Abstract
The combination of network function virtualization and software-defined networking allows various network functions to process flows according to their characteristics and requirements. Due to the highly dynamic nature of the workload, the network infrastructure needs to properly schedule the underlying resources in order to respond to workload changes in a timely manner. However, the existing NFV platform lacks a comprehensive solution for how to scale under workload variation, which may seriously hurt the overall system performance. To improve the scalability of the NFV platform and ensure consistent high performance under dynamic workloads, we propose AdaptNF, a novel NFV platform designed to support a combination of course-grained and fine-grained resource scheduling strategies. To deal with resource imbalance, which is the essential scheduling problem that leads to insufficient NFV performance, AdaptNF adopts a novel algorithm that can efficiently balance the workload among multiple network function instances through stateless flow migration. Our controlled experiments show that the AdaptNF scheme can optimize resource allocation and ensure outstanding performance after scaling. In terms of network throughput and latency, AdaptNF significantly improves the performance of the underlying NFV platform.
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