Abstract—The advancement of 5G technology and the In-ternet of Things (IoT) has enabled the development of appli-cations such as augmented reality and autonomous vehicles, which demand low latency and high computational power. These applications generate computationally intensive and delay-sensitive workloads, necessitating an efficient edge-cloud collaborative framework for optimal resource management. This study introduces a task offloading and service caching framework designed to enhance computational efficiency by dynamically distributing tasks between local processing and cloud-based execution at the edge, thereby reducing system overhead in terms of latency and energy consumption. To achieve this, a non-cooperative game theory-based distributed task offloading mechanism is employed to optimize offloading decisions. Additionally, a dynamic service caching strategy, formulated as a 0–1 knapsack optimization problem, is imple-mented to store frequently requested services at edge servers, improving task execution efficiency. As a result, task offload-ing decisions dynamically adapt to service caching updates, ensuring an optimal balance between performance, resource utilization, and system efficiency.
 
Keywords—game theory, service caching, task offloading


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