Scheduling Data Stream Jobs on Distributed Systems with Background Load

Cloud computing is used by numerous applications tailored for on-demand execution on elastic resources. While most cloud based applications rely mostly on virtualization, an emerging technology based on lightweight containers is starting to gain traction. While most research on job scheduling on clouds has focused on dedicated machines, the emergence and applicability of containers on a wider range of platforms including IoT, reopens the issue of scheduling on non-dedicated machines with high priority background load. In this paper we address this problem by proposing a model and several heuristics for scheduling non-preemptive data stream jobs on containers running on machines with background load. We also address the issue of estimating the container parameters. The heuristics are tested and analyzed based on real-life traces.