Skip to main content
SpringerLink
Log in

Resource optimization in edge and SDN-based edge computing: a comprehensive study

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The proliferation of mobile devices and the increasing use of networked applications have generated enormous data that require real-time processing and low-latency responses. For real-time applications, Edge Computing (EC) gained a lot of attention which helps to overcome the challenges with traditional computing systems. There is a massive growth of data generated at the web edge, but its limited computing resources and boundary dynamics pose significant challenges. For proficient resource utilization and network management, Software-Defined Networks (SDN) integration with EC can provide control and programmability in edge networking management. However, the optimization of resources is a fundamental need and challenge of EC based on SDN. Besides, the utilization of SDN in EC deals with a centralized and distributed infrastructure further enables the processing of data in closer proximity to its origin and helps in the growing demand for efficient and high-performance computing systems. This study presents a systematic review of the current state-of-the-art pertaining to resource optimization in EC and its integration with SDN. The review of challenges, benefits, and various proposed methodologies for managing resources in Edge and SDN-based Edge environments are investigated. This paper offers valuable insights into the current state and future directions of Edge, SDN-based EC systems, which can assist organizations in designing and deploying more efficient resource consumption solutions

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Botta, A., De Donato, W., Persico, V., Pescape, A.: Integration of cloud computing and internet of things: a survey. Future Gen. Comput. Syst. 56, 684–700 (2016)

    Article  Google Scholar 

  2. Varghese, B., Wang, N., Barbhuiya, S., Kilpatrick, P., Nikolopoulos, D.S.: Challenges and opportunities in edge computing. In: 2016 IEEE International Conference on Smart Cloud (SmartCloud), New York, NY, USA, pp. 20–26 (2016). https://doi.org/10.1109/SmartCloud.2016.18

  3. Nain, A., Sheikh, S.: Software-defined network: an emerging solution for IoT-CC-edge paradigm—an impeccable study. In: Recent Advances in Computing Sciences. CRC Press, New York (2023). pp. 201–205

  4. Li, J., Cai, J., Khan, F., Rehman, A.U., Balasubramaniam, V., Sun, J., Venu, P.: A secured framework for sdn-based edge computing in IOT-enabled healthcare system. IEEE Access 8, 135479–135490 (2020)

    Article  Google Scholar 

  5. Alomari, A., Subramaniam, S.K., Samian, N., Latip, R., Zukarnain, Z.: Resource management in SDN-based cloud and SDN-based fog computing: taxonomy study. Symmetry 13(5), 734 (2021)

    Article  ADS  Google Scholar 

  6. Peng, H., Ye, Q., Shen, X.S.: SDN-based resource management for autonomous vehicular networks: a multi-access edge computing approach. IEEE Wirel. Commun. 26(4), 156–162 (2019)

    Article  Google Scholar 

  7. Xu, X., Cao, H., Geng, Q., Liu, X., Dai, F., Wang, C.: Dynamic resource provisioning for workflow scheduling under uncertainty in edge computing environment. Concurr. Comput. 34(14), e5674 (2022)

    Article  Google Scholar 

  8. Hao, H., Zhang, J., Gu, Q.: Optimal IoT service offloading with uncertainty in SDN based mobile edge computing. Mob. Netw. Appl. 1, 1–10 (2021)

    Google Scholar 

  9. Anoushee, M., Fartash, M., Akbari Torkestani, J.: An intelligent resource management method in SDN based fog computing using reinforcement learning. Computing, 1–30 (2023)

  10. Kumhar, M., Bhatia, J.B.: Edge Computing in SDN-enabled IoT-based healthcare frameworks: challenges and future research directions. Int. J. Reliable Qual. E-Healthcare 11(4), 1–15 (2022)

    Article  Google Scholar 

  11. Jiang, C., Wan, J., Abbas, H.: An edge computing node deployment method based on improved k-means clustering algorithm for smart manufacturing. IEEE Syst. J. 15(2), 2230–2240 (2020)

    Article  ADS  Google Scholar 

  12. Haibeh, L.A., Yagoub, M.C., Jarray, A.: A survey on mobile edge computing infrastructure: Design, resource management, and optimization approaches. IEEE Access 10, 27591–27610 (2022)

    Article  Google Scholar 

  13. Bu, C., Wang, J.: Computing tasks assignment optimization among edge computing servers via SDN. Peer-to-Peer Netw. Appl. 14, 1190–1206 (2021)

    Article  Google Scholar 

  14. Li, C., Zhang, Y., Luo, Y.: Deep reinforcement learning-based resource allocation and seamless handover in multi-access edge computing based on SDN. Knowl. Inf. Syst. 63, 2479–2511 (2021)

    Article  Google Scholar 

  15. Taleb, T., Dutta, S., Ksentini, A., Iqbal, M., Flinck, H.: Mobile edge computing potential in making cities smarter. IEEE Commun. Mag. 55(3), 38–43 (2017)

    Article  Google Scholar 

  16. Raeisi-Varzaneh, M., et al.: Resource scheduling in edge computing: architecture, taxonomy, open issues and future research directions. IEEE Access 11, 25329–25350 (2023)

    Article  Google Scholar 

  17. Du, Jun, et al.: SDN-based resource allocation in edge and cloud computing systems: an evolutionary Stackelberg differential game approach. IEEE/ACM Trans. Netw. 30(4), 1613–1628 (2022)

  18. Jazaeri, S.S., et al.: Edge computing in SDN-IoT networks: a systematic review of issues, challenges and solutions. Clust. Comput. 1, 1–42 (2021)

    Google Scholar 

  19. Luo, Q., Hu, S., Li, C., Li, G., Shi, W.: Resource scheduling in edge computing: a survey. IEEE Commun. Surv. Tutor. 23(4), 2131–2165 (2021). https://doi.org/10.1109/COMST.2021.3106401

    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(5), 1–37 (2019)

    Article  Google Scholar 

  21. Keele, S.: Guidelines for performing systematic literature reviews in software engineering (2007)

  22. Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., et al.: The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, 71 (2021). https://doi.org/10.1136/bmj.n7

    Article  Google Scholar 

  23. Filali, A., Abouaomar, A., Cherkaoui, S., Kobbane, A., Guizani, M.: Multi-access edge computing: a survey. IEEE Access 8, 197017–197046 (2020). https://doi.org/10.1109/ACCESS.2020.3034136

    Article  Google Scholar 

  24. Kumar, N., Zeadally, S., Rodrigues, J.J.P.C.: Vehicular delay-tolerantnetworks for smart grid data management using mobile edge computing. IEEE Commun. Mag. 54(10), 660 (2016)

    Article  Google Scholar 

  25. Dama, S., Sathya, V., Kuchi, K., Pasca, T.V.: A feasible cellularInternet of Things: enabling edge computing and the IoT in dense futuristiccellular networks. IEEE Consum. Electron. Mag. 6(1), 66–72 (2017)

    Article  Google Scholar 

  26. Yu, Wei, et al.: A survey on the edge computing for the Internet of Things. IEEE Access 6, 6900–6919 (2017)

  27. Yu, W., et al.: A survey on the edge computing for the Internet of Things. IEEE Access 6, 6900–6919 (2018). https://doi.org/10.1109/ACCESS.2017.2778504

    Article  Google Scholar 

  28. Yu, W., Xu, G., Chen, Z., Moulema, P.: A cloud computing based architecture for cyber security situation awareness, In: Proceedngs of IEEE Conference in Communication Network Security (CNS), pp. 488–492 (Oct 2013)

  29. Chen, Z., et al.: A cloud computing based network monitoring and threat detection system for critical infrastructures. Big Data Res. 3, 10–23 (2016)

    Article  Google Scholar 

  30. Liu, J., Mao, Y., Zhang, J., Letaief, K.B.: Delay-optimal computation task scheduling for mobile-edge computing systems, In: Proceedings of IEEE International Symposium in Information Theory (ISIT), pp. 1451–1455 (Jun 2016)

  31. Sajjad, H.P., Danniswara, K., Al-Shishtawy, A., Vlassov, V.: SpanEdge: towards unifying stream processing over central and near the-edge data centers, In: Proceedings of IEEE/ACM Symposium in Edge Computation (SEC), pp. 168–178 (Oct 2016)

  32. Georgakopoulos, D., Jayaraman, P.P., Fazia, M., Villari, M., Ranjan, R.: Internet of Things and edge cloud computing roadmap for manufacturing. IEEE Cloud Comput. 3(4), 66–73 (2016)

    Article  Google Scholar 

  33. Rodrigues, T.G., Suto, K., Nishiyama, H., Kato, N., Temma, K.: Cloudlets activation scheme for scalable mobile edge computing with transmission power control and virtual machine migration. IEEE Trans. Comput. 67(9), 1287–1300 (2018). https://doi.org/10.1109/TC.2018.2818144

    Article  MathSciNet  Google Scholar 

  34. Liang, B., Gregory, M.A., Li, S.: Multi-access edge computing fundamentals, services, enablers and challenges: a complete survey. J. Netw. Comput. Appl. 199, 103308 (2022)

    Article  Google Scholar 

  35. Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., Zhao, W.: A survey on Internet of Things: Architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 4(5), 1125–1142 (2017)

    Article  Google Scholar 

  36. He, X., Jin, R., Dai, H.: Peace: Privacy-preserving and cost-efficient task offloading for mobile-edge computing. IEEE Trans. Wirel. Commun. 19(3), 1814–1824 (2020)

    Article  Google Scholar 

  37. Liu, H., Hua, S., Zhuo, X., Chen, D., Cheng, X.: Cooperative spectrum sharing of multiple primary users and multiple secondary users. Digit. Commun. Netw. 2(4), 191–195 (2016)

    Article  Google Scholar 

  38. Kiran, N., et al.: Joint resource allocation and computation offloading in mobile edge computing for SDN-based wireless networks. J. Commun. Netw. 22(1), 1–11 (2019)

    Article  ADS  Google Scholar 

  39. Piovesan, N., Gambin, A.F., Miozzo, M., Rossi, M., Dini, P.: Energy sustainable paradigms and methods for future mobile networks: A survey. Comput. Commun. 119, 101–117 (2018)

    Article  Google Scholar 

  40. Bi, S., Zhang, Y.: Computation rate maximization for wireless powered mobile-edge computing with binary computation offloading. IEEE Trans. Wireless Commun. 17(6), 4177–4190 (2018)

    Article  Google Scholar 

  41. Chen, W., Wang, D., Li, K.: Multi-user multi-task computation offloading in green mobile edge cloud computing. IEEE Trans. Serv. Comput. 12(5), 726–738 (2019)

    Article  Google Scholar 

  42. Zhou, B., Dastjerdi, A.V., Calheiros, R.N., Srirama, S.N., Buyya, R.: A context sensitive offloading scheme for mobile cloud computing service. In: Proceedings of IEEE 8th International Conference in Cloud Computing, p. 869876, July 2015

  43. Amjad, A., Rabby, F., Sadia, S., Patwary, M., Benkhelifa, E.: Cognitive edge computing based resource allocation framework for Internet of Things. In: Proceedings of 2nd International Conference in Fog Mobile Edge Computing (FMEC), p. 194200, May 2017

  44. Al-Shuwaili, Simeone, O.: Energy-efficient resource allocation for mobile edge computing-based augmented reality applications. IEEE Wirel. Commun. Lett., 6(3), 398401 (2017)

  45. Beraldi, R., Mtibaa, A., Alnuweiri, H.: Cooperative load balancingscheme for edge computing resources. In: Proceedings of 2nd International Conference in Fog Mobile Edge Computing (FMEC), p. 94100, May 2017

  46. Chen, X., Jiao, L., Li, W., Fu, X.: Efcient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Netw. 24(5), 27952808 (2016)

    Article  Google Scholar 

  47. Jiang, C., Cheng, X., Gao, H., Zhou, X., Wan, J.: Toward computation offloading in edge computing: a survey. IEEE Access 7, 131543–131558 (2019)

    Article  Google Scholar 

  48. Islam, A., Debnath, A., Ghose, M., Chakraborty, S.: A survey on task offloading in multi-access edge computing. J. Syst. Archit. 118, 102225 (2021)

    Article  Google Scholar 

  49. Li, S., Zhang, N., Lin, S., Kong, L., Katangur, A., Khan, M., Ni, M., Zhu, G.: Joint admission control and resource allocation in edge computing for Internet of Things. IEEE Netw. 32(1), 72–79 (2018). https://doi.org/10.1109/MNET.2018.1700163

    Article  Google Scholar 

  50. Xu, J., Palanisamy, B., Ludwig, H., Wang, Q.: [IEEE 2017 IEEE International Conference on Edge Computing (EDGE) - Honolulu, HI, USA (2017.6.25-2017.6.30)] 2017 IEEE International Conference on Edge Computing (EDGE) - Zenith: Utility-Aware Resource Allocation for Edge Computing, pp. 47–54. https://doi.org/10.1109/IEEE.EDGE.2017.15 (2017)

  51. Rimal, P., Van, D.P., Maier, M.: Mobile edge computing empowered fiber-wireless access networks in the 5G era. IEEE Commun. Mag. 55(2), 192200 (2017)

    Article  Google Scholar 

  52. Rimal, P., Van, D.P., Maier, M.: Cloudlet enhanced fiber-wirelessaccess networks for mobile-edge computing. IEEE Trans. Wirel. Commun. 16(6), 36013618 (2017)

    Article  Google Scholar 

  53. Chen, M., Hao, Y., Li, Y., Lai, C.-F., Wu, D.: On the computation offloading at ad hoc cloudlet: architecture and service modes. IEEE Commun. Mag. 53(6), 1824 (2015)

    Google Scholar 

  54. Satyanarayanan, M., Bahl, P., Caceres, R., Davies, N.: The case forVM-based cloudlets in mobile computing. IEEE Pervasive Comput. 8(4), 1423 (2009)

    Article  Google Scholar 

  55. Qin, Z., Qiu, X., Ye, J., Wang, L.: User-edge collaborative resource allocation and offloading strategy in edge computing. Wirel. Commun. Mob. Comput. https://doi.org/10.1155/2020/88671 (2020)

  56. Liu, X., Qin, Z., Gao, Y.: Resource allocation for edge computing in IoT networks via reinforcement learning. In: ICC 2019 - 2019 IEEE International Conference on Communications (ICC). https://doi.org/10.1109/icc.2019.8761385 (2019)

  57. Ha, K., Abe, Y., Chen, Z., Hu, W., Amos, B., Pillai, P., Satyanarayanan, M.: Adaptive VM handoff across cloudlets, Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA, Technical Report CMU-CS-15-113, Jun 2015

  58. Agarwal, R., Nayak, A.: DRAP: A decentralized public resourced cloudlet for ad-hoc networks. In: Proceedings of IEEE 4th International Conference in Cloud Networking (CloudNet), pp. 309314 (Oct. 2015)

  59. Tang, L., Chen, X., He, S.: When social network meets mobile cloud: a social group utility approach for optimizing computation offloading in cloudlet. IEEE Access 4, 58685879 (2016)

    Article  Google Scholar 

  60. Song, Y., Yau, S. S.: An approach to QoS-based task distribution in edge computing networks for IoT applications. In: IEEE 1st International Conference on Edge Computing (2017)

  61. Tham, C.-K., Chattopadhyay, R.: [IEEE 2017 IEEE 18th International Symposium on World of Wireless, Mobile and Multimedia Networks (WoWMoM) - Macau, China (2017.6.12-2017.6.15)] 2017 IEEE 18th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM) - A load balancing scheme for sensing and analytics on a mobile edge computing network, pp. 1–9, 2017. https://doi.org/10.1109/WoWMoM.2017.7974307

  62. Babou, C.S.M., Fall, D., Kashihara, S., Taenaka, Y., Bhuyan, M.H., Niang, I., Kadobayashi, Y.: Hierarchical load balancing and clustering technique for home edge computing. IEEE Access 8, 127593–127607 (2020). https://doi.org/10.1109/ACCESS.2020.3007944

    Article  Google Scholar 

  63. Kadhim, A.J., Naser, J.I.: Proactive load balancing mechanism for fog computing supported by parked vehicles in IoV-SDN. China Commun. 18(2), 271–289 (2021)

    Article  Google Scholar 

  64. Zhang, W.-Z., et al.: Secure and optimized load balancing for multitier IoT and edge-cloud computing systems. IEEE IoT J. 8(10), 8119–8132 (2020)

    Google Scholar 

  65. Laredo, J.L.J., Guinand, F., Olivier, D., Bouvry, P.: Load balancingat the edge of chaos: How self-organized criticality can lead to energy- efficient computing. IEEE Trans. Parallel Distrib. Syst. 28(2), 517529 (2017)

    Article  Google Scholar 

  66. Li, L., Li, Y., Hou, R.: A novel mobile edge computing-based architecture for future cellular vehicular networks, In: Proceedinds of IEEE Wireless Communication in Networking Conference (WCNC), p. 16, March 2017

  67. Mao, Y., Zhang, J., Letaief, K.B.: Dynamic computation offloadingfor mobile-edge computing with energy harvesting devices. IEEE J. Sel. Areas Commun. 34(12), 35903605 (2016)

    Article  Google Scholar 

  68. Mao, Y., Zhang, J., Letaief, K.B.: Joint task ofoading scheduling andtransmit power allocation for mobile-edge computing systems, In: Proceedinds in IEEE Wireless Communication in Networking Conference (WCNC), p. 16, March 2017

  69. Bohez, S., De Turck, J., Verbelen, T., Simoens, P., Dhoedt, B.: Mobile,collaborative augmented reality using cloudlets, In: Proceedings of International Conference in MOBILe Wireless MiddleWARE, Operating System, Application, pp. 4554, Nov 2013

  70. Moon, S., Lim, Y.: Task migration with partitioning for load balancingin collaborative edge computing. Appl. Sci. 12(3), 1168 (2022)

    Article  CAS  Google Scholar 

  71. Ali, J., Roh, B.: An effective approach for controller placement in software-defined Internet-of-Things (SD-IoT). Sensors 22(8), 2992 (2022)

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  72. Ali, J., et al.: A novel features prioritization mechanism for controllers in software-defined networking. Comput. Mater. 69, 267–282 (2021)

    Google Scholar 

  73. Singh, A., Kaur, N., Kaur, H.: Extensive performance analysis of OpenDayLight (ODL) and Open Network Operating System (ONOS) SDN controllers. Microprocess. Microsyst. 95, 104715 (2022)

    Article  Google Scholar 

  74. Ali, J., et al.: ESCALB: An effective slave controller allocation-based load balancing scheme for multi-domain SDN-enabled-IoT networks. J. King Saud Univ. 35(6), 101566 (2023)

    Google Scholar 

  75. Ali, J., Roh, B., Lee, S.: QoS improvement with an optimum controller selection for software-defined networks. PLoS ONE 14(5), e0217631 (2019)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Singh, A., Kaur, N., Kaur, H.: An extensive vulnerability assessment and countermeasures in open network operating system software defined networking controller. Concurr. Comput. 34(15), e6978 (2022)

    Article  Google Scholar 

  77. Singh, A., Kaur, H., Kaur, N.: A novel DDoS detection and mitigation technique using hybrid machine learning model and redirect illegitimate traffic in SDN network. Clust. Comput. 1, 1–21 (2023)

    Google Scholar 

  78. Bera, S., et al.: Soft-WSN: Software-defined WSN management system for IoT applications. IEEE Syst. J. 12(3), 2074–2081 (2016)

    Article  MathSciNet  ADS  Google Scholar 

  79. Ren, W., et al.: BLLC: A batch-level update mechanism with low cost for SDN-IoT networks. IEEE Internet Things J. 6(1), 1210–1222 (2018)

    Article  Google Scholar 

  80. Galluccio, L. et al.: SDN-WISE: Design, prototyping and experimentation of a stateful SDN solution for WIreless SEnsor networks. In: IEEE Conference on Computer Communications (INFOCOM). IEEE (2015)

  81. Ali, J., Roh, B.: A novel scheme for controller selection in Software-Defined Internet-of-Things (SD-IoT). Sensors 22(9), 3591 (2022)

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  82. Rahman, A., Chakraborty, C., Anwar, A., Karim, M.R., Islam, M.J., Kundu, D., Rahman, Z., Band, S.S.: SDN-IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic. Clust. Comput. (2021). https://doi.org/10.1007/s10586-021-03367-4

  83. Isyaku, B., MohdZahid, M.S., BteKamat, M., Abu Bakar, K., Ghaleb, F.A.: Software defined networking flow table management of openflow switches performance and security challenges: a survey. Futur. Internet. 12, 147 (2020). https://doi.org/10.3390/fi12090147

    Article  Google Scholar 

  84. Meneghello, F., Calore, M., Zucchetto, D., Polese, M., Zanella, A.: IoT: Internet of Threats? A survey of practical security vulnerabilities in real IoT Devices. IEEE Internet Things J. 6, 8182–8201 (2019). https://doi.org/10.1109/JIOT.2019.2935189

    Article  Google Scholar 

  85. Yurchenko, M., Cody, P., Coplan, A., Kennedy, R., Wood, T., Ramakrishnan, K.K.: OpenNetVM, pp. 1–2 (2018). https://doi.org/10.1145/3185467.3190786

  86. Aksymyuk, T., Brych, M., Dumych, S., Satria, D., Jo, M.: An IoT based monitoring framework for software defined 5G mobile networks. In: Proceedings of 11th International Conference in Ubiquitous Information Management and Communication IMCOM 2017, pp. 7–10 (2017). https://doi.org/10.1145/3022227.3022331

  87. Antonakakis, M., April, T., Bailey, M., Bernhard, M., Arbor, A., Bursztein, E., Cochran, J., Durumeric, Z., Halderman, J.A., Arbor, A., Invernizzi, L., Kallitsis, M., Network, M., Ma, Z., Mason, J., Menscher, D., Seaman, C., Sullivan, N., Thomas, K., Zhou, Y., Antonakakis, M., April, T., Bailey, M., Bernhard, M., Bursztein, E., Cochran, J.,Durumeric, Z., Halderman, J.A., Invernizzi, L., Kallitsis, M., Kumar, D., Lever, C., Ma, Z., Mason, J., Menscher, D., Seaman, C., Sullivan, N., Thomas, K., Zhou, Y.: Understanding the Mirai Botnet. USENIX Security, pp. 1093–1110 (2017). https://doi.org/10.1016/j.religion.2008.12.001

  88. Du, P., Putra, P., Yamamoto, S., Nakao, A.: A context-aware IoT architecture through software-defined data plane. In: Proceedings of 2016 IEEE Reg. 10 Symposium in TENSYMP 2016. pp. 315–320 (2016). https://doi.org/10.1109/TENCONSpring.2016.7519425

  89. Jazaeri, S.S., et al.: Composition of caching and classification in edge computing based on quality optimization for SDN-based IoT healthcare solutions. J. Supercomput. 1, 1–51 (2023)

    Google Scholar 

  90. Singh, J., et al.: Energy-efficient and secure load balancing technique for SDN-enabled fog computing. Sustainability 14(19), 12951 (2022)

    Article  Google Scholar 

  91. Chen, Y.-J., et al.: SDN-enabled traffic-aware load balancing for M2M networks. IEEE Internet Things J. 5(3), 1797–1806 (2018)

    Article  Google Scholar 

  92. Zhang, ., et al.: Task offloading in vehicular edge computing networks: a load-balancing solution. IEEE Trans. Vehicular Technol. 69(2), 2092–2104 (2019)

  93. Wang, A., et al.: Software-defined networking enhanced edge computing: a network-centric survey. Proc. IEEE 107(8), 1500–1519 (2019)

    Article  Google Scholar 

  94. Stamou, A., Kakkavas, G., Tsitseklis, K., Karyotis, V., Papavassiliou, S.: Autonomic network managementand cross-layer optimization in software defined radio environments. Future Internet 11, 37 (2019)

    Article  Google Scholar 

  95. Wang, Bo., et al.: A reliable IoT edge computing trust management mechanism for smart cities. IEEE Access 8, 46373–46399 (2020)

  96. Bhayo, J., Shah, S.A., Hameed, S., Ahmed, A., Nasir, J., Draheim, D.: Towards a machine learning-based framework for DDOS attack detection in softwaredefined IoT (SD-IoT) networks. Eng. Appl. Artif. Intell. 123, 106432 (2023)

    Article  Google Scholar 

  97. Mavromatis, A., et al.: A software-defined IoT device management framework for edge and cloud computing. IEEE Internet Things J. 7(3), 1718–1735 (2019)

    Article  Google Scholar 

  98. Eramo, V., Lavacca, F.G., Catena, T., Polverini, M., Cianfrani, A.: Effectiveness of segment routing technology in reducing the bandwidth and cloud resources provisioning times in network function virtualization architectures. Future Internet 11, 71 (2019)

    Article  Google Scholar 

  99. Akyildiz, I.F., Lin, S.-C., Wang, P.: Wireless software-defined networks (W-SDNs) and network function virtualization (NFV) for 5G cellular systems: An overview and qualitative evaluation. Comput. Netw. 93, 66–79 (2015)

    Article  Google Scholar 

  100. Moyano, R.F., Fernandez, D., Bellido, L., Merayo, N., Aguado, J.C., de Miguel, I.: Nfv-based qos provision for software defined optical access and residential networks, In: Quality of Service (IWQoS), 2017 IEEE/ACM 25th International Symposium on IEEE, 2017, pp. 1–5

  101. Yao, J., Han, Z., Sohail, M., Wang, L.: A robust security architecture for SDN-based 5G networks. Future Internet 11, 85 (2019)

    Article  Google Scholar 

  102. Mitsis, G., Apostolopoulos, P.A., Tsiropoulou, E.E., Papavassiliou, S.: Intelligent dynamic data offloadingin a competitive mobile edge computing market. Future Internet 11, 118 (2019)

    Article  Google Scholar 

  103. Leivadeas, A., Kesidis, G., Ibnkahla, M., Lambadaris, I.: VNF placement optimization at the edge and cloud. Future Internet 11, 69 (2019)

    Article  Google Scholar 

  104. Chen, Y., Ma, J., Huang, L., Zhang, X.: Energy-aware resource allocation and task scheduling in edge computing systems. IEEE Trans. Sustain. Comput. 6(1), 83–95 (2021)

    Google Scholar 

  105. Zhang, H., Yang, W., Xiang, Y., Xiang, Z.: Dynamic resource allocation for edge computing using software-defined networking. Future Gen. Comput. Syst. 110, 362–372 (2020)

    Google Scholar 

  106. Wang, S., Zhang, L., Wang, Y., Zhu, X.: Resource allocation scheme for multi-access edge computing using software-defined networking. J. Netw. Comput. Appl. 176, 102938 (2021)

    Google Scholar 

  107. Balasubramanian, V., et al.: Intelligent resource management at the edge for ubiquitous IoT: An SDN-based federated learning approach. IEEE Netw. 35(5), 114–121 (2021)

    Article  Google Scholar 

  108. Emmanouil, S., et al.: QoS-aware scheduling in LTE-A networks with SDN control. In: 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA). IEEE (2016)

  109. Zhang, J., Cui, Q., Zhang, X., Ni, W., Lyu, X., Pan, M., Tao, X., et al.: Online optimization of energy-efficient user association and workload offloading for mobile edge computing. IEEE Trans Vehicular Technol. 71, 2 (2022)

    Google Scholar 

  110. Guo, W., Mahendran, V., Radhakrishnan, S. et al.: Achieving throughput fairness in smart grid using sdn-based flow aggregation and scheduling. In: 2016 Ninth IEEE International Workshop on Selected Topics in Mobile and Wireless Computing

  111. Feng, T., Bi, J., Wang, K.: Allocation and scheduling of network resource for multiple control applications in sdn. China Commun. 12(6), 85–95 (2015)

    Article  Google Scholar 

  112. Sharma, A., Awasthi, L.K., et al.: Pr-CAI: priority based-context aware information scheduling for SDN-based vehicular network. Comput. Netw. 193, 108097 (2021)

    Article  Google Scholar 

  113. Chen, K., et al.: Improving integrated LTE-WiFi network performance with SDN based flow scheduling. In: 2018 27th International Conference on Computer Communication and Networks (ICCCN). IEEE (2018)

  114. Tayyaba, S.K. et al.: Software-defined network (sdn) based internet of things (iot) a road ahead. In: Proceedings of the International Conference on Future Networks and Distributed Systems (2017)

  115. Kalkan, K., Zeadally, S.: Securing internet of things with software defined networking. IEEE Commun. Mag. 56(9), 186–192 (2017)

    Article  Google Scholar 

  116. Alvizu, R., et al.: Comprehensive survey on T-SDN: software-defined networking for transport networks. IEEE Commun. Surv. Tutor. 19(4), 2232–2283 (2017)

    Article  Google Scholar 

  117. Bera, S., Misra, S., Vasilakos, A.V.: Software-defined networking for internet of things: a survey. IEEE Internet Things J. 4(6), 1994–2008 (2017)

    Article  Google Scholar 

  118. Carvalho, G., et al.: Edge computing: current trends, research challenges and future directions. Computing 103, 993–1023 (2021)

    Article  Google Scholar 

  119. Foukas, X., et al.: Network slicing in 5G: survey and challenges. IEEE Commun. Mag. 55(5), 94–100 (2017)

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Author notes

  1. Sophiya Sheikh, Mohammad Shahid, and Rohit Malik have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Application, Lovely Professional University, Phagwara, Punjab, 144411, India

    Ajay Nain, Sophiya Sheikh & Rohit Malik

  2. Department of Commerce, Aligarh Muslim University, Aligarh, Uttar Pradesh, 202002, India

    Mohammad Shahid

Authors
  1. Ajay Nain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sophiya Sheikh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Shahid
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rohit Malik
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Ajay Nain, Sophiya Sheikh and Mohammad Shahid contributed to the study conception and design of the model. The data acquistion, data analysis,manuscript prepration, and editing are completed by all authors.

Corresponding author

Correspondence to Sophiya Sheikh.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests.

Consent to participate

All authors gave explicit consent to participate in this work.

Consent for publish

All authors gave explicit consent to publish this manuscript.

Ethical approval

All authors have seen and agreed with the contents of the manuscript and are looking forward to publishing this paper on this journal.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nain, A., Sheikh, S., Shahid, M. et al. Resource optimization in edge and SDN-based edge computing: a comprehensive study. Cluster Comput (2024). https://doi.org/10.1007/s10586-023-04256-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10586-023-04256-8

Keywords

Search

Navigation