Strategies for Improving E-Commerce Performance through Massive Operations: Enhancing E-Commerce Efficiency Cloud Microservices
Keywords:
E-commerce optimization, microservices, cloud computing, network latencyAbstract
In the always changing field of e-commerce, ensuring flawless and efficient online operations is increasingly important. The complexity of maintaining high-performance digital platforms increases as businesses grow, so new approaches are needed to overcome challenges of running large-scale microservices on the clouds. Emphasizing load optimization, database correctness, reduction of network latency, and the use of sophisticated performance engineering techniques, this article investigates the difficult process of improving e-commerce performance. We investigate how well designed microservices enhance the responsiveness to changing market requirements and raise the robustness of e-commerce systems. We highlight technical breakthroughs that enable flawless online shopping experiences by means of careful analysis of important features such efficient load allocation to avoid bottlenecks, gaining exact database interactions for real-time user insights, and assuring fast network replies. We also discuss creative performance engineering techniques breaking the boundaries of e-commerce effectiveness. This talk aims to clarify the complex interplay of technology and strategies supporting the main e-commerce platforms, thereby offering understanding of the evolution of high-performance digital shopping environments that meet the constantly shifting demands of worldwide consumers.
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References
Sharma, S., & Chaturvedi, R. (2021). Optimizing Scalability and Performance in Cloud Services: Strategies and Solutions. ESP Journal of Engineering & Technology Advancements (ESP JETA), 1(2), 116-133.
Górski, T., & WOźniak, A. P. (2021). Optimization of business process execution in services architecture: A systematic literature review. IEEE Access, 9, 111833-111852.
Khan, S., Liu, X., Ali, S. A., & Alam, M. (2019). Bivariate, Cluster and Suitability Analysis of NoSQL Solutions for Different Application Areas. arXiv preprint arXiv:1911.11181.
Ben-Chen, M., Chazal, F., Guibas, L. J., & Ovsjanikov, M. (2017). 3.28 Qualitative and Multi-Attribute Learning from Diverse Data Collections. Functoriality in Geometric Data, 17.
Nag, N. (2020). Health state estimation. University of California, Irvine.
Lei, Y., Jasin, S., & Sinha, A. (2018). Joint dynamic pricing and order fulfillment for e-commerce retailers. Manufacturing & service operations management, 20(2), 269-284.
Li, H. J., Bu, Z., Wang, Z., & Cao, J. (2019). Dynamical clustering in electronic commerce systems via optimization and leadership expansion. IEEE Transactions on Industrial Informatics, 16(8), 5327-5334.
Netessine, S., Savin, S., & Xiao, W. (2006). Revenue management through dynamic cross selling in e-commerce retailing. Operations Research, 54(5), 893-913.
Weiss, R. M., & Mehrotra, A. K. (2001). Online dynamic pricing: Efficiency, equity and the future of e-commerce. Va. JL & Tech., 6, 1.
Wang, J., & Zhang, Y. (2013, July). Opportunity model for e-commerce recommendation: right product; right time. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval (pp. 303-312).
Menascé, D. A., Almeida, V. A., Fonseca, R., & Mendes, M. A. (2000). Business-oriented resource management policies for e-commerce servers. Performance Evaluation, 42(2-3), 223-239.
Barenji, A. V., Wang, W. M., Li, Z., & Guerra-Zubiaga, D. A. (2019). Intelligent E-commerce logistics platform using hybrid agent based approach. Transportation Research Part E: Logistics and Transportation Review, 126, 15-31.
Sun, Z., & Finnie, G. R. (2004). Intelligent techniques in e-commerce. Berlin: Springer.
Ricker, F., & Kalakota, R. (1999). Order fulfillment: the hidden key to e-commerce success. Supply chain management review, 11(3), 60-70.
Zeng, L., Ngu, A. H., Benatallah, B., Podorozhny, R., & Lei, H. (2008). Dynamic composition and optimization of web services. Distributed and Parallel Databases, 24, 45-72.
Katari, A., Muthsyala, A., & Allam, H. HYBRID CLOUD ARCHITECTURES FOR FINANCIAL DATA LAKES: DESIGN PATTERNS AND USE CASES.
Katari, A. Conflict Resolution Strategies in Financial Data Replication Systems.
Boda, V. V. R., & Immaneni, J. (2021). Healthcare in the Fast Lane: How Kubernetes and Microservices Are Making It Happen. Innovative Computer Sciences Journal, 7(1).
Immaneni, J. (2021). Using Swarm Intelligence and Graph Databases for Real-Time Fraud Detection. Journal of Computational Innovation, 1(1).
Gade, K. R. (2020). Data Mesh Architecture: A Scalable and Resilient Approach to Data Management. Innovative Computer Sciences Journal, 6(1).
Gade, K. R. (2020). Data Analytics: Data Privacy, Data Ethics, Data Monetization. MZ Computing Journal, 1(1).
Thumburu, S. K. R. (2021). Optimizing Data Transformation in EDI Workflows. Innovative Computer Sciences Journal, 7(1).
Thumburu, S. K. R. (2021). Performance Analysis of Data Exchange Protocols in Cloud Environments. MZ Computing Journal, 2(2).
Komandla, V. Strategic Feature Prioritization: Maximizing Value through User-Centric Roadmaps.
Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2021). Unified Data Architectures: Blending Data Lake, Data Warehouse, and Data Mart Architectures. MZ Computing Journal, 2(2).
Nookala, G. (2021). Automated Data Warehouse Optimization Using Machine Learning Algorithms. Journal of Computational Innovation, 1(1).
Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2020). Data Virtualization as an Alternative to Traditional Data Warehousing: Use Cases and Challenges. Innovative Computer Sciences Journal, 6(1).
Katari, A. (2019). Data Quality Management in Financial ETL Processes: Techniques and Best Practices. Innovative Computer Sciences Journal, 5(1).
Sarbaree Mishra. “Leveraging Cloud Object Storage Mechanisms for Analyzing Massive Datasets”. African Journal of Artificial Intelligence and Sustainable Development, vol. 1, no. 1, Jan. 2021, pp. 286-0
Sarbaree Mishra, et al. “A Domain Driven Data Architecture For Improving Data Quality In Distributed Datasets”. Journal of Artificial Intelligence Research and Applications, vol. 1, no. 2, Aug. 2021, pp. 510-31
Sarbaree Mishra. “Improving the Data Warehousing Toolkit through Low-Code No-Code”. Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, Oct. 2021, pp. 115-37
Sarbaree Mishra, and Jeevan Manda. “Incorporating Real-Time Data Pipelines Using Snowflake and Dbt”. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, Mar. 2021, pp. 205-2
Sarbaree Mishra. “Building A Chatbot For The Enterprise Using Transformer Models And Self-Attention Mechanisms”. Australian Journal of Machine Learning Research & Applications, vol. 1, no. 1, May 2021, pp. 318-40
Naresh Dulam, et al. “The AI Cloud Race: How AWS, Google, and Azure Are Competing for AI Dominance ”. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Dec. 2021, pp. 304-28
Naresh Dulam, et al. “Kubernetes Operators for AI ML: Simplifying Machine Learning Workflows”. African Journal of Artificial Intelligence and Sustainable Development, vol. 1, no. 1, June 2021, pp. 265-8
Naresh Dulam, et al. “Data Mesh in Action: Case Studies from Leading Enterprises”. Journal of Artificial Intelligence Research and Applications, vol. 1, no. 2, Dec. 2021, pp. 488-09
Naresh Dulam, et al. “Real-Time Analytics on Snowflake: Unleashing the Power of Data Streams”. Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, July 2021, pp. 91-114
Naresh Dulam, et al. “Serverless AI: Building Scalable AI Applications Without Infrastructure Overhead ”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, May 2021, pp. 519-42
Muneer Ahmed Salamkar. Batch Vs. Stream Processing: In-Depth Comparison of Technologies, With Insights on Selecting the Right Approach for Specific Use Cases. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Feb. 2020
Muneer Ahmed Salamkar, and Karthik Allam. Data Integration Techniques: Exploring Tools and Methodologies for Harmonizing Data across Diverse Systems and Sources. Distributed Learning and Broad Applications in Scientific Research, vol. 6, June 2020
Muneer Ahmed Salamkar, et al. The Big Data Ecosystem: An Overview of Critical Technologies Like Hadoop, Spark, and Their Roles in Data Processing Landscapes. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Sept. 2021, pp. 355-77
Muneer Ahmed Salamkar. Scalable Data Architectures: Key Principles for Building Systems That Efficiently Manage Growing Data Volumes and Complexity. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, Jan. 2021, pp. 251-70
Muneer Ahmed Salamkar, and Jayaram Immaneni. Automated Data Pipeline Creation: Leveraging ML Algorithms to Design and Optimize Data Pipelines. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, June 2021, pp. 230-5
Babulal Shaik. Network Isolation Techniques in Multi-Tenant EKS Clusters. Distributed Learning and Broad Applications in Scientific Research, vol. 6, July 2020
Babulal Shaik. Automating Compliance in Amazon EKS Clusters With Custom Policies . Journal of Artificial Intelligence Research and Applications, vol. 1, no. 1, Jan. 2021, pp. 587-10
Babulal Shaik. Developing Predictive Autoscaling Algorithms for Variable Traffic Patterns . Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, July 2021, pp. 71-90