ARINC 653-inspired regularity-based resource partitioning on xen

A multitude of cloud-native applications take up a significant share of today's world wide web, the majority of which implicitly require soft-real-time guarantees when hosted on servers at various data centers across the globe. With the rapid development of cloud computing and virtualization techniques, many applications have been moved onto cloud and edge platforms that require efficient virtualization techniques. This means a set of applications must be executed on a Virtual Machine (VM) and multiple VMs must be temporally and spatially scheduled on a set of CPUs. Designed to leverage the cloud infrastructure model, many of these cloud-native applications such as media servers strongly demand low data latency and high compute-resource availability, both of which must be predictable. However, state-of-art VM schedulers fail to satisfy these requirements simultaneously. The scheduling of cloud-native applications on VMs and the scheduling of VMs on physical resources (CPUs), collectively need to be real-time in nature as specified by the Hierarchical Real-Time Scheduling (HiRTS) framework. Conforming to the specifications of this framework, the Regularity-based Resource Partitioning (RRP) model has been proposed that introduces the concept of regularity to provide a near-ideal resource supply to all VMs. In this paper, we make the theoretically superior Regularity-based Resource Partitioning (RRP) model ready for prime time by implementing its associated resource partitioning algorithms for the first time ever on the popular x-86 open-source hypervisor Xen, i.e., RRP-Xen. This paper also compares and contrasts the real-time performance of RRP-Xen against contemporary Xen schedulers such as Credit and RTDS. Our contributions include: (1) a novel implementation of the RRP model on Xen's x-86 based hypervisor, thereby providing a test-bed for future researchers; (2) the first-ever multi-core ARINC 653 VM scheduler prototype on Xen; and (3) numerous experiments and theoretical analysis to determine the real-time performance of RRP-Xen under a stringent workload environment.