Cloud Cost, Reliability, and Speed: The Triangle Every Enterprise Struggles With
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V3I4P116Keywords:
Cloud Cost Optimization Strategies, Finops In Devops, Cloud Reliability Engineering, Cost Vs Speed Trade-Offs In Cloud, Enterprise Cloud Cost Management, AI Cloud Cost Optimization, ML-Driven Cloud Cost Forecasting, Finops for AI/ML Workloads, AI-Powered Capacity Planning, Machine Learning Cloud Resource OptimizationAbstract
The pace and uptake of cloud computing has influenced the enterprise IT infrastructural formation since it has allowed resource provisioning to be on blanket, adaptable, and on-demand. Nevertheless, organizations are also confronting a fundamental dilemma between three vital and competing goals namely cost efficiency, reliability of the system and speed of operation. Optimization on a single dimension tends to cause trade-offs in other dimensions as this can cause a continual optimization dilemma in current cloud systems. In spite of the tremendous developments in writings on cloud technologies, lack of unifying frameworks that would systematically address this tri-dimensional trade-off still persists. A conceptual and analytical framework proposed in this paper as a formalization of interdependences between cost, reliability, and performance is the Cloud Trade-Off Triangle, which attempts to introduce structure in understanding the interplay in these competing factors, along with managing them, according to workload characteristics and business priorities. To handle this, the research incorporates a multi-methodology which involves analytical modelling, objective design of a multi-layered optimization model, and a functional test in terms of simulated workloads and a case study based on real world scenario. The suggested framework combines the mechanisms of cost optimization, enhancement of the reliability, speeding up performance, and with adaptive mechanisms like auto-scaling, redundancy configuration and workload allocation based on latency. The experimental findings prove that it is impossible to set up one configuration to maximize the three dimensions, but it is possible to find optimal solutions that exist in a trade-off spectrum as determined by contextual priorities. The results indicate that cost-oriented solutions may lead to a decrease of costs other by 30% with possible reliability trade-offs, whereas reliability-oriented and performance-oriented solutions enhance availability and latency at a greater cost. The present paper adds to a synthesized model of decision-making, a framework of the optimization that can be scaled, and guidelines that can be applied to decision-makers in enterprise cloud architects, introducing the opportunity to optimize the tasks intelligently and context-consciously in the complex distributed environment.
References
[1] Wang, Z., Hayat, M. M., Ghani, N., & Shaban, K. B. (2016). Optimizing cloud-service performance: Efficient resource provisioning via optimal workload allocation. IEEE Transactions on parallel and Distributed Systems, 28(6), 1689-1702.
[2] Ravi, V. K., & Musunuri, A. (2020). Cloud cost optimization techniques in data engineering.
[3] Xiang, Y., Lan, T., Aggarwal, V., & Chen, Y. F. R. (2014). Joint latency and cost optimization for erasurecoded data center storage. ACM SIGMETRICS Performance Evaluation Review, 42(2), 3-14.
[4] Li, J., Peng, M., Yu, Y., & Ding, Z. (2016). Energy-efficient joint congestion control and resource optimization in heterogeneous cloud radio access networks. IEEE Transactions on Vehicular Technology, 65(12), 9873-9887.
[5] Ismail, L., & Fardoun, A. (2016). Eats: Energy-aware tasks scheduling in cloud computing systems. Procedia Computer Science, 83, 870-877.
[6] Fé, I., Matos, R., Dantas, J., Melo, C., Nguyen, T. A., Min, D., ... & Maciel, P. R. M. (2022). Performance-cost trade-off in auto-scaling mechanisms for cloud computing. Sensors, 22(3), 1221.
[7] Mukwevho, M. A., & Celik, T. (2018). Toward a smart cloud: A review of fault-tolerance methods in cloud systems. IEEE Transactions on Services Computing, 14(2), 589-605.
[8] Nezami, Z., Zamanifar, K., Djemame, K., & Pournaras, E. (2021). Decentralized edge-to-cloud load balancing: Service placement for the Internet of Things. Ieee Access, 9, 64983-65000.
[9] Vakilinia, S., Heidarpour, B., & Cheriet, M. (2016). Energy efficient resource allocation in cloud computing environments. IEEE Access, 4, 8544-8557.
[10] He, Z., Li, K., Li, K., & Zhou, W. (2021). Server configuration optimization in mobile edge computing: A cost‐performance tradeoff perspective. Software: Practice and Experience, 51(9), 1868-1895.
[11] Dazer, M., Stohrer, M., Kemmler, S., & Bertsche, B. (2016, September). Planning of reliability life tests within the accuracy, time and cost triangle. In 2016 IEEE Accelerated Stress Testing & Reliability Conference (ASTR) (pp. 1-9). IEEE.
[12] Sayadnavard, M. H., Haghighat, A. T., & Rahmani, A. M. (2022). A multi-objective approach for energy-efficient and reliable dynamic VM consolidation in cloud data centers. Engineering science and technology, an International Journal, 26, 100995.
[13] Ferrer, A. J., Hernández, F., Tordsson, J., Elmroth, E., Ali-Eldin, A., Zsigri, C., ... & Sheridan, C. (2012). OPTIMIS: A holistic approach to cloud service provisioning. Future Generation Computer Systems, 28(1), 66-77.
[14] Osypanka, P., & Nawrocki, P. (2020). Resource usage cost optimization in cloud computing using machine learning. IEEE Transactions on Cloud Computing, 10(3), 2079-2089.
[15] Welsh, T., & Benkhelifa, E. (2020). On resilience in cloud computing: A survey of techniques across the cloud domain. ACM computing surveys (CSUR), 53(3), 1-36.
[16] Wang, L., & Ranjan, R. (2015). Processing distributed internet of things data in clouds. IEEE Cloud Computing, 2(1), 76-80.
[17] Buyya, R., Yeo, C. S., & Venugopal, S. (2008, September). Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities. In 2008 10th IEEE international conference on high performance computing and communications (pp. 5-13). IEEE.
[18] Tzeng, G. H., & Huang, J. J. (2011). Multiple attribute decision making: methods and applications. CRC press.
[19] Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P. P., Kolodziej, J., Balaji, P., ... & Zomaya, A. (2016). A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing, 98(7), 751-774.
[20] Faniyi, F., & Bahsoon, R. (2015). A systematic review of service level management in the cloud. ACM Computing Surveys (CSUR), 48(3), 1-27.
[21] Luo, L., Meng, S., Qiu, X., & Dai, Y. (2019). Improving failure tolerance in large-scale cloud computing systems. IEEE Transactions on Reliability, 68(2), 620-632.
[22] Niedermaier, S., Koetter, F., Freymann, A., & Wagner, S. (2019, October). On observability and monitoring of distributed systems–an industry interview study. In International Conference on Service-Oriented Computing (pp. 36-52). Cham: Springer International Publishing.
[23] Ranjan, R., Benatallah, B., Dustdar, S., & Papazoglou, M. P. (2015). Cloud resource orchestration programming: overview, issues, and directions. IEEE Internet Computing, 19(5), 46-56.
[24] Bai, Q., Labi, S., & Sinha, K. C. (2012). Trade-off analysis for multiobjective optimization in transportation asset management by generating Pareto frontiers using extreme points nondominated sorting genetic algorithm II. Journal of Transportation Engineering, 138(6), 798-808.
[25] Chennareddy, R. K. (2020). Engineering Intelligence Systems Using Big Data and Cloud Architectures for Modern Data Intensive Applications. International Journal of AI, BigData, Computational and Management Studies, 1(2), 41-50.
[26] Chennareddy, R. K. (2021). Designing Data and Analytics Ecosystems for High Volume Transaction Processing Applications. International Journal of AI, BigData, Computational and Management Studies, 2(2), 95-106.
