Dynamic Task Offloading in Edge-Enabled AI Systems: An In-depth Analysis of Performance, Latency, and Resource Optimization

Abstract

This paper presents an in-depth analysis of dynamic task offloading in edge-enabled AI systems, focusing on performance, latency, and resource optimization. Using Python programming language with the matplotlib and numpy libraries, we simulated data to investigate various metrics over a specified time frame. The performance, latency, and resource optimization metrics were visualized through graphs, highlighting the dynamic nature of task offloading strategies. Additionally, we explored resource provisioning, resource scheduling, service placement, and task offloading mechanisms, examining their trends over time. Results indicate fluctuations in performance, latency, and resource optimization metrics, emphasizing the adaptability of edge-enabled AI systems to dynamic workload patterns. Notably, proactive resource management strategies and adaptive task offloading decisions play crucial roles in optimizing system performance and resource utilization. The observed variability underscores the importance of continuous monitoring and optimization to ensure efficient operation in real-world edge computing environments. Overall, this study contributes to a comprehensive understanding of dynamic task offloading in edge-enabled AI systems, showcasing their potential for diverse applications in latency-sensitive and resource-constrained environments.