Profile-based Resource Allocation for Virtualized Network Functions
This work addresses the problem of deterministic performance in dynamic cloud environments for service operators, representing an incremental improvement in resource allocation techniques.
The paper tackles the challenge of mapping Service Level Agreement (SLA) performance specifications to resource allocations in virtualized network functions (VNFs) by proposing a profiling-based method that predicts performance based on workload and resources, enabling optimal resource allocation to meet SLA requirements and avoid unnecessary scaling.
The virtualization of compute and network resources enables an unseen flexibility for deploying network services. A wide spectrum of emerging technologies allows an ever-growing range of orchestration possibilities in cloud-based environments. But in this context it remains challenging to rhyme dynamic cloud configurations with deterministic performance. The service operator must somehow map the performance specification in the Service Level Agreement (SLA) to an adequate resource allocation in the virtualized infrastructure. We propose the use of a VNF profile to alleviate this process. This is illustrated by profiling the performance of four example network functions (a virtual router, switch, firewall and cache server) under varying workloads and resource configurations. We then compare several methods to derive a model from the profiled datasets. We select the most accurate method to further train a model which predicts the services' performance, in function of incoming workload and allocated resources. Our presented method can offer the service operator a recommended resource allocation for the targeted service, in function of the targeted performance and maximum workload specified in the SLA. This helps to deploy the softwarized service with an optimal amount of resources to meet the SLA requirements, thereby avoiding unnecessary scaling steps.