Search

With the development of the Internet of Things, trust has become a limited factor in the integration of heterogeneous IoT networks. In this regard, we use the combination of blockchain technology and SDN/NFV to build a heterogeneous IoT network resource management model based on the consortium chain. In order to solve the efficiency problem caused by the full amount of data on the chain, we deploy light nodes and full nodes for the consortium chain. At the same time, we use the idea of identification to realize the separation of identification and resource information, build the application mode of on-chain identification and off-chain information, and realize resources endogenous trust management. We also propose a practical Byzantine fault-tolerant consensus mechanism based on reputation value to save consensus costs and improve efficiency. Combined with artificial intelligence technology, we introduce deep reinforcement learning for service function chain orchestration, and design a service function chain orchestration algorithm based on Asynchronous Advantage Actor-Critic to optimize orchestration costs. The final simulation results show that the consensus algorithm and service function chain orchestration algorithm we designed have good performance in terms of cost saving and efficiency improvement. [ABSTRACT FROM AUTHOR]

In-network computing indicates the convergence of computing and network, which can be further considered as computing-based networking and network-enabled computing, from which comes the edge in-network computing paradigm. In this newly proposed paradigm, the computing, caching, and communication resources are deployed on in-transit edge nodes, such as routers or switches; hence, services are provided with less delay and energy consumption by the deep aggregation of edge-side resources. However, open challenges still exist in dealing with heterogeneous resource sharing, scheduling, and ensuring credibility of service processing. Given this background, this article aims to address the edge-side heterogeneous resource trust management in multi-domain scenarios. A multi-domain heterogeneous resource trust management architecture is first constructed to achieve trusted resource sharing and transaction. Then, under the proposed architecture, a smart-contract-based resource sharing mechanism is formulated, including an incentive mechanism based on end-side node contribution. Furthermore, a differential-evolution-enabled containerized microservice orchestration algorithm is finally designed to minimize operation delay and load imbalance during the container deployment. The application of this platform in a highly elastic power grid scenario has been tested as an example. [ABSTRACT FROM AUTHOR]

To meet the execution requirements of delay-sensitive services in vehicular edge computing (VEC) networks, vehicular services need to be offloaded to edge computing nodes. For complex, large-scale services, the services need to be migrated if the services are not completed before the vehicles leave the coverage of edge computing nodes. Trust and resource matching between areas thus become major problems. This paper studies the decision model of vehicular service offloading and migration. First, software-defined network (SDN) technology is introduced into the traditional network architecture, and a two-layer distributed SDN-controlled VEC network architecture is designed, which is divided into a domain control layer and an area control layer. In this framework, we use the consortium blockchain as a carrier to share network topology information between SDN controllers to prevent information leakage. We then established a service offloading and migration optimization problem model to minimize service execution delay, reduce energy consumption and maximize the throughput of the blockchain system. We describe the problem model as a Markov Decision Process (MDP), introduce a deep reinforcement learning (DRL) algorithm named asynchronous advantage actor-critic (A3C) and design a dynamic service offloading and migration algorithm (DSOMA) based on A3C to solve the problem. Simulation results show that DSOMA can increase the throughput of the blockchain system, and DSOMA is superior to the deep Q-learning (DQN) algorithm and greedy offloading algorithm in reducing service execution delay and system energy consumption. [ABSTRACT FROM AUTHOR]