Abstract
The transition to beyond fifth generation (B5G) and sixth generation (6G) networks will lead to the support of new and future services, like virtual reality, vehicle collision avoidance or Internet-of-Things applications, among others. Such applications will be deployed based on loosely-coupled and independent microservices under different Quality of Service (QoS) requirements. Moreover, future applications will be either delay-tolerant or delay-sensitive, resulting in different application categories. Therefore, any provisioning technique will have to meet a large range of requirements, with flexibility and optimally, to accommodate the different service demands onto a common network. This is a major challenge for current networks, having to provide such services under strict QoS requirements, at minimum cost, and in a reasonable time. Currently, multi-access edge computing (MEC) has been proposed to deploy time-sensitive microservice-based applications, increasing the complexity of an optimal deployment in resource-constrained systems. Moreover, traditional heuristic algorithms do follow static solving strategies, resulting in sub-optimal provisioning decisions. To address this problem, we propose a combined heuristic and artificial intelligence technique that minimizes the cost of deployment at the edge of the network, optimally provisioning, in reasonable time, different types of applications with different requirements, to be done on single or multiple MECs, according to the category of the requested application and current MEC state features. Extensive simulation results show that the use of an intelligent algorithm significantly improves most of the performance metrics used, especially the minimization deployment cost, compared to other baseline techniques.
Similar content being viewed by others
References
Han, C., Wu, Y., Chen, Z., et al.: Network 2030 a blueprint of technology, applications and market drivers towards teh year 2030 and beyond. International Telecommunication Union (2018)
Clemm, A., Zhani, M.F., Boutaba, R.: Network management 2030: operations and control of network 2030 services. J. Netw. Syst. Manag. 28(4), 721–750 (2020)
Katz, M., Matinmikko-Blue, M., Latva-Aho, M.: 6 genesis flagship program: Building the bridges towards 6g-enabled wireless smart society and ecosystem. In: Proceedings of the 2018 IEEE 10th Latin-American Conference on Communications (LATINCOM), pp. 1–9 (2018)
He, X., Tu, Z., Xu, X., Wang, Z.: Programming framework and infrastructure for self-adaptation and optimized evolution method for microservice systems in cloud-edge environments. Future Gener. Comput. Syst. 118, 263–281 (2021)
Premsankar, G., Di Francesco, M., Taleb, T.: Edge computing for the internet of things: a case study. IEEE Internet Things J. 5(2), 1275–1284 (2018)
Wang, S., Parsons, M., Stone-McLean, J., Rogers, P., Boyd, S., Hoover, K., Meruvia-Pastor, O., Gong, M., Smith, A.: Augmented reality as a telemedicine platform for remote procedural training. Sensors 17(10), 2294 (2017)
Jamiat, N., Othman, N.F.N.: Effects of augmented reality mobile apps on early childhood education students’ achievement. In: Proceedings of the 2019 The 3rd International Conference on Digital Technology in Education, pp. 30–33 (2019)
Huang, C., Hu, S., Alexandropoulos, G.C., Zappone, A., Yuen, C., Zhang, R., Di Renzo, M., Debbah, M.: Holographic mimo surfaces for 6g wireless networks: opportunities, challenges, and trends. IEEE Wirel. Commun. 27(5), 118–125 (2020)
Yastrebova, A., Kirichek, R., Koucheryavy, Y., Borodin, A., Koucheryavy, A.: Future networks 2030: Architecture & requirements. In: Proceedings of the 10th International congress on ultra modern telecommunications and control systems and workshops (ICUMT). IEEE 2018, pp. 1–8 (2018)
Elbamby, M.S., Perfecto, C., Bennis, M., Doppler, K.: Toward low-latency and ultra-reliable virtual reality. IEEE Netw. 32(2), 78–84 (2018)
Nguyen, N.D., Phan, L.-A., Park, D.-H., Kim, S., Kim, T.: Elasticfog: elastic resource provisioning in container-based fog computing. IEEE Access 8, 183879–183890 (2020)
Hu, Y.C., Patel, M., Sabella, D., Sprecher, N., Young, V.: Mobile edge computing-a key technology towards 5g. ETSI White Pap. 11(11), 1–16 (2015)
Computing, E.M.E., Initiative, M, et al.: Mobile-edge computing: introductory technical white paper, ETSI: Sophia Antipolis, France, pp. 1–36 (2014)
Huang, X., He, L., Chen, X., Liu, G., Li, F.: A more refined mobile edge cache replacement scheme for adaptive video streaming with mutual cooperation in multi-mec servers. In: Proceedings of the 2020 IEEE International Conference on Multimedia and Expo (ICME). IEEE, pp. 1–6 (2020)
Karimi, A., Pedersen, K.M., Mahmood, N.H., Berardinelli, G., Mogensen, P.: On the multiplexing of data and metadata for ultra-reliable low-latency communications in 5g. IEEE Trans. Veh. Technol. 69(10), 12136–12147 (2020)
Deng, M., Tian, H., Fan, B.: Fine-granularity based application offloading policy in cloud-enhanced small cell networks. In: Proceedings of the 2016 IEEE International Conference on Communications Workshops (ICC). IEEE, pp. 638–643 (2016)
FG-NET2030, I.: New services and capabilities for network 2030: description, technical gap and performance target analysis, FG-NET2030 document NET2030-O-027 (2019)
Chakareski, J.: Vr/ar immersive communication: caching, edge computing, and transmission trade-offs. In: Proceedings of the Workshop on Virtual Reality and Augmented Reality Network, pp. 36–41 (2017)
Ren, J., He, Y., Huang, G., Yu, G., Cai, Y., Zhang, Z.: An edge-computing based architecture for mobile augmented reality. IEEE Netw. 33(4), 162–169 (2019)
Rauschnabel, P.A.: Augmented reality is eating the real-world! the substitution of physical products by holograms. Int. J. Inf. Manag. 57, 102279 (2021)
Lu, L., Wang, H., Liu, P., Liu, R., Zhang, J., Xie, Y., Liu, S., Huo, T., Xie, M., Wu, X., et al.: Applications of mixed reality technology in orthopedics surgery: a pilot study. Front. Bioeng. Biotechnol. 10, 740507 (2022)
Balalaie, A., Heydarnoori, A., Jamshidi, P.: Microservices architecture enables devops: migration to a cloud-native architecture. IEEE Softw. 33(3), 42–52 (2016)
Cherradi, G., El Bouziri, A., Boulmakoul, A., Zeitouni, K.: Real-time hazmat environmental information system: a micro-service based architecture. Procedia Comput. Sci. 109, 982–987 (2017)
Filip, I.-D., Pop, F., Serbanescu, C., Choi, C.: Microservices scheduling model over heterogeneous cloud-edge environments as support for iot applications. IEEE Internet Things J. 5(4), 2672–2681 (2018)
Di Francesco, P., Lago, P., Malavolta, I.: Migrating towards microservice architectures: an industrial survey. In: Proceedings of the 2018 IEEE International Conference on Software Architecture (ICSA). IEEE, pp. 29–2909 (2018)
Boyer, F., Etchevers, X., De Palma, N., Tao, X.: Architecture-based automated updates of distributed microservices. In: Proceedings of the Service-Oriented Computing: 16th International Conference, ICSOC 2018. Springer, Hangzhou, China, 12–15 Nov, pp. 21–36 (2018)
Voegler, M., Schleicher, J.M., Inzinger, C., Dustdar, S.: Optimizing elastic iot application deployments. IEEE Trans. Serv. Comput. 11(5), 879–892 (2016)
Abdullah, M., Iqbal, W., Erradi, A.: Unsupervised learning approach for web application auto-decomposition into microservices. J. Syst. Softw. 151, 243–257 (2019)
Wan, X., Guan, X., Wang, T., Bai, G., Choi, B.-Y.: Application deployment using microservice and docker containers: framework and optimization. J. Netw. Comput. Appl. 119, 97–109 (2018)
Tan, B., Ma, H., Mei, Y., A nsga-II-based approach for multi-objective micro-service allocation in container-based clouds. In: Proceedings of the 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID). IEEE 2020, pp. 282–289 (2020)
Fazio, M., Celesti, A., Ranjan, R., Liu, C., Chen, L., Villari, M.: Open issues in scheduling microservices in the cloud. IEEE Cloud Comput. 3(5), 81–88 (2016)
Guerrero, C., Lera, I., Juiz, C.: Resource optimization of container orchestration: a case study in multi-cloud microservices-based applications. J. Supercomput. 74(7), 2956–2983 (2018)
Niu, Y., Liu, F., Li, Z.: Load balancing across microservices. In: Proceedings of the IEEE INFOCOM 2018-IEEE Conference on Computer Communications. IEEE, pp. 198–206 (2018)
Bhamare, D., Samaka, M., Erbad, A., Jain, R., Gupta, L., Chan, H.A., Multi-objective scheduling of micro-services for optimal service function chains. In: Proceedings of the IEEE International Conference on Communications (ICC). IEEE 2017, 1–6 (2017)
Wen, Z., Lin, T., Yang, R., Ji, S., Ranjan, R., Romanovsky, A., Lin, C., Xu, J.: Ga-par: dependable microservice orchestration framework for geo-distributed clouds. IEEE Trans. Parallel Distrib. Syst. 31(1), 129–143 (2019)
Wang, L., Jiao, L., He, T., Li, J., Mühlhäuser, M.: Service entity placement for social virtual reality applications in edge computing. In: Proceedings of the IEEE INFOCOM 2018-IEEE Conference on Computer Communications. IEEE, pp. 468–476 (2018)
Deng, S., Xiang, Z., Taheri, J., Khoshkholghi, M.A., Yin, J., Zomaya, A.Y., Dustdar, S.: Optimal application deployment in resource constrained distributed edges. IEEE Trans. Mob. Comput. 20(5), 1907–1923 (2020)
Wang, S., Urgaonkar, R., He, T., Chan, K., Zafer, M., Leung, K.K.: Dynamic service placement for mobile micro-clouds with predicted future costs. IEEE Trans. Parallel Distrib. Syst. 28(4), 1002–1016 (2016)
Ouyang, T., Zhou, Z., Chen, X.: Follow me at the edge: mobility-aware dynamic service placement for mobile edge computing. IEEE J. Sel. Areas Commun. 36(10), 2333–2345 (2018)
Ren, J., Yu, G., Cai, Y., He, Y.: Latency optimization for resource allocation in mobile-edge computation offloading. IEEE Trans. Wirel. Commun. 17(8), 5506–5519 (2018)
Chen, L., Xu, Y., Lu, Z., Wu, J., Gai, K., Hung, P.C., Qiu, M.: Iot microservice deployment in edge-cloud hybrid environment using reinforcement learning. IEEE Internet Things J. 8(16), 12610–12622 (2020)
He, T., Khamfroush, H., Wang, S., La Porta, T., Stein, S.: It’s hard to share: Joint service placement and request scheduling in edge clouds with sharable and non-sharable resources. In: Proceedings of the 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS). IEEE, pp. 365–375 (2018)
Chen, F., Zhou, J., Xia, X., Jin, H., He, Q.: Optimal application deployment in mobile edge computing environment. In: Proceedings of the 2020 IEEE 13th International Conference on Cloud Computing (CLOUD). IEEE, pp. 184–192 (2020)
Faticanti, F., De Pellegrini, F., Siracusa, D., Santoro, D., Cretti, S.: Throughput-aware partitioning and placement of applications in fog computing. IEEE Trans. Netw. Serv. Manag. 17(4), 2436–2450 (2020)
Li, B., He, Q., Cui, G., Xia, X., Chen, F., Jin, H., Yang, Y.: Read: Robustness-oriented edge application deployment in edge computing environment. IEEE Trans. Serv. Comput. 15, 1746 (2020)
Lv, Z., Xiu, W.: Interaction of edge-cloud computing based on sdn and nfv for next generation iot. IEEE Internet Things J. 7(7), 5706–5712 (2019)
Chen, M., Hao, Y.: Task offloading for mobile edge computing in software defined ultra-dense network. IEEE J. Sel. Areas Commun. 36(3), 587–597 (2018)
Mu, S., Zhong, Z., Zhao, D., Ni, M.: Joint job partitioning and collaborative computation offloading for internet of things. IEEE Internet Things J. 6(1), 1046–1059 (2018)
Lv, Z., Qiao, L.: Optimization of collaborative resource allocation for mobile edge computing. Comput. Commun. 161, 19–27 (2020)
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015)
Chen, J., Chen, S., Wang, Q., Cao, B., Feng, G., Hu, J.: iraf: a deep reinforcement learning approach for collaborative mobile edge computing iot networks. IEEE Internet Things J. 6(4), 7011–7024 (2019)
Huang, L., Feng, X., Feng, A., Huang, Y., Qian, L.P.: Distributed deep learning-based offloading for mobile edge computing networks. Mob. Netw. Appl. 2018, 1–8 (2018)
Huang, L., Bi, S., Zhang, Y.-J.A.: Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks. IEEE Trans. Mob. Comput. 19(11), 2581–2593 (2019)
Xiong, X., Zheng, K., Lei, L., Hou, L.: Resource allocation based on deep reinforcement learning in iot edge computing. IEEE J. Sel. Areas Commun. 38(6), 1133–1146 (2020)
Lu, H., He, X., Du, M., Ruan, X., Sun, Y., Wang, K.: Edge qoe: computation offloading with deep reinforcement learning for internet of things. IEEE Internet Things J. 7(10), 9255–9265 (2020)
Qiu, X., Zhang, W., Chen, W., Zheng, Z.: Distributed and collective deep reinforcement learning for computation offloading: a practical perspective. IEEE Trans. Parallel Distrib. Syst. 32(5), 1085–1101 (2020)
Simsek, M., Aijaz, A., Dohler, M., Sachs, J., Fettweis, G.: 5g-enabled tactile internet. IEEE J. Sel. Areas Commun. 34(3), 460–473 (2016)
Bittencourt, L.F., Lopes, M.M., Petri, I., Rana, O.F.: Towards virtual machine migration in fog computing. In: Proceedings of the 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC). IEEE, pp. 1–8 (2015)
Gong, L., Wen, Y., Zhu, Z., Lee, T.: Toward profit-seeking virtual network embedding algorithm via global resource capacity. In: Proceedings of the IEEE INFOCOM 2014-IEEE Conference on Computer Communications. IEEE, pp. 1–9 (2014)
Amiri, R., Mehrpouyan, H., Fridman, L., Mallik, R.K., Nallanathan, A., Matolak, D., A machine learning approach for power allocation in hetnets considering qos. In: Proceedings of the IEEE international conference on communications (ICC). IEEE 2018, 1–7 (2018)
Sun, G., Li, Y., Liao, D., Chang, V.: Service function chain orchestration across multiple domains: a full mesh aggregation approach. IEEE Trans. Netw. Serv. Manage. 15(3), 1175–1191 (2018)
Kibalya, G., Serrat, J., Gorricho, J.-L., Okello, D., Zhang, P.: A deep reinforcement learning-based algorithm for reliability-aware multi-domain service deployment in smart ecosystems. Neural Comput. Appl. 2020, 1–23 (2020)
Kibalya, G., Serrat, J., Gorricho, J.-L., Yao, H., Zhang, P.: A novel dynamic programming inspired algorithm for embedding of virtual networks in future networks. Comput. Netw. 179, 107349 (2020)
Pei, J., Hong, P., Xue, K., Li, D.: Efficiently embedding service function chains wif dynamic virtual network function placement in geo-distributed cloud system. IEEE Trans. Parallel Distrib. Syst. 30(10), 2179–2192 (2018)
Lai, P., He, Q., Cui, G., Xia, X., Abdelrazek, M., Chen, F., Hosking, J., Grundy, J., Yang, Y.: Qoe-aware user allocation in edge computing systems wif dynamic qos. Future Gener. Comput. Syst. 112, 684–694 (2020)
Wang, J., Hu, J., Min, G., Zomaya, A.Y., Georgalas, N.: Fast adaptive task offloading in edge computing based on meta reinforcement learning. IEEE Trans. Parallel Distrib. Syst. 32(1), 242–253 (2020)
Machen, A., Wang, S., Leung, K.K., Ko, B.J., Salonidis, T.: Live service migration in mobile edge clouds. IEEE Wirel. Commun. 25(1), 140–147 (2017)
KIbalya, G., Serrat-Fernandez, J., Gorricho, J.L., Zhang, P.: A reinforcement learning approach for virtual network function chaining and sharing in softwarized networks. IEEE Trans. Netw. Serv. Manag. 19(3), 3352–3370 (2022)
Pekar, A., Mocnej, J., Seah, W.K., Zolotova, I.: Application domain-based overview of iot network traffic characteristics. ACM Comput. Surv. (CSUR) 53(4), 1–33 (2020)
Raaen, K., Kjellmo, I.: Measuring latency in virtual reality systems. In: Chorianopoulos, K., Divitini, M., Baalsrud-Hauge, J., Jaccheri, L., Malaka, R. (eds.) Entertainment Computing - ICEC 2015, pp. 457–462. Springer, Cham (2015)
Ahmaniemi, T., Lindholm, H., Muller, K., Taipalus, T.: Virtual reality experience as a stress recovery solution in workplace. IEEE Life Sci. Conf. (LSC) 2017, 206–209 (2017)
De Maio, V., Brandic, I.: First hop mobile offloading of dag computations. In: Proceedings of the 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). IEEE 2018, pp. 83–92 (2018)
Acknowledgements
This work has been partially funded by the project "UNICO-5G I+D-OPTIMAIX-TSI-063000-2021-34".
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no competing interests.
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.
About this article
Cite this article
Ssemakula, J.B., Gorricho, JL., Kibalya, G. et al. Deployment of Future Services in a Multi-access Edge Computing Environment Using Intelligence at the Edge. J Netw Syst Manage 31, 72 (2023). https://doi.org/10.1007/s10922-023-09761-0
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10922-023-09761-0