A scheduling architecture for enforcing quality of service in multi-process systems

There is a massive deployment of multi-core CPUs. It requires a significant drive to consolidate multiple services while still achieving high performance on these off-the-shelf CPUs. Each function had earlier an own execution environment, which guaranteed a certain Quality of Service (QoS). Consolidating multiple services can give rise to shared resource congestions, resulting in lower and non-deterministic QoS. We describe a method to increase the overall system performance by assisting the operating system process scheduler to utilize shared resources more efficiently. Our method utilizes hardware- and system-level performance counters to profile the shared resource usage of each process. We also use a big-data approach to analyzing statistics from many nodes. The outcome of the analysis is a decision support model that is utilized by the process scheduler when allocating and scheduling process. Our scheduler can efficiently distribute processes compared to traditional CPU-load based process schedulers by considering the hardware capacity and previous scheduling- and allocation decisions.