PredictOptiCloud: A hybrid framework for predictive optimization in hybrid workload cloud task scheduling

Abstract

In the realm of e-commerce, the growing complexity of dynamic workloads and resource management poses a substantial challenge for platforms aiming to optimize user experiences and operational efficiency. To address this issue, the PredictOptiCloud framework is introduced, offering a solution that combines sophisticated methodologies with comprehensive performance analysis. The framework encompasses a domain-specific approach that extracts and processes historical workload data, utilizing Domain-specific Hierarchical Attention Bi LSTM networks. This enables PredictOptiCloud to effectively predict and manage both stable and dynamic workloads. Furthermore, it employs the Spider Wolf Optimization (SWO) for load balancing and offloading decisions, optimizing resource allocation and enhancing user experiences. The performance analysis of PredictOptiCloud involves a multifaceted evaluation, with key metrics including response time, throughput, resource utilization rate, cost-efficiency, conversion rate, rate of successful task offloading, precision, accuracy, task volume, and churn rate. By meticulously assessing these metrics, PredictOptiCloud demonstrates its prowess in not only predicting and managing workloads but also in optimizing user satisfaction, operational efficiency, and cost-effectiveness, ultimately positioning itself as an invaluable asset for e-commerce platforms striving for excellence in an ever-evolving landscape.