Autonomous Multi-Cloud Kubernetes Orchestration Using Custom Resource Definitions and Controller Patterns
DOI:
https://doi.org/10.22399/ijcesen.5545Keywords:
Autonomous Kubernetes orchestration, Custom Resource Definitions, Multi-cloud control planes, Controller reconciliation, Policy-aware cloud federationAbstract
With the proliferation of cloud-native systems across multiple cloud providers, regions, and edge environments, multi-cloud Kubernetes orchestration, without any central management, is becoming a critical research challenge. This review explores the use of Custom Resource Definitions (CRD) and controller patterns to underpin policy-driven, self-adaptive orchestration of heterogeneous clusters of Kubernetes. A structured technical coding approach has been used to review literature from the field of cloud federation, container orchestration, model-driven deployment, self-adaptive systems, feedback control and edge/fog computing. The results of this review indicate that previous work has set solid ground for the analysis of declarative intent, placement optimisation, portability and adaptive control, although evidence is scattered across different research communities. In the coded comparison of representative studies, placement and resource allocation are given significantly more attention than the following: controller convergence, policy conflict handling, CRD schema quality and assurance. Five core domains of orchestration are identified: declarative intent modelling, reconciliation control, cross-cloud placement, interoperability, and observability-driven adaptability. The results indicate that CRDs should not be treated as simple configuration objects but rather as runtime models of desired state, constraints, and status. Controllers should be tested as feedback systems that exhibit measurable convergence, retry behaviour and interaction effects. A number of future research directions are worth exploring, such as a reproducible multi-cloud benchmark, formalised CRD schema design, secure controller authority boundaries, multi-controller coordination, and safe constrained learning for adaptive placement. The future contribution of this field lies in making multi-cloud automation measurable, explainable, and bounded.
References
[1] Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R. H., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58. DOI: https://doi.org/10.1145/1721654.1721672
[2] Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, 25(6), 599–616. DOI: https://doi.org/10.1016/j.future.2008.12.001
[3] Toosi, A. N., Calheiros, R. N., & Buyya, R. (2014). Interconnected cloud computing environments: Challenges, taxonomy, and survey. ACM Computing Surveys, 47(1), 1–47. DOI: https://doi.org/10.1145/2593512
[4] Varghese, B., & Buyya, R. (2018). Next generation cloud computing: New trends and research directions. Future Generation Computer Systems, 79, 849–861. DOI: https://doi.org/10.1016/j.future.2017.09.020
[5] Hellerstein, J. L., Diao, Y., Parekh, S., & Tilbury, D. M. (2004). Feedback control of computing systems. Wiley-IEEE Press. DOI: https://doi.org/10.1002/047166880X
[6] Jamshidi, P., Pahl, C., Mendonça, N. C., Lewis, J., & Tilkov, S. (2018). Microservices: The journey so far and challenges ahead. IEEE Software, 35(3), 24–35. DOI: https://doi.org/10.1109/MS.2018.2141039
[7] Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646. DOI: https://doi.org/10.1109/JIOT.2016.2579198
[8] Burns, B., Grant, B., Oppenheimer, D., Brewer, E., & Wilkes, J. (2016). Borg, Omega, and Kubernetes. Communications of the ACM, 59(5), 50–57. DOI: https://doi.org/10.1145/2890784
[9] Rodriguez, M. A., & Buyya, R. (2019). Container-based cluster orchestration systems: A taxonomy and future directions. Software: Practice and Experience, 49(5), 698–719. DOI: https://doi.org/10.1002/spe.2660
[10] Pahl, C., Brogi, A., Soldani, J., & Jamshidi, P. (2019). Cloud container technologies: A state-of-the-art review. IEEE Transactions on Cloud Computing, 7(3), 677–692. DOI: https://doi.org/10.1109/TCC.2017.2702586
[11] Grozev, N., & Buyya, R. (2014). Inter-cloud architectures and application brokering: Taxonomy and survey. Software: Practice and Experience, 44(3), 369–390. DOI: https://doi.org/10.1002/spe.2168
[12] Petcu, D. (2014). Consuming resources and services from multiple clouds. Journal of Grid Computing, 12(2), 321–345. DOI: https://doi.org/10.1007/s10723-013-9290-3
[13] Ferry, N., Chauvel, F., Song, H., Rossini, A., Lushpenko, M., & Solberg, A. (2018). CloudMF: Applying MDE to tame the complexity of managing multi-cloud applications. ACM Transactions on Internet Technology, 18(2), 1–24. DOI: https://doi.org/10.1145/3125621
[14] Weyns, D., Malek, S., & Andersson, J. (2012). FORMS: Unifying reference model for formal specification of distributed self-adaptive systems. ACM Transactions on Autonomous and Adaptive Systems, 7(1), 1–61. DOI: https://doi.org/10.1145/2168260.2168268
[15] Garlan, D., Cheng, S. W., Huang, A. C., Schmerl, B., & Steenkiste, P. (2004). Rainbow: Architecture-based self-adaptation with reusable infrastructure. Computer, 37(10), 46–54. DOI: https://doi.org/10.1109/MC.2004.175
[16] De Lemos, R., Giese, H., Müller, H. A., Shaw, M., Andersson, J., Litoiu, M., Schmerl, B., Tamura, G., Villegas, N. M., Vogel, T., Weyns, D., Baresi, L., Becker, B., Bencomo, N., Brun, Y., Cukic, B., Desmarais, R., Dustdar, S., Engels, G., ... Whittle, J. (2013). Software engineering for self-adaptive systems: A second research roadmap. In R. De Lemos, H. Giese, H. A. Müller, & M. Shaw (Eds.), Software engineering for self-adaptive systems II (pp. 1–32). Springer. DOI: https://doi.org/10.1007/978-3-642-35813-5_1
[17] Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017). A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys & Tutorials, 19(4), 2322–2358. DOI: https://doi.org/10.1109/COMST.2017.2745201
[18] Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R. H., Morrow, M. J., & Polakos, P. A. (2018). A comprehensive survey on fog computing: State-of-the-art and research challenges. IEEE Communications Surveys & Tutorials, 20(1), 416–464. DOI: https://doi.org/10.1109/COMST.2017.2771153
[19] Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering: A systematic literature review. Information and Software Technology, 51(1), 7–15. DOI: https://doi.org/10.1016/j.infsof.2008.09.009
[20] Petersen, K., Vakkalanka, S., & Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 64, 1–18. DOI: https://doi.org/10.1016/j.infsof.2015.03.007
[21] Jain, R. (1991). The art of computer systems performance analysis: Techniques for experimental design, measurement, simulation, and modeling. Wiley.
[22] Lazowska, E. D., Zahorjan, J., Graham, G. S., & Sevcik, K. C. (1984). Quantitative system performance: Computer system analysis using queueing network models. Prentice Hall.
[23] Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose, C. A. F., & Buyya, R. (2011). CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 41(1), 23–50. DOI: https://doi.org/10.1002/spe.995
[24] Piraghaj, S. F., Dastjerdi, A. V., Calheiros, R. N., & Buyya, R. (2017). ContainerCloudSim: An environment for modeling and simulation of containers in cloud data centers. Software: Practice and Experience, 47(4), 505–521. DOI: https://doi.org/10.1002/spe.2422
[25] Gupta, H., Dastjerdi, A. V., Ghosh, S. K., & Buyya, R. (2017). iFogSim: A toolkit for modeling and simulation of resource management techniques in Internet of Things, edge and fog computing environments. Software: Practice and Experience, 47(9), 1275–1296. DOI: https://doi.org/10.1002/spe.2509
[26] Sonmez, C., Ozgovde, A., & Ersoy, C. (2018). EdgeCloudSim: An environment for performance evaluation of edge computing systems. Transactions on Emerging Telecommunications Technologies, 29(11), e3493. DOI: https://doi.org/10.1002/ett.3493
[27] Skarlat, O., Nardelli, M., Schulte, S., Borkowski, M., & Leitner, P. (2017). Optimized IoT service placement in the fog. Service Oriented Computing and Applications, 11(4), 427–443. DOI: https://doi.org/10.1007/s11761-017-0219-8
[28] Wen, Z., Yang, R., Garraghan, P., Lin, T., Xu, J., & Rovatsos, M. (2017). Fog orchestration for Internet of Things services. IEEE Internet Computing, 21(2), 16–24. DOI: https://doi.org/10.1109/MIC.2017.36
[29] Tuli, S., Mahmud, R., Tuli, S., & Buyya, R. (2019). FogBus: A blockchain-based lightweight framework for edge and fog computing. Journal of Systems and Software, 154, 22–36. DOI: https://doi.org/10.1016/j.jss.2019.04.050
[30] Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generation Computer Systems, 28(5), 755–768. DOI: https://doi.org/10.1016/j.future.2011.04.017
[31] Ghobaei-Arani, M., Souri, A., & Rahmanian, A. A. (2020). Resource management approaches in fog computing: A comprehensive review. Journal of Grid Computing, 18(1), 1–42. DOI: https://doi.org/10.1007/s10723-019-09491-1
[32] Brogi, A., Forti, S., Guerrero, C., & Lera, I. (2020). How to place your apps in the fog: State of the art and open challenges. Software: Practice and Experience, 50(5), 719–740. DOI: https://doi.org/10.1002/spe.2766
[33] Kephart, J. O., & Chess, D. M. (2003). The vision of autonomic computing. Computer, 36(1), 41–50. DOI: https://doi.org/10.1109/MC.2003.1160055
[34] Salehie, M., & Tahvildari, L. (2009). Self-adaptive software: Landscape and research challenges. ACM Transactions on Autonomous and Adaptive Systems, 4(2), 1–42. DOI: https://doi.org/10.1145/1516533.1516538
[35] Cheng, B. H. C., De Lemos, R., Giese, H., Inverardi, P., Magee, J., Andersson, J., Becker, B., Bencomo, N., Brun, Y., Cukic, B., Di Marzo Serugendo, G., Dustdar, S., Finkelstein, A., Gacek, C., Geihs, K., Grassi, V., Karsai, G., Kienle, H. M., Kramer, J., ... Whittle, J. (2009). Software engineering for self-adaptive systems: A research roadmap. In B. H. C. Cheng, R. De Lemos, H. Giese, P. Inverardi, & J. Magee (Eds.), Software engineering for self-adaptive systems (pp. 1–26). Springer. DOI: https://doi.org/10.1007/978-3-642-02161-9_1
[36] Huebscher, M. C., & McCann, J. A. (2008). A survey of autonomic computing—degrees, models, and applications. ACM Computing Surveys, 40(3), 1–28. DOI: https://doi.org/10.1145/1380584.1380585
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