Blockchain-Enabled Joint Resource Allocation for Virtualized Video Service Functions

Abstract

Power information is an important guarantee for energy security. As an important technical means of safety management and risk control, video monitoring is widely used in the power industry. Power video monitoring system uses efficient processing of multimodal video data and automatically identifies abnormal events and equipment status, replacing human monitoring with machine. Video monitoring data of power substations usually contain both visual information and auditory information, and the data types are diversified. The multimodal video data provides a rich underlying data source for the intelligent monitoring function, but it requires multiple service forms for efficient processing. Most intelligent edge monitoring equipment are only equipped with lightweight computing resources and limited battery supply, limited resources, and weak local processing data capabilities. Power video monitoring system has the characteristics of distribution, openness, interconnection, and intellectualization. Its intelligent edge video equipment is widely distributed, which also brings convenience and also brings security risks in terms of data security and reliability. For the outdoor multimodal power video monitoring system scenario, this paper adopts the edge-cloud distributed system architecture to solve the problem of resource shortage and adopts the first proposed service function virtualization (SFV) to solve the problem of multimodal video data processing. At the same time, the problem of security protection is solved by introducing blockchain to establish trust among intelligent video equipment and service providers. Under the security protection of virtualized service consortium blockchain (VSCB), virtualization technology is introduced into the service function chain (SFC) to realize SFV and solve the resource optimal allocation problem of multimodal video data processing. The work mainly involves the joint mapping of virtual resources, physical resources, and the joint optimization of computing and communication resources. Problems such as large state space and high dimensionality of action space have an impact on resource allocation. The stochastic optimization problem of resource allocation is established as a Markov decision process (MDP) model, and SFV technology is used to optimize cost and delay. The resource allocation optimization algorithm (RAOA-A3C) based on asynchronous advantage actor-critic algorithm (A3C) is proposed. Simulation experiments show that the RAOA-A3C proposed in this paper is more suitable for high-dynamic, multidimensional, and distributed power video monitoring system scenario and has achieved better optimization results in reducing time delay and deployment costs.