APPM: Adaptive Parallel Processing Mechanism for Service Function Chains

By replacing traditional hardware-based middleboxes with software-based Virtual Network Functions (VNFs) running on general-purpose servers, network function virtualization represents a promising technique to reduce the cost of service creation and increase the agility of network operations. Typically, Service Function Chains (SFCs) are adopted to orchestrate dynamical network services and facilitate management of network applications. Recently, SFC parallelism that implements parallel processing of VNFs has been investigated to further improve SFC service quality. However, the unreasonable service graph of parallel processing in existing parallelized SFCs (PSFCs) might cause excessive resource consumption; incoordination between PSFC deployment and scheduling also increases the queuing delay of VNFs and degrades PSFC performance. In this paper, an adaptive parallel processing optimization mechanism (APPM) is proposed to self-adaptively adjust the service graph of PSFCs and intelligently solve the joint problem of PSFC deployment and scheduling. Specifically, APPM uses a parallelism optimization algorithm (POA) based on the bin packing problem with soft bin capacity to optimize the structure of the PSFC service graph. Afterward, APPM employs a joint optimization algorithm based on reinforcement learning (JORL) to jointly deploy and schedule the PSFCs optimized by POA via the online perception of environment status. Simulation results showed that POA reduces the SFC parallelism degree and resource consumption by about 35%; JORL lowers SFC delay by reducing the queuing delay and has better overall performance than the state of the art algorithms even with limited resources.