VNF Embedding and Assignment for Network Function Parallelism

The emergence of Network Function Virtualization (NFV) and Service Function Chaining (SFC) together enable flexible and agile network management and traffic engineering. Recently, Network Function Parallelism (NFP) has been proposed to break the need for sequential services and, hence, significantly reduce the latency of SFC. While some studies have investigated how to identify parallel paths to enhance the efficiency of parallelism, less effort has been paid to efficient network function embedding with consideration of parallelism opportunities. Hence, this work aims at solving the instance deployment problem so as to prevent parallel functions from waiting for each other before continuing to the next stage. As it is extremely difficult to optimize VNF (Virtual Network Functions) embedding for uncertain parallelism opportunities, we propose a practical embedding scoring mechanism to enhance the likelihood of low-cost function parallelism. Our scoring design tends to cluster independent functions for efficient parallelism while ensuring load balancing. After instance deployment, we then assign function instances to parallel chains so as to minimize their end-to-end latency while balancing the workload of instances. Our evaluation results show that the proposed scoringbased embedding scheme ensures homogeneous delays of parallel functions and, hence, reduces the end-to-end latency for NFP by up to 22% as compared to the embedding algorithm without considering parallelism opportunities.