Orca

Onboarding network functions onto current clouds requires labor-intensive configuration of the virtual environment. Developers need to dimension the resources available to each virtual machine such as CPU and memory, define thresholds for scaling dynamically and create configuration files that operators can use to execute the network services. This process is time consuming and dependent on the server architecture. As resources are managed on an individual virtual machine basis, services cannot be orchestrated end to end without significant expertise. In this paper, we argue that much of the manual configuration needed for onboarding services onto a cloud can be automated. Moreover, we can automatically generate abstractions that consider services end-to-end and enable their holistic orchestration. We propose a framework that benchmarks network services during the onboarding process and generates an elastic model which relates workload mixes to resource requirements, identifies component dependencies and automates service operation on heterogeneous stacks. We have evaluated our framework using a real-time communication service that handles multiple classes of workloads. Results show that underprovisioning can be eliminated for regular daily traffic, reducing resource provisioning time by at least 5X for the most stressing traffic surges, while improving key performance indicators by at least 40%.