Containers are effective lightweight virtualization technology due to their common host operating system, rapid launch times, scalability, portability, and quick deployment. With the help of containers, programs can create run-time environment that is independent on the platform that boosts mobility and efficiency by encapsulating all required dependencies (code, running time, system libraries, and tools) into a virtual environment. The scheduler is an essential and critical part of optimizing performance and decreasing the cost of container services. Conventional Scheduling methods which are single-criteria-based algorithms (i.e. First Comes First Serves (FCFS), Round Robin, and Shortest Job First) cannot meet the demands of cloud computing. Multi-objectives-based container scheduling algorithms are therefore required to satisfy efficient resource usage optimization by optimally mapping containers to VMS. A novel methodology to the aforementioned issue may be provided by clustering-based task scheduling algorithms, which facilitate the efficient scheduling of a huge number of containers depending on container multi-criteria. This paper suggests a k-mean clustering algorithm for containers to enhance the load balancing that shortens resource execution times while also boosting the resource utilization rate. According to the experimental findings, the suggested algorithm outperforms FCFS algorithm in with respect to the execution time and maintains significant improvement of resource utilization among virtual machines and physical machines.