With the upcoming 5G deployment and the exponentially increasing data transmitted over cellular networks, off the shelf hardware won't provide enough performance to cope with the data being transferred over cellular networks. To tackle that problem, hardware accelerators will be of great support thanks to their better performances and lower energy consumption. However, hardware accelerators are not a silver bullet as their very nature prevents them to be as flexible as CPUs. Hardware accelerators integration into Kubernetes and Docker, respectively the most used tools for orchestration and containerization, is still not as flexible as it would need. In this thesis, we developed a framework that allows for a more flexible integration of these accelerators into a Kubernetes cluster using Docker containers making use of an abstraction layer instead of the classic virtualization process. Our results compare the performance of an execution with and without the framework that was developed during this thesis. We found that the framework's overhead depends on the size of the data being processed by the accelerator but does not go over a very low percentage of the total execution time. This framework provides an abstraction for hardware accelerators and thus provides an easy way to integrate hardware accelerated applications into a heterogeneous cluster or even across different clusters with different hardware accelerators types. This framework also moves the hardware specific parts of an accelerated program from the containers to the infrastructure and enables a new kind of service, OpenCL as a service.