[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Log in

A survey on nature-inspired techniques for computation offloading and service placement in emerging edge technologies

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Internet of Things (IoT) aims to make an environment more innovative and productive by connecting physical things to the internet. Processing generated data from IoT devices and actuation warranted in real-time requires computational infrastructure near the edge to get the outcome without delay. Emerging edge technologies such as Fog computing, Multi-Access Edge Computing, and Cloudlet provide computing resources near the edge, i.e. closer to the IoT devices, where devices can place their services/applications or offload their computational job for processing. The utilization of computing resources provided by emerging edge technologies addresses the issue of delay in the outcome and increases the battery life of IoT devices/End-user devices. Computational resources provided by the edge technologies, i.e. edge/fog nodes, can be heterogeneous, dynamic and mobile. Therefore, service placement and computation offloading on edge/fog nodes are challenging issues, and the problem to finding the best suitable fog/edge nodes is NP-Hard. Nature-inspired algorithms provide robust solutions to NP-Hard problems. Nowadays, nature-inspired algorithms have been widely applied for resource allocation for service placement and computation offloading in emerging edge technologies. In this work, we provide a detailed study on the applications of nature-inspired algorithms in emerging edge computing domains. We provide an overview of emerging edge technologies, related quality parameters and nature-inspired algorithms followed by the basic formulation of service placement and computation offloading in emerging edge computing systems. In this work, we classify the works in emerging edge computing applying nature-inspired algorithms into two categories: works related to service placement and works related to offloading. We provide a thorough review and comparison of the existing nature-inspired approaches in each category. We discuss various open issues at the end to set future research directions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3.
Figure 4
Figure 5
Figure 6.
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23

Similar content being viewed by others

References

  1. Kumar, D., Maurya, A.K., Baranwal, G.: IoT services in healthcare industry with fog/edge and cloud computing. In: IoT-Based Data Analytics for the Healthcare Industry, pp. 81–103. Academic Press (2021)

    Chapter  Google Scholar 

  2. Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: A complete survey. J. Syst. Archit. 98, 289–330 (2019). https://doi.org/10.1016/j.sysarc.2019.02.009

    Article  Google Scholar 

  3. Singh, M., Baranwal, G.: Quality of Service (QoS) in Internet of Things. In: Proceedings - 2018 3rd International Conference On Internet of Things: Smart Innovation and Usages, IoT-SIU 2018 (2018)

  4. Salaht, F.A., Desprez, F., Lebre, A.: An Overview of Service Placement Problem in Fog and Edge Computing, (2020)

    Google Scholar 

  5. Guerrero, C., Lera, I., Juiz, C.: Evaluation and efficiency comparison of evolutionary algorithms for service placement optimization in fog architectures. Futur. Gener. Comput. Syst. 97, 131–144 (2019). https://doi.org/10.1016/j.future.2019.02.056

    Article  Google Scholar 

  6. Yang, X.S.: Nature-inspired optimization algorithms: Challenges and open problems. J. Comput. Sci. 46, (2020). https://doi.org/10.1016/j.jocs.2020.101104

  7. Nayeri, Z.M., Ghafarian, T., Javadi, B.: Application placement in Fog computing with AI approach: Taxonomy and a state of the art survey, (2021)

  8. Hedhli, A., Mezni, H.: A Survey of Service Placement in Cloud Environments. J. Grid Comput. 19, (2021). https://doi.org/10.1007/s10723-021-09565-z

  9. Shakarami, A., Shahidinejad, A., Ghobaei-Arani, M.: A review on the computation offloading approaches in mobile edge computing: A game-theoretic perspective. Softw. - Pract. Exp. 50, 1719–1759 (2020). https://doi.org/10.1002/spe.2839

    Article  Google Scholar 

  10. Shakarami, A., Ghobaei-Arani, M., Masdari, M., Hosseinzadeh, M.: A Survey on the Computation Offloading Approaches in Mobile Edge/Cloud Computing Environment: A Stochastic-based Perspective, (2020)

  11. Masdari, M., Khezri, H.: Efficient offloading schemes using Markovian models: a literature review. Computing. 102, 1673–1716 (2020). https://doi.org/10.1007/s00607-020-00812-x

    Article  MathSciNet  Google Scholar 

  12. Saeik, F., Avgeris, M., Spatharakis, D., Santi, N., Dechouniotis, D., Violos, J., Leivadeas, A., Athanasopoulos, N., Mitton, N., Papavassiliou, S.: Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions. Comput. Networks. 195, (2021). https://doi.org/10.1016/j.comnet.2021.108177

  13. Asim, M., Wang, Y., Wang, K., Huang, P.Q.: A Review on Computational Intelligence Techniques in Cloud and Edge Computing, (2020)

  14. Guzek, M., Bouvry, P., Talbi, E.G.: A survey of evolutionary computation for resource management of processing in cloud computing [review article], (2015)

  15. Balusamy, B., Sridhar, J., Dhamodaran, D., Krishna, P.V.: Bio-inspired algorithms for cloud computing: A review. Int. J. Innov. Comput. Appl. 6, 181–202 (2015). https://doi.org/10.1504/ijica.2015.073007

    Article  Google Scholar 

  16. Zhan, Z.H., Liu, X.F., Gong, Y.J., Zhang, J., Chung, H.S.H., Li, Y.: Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Comput. Surv. 47, (2015). https://doi.org/10.1145/2788397

  17. Milan, S.T., Rajabion, L., Ranjbar, H., Navimipour, N.J.: Nature inspired meta-heuristic algorithms for solving the load-balancing problem in cloud environments, (2019)

  18. Asghari, S., Navimipour, N.J.: Nature inspired meta-heuristic algorithms for solving the service composition problem in the cloud environments. Int. J. Commun. Syst. 31, (2018). https://doi.org/10.1002/dac.3708

  19. Ramezani, F., Naderpour, M., Taheri, J., Romanous, J., Zomaya, A.Y.: Task Scheduling in Cloud Environments. In: Evolutionary Computation in Scheduling. pp. 213–255. Wiley (2020)

  20. Sarathambekai, S., Umamaheswari, K.: Task Scheduling in Heterogeneous Computing Systems Using Swarm Intelligence. In: Evolutionary Computation in Scheduling. pp. 73–103 (2020)

  21. Masdari, M., Salehi, F., Jalali, M., Bidaki, M.: A Survey of PSO-Based Scheduling Algorithms in Cloud Computing. J. Netw. Syst. Manag. 25, 122–158 (2017). https://doi.org/10.1007/s10922-016-9385-9

    Article  Google Scholar 

  22. Gasmi, K., Dilek, S., Tosun, S., Ozdemir, S.: A survey on computation offloading and service placement in fog computing-based IoT. J. Supercomput. 1–32, (2021). https://doi.org/10.1007/s11227-021-03941-y

  23. Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: MCC’12 - Proceedings of the 1st ACM Mobile Cloud Computing Workshop. pp. 13–15 (2012)

  24. OpenFog Consortium Architecture Working Group: OpenFog Architecture Overview. OpenFogConsortium. 1–35 (2016)

  25. OpenfogConsortium: OpenFog Reference Architecture for Fog Computing Produced. (2017)

  26. Marín-Tordera, E., Masip-Bruin, X., García-Almiñana, J., Jukan, A., Ren, G.J., Zhu, J.: Do we all really know what a fog node is? Current trends towards an open definition. Comput. Commun. 109, 117–130 (2017). https://doi.org/10.1016/j.comcom.2017.05.013

    Article  Google Scholar 

  27. Chiang, M., Ha, S., Chih-Lin, I., Risso, F., Zhang, T.: Clarifying Fog Computing and Networking: 10 Questions and Answers, (2017)

  28. Satyanarayanan, M., Bahl, P., Cáceres, R., Davies, N.: The case for VM-based cloudlets in mobile computing. IEEE Pervasive Comput. 8, 14–23 (2009). https://doi.org/10.1109/MPRV.2009.82

    Article  Google Scholar 

  29. Ha, K., Chen, Z., Hu, W., Richter, W., Pillai, P., Satyanarayanan, M.: Towards wearable cognitive assistance. In: MobiSys 2014 - Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services. pp. 68–81 (2014)

  30. Elazhary, H.: Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions, (2019)

  31. OpenfogConsortium: OpenFog Reference Architecture for Fog Computing Produced. (2017)

  32. Jalali, F.: Energy Consumption of Cloud Computing and Fog Computing Applications, https://minerva-access.unimelb.edu.au/bitstream/handle/11343/58849/Jalali_Fa_thesis.pdf?sequence=1, (2015)

  33. Brogi, A., Forti, S., Guerrero, C., Lera, I.: How to place your apps in the fog: State of the art and open challenges. In: Software - Practice and Experience. pp. 719–740 (2020)

  34. Wang, J., Pan, J., Esposito, F., Calyam, P., Yang, Z., Mohapatra, P.: Edge cloud offloading algorithms: Issues, methods, and perspectives. ACM Comput. Surv. 52, (2019). https://doi.org/10.1145/3284387

  35. Aazam, M., Zeadally, S., Harras, K.A.: Offloading in fog computing for IoT: Review, enabling technologies, and research opportunities. Futur. Gener. Comput. Syst. 87, 278–289 (2018). https://doi.org/10.1016/j.future.2018.04.057

    Article  Google Scholar 

  36. Peng, G., Wu, H., Wu, H., Wolter, K.: Constrained Multiobjective Optimization for IoT-Enabled Computation Offloading in Collaborative Edge and Cloud Computing. IEEE Internet Things J. 8, 13723–13736 (2021). https://doi.org/10.1109/JIOT.2021.3067732

    Article  Google Scholar 

  37. Skarlat, O., Nardelli, M., Schulte, S., Dustdar, S.: Towards QoS-Aware Fog Service Placement. In: Proceedings - 2017 IEEE 1st International Conference on Fog and Edge Computing, ICFEC 2017. pp. 89–96 (2017)

  38. Kumar, D., Raza, Z.: A PSO based VM resource scheduling model for cloud computing. In: Proceedings - 2015 IEEE International Conference on Computational Intelligence and Communication Technology, CICT 2015. pp. 213–219. IEEE (2015)

  39. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks - Conference Proceedings. pp. 1942–1948 (1995)

  40. Houssein, E.H., Gad, A.G., Hussain, K., Suganthan, P.N.: Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application. Swarm Evol. Comput. 63, (2021). https://doi.org/10.1016/j.swevo.2021.100868

  41. Dorigo, M., Di Caro, G.: Ant colony optimization: A new meta-heuristic. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999. pp. 1470–1477 (1999)

  42. Dorigo, M., Blum, C.: Ant colony optimization theory: A survey. Theor. Comput. Sci. 344, 243–278 (2005). https://doi.org/10.1016/j.tcs.2005.05.020

    Article  MathSciNet  MATH  Google Scholar 

  43. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. J. Glob. Optim. 39, 459–471 (2007). https://doi.org/10.1007/s10898-007-9149-x

    Article  MathSciNet  MATH  Google Scholar 

  44. Goldberg, D.E., Holland, J.H.: Genetic Algorithms and Machine Learning, (1988)

  45. Coello, C.A.C.: An Updated Survey of GA-Based Multiobjective Optimization Techniques. ACM Comput. Surv. 32, 109–143 (2000). https://doi.org/10.1145/358923.358929

    Article  Google Scholar 

  46. Vidyarthi, D.P., Tripathi, A.K.: Maximizing reliability of distributed computing system with task allocation using simple genetic algorithm. J. Syst. Archit. 47, 549–554 (2001). https://doi.org/10.1016/s1383-7621(01)00013-3

    Article  Google Scholar 

  47. Rani, S., Ahmed, S.H., Rastogi, R.: Dynamic clustering approach based on wireless sensor networks genetic algorithm for IoT applications. Wirel. Networks. 26, 2307–2316 (2020). https://doi.org/10.1007/s11276-019-02083-7

    Article  Google Scholar 

  48. Raza, Z., Vidyarthi, D.P.: A computational grid scheduling model to maximize reliability using modified GA. Int. J. Grid High Perform. Comput. 3, 1–20 (2011). https://doi.org/10.4018/jghpc.2011010101

    Article  Google Scholar 

  49. Canali, C., Lancellotti, R.: GASP: Genetic algorithms for service placement in fog computing systems. Algorithms. 12, (2019). https://doi.org/10.3390/a12100201

  50. Knowles, J.D., Corne, D.W.: M-PAES: A memetic algorithm for multiobjective optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, CEC 2000. pp. 325–332 (2000)

  51. Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An Application Placement Technique for Concurrent IoT Applications in Edge and Fog Computing Environments. IEEE Trans. Mob. Comput. 20, 1298–1311 (2021). https://doi.org/10.1109/TMC.2020.2967041

    Article  Google Scholar 

  52. Zitzler, E., Thiele, L.: An Evolutionary Algorithm for Multiobjective Optimization : The Strength Pareto Approach. Technical Report 43, Computer Engineering and Communication Networks Lab (TIK). TIK-Report. (1998)

  53. Ayoubi, M., Ramezanpour, M., Khorsand, R.: An autonomous IoT service placement methodology in fog computing. Softw. - Pract. Exp. 51, 1097–1120 (2021). https://doi.org/10.1002/spe.2939

    Article  Google Scholar 

  54. Xu, X., Liu, Q., Luo, Y., Peng, K., Zhang, X., Meng, S., Qi, L.: A computation offloading method over big data for IoT-enabled cloud-edge computing. Futur. Gener. Comput. Syst. 95, 522–533 (2019). https://doi.org/10.1016/j.future.2018.12.055

    Article  Google Scholar 

  55. Peng, K., Zhu, M., Zhang, Y., Liu, L., Zhang, J., Leung, V.C.M., Zheng, L.: An energy- and cost-aware computation offloading method for workflow applications in mobile edge computing. Eurasip J. Wirel. Commun. Netw. 2019, (2019). https://doi.org/10.1186/s13638-019-1526-x

  56. Hussein, M.K., Mousa, M.H.: Efficient task offloading for IoT-Based applications in fog computing using ant colony optimization. IEEE Access. 8, 37191–37201 (2020). https://doi.org/10.1109/ACCESS.2020.2975741

    Article  Google Scholar 

  57. Natesha, B.V., Guddeti, R.M.R.: Adopting elitism-based Genetic Algorithm for minimizing multi-objective problems of IoT service placement in fog computing environment. J. Netw. Comput. Appl. 178, (2021). https://doi.org/10.1016/j.jnca.2020.102972

  58. Sami, H., Mourad, A.: Dynamic On-Demand Fog Formation Offering On-the-Fly IoT Service Deployment. IEEE Trans. Netw. Serv. Manag. 17, 1026–1039 (2020). https://doi.org/10.1109/TNSM.2019.2963643

    Article  Google Scholar 

  59. Huang, T., Lin, W., Xiong, C., Pan, R., Huang, J.: An Ant Colony Optimization-Based Multiobjective Service Replicas Placement Strategy for Fog Computing. IEEE Trans. Cybern. 1–14, (2020). https://doi.org/10.1109/tcyb.2020.2989309

  60. Skarlat, O., Nardelli, M., Schulte, S., Borkowski, M., Leitner, P.: Optimized IoT service placement in the fog. Serv. Oriented Comput. Appl. 11, 427–443 (2017). https://doi.org/10.1007/s11761-017-0219-8

    Article  Google Scholar 

  61. Djemai, T., Stolf, P., Monteil, T., Pierson, J.M.: A discrete particle swarm optimization approach for energy-efficient IoT services placement over fog infrastructures. In: Proceedings - 2019 18th International Symposium on Parallel and Distributed Computing, ISPDC 2019. pp. 32–40 (2019)

  62. Sami, H., Mourad, A., El-Hajj, W.: Vehicular-OBUs-As-On-Demand-Fogs: Resource and Context Aware Deployment of Containerized Micro-Services. IEEE/ACM Trans. Netw. 28, 778–790 (2020). https://doi.org/10.1109/TNET.2020.2973800

    Article  Google Scholar 

  63. Al-Tarawneh, M.A.B.: Bi-objective optimization of application placement in fog computing environments. J. Ambient Intell. Humaniz. Comput. (2021). https://doi.org/10.1007/s12652-021-02910-w

  64. Mseddi, A., Jaafar, W., Elbiaze, H., Ajib, W.: Joint Container Placement and Task Provisioning in Dynamic Fog Computing. IEEE Internet Things J. 6, 10028–10040 (2019). https://doi.org/10.1109/JIOT.2019.2935056

    Article  Google Scholar 

  65. Moallemi, R., Bozorgchenani, A., Tarchi, D.: An evolutionary-based algorithm for smart-living applications placement in fog networks. In: 2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings (2019)

  66. Wen, Z., Yang, R., Garraghan, P., Lin, T., Xu, J., Rovatsos, M.: Fog orchestration for internet of things services. IEEE Internet Comput. 21, 16–24 (2017). https://doi.org/10.1109/MIC.2017.36

    Article  Google Scholar 

  67. Xu, X., Liu, X., Xu, Z., Dai, F., Zhang, X., Qi, L.: Trust-Oriented IoT Service Placement for Smart Cities in Edge Computing. IEEE Internet Things J. 7, 4084–4091 (2020). https://doi.org/10.1109/JIOT.2019.2959124

    Article  Google Scholar 

  68. Roy, P., Sarker, S., Razzaque, M.A., Hassan, M.M., AlQahtani, S.A., Aloi, G., Fortino, G.: AI-enabled mobile multimedia service instance placement scheme in mobile edge computing. Comput. Networks. 182, (2020). https://doi.org/10.1016/j.comnet.2020.107573

  69. Hosseinzadeh, M., Masdari, M., Rahmani, A.M., Mohammadi, M., Aldalwie, A.H.M., Majeed, M.K., Karim, S.H.T.: Improved Butterfly Optimization Algorithm for Data Placement and Scheduling in Edge Computing Environments. J. Grid Comput. 19, (2021). https://doi.org/10.1007/s10723-021-09556-0

  70. Fang, J., Ma, A.: IoT Application Modules Placement and Dynamic Task Processing in Edge-Cloud Computing. IEEE Internet Things J. 8, 12771–12781 (2021). https://doi.org/10.1109/JIOT.2020.3007751

    Article  Google Scholar 

  71. Maia, A.M., Ghamri-Doudane, Y., Vieira, D., Franklin De Castro, M.: Dynamic service placement and load distribution in edge computing. In: 16th International Conference on Network and Service Management, CNSM 2020, 2nd International Workshop on Analytics for Service and Application Management, AnServApp 2020 and 1st International Workshop on the Future Evolution of Internet Protocols, IPFutu. pp. 1–9. IEEE (2020)

  72. Maia, A.M., Ghamri-Doudane, Y., Vieira, D., De Castro, M.F.: Optimized placement of scalable IoT services in edge computing. In: 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019. pp. 189–197 (2019)

  73. Maia, A.M., Ghamri-Doudane, Y., Vieira, D., De Castro, M.F.: A multi-objective service placement and load distribution in edge computing. 2019 IEEE Glob. Commun. Conf. GLOBECOM 2019 - Proc. (2019). https://doi.org/10.1109/GLOBECOM38437.2019.9014303

  74. Lin, B., Zhu, F., Zhang, J., Chen, J., Chen, X., Xiong, N.N., Lloret Mauri, J.: A Time-Driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing. IEEE Trans. Ind. Informatics. 15, 4254–4265 (2019). https://doi.org/10.1109/TII.2019.2905659

    Article  Google Scholar 

  75. Mennes, R., Spinnewyn, B., Latre, S., Botero, J.F.: GRECO: A Distributed Genetic Algorithm for Reliable Application Placement in Hybrid Clouds. In: Proceedings - 2016 5th IEEE International Conference on Cloud Networking, CloudNet 2016. pp. 14–20 (2016)

  76. Zou, G., Qin, Z., Deng, S., Li, K.C., Gan, Y., Zhang, B.: Towards the optimality of service instance selection in mobile edge computing. Knowledge-Based Syst. 217, (2021). https://doi.org/10.1016/j.knosys.2021.106831

  77. Wang, Z., Gao, F., Jin, X.: Optimal deployment of cloudlets based on cost and latency in Internet of Things networks. Wirel. Networks. 26, 6077–6093 (2020). https://doi.org/10.1007/s11276-020-02418-9

    Article  Google Scholar 

  78. Yang, L., Cao, J., Liang, G., Han, X.: Cost Aware Service Placement and Load Dispatching in Mobile Cloud Systems. IEEE Trans. Comput. 65, 1440–1452 (2016). https://doi.org/10.1109/TC.2015.2435781

    Article  MathSciNet  MATH  Google Scholar 

  79. Ghanavati, S., Abawajy, J.H., Izadi, D.: An Energy Aware Task Scheduling Model Using Ant-Mating Optimization in Fog Computing Environment. IEEE Trans. Serv. Comput. 1–1, (2020). https://doi.org/10.1109/tsc.2020.3028575

  80. Ghobaei-Arani, M., Souri, A., Safara, F., Norouzi, M.: An efficient task scheduling approach using moth-flame optimization algorithm for cyber-physical system applications in fog computing. Trans. Emerg. Telecommun. Technol. 31, (2020). https://doi.org/10.1002/ett.3770

  81. Li, X., Zang, Z., Shen, F., Sun, Y.: Task Offloading Scheme Based on Improved Contract Net Protocol and Beetle Antennae Search Algorithm in Fog Computing Networks. Mob. Networks Appl. 25, 2517–2526 (2020). https://doi.org/10.1007/s11036-020-01593-5

    Article  Google Scholar 

  82. Adhikari, M., Gianey, H.: Energy efficient offloading strategy in fog-cloud environment for IoT applications. Internet of Things. 6, 100053 (2019). https://doi.org/10.1016/j.iot.2019.100053

    Article  Google Scholar 

  83. Keshavarznejad, M., Rezvani, M.H., Adabi, S.: Delay-aware optimization of energy consumption for task offloading in fog environments using metaheuristic algorithms. Cluster Comput. 24, 1825–1853 (2021). https://doi.org/10.1007/s10586-020-03230-y

    Article  Google Scholar 

  84. Zhang, D., Haider, F., St-Hilaire, M., Makaya, C.: Model and algorithms for the planning of fog computing networks. IEEE Internet Things J. 6, 3873–3884 (2019). https://doi.org/10.1109/JIOT.2019.2892940

    Article  Google Scholar 

  85. Alli, A.A., Alam, M.M.: SecOFF-FCIoT: Machine learning based secure offloading in Fog-Cloud of things for smart city applications. Internet Things (Netherlands). 7, 100070 (2019). https://doi.org/10.1016/j.iot.2019.100070

    Article  Google Scholar 

  86. Adhikari, M., Srirama, S.N., Amgoth, T.: Application Offloading Strategy for Hierarchical Fog Environment Through Swarm Optimization. IEEE Internet Things J. 7, 4317–4328 (2020). https://doi.org/10.1109/JIOT.2019.2958400

    Article  Google Scholar 

  87. Shahryari, O.K., Pedram, H., Khajehvand, V., TakhtFooladi, M.D.: Energy and task completion time trade-off for task offloading in fog-enabled IoT networks. Pervasive Mob. Comput. 74, (2021). https://doi.org/10.1016/j.pmcj.2021.101395

  88. Hussain, M.M., Beg, M.M.S.: CODE-V: Multi-hop computation offloading in Vehicular Fog Computing. Futur. Gener. Comput. Syst. 116, 86–102 (2021). https://doi.org/10.1016/j.future.2020.09.039

    Article  Google Scholar 

  89. Li, X., Zhou, L., Sun, Y., Ulziinyam, B.: Multi-task offloading scheme for UAV-enabled fog computing networks. Eurasip J. Wirel. Commun. Netw. 2020, (2020). https://doi.org/10.1186/s13638-020-01825-y

  90. Zhu, C., Tao, J., Pastor, G., Xiao, Y., Ji, Y., Zhou, Q., Li, Y., Yla-Jaaski, A.: Folo: Latency and quality optimized task allocation in vehicular fog computing. IEEE Internet Things J. 6, 4150–4161 (2019). https://doi.org/10.1109/JIOT.2018.2875520

    Article  Google Scholar 

  91. Sun, Y., Song, C., Yu, S., Liu, Y., Pan, H., Zeng, P.: Energy-efficient task offloading based on differential evolution in edge computing system with energy harvesting. IEEE Access. (2021). https://doi.org/10.1109/ACCESS.2021.3052901

  92. Peng, K., Huang, H., Wan, S., Leung, V.C.M.: End-edge-cloud collaborative computation offloading for multiple mobile users in heterogeneous edge-server environment. Wirel. Networks. (2020). https://doi.org/10.1007/s11276-020-02385-1

  93. Song, Y., Yau, S.S., Yu, R., Zhang, X., Xue, G.: An Approach to QoS-based Task Distribution in Edge Computing Networks for IoT Applications. In: Proceedings - 2017 IEEE 1st International Conference on Edge Computing, EDGE 2017. pp. 32–39 (2017)

  94. Bi, J., Yuan, H., Duanmu, S., Zhou, M., Abusorrah, A.: Energy-Optimized Partial Computation Offloading in Mobile-Edge Computing with Genetic Simulated-Annealing-Based Particle Swarm Optimization. IEEE Internet Things J. 8, 3774–3785 (2021). https://doi.org/10.1109/JIOT.2020.3024223

    Article  Google Scholar 

  95. Saleem, U., Liu, Y., Jangsher, S., Li, Y., Jiang, T.: Mobility-Aware Joint Task Scheduling and Resource Allocation for Cooperative Mobile Edge Computing. IEEE Trans. Wirel. Commun. 20, 360–374 (2021). https://doi.org/10.1109/TWC.2020.3024538

    Article  Google Scholar 

  96. Zakaryia, S.A., Ahmed, S.A., Hussein, M.K.: Evolutionary offloading in an edge environment. Egypt. Informatics J. 22, 257–267 (2021). https://doi.org/10.1016/j.eij.2020.09.003

    Article  Google Scholar 

  97. Yang, L., Zhang, H., Li, M., Guo, J., Ji, H.: Mobile edge computing empowered energy efficient task offloading in 5G. IEEE Trans. Veh. Technol. 67, 6398–6409 (2018). https://doi.org/10.1109/TVT.2018.2799620

    Article  Google Scholar 

  98. Huynh, L.N.T., Pham, Q.V., Pham, X.Q., Nguyen, T.D.T., Hossain, M.D., Huh, E.N.: Efficient computation offloading in multi-tier multi-access edge computing systems: A particle swarm optimization approach. Appl. Sci. 10, (2020). https://doi.org/10.3390/app10010203

  99. Guo, F., Zhang, H., Ji, H., Li, X., Leung, V.C.M.: An efficient computation offloading management scheme in the densely deployed small cell networks with mobile edge computing. IEEE/ACM Trans. Netw. 26, 2651–2664 (2018). https://doi.org/10.1109/TNET.2018.2873002

    Article  Google Scholar 

  100. Xu, X., Liu, X., Yin, X., Wang, S., Qi, Q., Qi, L.: Privacy-aware offloading for training tasks of generative adversarial network in edge computing. Inf. Sci. (Ny). 532, 1–15 (2020). https://doi.org/10.1016/j.ins.2020.04.026

    Article  MathSciNet  Google Scholar 

  101. Li, Z., Zhu, Q.: Genetic algorithm-based optimization of offloading and resource allocation in mobile-edge computing. Information 11, (2020). https://doi.org/10.3390/info11020083

  102. Deng, X., Sun, Z., Li, D., Luo, J., Wan, S.: User-Centric Computation Offloading for Edge Computing. IEEE Internet Things J. 8, 12559–12568 (2021). https://doi.org/10.1109/JIOT.2021.3057694

    Article  Google Scholar 

  103. Xu, X., Wu, Q., Qi, L., Dou, W., Tsai, S.B., Bhuiyan, M.Z.A.: Trust-Aware Service Offloading for Video Surveillance in Edge Computing Enabled Internet of Vehicles. IEEE Trans. Intell. Transp. Syst. 22, 1787–1796 (2021). https://doi.org/10.1109/TITS.2020.2995622

    Article  Google Scholar 

  104. Qi, Q., Wang, J., Li, Q., Li, T., Cao, Y.: Resource orchestration for multi-Task application in home-To-home cloud. IEEE Trans. Consum. Electron. 62, 191–199 (2016). https://doi.org/10.1109/TCE.2016.7514719

    Article  Google Scholar 

  105. Jiang, C., Li, Y., Su, J., Chen, Q.: Research on new edge computing network architecture and task offloading strategy for Internet of Things. Wirel. Netw. (2021). https://doi.org/10.1007/s11276-020-02516-8

  106. Song, F., Xing, H., Luo, S., Zhan, D., Dai, P., Qu, R.: A Multiobjective Computation Offloading Algorithm for Mobile-Edge Computing. IEEE Internet Things J. 7, 8780–8799 (2020). https://doi.org/10.1109/JIOT.2020.2996762

    Article  Google Scholar 

  107. Liu, J., Zhang, Q.: Code-partitioning offloading schemes in mobile edge computing for augmented reality. IEEE Access. 7, 11222–11236 (2019). https://doi.org/10.1109/ACCESS.2019.2891113

    Article  Google Scholar 

  108. Xu, X., Zhang, X., Liu, X., Jiang, J., Qi, L., Bhuiyan, M.Z.A.: Adaptive Computation Offloading with Edge for 5G-Envisioned Internet of Connected Vehicles. IEEE Trans. Intell. Transp. Syst. 22, 5213–5222 (2021). https://doi.org/10.1109/TITS.2020.2982186

    Article  Google Scholar 

  109. Chen, C., Chen, L., Liu, L., He, S., Yuan, X., Lan, D., Chen, Z.: Delay-optimized V2V-based computation offloading in urban vehicular edge computing and networks. IEEE Access. 8, 18863–18873 (2020). https://doi.org/10.1109/ACCESS.2020.2968465

    Article  Google Scholar 

  110. Luo, Q., Li, C., Luan, T., Shi, W.: Minimizing the Delay and Cost of Computation Offloading for Vehicular Edge Computing. IEEE Trans. Serv. Comput. (2021). https://doi.org/10.1109/TSC.2021.3064579

  111. Dai, S., Li Wang, M., Gao, Z., Huang, L., Du, X., Guizani, M.: An Adaptive Computation Offloading Mechanism for Mobile Health Applications. IEEE Trans. Veh. Technol. 69, 998–1007 (2020). https://doi.org/10.1109/TVT.2019.2954887

    Article  Google Scholar 

  112. Xu, X., Gu, R., Dai, F., Qi, L., Wan, S.: Multi-objective computation offloading for Internet of Vehicles in cloud-edge computing. Wirel. Netw. 26, 1611–1629 (2020). https://doi.org/10.1007/s11276-019-02127-y

    Article  Google Scholar 

  113. Hou, X., Ren, Z., Wang, J., Cheng, W., Ren, Y., Chen, K.C., Zhang, H.: Reliable Computation Offloading for Edge-Computing-Enabled Software-Defined IoV. IEEE Internet Things J. 7, 7097–7111 (2020). https://doi.org/10.1109/JIOT.2020.2982292

    Article  Google Scholar 

  114. Pham, H.G.T., Pham, Q.V., Pham, A.T., Nguyen, C.T.: Joint Task Offloading and Resource Management in NOMA-Based MEC Systems: A Swarm Intelligence Approach. IEEE Access. 8, 190463–190474 (2020). https://doi.org/10.1109/ACCESS.2020.3031614

    Article  Google Scholar 

  115. Xia, W., Shen, L.: Joint Resource Allocation at Edge Cloud Based on Ant Colony Optimization and Genetic Algorithm. Wirel. Pers. Commun. 117, 355–386 (2021). https://doi.org/10.1007/s11277-020-07873-3

    Article  Google Scholar 

  116. Lin, B., Huang, Y., Zhang, J., Hu, J., Chen, X., Li, J.: Cost-Driven Off-Loading for DNN-Based Applications over Cloud, Edge, and End Devices. IEEE Trans. Ind. Informatics. 16, 5456–5466 (2020). https://doi.org/10.1109/TII.2019.2961237

    Article  Google Scholar 

  117. Soula, M., Karanika, A., Kolomvatsos, K., Anagnostopoulos, C., Stamoulis, G.: Intelligent tasks allocation at the edge based on machine learning and bio-inspired algorithms. Evol. Syst. (2021). https://doi.org/10.1007/s12530-021-09379-0

  118. Jiang, F., Wang, K., Dong, L., Pan, C., Xu, W., Yang, K.: Deep-Learning-Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks. IEEE Internet Things J. 7, 6252–6265 (2020). https://doi.org/10.1109/JIOT.2019.2954503

    Article  Google Scholar 

  119. Chen, J., Zhao, Y., Xu, Z., Zheng, H.: Resource allocation strategy for D2D-assisted edge computing system with hybrid energy harvesting. IEEE Access. 8, 192643–192658 (2020). https://doi.org/10.1109/ACCESS.2020.3032033

    Article  Google Scholar 

  120. Huang, T., Ruan, F., Xue, S., Qi, L., Duan, Y.: Computation offloading for multimedia workflows with deadline constraints in cloudlet-based mobile cloud. Wirel. Networks. 26, 5535–5549 (2020). https://doi.org/10.1007/s11276-019-02053-z

    Article  Google Scholar 

  121. Xu, X., Fu, S., Yuan, Y., Luo, Y., Qi, L., Lin, W., Dou, W.: Multiobjective computation offloading for workflow management in cloudlet-based mobile cloud using NSGA-II. Comput. Intell. 35, 476–495 (2019). https://doi.org/10.1111/coin.12197

    Article  MathSciNet  Google Scholar 

  122. Liu, L., Du, Y.: An improved multi-objective evolutionary algorithm for computation offloading in the multi-cloudlet environment, (2021)

  123. Guan, S., Boukerche, A., Loureiro, A.: Novel Sustainable and Heterogeneous Offloading Management Techniques in Proactive Cloudlets. IEEE Trans. Sustain. Comput. 6, 334–346 (2021). https://doi.org/10.1109/TSUSC.2020.2980847

    Article  Google Scholar 

  124. Midya, S., Roy, A., Majumder, K., Phadikar, S.: Multi-objective optimization technique for resource allocation and task scheduling in vehicular cloud architecture: A hybrid adaptive nature inspired approach. J. Netw. Comput. Appl. 103, 58–84 (2018). https://doi.org/10.1016/j.jnca.2017.11.016

    Article  Google Scholar 

  125. Manukumar, S.T., Muthuswamy, V.: A Novel Multi-Objective Efficient Offloading Decision Framework in Cloud Computing for Mobile Computing Applications. Wirel. Pers. Commun. 107, 1625–1642 (2019). https://doi.org/10.1007/s11277-019-06348-4

    Article  Google Scholar 

  126. Shi, Y., Chen, S., Xu, X.: MAGA: A Mobility-Aware Computation Offloading Decision for Distributed Mobile Cloud Computing. IEEE Internet Things J. 5, 164–174 (2018). https://doi.org/10.1109/JIOT.2017.2776252

    Article  Google Scholar 

  127. Wang, Y., Wu, L., Yuan, X., Liu, X., Li, X.: An Energy-Efficient and Deadline-Aware Task Offloading Strategy Based on Channel Constraint for Mobile Cloud Workflows. IEEE Access. 7, 69858–69872 (2019). https://doi.org/10.1109/ACCESS.2019.2919319

    Article  Google Scholar 

  128. Wang, T., Wei, X., Tang, C., Fan, J.: Efficient multi-tasks scheduling algorithm in mobile cloud computing with time constraints. Peer-to-Peer Netw. Appl. 11, 793–807 (2018). https://doi.org/10.1007/s12083-017-0561-9

    Article  Google Scholar 

  129. Kaur, P., Mehta, S.: Efficient computation offloading using grey wolf optimization algorithm. In: AIP Conference Proceedings (2019)

  130. Mehta, S., Kaur, P.: Efficient Computation Offloading in Mobile Cloud Computing with Nature-Inspired Algorithms. Int. J. Comput. Intell. Appl. 18, (2019). https://doi.org/10.1142/S1469026819500238

  131. Zhang, J., Zhou, Z., Li, S., Gan, L., Zhang, X., Qi, L., Xu, X., Dou, W.: Hybrid computation offloading for smart home automation in mobile cloud computing. Pers. Ubiquitous Comput. 22, 121–134 (2018). https://doi.org/10.1007/s00779-017-1095-0

    Article  Google Scholar 

  132. Sundararaj, V.: Optimal Task Assignment in Mobile Cloud Computing by Queue Based Ant-Bee Algorithm. Wirel. Pers. Commun. 104, 173–197 (2019). https://doi.org/10.1007/s11277-018-6014-9

    Article  Google Scholar 

  133. Guo, S., Wang, Y., Meng, S., Ma, N.: Delay optimization for mobile cloud computing application offloading in smart cities. In: Advances in Intelligent Systems and Computing. pp. 456–466 (2019)

  134. Tout, H., Talhi, C., Kara, N., Mourad, A.: Selective mobile cloud offloading to augment multi-persona performance and viability. IEEE Trans. Cloud Comput. 7, 314–328 (2019). https://doi.org/10.1109/TCC.2016.2535223

    Article  Google Scholar 

  135. Sheikh, I., Das, O.: Modeling the Effect of Parallel Execution on Multi-site Computation Offloading in Mobile Cloud Computing. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 219–234 (2018)

  136. Abd, S.K., Al-Haddad, S.A.R., Hashim, F., Abdullah, A.B.H.J., Yussof, S.: Energy-Aware Fault Tolerant Task offloading of Mobile Cloud Computing. In: Proceedings - 5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2017. pp. 161–164 (2017)

  137. Qi, H., Mu, X., Shi, Y.: A task unloading strategy of IoT devices using deep reinforcement learning based on mobile cloud computing environment. Wirel. Netw. (2020). https://doi.org/10.1007/s11276-020-02471-4

  138. Roberge, V., Tarbouchi, M., Labonte, G.: Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans. Ind. Informatics. 9, 132–141 (2013). https://doi.org/10.1109/TII.2012.2198665

    Article  Google Scholar 

  139. Niu, X., Shao, S., Xin, C., Zhou, J., Guo, S., Chen, X., Qi, F.: Workload Allocation Mechanism for Minimum Service Delay in Edge Computing-Based Power Internet of Things. IEEE Access. 7, 83771–83784 (2019). https://doi.org/10.1109/ACCESS.2019.2920325

    Article  Google Scholar 

  140. Li, X., Yao, X.: Cooperatively coevolving particle swarms for large scale optimization. IEEE Trans. Evol. Comput. 16, 210–224 (2012). https://doi.org/10.1109/TEVC.2011.2112662

    Article  Google Scholar 

  141. Trivedi, A., Srinivasan, D., Sanyal, K., Ghosh, A.: A survey of multiobjective evolutionary algorithms based on decomposition. IEEE Trans. Evol. Comput. 21, 440–462 (2017). https://doi.org/10.1109/TEVC.2016.2608507

    Article  Google Scholar 

  142. Zhou, A., Qu, B.Y., Li, H., Zhao, S.Z., Suganthan, P.N., Zhangd, Q.: Multiobjective evolutionary algorithms: A survey of the state of the art, (2011)

  143. Li, H., Zhang, Q.: Multiobjective optimization problems with complicated pareto sets, MOEA/ D and NSGA-II. IEEE Trans. Evol. Comput. 13, 284–302 (2009). https://doi.org/10.1109/TEVC.2008.925798

    Article  Google Scholar 

  144. Tan, K.C., Tay, A., Cai, J.: Design and implementation of a distributed evolutionary computing software. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 33, 325–338 (2003). https://doi.org/10.1109/TSMCC.2003.817359

    Article  Google Scholar 

  145. Yadav, R., Baranwal, G.: Trust-aware Framework for Application Placement in Fog Computing. In: International Symposium on Advanced Networks and Telecommunication Systems, ANTS (2019)

  146. Tclouds-project: Tclouds-project, https://tclouds.technikon.com/downloads/TCLOUDS_poster_20130827.pdf

  147. Di Nitto, E., Da Silva, M.A.A., Ardagna, D., Casale, G., Craciun, C.D., Ferry, N., Muntes, V., Solberg, A.: Supporting the development and operation of multi-cloud applications: The MODAClouds approach. In: Proceedings - 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2013. pp. 417–423 (2013)

  148. Kamateri, E., Loutas, N., Zeginis, D., Ahtes, J., D’Andria, F., Bocconi, S., Gouvas, P., Ledakis, G., Ravagli, F., Lobunets, O., Tarabanis, K.A.: Cloud4SOA: A semantic-interoperability paas solution for multi-cloud platform management and portability. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 64–78 (2013)

  149. Sadovykh, A., Hein, C., Morin, B., Mohagheghi, P., Berre, A.J.: REMICS- REuse and migration of legacy applications to interoperable cloud services. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 315–316 (2011)

  150. Grozev, N., Buyya, R.: Inter-Cloud architectures and application brokering: Taxonomy and survey. Softw. - Pract. Exp. 44, 369–390 (2014). https://doi.org/10.1002/spe.2168

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gaurav Baranwal.

Ethics declarations

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

This article belongs to the Topical Collection: Special Issue on Resource Management at the Edge for Future Web, Mobile and IoT Applications

Guest Editors: Qiang He, Fang Dong, Chenshu Wu, and Yun Yang

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, D., Baranwal, G., Shankar, Y. et al. A survey on nature-inspired techniques for computation offloading and service placement in emerging edge technologies. World Wide Web 25, 2049–2107 (2022). https://doi.org/10.1007/s11280-022-01053-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-022-01053-y

Keywords

Navigation