Improving user experience through predictive workload optimization in e commerce cloud systems

Abstract

The rapid evolution of e-commerce has led to an unprecedented newlineincrease in digital transactions, user interactions, and data-intensive newlineoperations, placing significant pressure on computational resources and newlineworkload management. As consumers engage in online shopping, newlinepersonalized recommendations, and real-time transactions, e-commerce newlineplatforms must efficiently handle dynamic workload fluctuations, especially newlineduring high-demand events like flash sales and seasonal promotions. newlineTraditional workload prediction and resource allocation methods struggle to newlineadapt to non-linear and highly variable traffic patterns. These conventional newlineapproaches often fail to capture intricate workload dependencies, leading to newlineinefficient resource utilization, higher response time, and increased newlineoperational cost. Consequently, suboptimal workload scheduling degrades newlineuser experience, increases latency, and negatively impacts conversion rates, newlinemaking it imperative to develop a more adaptive and intelligent workload newlinemanagement solution. The research presents PredictOptiCloud as a new newlinesolution to improve workload prediction and optimize resource management newlineand scheduling for e-commerce platforms using advanced deep learning and newlineoptimization methods. Workload predictions from PredictOptiCloud are made newlineusing a Domain-Specific Hierarchical Attention Bidirectional Long Short newlineTerm Memory (DSHA-BiLSTM) network that processes historical data newlineconsisting of user interactions and server performance and transaction logs. newlineBy implementing the hierarchical attention mechanism, the model learns to newlineidentify important workload patterns that boost its predictive power through newlinethe newlinedetection of extended relationships within e-commerce traffic. newline