). IEEE, pp 1–6

  51. 51.

    Jiang X, Hu P, Li Y, Yuan C, Masood I, Jelodar H, Rabbani M, Wang Y (2018) A survey of real-time approximate nearest neighbor query over streaming data for fog computing. J Parallel Distrib Comput 116:50–62

    Article  Google Scholar 

  52. 52.

    Kai K, Cong W, Tao L (2016) Fog computing for vehicular ad-hoc networks: paradigms, scenarios, and issues. J China Univ Posts Telecommun 23(2):56–96

    Article  Google Scholar 

  53. 53.

    Kamal MB, Javaid N, Naqvi SAA, Butt H, Saif T, Kamal MD (2018) Heuristic min-conflicts optimizing technique for load balancing on fog computing. In: International Conference on Intelligent Networking and Collaborative Systems. Springer, pp 207–219

  54. 54.

    Kaur M, Aron R (2020) Equal distribution based load balancing technique for fog-based cloud computing. In: International Conference on Artificial Intelligence: Advances and Applications 2019. Springer, pp 189–198

  55. 55.

    Keshvadi S, Faghih B (2016) A multi-agent based load balancing system in iaas cloud environment. Int Robot Autom J 1(1):1–6

    Google Scholar 

  56. 56.

    Khan S, Parkinson S, Qin Y (2017) Fog computing security: a review of current applications and security solutions. J Cloud Comput 6(1):19

    Article  Google Scholar 

  57. 57.

    Khattak HA, Arshad H, ul Islam S, Ahmed G, Jabbar S, Sharif AM, Khalid S (2019) Utilization and load balancing in fog servers for health applications. EURASIP J Wirel Commun Netw 1:91

    Article  Google Scholar 

  58. 58.

    Kitchenham B (2004) Procedures for performing systematic reviews. Keele UK Keele Univ 33(2004):1–26

    Google Scholar 

  59. 59.

    Kumar N, Shukla D (2018) Load balancing mechanism using fuzzy row penalty method in cloud computing environment. In: Information and Communication Technology for Sustainable Development. Springer, pp 365–373

  60. 60.

    Kumar P, Kumar R (2019) Issues and challenges of load balancing techniques in cloud computing: a survey. ACM Comput Surv 51(6):1–35

    Article  Google Scholar 

  61. 61.

    Kunal S, Saha A, Amin R (2019) An overview of cloud-fog computing: architectures, applications with security challenges. Secur Priv 2(4):e72

    Google Scholar 

  62. 62.

    LD DB, Krishna PV (2013) Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl Soft Comput 13(5):2292–2303

    Article  Google Scholar 

  63. 63.

    Letić J (2019) Internet of things statistics for 2020—taking things apart. https://dataprot.net/statistics/iot-statistics/

  64. 64.

    Li C, Zhuang H, Wang Q, Zhou X (2018) Sslb: self-similarity-based load balancing for large-scale fog computing. Arab J Sci Eng 43:7487–7498

    Article  Google Scholar 

  65. 65.

    Li K, Xu G, Zhao G, Dong Y, Wang D (2011) Cloud task scheduling based on load balancing ant colony optimization. In: 2011 Sixth Annual ChinaGrid Conference. IEEE, pp 3–9

  66. 66.

    Liu L, Qi D, Zhou N, Wu Y (2018) A task scheduling algorithm based on classification mining in fog computing environment. Wirel Commun Mob Comput 2018:1–11

    Google Scholar 

  67. 67.

    Manju A, Sumathy S (2019) Efficient load balancing algorithm for task preprocessing in fog computing environment. In: Smart Intelligent Computing and Applications. Springer, pp 291–298

  68. 68.

    Mao Y, Ren D, Chen X (2013) Adaptive load balancing algorithm based on prediction model in cloud computing. In: Proceedings of the Second International Conference on Innovative Computing and Cloud Computing. ACM, p 165

  69. 69.

    Meftah A, Youssef AE, Zakariah M (2018) Effect of service broker policies and load balancing algorithms on the performance of large scale internet applications in cloud datacenters. Int J Adv Comput Sci Appl 9(5):219–227

    Google Scholar 

  70. 70.

    Menon H, Bhatele A, Fourestier S, Kale L, Pellegrini F (2015) Applying graph partitioning methods in measurement-based dynamic load balancing. Technical report

  71. 71.

    Mishra SK, Sahoo B, Parida PP (2018) Load balancing in cloud computing: a big picture. J King Saud Univ Comput Inf Sci 32(3):149–158

    Google Scholar 

  72. 72.

    Mohanty S, Patra PK, Ray M, Mohapatra S (2018) A novel meta-heuristic approach for load balancing in cloud computing. IJKBO 8(1):29–49

    Google Scholar 

  73. 73.

    Mouradian C, Naboulsi D, Yangui S, Glitho RH, Morrow MJ, Polakos PA (2018) A comprehensive survey on fog computing: state-of-the-art and research challenges. IEEE Commun Surv Tutor 20(1):416–464

    Article  Google Scholar 

  74. 74.

    Moysiadis V, Sarigiannidis P, Moscholios I (2018) Towards distributed data management in fog computing. Wirel Commun Mob Comput 2018:1–14

    Article  Google Scholar 

  75. 75.

    Nahir A, Orda A, Raz D (2016) Replication-based load balancing. IEEE Trans Parallel Distrib Syst 27(2):494–507

    Article  Google Scholar 

  76. 76.

    Naqvi SAA, Javaid N, Butt H, Kamal MB, Hamza A, Kashif M (2018) Metaheuristic optimization technique for load balancing in cloud-fog environment integrated with smart grid. In: International Conference on Network-Based Information Systems. Springer, pp. 700–711

  77. 77.

    Naranjo PGV, Pooranian Z, Shojafar M, Conti M, Buyya R (2018) Focan: a fog-supported smart city network architecture for management of applications in the internet of everything environments. J Parallel Distrib Comput 132(2019):274–283

    Google Scholar 

  78. 78.

    Nath SB, Gupta H, Chakraborty S, Ghosh SK (2018) A survey of fog computing and communication: current researches and future directions. arXiv preprint arXiv:1804.04365

  79. 79.

    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. In: International Conference on Intelligent Networking and Collaborative Systems. Springer, pp 34–46

  80. 80.

    Ningning S, Chao G, Xingshuo A, Qiang Z (2016) Fog computing dynamic load balancing mechanism based on graph repartitioning. China Commun 13(3):156–164

    Article  Google Scholar 

  81. 81.

    Oueis J, Strinati EC, Barbarossa S (2015) The fog balancing: load distribution for small cell cloud computing. In: 2015 IEEE 81st Vehicular Technology Conference (VTC Spring). IEEE, pp 1–6

  82. 82.

    Patel D, Rajawat AS (2015) Efficient throttled load balancing algorithm in cloud environment. Int J Mod Trends Eng Res 2(03):463–480

    Google Scholar 

  83. 83.

    Peng M, Yan S, Zhang K, Wang C (2015) Fog computing based radio access networks: issues and challenges. arXiv preprint arXiv:1506.04233

  84. 84.

    Pourghebleh B, Hayyolalam V (2019) A comprehensive and systematic review of the load balancing mechanisms in the internet of things. Clust Comput 1–21

  85. 85.

    Puthal D, Ranjan R, Nanda A, Nanda P, Jayaraman PP, Zomaya AY (2019) Secure authentication and load balancing of distributed edge datacenters. J Parallel Distrib Comput 124:60–69

    Article  Google Scholar 

  86. 86.

    Qiao H, Pal P (2017) On maximum-likelihood methods for localizing more sources than sensors. IEEE Signal Process Lett 24(5):703–706

    Article  Google Scholar 

  87. 87.

    Rafique H, Shah MA, Islam SU, Maqsood T, Khan S, Maple C (2019) A novel bio-inspired hybrid algorithm (nbiha) for efficient resource management in fog computing. IEEE Access 7:115760–115773

    Article  Google Scholar 

  88. 88.

    Rathore N, Chana I (2014) Load balancing and job migration techniques in grid: a survey of recent trends. Wireless Pers Commun 79(3):2089–2125

    Article  Google Scholar 

  89. 89.

    Rehman AU, Ahmad Z, Jehangiri AI, Ala’Anzy MA, Othman M, Umar AI, Ahmad J (2020) Dynamic energy efficient resource allocation strategy for load balancing in fog environment. IEEE Access 8:199829–199839

    Article  Google Scholar 

  90. 90.

    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. In: International Conference on Broadband and Wireless Computing, Communication and Applications. Springer, pp. 15–27

  91. 91.

    Rufino J, Alam M, Ferreira J, Rehman A, Tsang KF (2017) Orchestration of containerized microservices for iiot using docker. In: 2017 IEEE International Conference on Industrial Technology (ICIT). IEEE, pp 1532–1536

  92. 92.

    Saharan K, Kumar A (2015) Fog in comparison to cloud: a survey. Int J Comput Appl 122(3):10–12

    Google Scholar 

  93. 93.

    Saroa MK, Aron R (2018) Fog computing and its role in development of smart applications. In: 2018 IEEE International Conference on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom). IEEE, pp 1120–1127

  94. 94.

    Shahid MH, Hameed AR, ul Islam S, Khattak HA, Din IU, Rodrigues JJ (2020) Energy and delay efficient fog computing using caching mechanism. Comput Commun 154:534–541

    Article  Google Scholar 

  95. 95.

    Sharma H, Sekhon GS (2017) A review on load balancing in cloud using enhanced genetic algorithm. Int J Comput Eng Technol 8(2)

  96. 96.

    Shaw SB, Singh A (2014) A survey on scheduling and load balancing techniques in cloud computing environment. In: 2014 International Conference on Computer and Communication Technology (ICCCT). IEEE, pp 87–95

  97. 97.

    Shuminoski T, Kitanov S, Janevski T (2018) Advanced qos provisioning and mobile fog computing for 5g. Wirel Commun Mob Comput 2018:1–13

    Article  Google Scholar 

  98. 98.

    Simmhan Y (2017) Big data and fog computing. arXiv preprint arXiv:1712.09552

  99. 99.

    Singh A, Juneja D, Malhotra M (2017) A novel agent based autonomous and service composition framework for cost optimization of resource provisioning in cloud computing. J King Saud Univ Comput Inf Sci 29(1):19–28

    Article  Google Scholar 

  100. 100.

    Singh GS, Vivek T (2015) Implementation of a hybrid load balancing algorithm for cloud computing. Int J Adv Technol Eng Sci 3(1):73–81

    Google Scholar 

  101. 101.

    Singh SP, Kumar R, Sharma A, Nayyar A (2020) Leveraging energy-efficient load balancing algorithms in fog computing. Concurr Comput Pract Exp e5913:1–28

    Google Scholar 

  102. 102.

    Singh SP, Sharma A, Kumar R (2020) Design and exploration of load balancers for fog computing using fuzzy logic. Simul Model Pract Theory 101:102017

    Article  Google Scholar 

  103. 103.

    Sotomayor B, Montero RS, Llorente IM, Foster I (2009) Virtual infrastructure management in private and hybrid clouds. IEEE Internet Comput 13(5):14–22

    Article  Google Scholar 

  104. 104.

    Stantchev V, Barnawi A, Ghulam S, Schubert J, Tamm G (2015) Smart items, fog and cloud computing as enablers of servitization in healthcare. Sens Transducers 185(2):121

    Google Scholar 

  105. 105.

    Talaat FM, Saraya MS, Saleh AI, Ali HA, Ali SH (2020) A load balancing and optimization strategy (lbos) using reinforcement learning in fog computing environment. J Ambient Intell Hum Comput 1–16

  106. 106.

    Tang B, Chen Z, Hefferman G, Wei T, He H, Yang Q (2015) A hierarchical distributed fog computing architecture for big data analysis in smart cities. In: Proceedings of the ASE BigData & SocialInformatics 2015. ACM, p 28

  107. 107.

    Téllez N, Jimeno M, Salazar A, Nino-Ruiz E (2018) A tabu search method for load balancing in fog computing. Int J Artif Intell 16(2):1–30

    Google Scholar 

  108. 108.

    Vaquero LM, Rodero-Merino L (2014) Finding your way in the fog: towards a comprehensive definition of fog computing. ACM SIGCOMM Comput Commun Rev 44(5):27–32

    Article  Google Scholar 

  109. 109.

    Velde V, Rama B (2017) An advanced algorithm for load balancing in cloud computing using fuzzy technique. In: 2017 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, pp 1042–1047

  110. 110.

    Verba N, Chao KM, Lewandowski J, Shah N, James A, Tian F (2019) Modeling industry 4.0 based fog computing environments for application analysis and deployment. Future Gen Comput Syst 91:48–60

    Article  Google Scholar 

  111. 111.

    Verma M, Bhardwaj N, Yadav AK (2016) Real time efficient scheduling algorithm for load balancing in fog computing environment. Int J Inf Technol Comput Sci 8(4):1–10

    Google Scholar 

  112. 112.

    Verma S, Yadav AK, Motwani D, Raw R, Singh HK (2016) An efficient data replication and load balancing technique for fog computing environment. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, pp 2888–2895

  113. 113.

    Wan J, Chen B, Wang S, Xia M, Li D, Liu C (2018) Fog computing for energy-aware load balancing and scheduling in smart factory. IEEE Trans Ind Inf 14(10):4548–4556

    Article  Google Scholar 

  114. 114.

    Wang J, Li D, Hu MY (2020) Fog nodes deployment based on space–time characteristics in smart factory. IEEE Trans Ind Inform 1–9

  115. 115.

    Wang SC, Yan KQ, Liao WP, Wang SS (2010) Towards a load balancing in a three-level cloud computing network. In: 2010 3rd International Conference on Computer Science and Information Technology, vol 1. IEEE, pp 108–113

  116. 116.

    Xu X, Fu S, Cai Q, Tian W, Liu W, Dou W, Sun X, Liu AX (2018) Dynamic resource allocation for load balancing in fog environment. Wirel Commun Mob Comput 2018:1–15

    Google Scholar 

  117. 117.

    Younis MRHJ, El-Halees AM (2015) Hybrid load balancing algorithm in heterogeneous cloud environment. Hybrid Load Balanc Algorithm Heterog Cloud Environ 5(3):2231–2307

    Google Scholar 

  118. 118.

    Yu Y, Li X, Qian C (2017) Sdlb: a scalable and dynamic software load balancer for fog and mobile edge computing. In: Proceedings of the Workshop on Mobile Edge Communications. ACM, pp 55–60

  119. 119.

    Zahid M, Javaid N, Ansar K, Hassan K, Khan MK, Waqas M (2018) Hill climbing load balancing algorithm on fog computing. In: International Conference on P2P, Parallel, Grid, Cloud and Internet Computing. Springer, pp 238–251

  120. 120.

    Zakria M, Javaid N, Ismail M, Zubair M, Zaheer MA, Saeed F (2018) Cloud-fog based load balancing using shortest remaining time first optimization. In: International Conference on P2P, Parallel, Grid, Cloud and Internet Computing. Springer, pp 199–211

  121. 121.

    Zikopoulos PC, Eaton C, DeRoos D, Deutsch T, Lapis G (2012) Understanding big data: analytics for enterprise class hadoop and streaming data. McGraw-Hill, New York

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Mandeep Kaur.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kaur, M., Aron, R. A systematic study of load balancing approaches in the fog computing environment. J Supercomput (2021). https://doi.org/10.1007/s11227-020-03600-8

Download citation


  • Fog computing
  • Resource management
  • Internet of Things (IoT)
  • Load balancing
  • Cloud computing