Skip to main content
Log in

A Survey on Auction based Approaches for Resource Allocation and Pricing in Emerging Edge Technologies

  • Published:
Journal of Grid Computing Aims and scope Submit manuscript

Abstract

The advancements in sensing technologies, smart devices, wearable gadgets, and communication paradigm enable the vision of the internet of things, smart city, virtual and augmented reality, pervasive healthcare, to name a few. These applications have strict requirements of low latency delivery, high data rate, and instant response. To support this, various new technologies, such as fog computing, mobile edge computing, cloudlet, Micro, and Nano centers, mini and micro clouds, etc., have emerged. The entire set of emerging edge computing paradigms are commonly referred as "edge technologies" in which computational resources and storage are closer to the user/terminal devices somewhere between the device and the cloud data center. The edge technologies aim to deliver computing services with minimal delay by reducing the downward and upward time and data traffic volume. Like cloud service providers, edge service providers are emerging, and a market of edge computing resources has been created. Therefore, Auction theory, a subfield of Economics, is being widely applied for the allocation of resources in emerging edge technologies. This work presents a comprehensive survey on auction-based resource allocation and pricing approaches in emerging edge technologies. An overview of edge technologies and auction theory is given, followed by a thorough review and comparison of the existing auction-based approaches applied in edge technologies for resource allocation and pricing in terms of economic properties. Various open research issues have been deliberated to set the future research direction at the end.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Lueth, K.L.: State of the IoT 2018: Number of IoT devices now at 7B, https://iot-analytics.com/state-of-the-iot-update-q1-q2-2018-number-of-iot-devices-now-7b/

  2. Ren, J., Zhang, Y., Deng, R., Zhang, N., Zhang, D., Shen, X.S.: Joint channel access and sampling rate control in energy harvesting cognitive radio sensor networks. IEEE Trans. Emerg. Top. Comput. 7, 149–161 (2019). https://doi.org/10.1109/TETC.2016.2555806

    Article  Google Scholar 

  3. Cisco, T.: Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update 2014–2019 White Paper. Growth Lakel. 2011, 2010–2015 (2011)

    Google Scholar 

  4. Doukas, C., Maglogiannis, I.: Bringing IoT and cloud computing towards pervasive healthcare. In: Proceedings - 6th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2012. pp. 922–926 (2012)

  5. Habiba, U., Hossain, E.: Auction mechanisms for virtualization in 5g cellular networks: Basics, trends, and open challenges. IEEE Commun. Surv. Tutorials. 20, 2264–2293 (2018). https://doi.org/10.1109/COMST.2018.2811395

    Article  Google Scholar 

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

  7. 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 

  8. Mao, Y., You, C., Zhang, J., Huang, K., Letaief, K.B.: A survey on mobile edge computing: the communication perspective, (2017)

  9. Avelar, V.: Cost Benefit Analysis of Edge Micro Data Center Deployments. (2017)

  10. Laoutaris, N., Rodriguez, P., Massoulie, L.: ECHOS: Edge capacity hosting overlays of nano data centers. In: Computer Communication Review. pp. 51–54 (2008)

  11. Ghobaei-Arani, M., Souri, A., Rahmanian, A.A.: Resource management approaches in fog computing: a comprehensive review, (2020)

  12. Baranwal, G., Kumar, D., Raza, Z., Vidyarthi, D.P.: Auction theory. In: SpringerBriefs in Computer Science. pp. 17–31. Springer, Singapore (2018)

  13. Baranwal, G., Kumar, D., Raza, Z., Vidyarthi, D.P.: Auction based resource provisioning in cloud computing. Springer Singapore, Singapore (2018)

  14. Tang, W., Jain, R.: Hierarchical auction mechanisms for network resource allocation. IEEE J. Sel. Areas Commun. 30, 2117–2125 (2012). https://doi.org/10.1109/JSAC.2012.121204

    Article  Google Scholar 

  15. Lin, P., Feng, X., Zhang, Q.: Springer briefs in computer science auction design for the wireless spectrum market. Springer International Publishing, Cham (2014)

    Book  Google Scholar 

  16. Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R.H., Morrow, M.J., Polakos, P.A.: A comprehensive survey on fog computing: state-of-the-art and research challenges, (2018)

  17. Bilal, K., Khalid, O., Erbad, A., Khan, S.U.: Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers. Comput. Networks. 130, 94–120 (2018). https://doi.org/10.1016/j.comnet.2017.10.002

    Article  Google Scholar 

  18. 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)

  19. Moura, J., Hutchison, D.: Game theory for multi-access edge computing: Survey, use cases, and future trends. IEEE Commun. Surv. Tutorials. 21, 260–288 (2019). https://doi.org/10.1109/COMST.2018.2863030

    Article  Google Scholar 

  20. Hong, C.H., Varghese, B.: Resource management in fog/Edge computing: A survey on architectures, infrastructure, and algorithms. ACM Comput. Surv. 52, (2019). https://doi.org/10.1145/3326066

  21. Luong, N.C., Wang, P., Niyato, D., Wen, Y., Han, Z.: Resource management in cloud networking using economic analysis and pricing models: a survey. IEEE Commun. Surv. Tutorials. 19, 954–1001 (2017). https://doi.org/10.1109/COMST.2017.2647981

    Article  Google Scholar 

  22. Zhang, Y., Xu, K.: A Survey of Resource Management in Cloud and Edge Computing. Netw. Manag. Cloud Edge Comput. 15–32 (2020). https://doi.org/10.1007/978-981-15-0138-8_2

  23. Zhang, Y., Lee, C., Niyato, D., Wang, P.: Auction approaches for resource allocation in wireless systems: A survey. IEEE Commun. Surv. Tutorials. 15, 1020–1041 (2013). https://doi.org/10.1109/SURV.2012.110112.00125

    Article  Google Scholar 

  24. 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)

  25. Xu, K., Li, Y., Ren, F.: An energy-efficient compressive sensing framework incorporating online dictionary learning for long-term wireless health monitoring. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. pp. 804–808 (2016)

  26. 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

  27. 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

  28. Kemp, R., Palmer, N., Kielmann, T., Seinstra, F., Drost, N., Maassen, J., Bal, H.: eyeDentify: Multimedia cyber foraging from a smartphone. In: ISM 2009 - 11th IEEE International Symposium on Multimedia. pp. 392–399 (2009)

  29. Shi, B., Yang, J., Huang, Z., Hui, P.: Offloading guidelines for augmented reality applications on wearable devices. In: MM 2015 - Proceedings of the 2015 ACM Multimedia Conference. pp. 1271–1274 (2015)

  30. 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)

  31. Cortés, R., Bonnaire, X., Marin, O., Sens, P.: Stream processing of healthcare sensor data: Studying user traces to identify challenges from a big data perspective. In: Procedia Computer Science. pp. 1004–1009 (2015)

  32. Abowd, G.D., Dey, A.K., Brown, P.J., Davies, N., Smith, M., Steggles, P.: Towards a better understanding of context and context-awareness. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 304–307 (1999)

  33. 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 

  34. 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)

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

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

  37. Beck, M.T., Feld, S., Linnhoff-Popien, C., Pützschler, U.: Mobile edge computing (MEC) framework and reference architecture. Informatik-Spektrum 39, 108–114 (2016)

    Article  Google Scholar 

  38. 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 

  39. Satyanarayanan, M.: The role of cloudlets in hostile environments. Presented at the (2013)

  40. Bahl, V.: Emergence of Micro Datacenter (Cloudlets/Edges) for Mobile Computing, https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/Micro-Data-Centers-mDCs-for-Mobile-Computing-1.pdf, (2015)

  41. Anixter Inc.: Micro Data Center Solutions. (2017)

  42. Jalali, F., Ayre, R., Vishwanath, A., Hinton, K., Alpcan, T., Tucker, R.: Energy consumption of content distribution from nano data centers versus centralized data centers. In: Performance Evaluation Review. pp. 49–54 (2014)

  43. Valancius, V., Laoutaris, N., Massoulié, L., Diot, C., Rodriguez, P.: Greening the internet with nano data centers. In: CoNEXT’09 - Proceedings of the 2009 ACM Conference on Emerging Networking Experiments and Technologies. pp. 37–48 (2009)

  44. Asimakopoulou, E., Sotiriadis, S., Bessis, N., Dobre, C., Cristea, V.: Centralized micro-clouds: An infrastructure for service distribution in collaborative smart devices. In: Procedia Computer Science. pp. 83–90 (2013)

  45. Sotiriadis, S., Asimakopoulou, E., Bessis, N., Pop, F., Cristea, V.: Performance evaluation of interoperable micro-clouds. In: Procedia Computer Science. pp. 99–106 (2013)

  46. Mejías, B., Van Roy, P.: From mini-clouds to cloud computing. In: Proceedings - 2010 4th IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshop, SASOW 2010. pp. 234–238 (2010)

  47. Narendra, N.C., Koorapati, K., Ujja, V.: Towards Cloud-Based Decentralized Storage for Internet of Things Data. In: Proceedings - 2015 IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2015. pp. 160–168 (2016)

  48. Chang, H., Hari, A., Mukherjee, S., Lakshman, T. V.: Bringing the cloud to the edge. In: Proceedings - IEEE INFOCOM. pp. 346–351 (2014)

  49. Wellman, M.P.: A market-oriented programming environment and its application to distributed multicommodity flow problems J. . Artif. Intell. Res. 1, 1–23 (1993). https://doi.org/10.1613/jair.2

    Article  MATH  Google Scholar 

  50. Chen, Y., Zhang, Q.: Dynamic spectrum auction. Springer International Publishing, Cham (2015)

    Book  Google Scholar 

  51. Sun, W., Liu, J., Yue, Y., Zhang, H.: Double auction-based resource allocation for mobile edge computing in industrial internet of things. IEEE Trans. Ind. Informatics. 14, 4692–4701 (2018). https://doi.org/10.1109/TII.2018.2855746

    Article  Google Scholar 

  52. Wang, Q., Ren, K., Meng, X.: When cloud meets eBay: Towards effective pricing for cloud computing. In: Proceedings - IEEE INFOCOM. pp. 936–944 (2012)

  53. Baranwal, G., Kumar, D., Raza, Z., Vidyarthi, D.P.: Forward auction-based cloud resource provisioning. In: SpringerBriefs in Computer Science. pp. 33–51. Springer, Singapore (2018)

  54. Kumar, D., Baranwal, G., Raza, Z., Vidyarthi, D.P.: A survey on spot pricing in cloud computing. J. Netw. Syst. Manag. 26, 809–856 (2018). https://doi.org/10.1007/s10922-017-9444-x

    Article  Google Scholar 

  55. Wurman, P.R., Wellman, M.P., Walsh, W.E.: A parametrization of the auction design space. Games Econ. Behav. 35, 304–338 (2001). https://doi.org/10.1006/game.2000.0828

    Article  MathSciNet  MATH  Google Scholar 

  56. Shoham, Y.: The zoology of auctions:, (2001)

  57. Miller, H.L.: Motivation and personality. SAGE Encycl. Theory Psychol. (2016). https://doi.org/10.4135/9781483346274.n199

    Article  Google Scholar 

  58. Daniel, T.E.: Pitfalls in the theory of fairness-Comment, (1978)

  59. Sawyer, R.L., Cole, N.S., Cole, J.W.L.: Utilities and the issue of fairness in a decision theoretic model for selection. J. Educ. Meas. 13, 59–76 (1976). https://doi.org/10.1111/j.1745-3984.1976.tb00182.x

    Article  Google Scholar 

  60. Endriss, U., Maudet, N., Sadri, F., Toni, F.: Negotiating socially optimal allocations of resources. J. Artif. Intell. Res. 25, 315–348 (2006). https://doi.org/10.1613/jair.1870

    Article  MathSciNet  Google Scholar 

  61. Rothkopf, M.H., Pekeč, A., Harstad, R.M.: Computationally manageable combinational auctions. Manage. Sci. 44, 1131–1147 (1998). https://doi.org/10.1287/mnsc.44.8.1131

    Article  MATH  Google Scholar 

  62. Sandholm, T.: An algorithm for optimal winner determination in combinatorial auctions. In: IJCAI International Joint Conference on Artificial Intelligence. pp. 542–547 (1999)

  63. van Hoesel, S., Müller, R.: Optimization in electronic markets: examples in combinatorial auctions. NETNOMICS 3, 23–33 (2001). https://doi.org/10.1023/A:1009940607600

    Article  Google Scholar 

  64. Sandholm, T., Suri, S., Gilpin, A., Levine, D.: CABOB: A fast optimal algorithm for winner determination in combinatorial auctions. Manage. Sci. 51, 374–390 (2005). https://doi.org/10.1287/mnsc.1040.0336

    Article  MATH  Google Scholar 

  65. Fujishima, Y., Leyton-Brown, K., Shoham, Y.: Taming the computational complexity of combinatorial auctions: Optimal and approximate approaches. IJCAI Int. Jt. Conf. Artif. Intell. 1, 548–553 (1999)

    Google Scholar 

  66. Andersson, A., Tenhunen, M., Ygge, F.: Integer programming for combinatorial auction winner determination. In: Proceedings - 4th International Conference on MultiAgent Systems, ICMAS 2000. pp. 39–46 (2000)

  67. De Vries, S., Vohra, R. V.: Combinatorial auctions: A survey, (2003)

  68. Leyton-Brown, K., Pearson, M., Shoham, Y.: Towards a universal test suite for combinatorial auction algorithms. Presented at the (2000)

  69. Lehmann, D., O’Callaghan, L.I., Shoham, Y.: Truth revelation in approximately efficient combinatorial auctions. J. ACM. 49, 577–602 (2002). https://doi.org/10.1145/585265.585266

    Article  MathSciNet  MATH  Google Scholar 

  70. Kelly, T.: Generalized knapsack solvers for multi-unit combinatorial auctions: Analysis and application to computational resource allocation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 73–86 (2005)

  71. Kellerer, H., Pferschy, U., Pisinger, D.: Knapsack Problems. (2004)

  72. Lavi, R., Swamy, C.: Truthful and near-optimal mechanism design via linear programming. In: Journal of the ACM (2011)

  73. Vickrey, W.: Counterspeculation, auctions, and competitive sealed tenders. J. Finance. 16, 8 (1961). https://doi.org/10.2307/2977633

    Article  MathSciNet  Google Scholar 

  74. Clarke, E.H.: Multipart pricing of public goods. Public Choice 11, 17–33 (1971). https://doi.org/10.1007/BF01726210

    Article  Google Scholar 

  75. Groves, T.: Incentives in teams. Econometrica 41, 617 (1973). https://doi.org/10.2307/1914085

    Article  MathSciNet  MATH  Google Scholar 

  76. Baranwal, G., Vidyarthi, D.P.: A truthful and fair multi-attribute combinatorial reverse auction for resource procurement in cloud computing. IEEE Trans. Serv. Comput. 12, 851–864 (2019). https://doi.org/10.1109/TSC.2016.2632719

    Article  Google Scholar 

  77. Kumar, D., Baranwal, G., Raza, Z., Vidyarthi, D.P.: A truthful combinatorial double auction-based marketplace mechanism for cloud computing. J. Syst. Softw. 140, 91–108 (2018). https://doi.org/10.1016/j.jss.2018.03.003

    Article  Google Scholar 

  78. Kumar, D., Baranwal, G., Raza, Z., Vidyarthi, D.P.: Fair mechanisms for combinatorial reverse auction-based cloud market. In: Smart Innovation. Systems and Technologies, pp. 267–277. Springer, Singapore (2019)

  79. Baranwal, G., Kumar, D., Vidyarthi, D.P.: Feasibility of providers’ coalition in reverse auction-based cloud market. In: Handling Priority Inversion in Time-Constrained Distributed Databases. pp. 119–129 (2020)

  80. Grosu, D., Das, A.: Auction-based resource allocation protocols in grids. In: Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Systems. pp. 20–27 (2004)

  81. Wang, W., Liang, B., Li, B.: Designing truthful spectrum double auctions with local markets. IEEE Trans. Mob. Comput. 13, 75–88 (2014). https://doi.org/10.1109/TMC.2012.212

    Article  Google Scholar 

  82. Zhai, Y., Huang, L., Chen, L., Xiao, N., Geng, Y.: COUSTIC: Combinatorial double auction for crowd sensing task assignment in device-to-device clouds. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 636–651 (2018)

  83. Bi, F., Stein, S., Gerding, E., Jennings, N., La Porta, T.: A truthful online mechanism for resource allocation in fog computing. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 363–376 (2019)

  84. Kumar, D., Baranwal, G., Vidyarthi, D.P.: Fair resource allocation policies in reverse auction-based cloud market. SN Comput. Sci. 2, 483 (2021). https://doi.org/10.1007/s42979-021-00907-y

    Article  Google Scholar 

  85. 451 Research: Size and Impact of Fog Computing Market, https://www.openfogconsortium.org/wp-content/uploads/451-Research-report-on-5-year-Market-Sizing-of-Fog-Oct-2017.pdf

  86. Weinman, J.: Fogonomics-The Strategic, Economic, and Financial Aspects of the Cloud. In: Proceedings - International Computer Software and Applications Conference. p. 705 (2017)

  87. Kim, W.S., Chung, S.H.: User incentive model and its optimization scheme in user-participatory fog computing environment. Comput. Networks. 145, 76–88 (2018). https://doi.org/10.1016/j.comnet.2018.08.011

    Article  Google Scholar 

  88. Pérez, A., Moltó, G., Caballer, M., Calatrava, A.: Serverless computing for container-based architectures. Futur. Gener. Comput. Syst. 83, 50–59 (2018). https://doi.org/10.1016/j.future.2018.01.022

    Article  Google Scholar 

  89. Zhang, D., Tan, L., Ren, J., Awad, M.K., Zhang, S., Zhang, Y., Wan, P.J.: Near-optimal and truthful online auction for computation offloading in green edge-computing systems. IEEE Trans. Mob. Comput. 19, 880–893 (2020). https://doi.org/10.1109/TMC.2019.2901474

    Article  Google Scholar 

  90. Luo, S., Wen, Y., Xu, W., Puthal, D.: Adaptive task offloading auction for industrial CPS in mobile edge computing. IEEE Access. 7, 169055–169065 (2019). https://doi.org/10.1109/ACCESS.2019.2954898

    Article  Google Scholar 

  91. Gao, Z., Yao, C., Xiao, K., Mo, Z., Wang, Q., Yang, Y.: A real-time task offloading strategy based on double auction for optimal resource allocation in edge computing. In: Proceedings - 2019 International Conference on Future Internet of Things and Cloud, FiCloud 2019. pp. 9–16 (2019)

  92. Xu, Q., Su, Z., Wang, Y., Dai, M.: A trustworthy content caching and bandwidth allocation scheme with edge computing for smart campus. IEEE Access. 6, 63868–63879 (2018). https://doi.org/10.1109/ACCESS.2018.2872740

    Article  Google Scholar 

  93. Prasad, A.S., Arumaithurai, M., Koll, D., Fu, X.: RAERA: A robust auctioning approach for edge resource allocation. In: MECOMM 2017 - Proceedings of the 2017 Workshop on Mobile Edge Communications, Part of SIGCOMM 2017. pp. 49–54 (2017)

  94. Li, Q., Yao, H., Mai, T., Jiang, C., Zhang, Y.: Reinforcement-learning-and belief-learning-based double auction mechanism for edge computing resource allocation. IEEE Internet Things J. 7, 5976–5985 (2020). https://doi.org/10.1109/JIOT.2019.2953108

    Article  Google Scholar 

  95. Galanopoulos, A., Iosifidis, G., Salonidis, T.: Poster: Cooperative analytics for the internet of things. In: Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc). pp. 395–396 (2019)

  96. Avasalcai, C., Tsigkanos, C., Dustdar, S.: Decentralized resource auctioning for latency-sensitive edge computing. In: Proceedings - 2019 IEEE International Conference on Edge Computing, EDGE 2019 - Part of the 2019 IEEE World Congress on Services. pp. 72–76 (2019)

  97. Barrett, C., Tinelli, C.: Satisfiability modulo theories. In: Handbook of Model Checking. pp. 305–343 (2018)

  98. Meng, S., Li, Q., Wu, T., Huang, W., Zhang, J., Li, W.: A fault-tolerant dynamic scheduling method on hierarchical mobile edge cloud computing. Comput. Intell. 35, 577–598 (2019). https://doi.org/10.1111/coin.12219

    Article  MathSciNet  Google Scholar 

  99. Weinman, J.: The 10 laws of fogonomics. IEEE Cloud Comput. 4, 8–14 (2017). https://doi.org/10.1109/MCC.2018.1081060039

    Article  Google Scholar 

  100. Zu, Y., Shen, F., Yan, F., Yang, Y., Zhang, Y., Bu, Z., Shen, L.: An auction-based mechanism for task offloading in fog networks. In: IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC (2019)

  101. Besharati, R., Rezvani, M.H.: A prototype auction-based mechanism for computation offloading in fog-cloud environments. In: 2019 IEEE 5th Conference on Knowledge Based Engineering and Innovation, KBEI 2019. pp. 542–547 (2019)

  102. 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)

  103. Kayal, P., Liebeherr, J.: Distributed service placement in fog computing: An iterative combinatorial auction approach. In: Proceedings - International Conference on Distributed Computing Systems. pp. 2145–2156 (2019)

  104. Lee, Y., Jeong, S., Masood, A., Park, L., Dao, N.N., Cho, S.: Trustful resource management for service allocation in fog-enabled intelligent transportation systems. IEEE Access. 8, 147313–147322 (2020). https://doi.org/10.1109/ACCESS.2020.3015550

    Article  Google Scholar 

  105. Su, Z., Xu, Q., Luo, J., Pu, H., Peng, Y., Lu, R.: A secure content caching scheme for disaster backup in fog computing enabled mobile social networks. IEEE Trans. Ind. Informatics. 14, 4579–4589 (2018). https://doi.org/10.1109/TII.2018.2849984

    Article  Google Scholar 

  106. Fawcett, L., Broadbent, M., Race, N.: Combinatorial auction-based resource allocation in the fog. In: Proceedings - European Workshop on Software-Defined Networks, EWSDN . pp. 62–63 (2017)

  107. Ge, H., Berry, R.A.: A hierarchical quantized auction for fog resources. In: INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019. pp. 7–12 (2019)

  108. Bandyopadhyay, A., Roy, T.S., Sarkar, V., Mallik, S.: Combinatorial auction-based fog service allocation mechanism for IoT applications. In: Proceedings of the Confluence 2020 - 10th International Conference on Cloud Computing, Data Science and Engineering. pp. 518–524 (2020)

  109. Hayakawa, K., Gerding, E.H., Stein, S., Shiga, T.: Price-based online mechanisms for settings with uncertain future procurement costs and multi-unit demand. In: AAMAS ’18 Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems. pp. 309–317. , Stockholm (2018)

  110. Zhang, F., Tang, Z., Chen, M., Zhou, X., Jia, W.: A dynamic resource overbooking mechanism in fog computing. In: Proceedings - 15th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2018. pp. 89–97 (2018)

  111. McAfee, R.P.: A dominant strategy double auction. J. Econ. Theory. 56, 434–450 (1992). https://doi.org/10.1016/0022-0531(92)90091-U

    Article  MathSciNet  MATH  Google Scholar 

  112. Guo, Y., Saito, T., Oma, R., Nakamura, S., Enokido, T., Takizawa, M.: Distributed approach to fog computing with auction method. In: Advances in Intelligent Systems and Computing. pp. 268–275 (2020)

  113. Zhang, Y., Wang, C.Y., Wei, H.Y.: Parking reservation auction for parked vehicle assistance in vehicular fog computing. IEEE Trans. Veh. Technol. 68, 3126–3139 (2019). https://doi.org/10.1109/TVT.2019.2899887

    Article  Google Scholar 

  114. Peng, X., Ota, K., Dong, M.: Multiattribute-based double auction toward resource allocation in vehicular fog computing. IEEE Internet Things J. 7, 3094–3103 (2020). https://doi.org/10.1109/JIOT.2020.2965009

    Article  Google Scholar 

  115. Safianowska, M.B., Chang, Y.C.P., Wang, T.J., Huang, C.W., Huang, C.Y.: An auction based smart service robot implemented on a Fog Computing node. In: 2017 IEEE Fog World Congress, FWC 2017. pp. 1–5 (2018)

  116. Jiao, Y., Wang, P., Niyato, D., Suankaewmanee, K.: Auction mechanisms in cloud/fog computing resource allocation for public blockchain networks. IEEE Trans. Parallel Distrib. Syst. 30, 1975–1989 (2019). https://doi.org/10.1109/TPDS.2019.2900238

    Article  Google Scholar 

  117. Luong, N.C., Jiao, Y., Wang, P., Niyato, D., Kim, D.I., Han, Z.: A machine-learning-based auction for resource trading in fog computing. IEEE Commun. Mag. 58, 82–88 (2020). https://doi.org/10.1109/MCOM.001.1900136

    Article  Google Scholar 

  118. Luong, N.C., Xiong, Z., Wang, P., Niyato, D.: Optimal auction for edge computing resource management in mobile blockchain networks: a deep learning approach. In: IEEE International Conference on Communications (2018)

  119. Debe, M., Salah, K., Rehman, M.H.U., Svetinovic, D.: Blockchain-based decentralized reverse bidding in fog computing. IEEE Access. 8, 81686–81697 (2020). https://doi.org/10.1109/ACCESS.2020.2991261

    Article  Google Scholar 

  120. Peng, M., Yan, S., Zhang, K., Wang, C.: Fog-computing-based radio access networks: Issues and challenges. IEEE Netw. 30, 46–53 (2016). https://doi.org/10.1109/MNET.2016.7513863

    Article  Google Scholar 

  121. Checko, A., Christiansen, H.L., Yan, Y., Scolari, L., Kardaras, G., Berger, M.S., Dittmann, L.: Cloud RAN for mobile networks - a technology overview. IEEE Commun. Surv. Tutorials. 17, 405–426 (2015). https://doi.org/10.1109/COMST.2014.2355255

    Article  Google Scholar 

  122. Han, C., Wang, W., Zhang, P., Wang, Y., Zhang, Z.: Computational resource constrained multi-cell joint processing in fog radio access networks. In: 2018 10th International Conference on Wireless Communications and Signal Processing, WCSP 2018 (2018)

  123. Han, C., Zhang, P., Wang, W., Wang, Y., Zhang, Z.: Delay-optimal joint processing in computation-constrained fog radio access networks. IEEE Access. 7, 58857–58865 (2019). https://doi.org/10.1109/ACCESS.2019.2913147

    Article  Google Scholar 

  124. Yang, S.: A task offloading solution for internet of vehicles using combination auction matching model based on mobile edge computing. IEEE Access. 8, 53261–53273 (2020). https://doi.org/10.1109/ACCESS.2020.2980567

    Article  Google Scholar 

  125. Habiba, U., Maghsudi, S., Hossain, E.: A reverse auction model for efficient resource allocation in mobile edge computation offloading. In: 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings (2019)

  126. Mashhadi, F., Monroy, S.A.S., Bozorgchenani, A., Tarchi, D.: Optimal auction for delay and energy constrained task offloading in mobile edge computing. Comput. Networks. 183, (2020). https://doi.org/10.1016/j.comnet.2020.107527

  127. Li, F., Yao, H., Du, J., Jiang, C., Han, Z., Liu, Y.: Auction Design for Edge Computation Ofloading in SDN-based Ultra Dense Networks. IEEE Trans. Mob. Comput. 1–1 (2020). https://doi.org/10.1109/tmc.2020.3026319

  128. Bahreini, T., Badri, H., Grosu, D.: An envy-free auction mechanism for resource allocation in edge computing systems. In: Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing, SEC 2018. pp. 313–322 (2018)

  129. Hung, Y.H., Wang, C.Y., Hwang, R.H.: Optimizing social welfare of live video streaming services in mobile edge computing. IEEE Trans. Mob. Comput. 19, 922–934 (2020). https://doi.org/10.1109/TMC.2019.2901786

    Article  Google Scholar 

  130. Cao, X., Zhang, J., Poor, H.V.: An optimal auction mechanism for mobile edge caching. In: Proceedings - International Conference on Distributed Computing Systems. pp. 388–399 (2018)

  131. Zhang, T., Fang, X., Liu, Y., Li, G.Y., Xu, W.: D2D-enabled mobile user edge caching: a multi-winner auction approach. IEEE Trans. Veh. Technol. 68, 12314–12328 (2019). https://doi.org/10.1109/TVT.2019.2947334

    Article  Google Scholar 

  132. Wang, S., Zhang, Z., Yu, R., Zhang, Y.: Low-latency caching with auction game in vehicular edge computing. In: 2017 IEEE/CIC International Conference on Communications in China, ICCC 2017. pp. 1–6 (2018)

  133. Garmehi, M., Analoui, M., Pathan, M., Buyya, R.: An economic mechanism for request routing and resource allocation in hybrid CDN-P2P networks. Int. J. Netw. Manag. 25, 375–393 (2015). https://doi.org/10.1002/nem.1891

    Article  Google Scholar 

  134. Zhang, R., Shi, W., Zhang, J., Liu, W.: An auction scheme for computing resource allocation in D2D-assisted mobile edge computing. In: 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings (2019)

  135. Zhang, H., Guo, F., Ji, H., Zhu, C.: Combinational auction-based service provider selection in mobile edge computing networks. IEEE Access. 5, 13455–13464 (2017). https://doi.org/10.1109/ACCESS.2017.2721957

    Article  Google Scholar 

  136. Yue, Y., Sun, W., Liu, J.: A Double Auction-Based Approach for Multi-User Resource Allocation in Mobile Edge Computing. In: 2018 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018. pp. 805–810 (2018)

  137. Yue, Y., Sun, W., Liu, J.: Multi-Task Cross-Server Double Auction for Resource Allocation in Mobile Edge Computing. In: IEEE International Conference on Communications (2019)

  138. Xu, X., Cai, Q., Zhang, G., Zhang, J., Tian, W., Zhang, X., Liu, A.X.: An incentive mechanism for crowdsourcing markets with social welfare maximization in cloud-edge computing. Concurr. Comput. Pract. Exp. 33, 1 (2021). https://doi.org/10.1002/cpe.4961

    Article  Google Scholar 

  139. Li, Z., Wang, H.: Edge computing resource allocation algorithm based on auction game. In: Communications in Computer and Information Science. pp. 352–359 (2019)

  140. Wu, X., Jiang, W., Zhang, Y., Yu, W.: Online combinatorial based mechanism for MEC network resource allocation. Int. J. Commun. Syst. 32, (2019). https://doi.org/10.1002/dac.3928

  141. Le, T.H.T., Tran, N.H., Leanh, T., Oo, T.Z., Kim, K., Ren, S., Hong, C.S.: Auction mechanism for dynamic bandwidth allocation in multi-tenant edge computing. IEEE Trans. Veh. Technol. 69, 15162–15176 (2020). https://doi.org/10.1109/TVT.2020.3036470

    Article  Google Scholar 

  142. Li, Y., Wu, J., Chen, L.: POEM+: Pricing longer for mobile blockchain computation offloading with edge computing. In: Proceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019. pp. 162–167 (2019)

  143. Yu, Q., Wu, J., Chen, L.: POEM: Pricing longer for edge computing in the device cloud. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 355–369 (2018)

  144. Xia, C., Chen, H., Liu, X., Wu, J., Chen, L.: ETRA: Efficient three-stage resource allocation auction for mobile blockchain in edge computing. In: Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS. pp. 701–705 (2019)

  145. Gao, Z., Lin, B., Xiao, K., Wang, Q., Mo, Z., Yang, Y.: A dynamic resource allocation algorithm based on auction model in mobile blockchain network. In: 2019 IEEE 3rd International Conference on Electronic Information Technology and Computer Engineering, EITCE 2019. pp. 1605–1610 (2019)

  146. Zavodovski, A., Bayhan, S., Mohan, N., Zhou, P., Wong, W., Kangasharju, J.: DeCloud: Truthful decentralized double auction for edge clouds. In: Proceedings - International Conference on Distributed Computing Systems. pp. 2157–2167 (2019)

  147. Liu, X., Wu, J., Chen, L., Xia, C.: Efficient auction mechanism for edge computing resource allocation in mobile blockchain. In: Proceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019. pp. 871–876 (2019)

  148. Liu, T., Wu, J., Chen, L., Wu, Y., Li, Y.: Smart contract-based long-term auction for mobile blockchain computation offloading. IEEE Access. 8, 36029–36042 (2020). https://doi.org/10.1109/ACCESS.2020.2974750

    Article  Google Scholar 

  149. Zhou, C., Tham, C.K.: Where to process: deadline-aware online resource auction in mobile edge computing. In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018. pp. 675–680 (2018)

  150. Zhou, B., Srirama, S.N., Buyya, R.: An auction-based incentive mechanism for heterogeneous mobile clouds. J. Syst. Softw. 152, 151–164 (2019). https://doi.org/10.1016/j.jss.2019.03.003

    Article  Google Scholar 

  151. Misra, S., Wolfinger, B.E., Achuthananda, M.P.A., Chakraborty, T., Das, S.N., Das, S.: Auction-based optimal task offloading in mobile cloud computing. IEEE Syst. J. 13, 2978–2985 (2019). https://doi.org/10.1109/JSYST.2019.2898903

    Article  Google Scholar 

  152. Jin, A.L., Song, W., Zhuang, W.: Auction-based resource allocation for sharing cloudlets in mobile cloud computing. IEEE Trans. Emerg. Top. Comput. 6, 45–57 (2018). https://doi.org/10.1109/TETC.2015.2487865

    Article  Google Scholar 

  153. Zhou, G., Wu, J., Chen, L., Jiang, G., Lam, S.K.: Efficient three-stage auction schemes for cloudlets deployment in wireless access network. Wirel. Networks. 25, 3335–3349 (2019). https://doi.org/10.1007/s11276-018-1725-0

    Article  Google Scholar 

  154. Zhou, C., Tham, C.K., Motani, M.: Online auction for truthful stochastic offloading in mobile cloud computing. In: 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings. pp. 1–6 (2017)

  155. Chen, S., Jiao, L., Wang, L., Liu, F.: An online market mechanism for edge emergency demand response via cloudlet control. In: Proceedings - IEEE INFOCOM. pp. 2566–2574 (2019)

  156. Kiani, A., Ansari, N.: Toward hierarchical mobile edge computing: an auction-based profit maximization approach. IEEE Internet Things J. 4, 2082–2091 (2017). https://doi.org/10.1109/JIOT.2017.2750030

    Article  Google Scholar 

  157. Tasiopoulos, A.G., Ascigil, O., Psaras, I., Pavlou, G.: Edge-MAP: Auction markets for edge resource provisioning. In: 19th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2018 (2018)

  158. Tasiopoulos, A., Ascigil, O., Psaras, I., Toumpis, S., Pavlou, G.: FogSpot: spot pricing for application provisioning in edge/fog computing. IEEE Trans. Serv. Comput. 1–1 (2019). https://doi.org/10.1109/tsc.2019.2895037

  159. Amazon: Amazon EC2 Pricing, https://aws.amazon.com/ec2/instance-types/

  160. Dai, S., Hai, L., Li, Y., Zhang, Z.: An incentive auction-based cooperative resource provisioning scheme for edge computing over passive optical networks. In: 2019 18th International Conference on Optical Communications and Networks, ICOCN 2019 (2019)

  161. Jin, A.L., Song, W., Wang, P., Niyato, D., Ju, P.: Auction mechanisms toward efficient resource sharing for cloudlets in mobile cloud computing. IEEE Trans. Serv. Comput. 9, 895–909 (2016). https://doi.org/10.1109/TSC.2015.2430315

    Article  Google Scholar 

  162. Yassine, A., Hossain, M.S., Muhammad, G., Guizani, M.: Double auction mechanisms for dynamic autonomous electric vehicles energy trading. IEEE Trans. Veh. Technol. 68, 7466–7476 (2019). https://doi.org/10.1109/TVT.2019.2920531

    Article  Google Scholar 

  163. Wang, Q., Guo, S., Liu, J., Pan, C., Yang, L.: Profit Maximization Incentive Mechanism for Resource Providers in Mobile Edge Computing. IEEE Trans. Serv. Comput. 1–1 (2019). https://doi.org/10.1109/tsc.2019.2924002

  164. Li, Z., Yang, Z., Xie, S.: Computing resource trading for edge-cloud-assisted internet of things. IEEE Trans. Ind. Informatics. 15, 3661–3669 (2019). https://doi.org/10.1109/TII.2019.2897364

    Article  Google Scholar 

  165. Guo, S., Dai, Y., Guo, S., Qiu, X., Qi, F.: Blockchain meets edge computing: stackelberg game and double auction based task offloading for mobile blockchain. IEEE Trans. Veh. Technol. 69, 5549–5561 (2020). https://doi.org/10.1109/TVT.2020.2982000

    Article  Google Scholar 

  166. Samanta, A., Jiao, L., Muhlhauser, M., Wang, L.: Incentivizing microservices for online resource sharing in edge clouds. In: Proceedings - International Conference on Distributed Computing Systems. pp. 420–430 (2019)

  167. Gao, G., Xiao, M., Wu, J., Huang, H., Wang, S., Chen, G.: Auction-based VM allocation for deadline-sensitive tasks in distributed edge cloud. IEEE Trans. Serv. Comput. 1–1 (2019). https://doi.org/10.1109/tsc.2019.2902549

  168. Nisan, N., Ronen, A.: Computationally feasible VCG mechanisms. J. Artif. Intell. Res. 29, 19–47 (2007). https://doi.org/10.1613/jair.2046

    Article  MathSciNet  MATH  Google Scholar 

  169. Baranwal, G., Vidyarthi, D.P.: FONS: a fog orchestrator node selection model to improve application placement in fog computing. J. Supercomput. 77, 10562–10589 (2021). https://doi.org/10.1007/s11227-021-03702-x

    Article  Google Scholar 

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

  171. Mukwevho, M.A., Celik, T.: Toward a smart cloud: a review of fault-tolerance methods in cloud systems. IEEE Trans. Serv. Comput. 14, 589–605 (2021). https://doi.org/10.1109/TSC.2018.2816644

    Article  Google Scholar 

  172. Mukherjee, M., Matam, R., Shu, L., Maglaras, L., Ferrag, M.A., Choudhury, N., Kumar, V.: Security and privacy in fog computing: challenges. IEEE Access. 5, 19293–19304 (2017). https://doi.org/10.1109/ACCESS.2017.2749422

    Article  Google Scholar 

  173. Alrawais, A., Alhothaily, A., Hu, C., Cheng, X.: Fog computing for the internet of things: security and privacy issues. IEEE Internet Comput. 21, 34–42 (2017). https://doi.org/10.1109/MIC.2017.37

    Article  Google Scholar 

  174. Song, B., Hassan, M.M., Huh, E.N.: A novel cloud market infrastructure for trading service. In: Proceedings of the 2009 International Conference on Computational Science and Its Applications, ICCSA 2009. pp. 44–50 (2009)

  175. Xue, Y., Baochun, L.I., Nahrstedt, K.: Optimal resource allocation in wireless ad hoc networks: A price-based approach. IEEE Trans. Mob. Comput. 5, 347–364 (2006). https://doi.org/10.1109/TMC.2006.1599404

    Article  MATH  Google Scholar 

  176. Pla, A., López, B., Murillo, J., Maudet, N.: Multi-attribute auctions with different types of attributes: Enacting properties in multi-attribute auctions. Expert Syst. Appl. 41, 4829–4843 (2014). https://doi.org/10.1016/j.eswa.2014.02.023

    Article  Google Scholar 

  177. Pla, A., López, B., Murillo, J.: Multi-dimensional fairness for auction-based resource allocation. Knowledge-Based Syst. 73, 134–148 (2015). https://doi.org/10.1016/j.knosys.2014.09.009

    Article  Google Scholar 

  178. Baranwal, G., Vidyarthi, D.P.: A fair multi-attribute combinatorial double auction model for resource allocation in cloud computing. J. Syst. Softw. 108, 60–76 (2015). https://doi.org/10.1016/j.jss.2015.06.025

    Article  Google Scholar 

  179. Xiao, K., Shi, W., Gao, Z., Yao, C., Qiu, X.: DAER: a resource preallocation algorithm of edge computing server by using blockchain in intelligent driving. IEEE Internet Things J. 7, 9291–9302 (2020). https://doi.org/10.1109/JIOT.2020.2984553

    Article  Google Scholar 

  180. Liu, J., Guo, S., Shi, Y., Feng, L., Wang, C.: Decentralized caching framework toward edge network based on blockchain. IEEE Internet Things J. 7, 9158–9174 (2020). https://doi.org/10.1109/JIOT.2020.3003700

    Article  Google Scholar 

  181. Sun, W., Liu, J., Yue, Y., Wang, P.: joint resource allocation and incentive design for blockchain-based mobile edge computing. IEEE Trans. Wirel. Commun. 19, 6050–6064 (2020). https://doi.org/10.1109/TWC.2020.2999721

    Article  Google Scholar 

  182. Lin, H., Yang, Z., Hong, Z., Li, S., Chen, W.: Smart contract-based hierarchical auction mechanism for edge computing in blockchain-empowered IoT. In: Proceedings - 21st IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2020. pp. 147–156 (2020)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gaurav Baranwal.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, D., Baranwal, G. & Vidyarthi, D.P. A Survey on Auction based Approaches for Resource Allocation and Pricing in Emerging Edge Technologies. J Grid Computing 20, 3 (2022). https://doi.org/10.1007/s10723-021-09593-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10723-021-09593-9

Keywords

Navigation