Edge computing provides benefits such as reduced latency by processing data closer to its source, but it faces challenges due to limited processing power, memory, and storage capacity. To address these limitations and enhance system efficiency, task classification becomes crucial in edge computing. By categorizing tasks, resources can be allocated effectively. This paper presents a novel approach for classifying tasks and edge servers based on CPU and memory capacities. The proposed model treats tasks and edge servers as points and applies the K-means method by adding multi-objective constraints Chebyshev distance. The distance metric considers two objectives: The distance between points and the number of tasks and edge servers. Unlike traditional K-means algorithms that often result in clusters with either one server or clusters without servers, our model ensures that nearly every cluster contains at least one server. Simulation results demonstrate the fast convergence of the proposed model, evaluated through clustering using inertia and silhouette coefficient. It is important to note that this classification does not directly influence task scheduling or resource allocation processes. Instead, it serves as a preliminary step to improve the effectiveness of these subsequent processes. By strategically placing more powerful tasks on higher-capacity servers, and vice versa, our approach aims to reduce the workload of the scheduler and enhance resource allocation or task scheduling. This classification framework has the potential to achieve efficient task and resource management in edge computing environments.