In software defined network/network function virtualization (SDN/NFV)-enabled cloud environment, cloud services can be implemented as service function chains (SFCs), which consist of a series of ordered virtual network functions. However, due to fluctuations of cloud traffic and without knowledge of cloud computing network configuration, designing SFC optimization approach to obtain flexible cloud services in dynamic cloud environment is a pivotal challenge. In this paper, we propose a fault tolerance oriented SFC optimization approach based on deep reinforcement learning. We model fault tolerance oriented SFC elastic optimization problem as a Markov decision process, in which the reward is modeled as a weighted function, including minimizing energy consumption and migration cost, maximizing revenue benefit and load balancing. Then, taking binary integer programming model as constraints of quality of cloud services, we design optimization approaches for single-agent double deep Q-network (SADDQN) and multi-agent DDQN (MADDQN). Among them, MADDQN decentralizes training tasks from control plane to data plane to reduce the probability of single point of failure for the centralized controller. Experimental results show that the designed approaches have better performance. MADDQN can almost reach the upper bound of theoretical solution obtained by assuming a prior knowledge of the dynamics of cloud traffic.

This paper studies the energy management problem of a seaport integrated energy system under the polymorphic network. Firstly, with the diversity of energy devices, a seaport integrated energy system based on the polymorphic network is established to ensure information exchange and energy interaction between heterogeneous devices, including the service layer, control layer, and data layer. Secondly, by analyzing the characteristics of different loads and the energy conversion hub, such as the power to gas (P2G) and combined cooling heating and power (CCHP), the energy management model for the seaport integrated energy system is constructed. Finally, we obtain the optimal solution by mixed integer linear programming, and the proposed strategy is used to a seaport integrated energy system including CCHP, P2G, clean energy and energy storage device. By comparing four different cases, the simulation results show a reduction in the cost of energy purchase and carbon emissions when applying our strategy with various device types and device failures. Moreover, considering the application of the proposed energy management strategy under seasonal variations, the optimal solution for the energy management problem of the seaport integrated energy system is obtained.

Cloud computing enables Network Function Virtualization to dynamically provide and deploy network functions (NFs) to meet business-specific requirements. This approach streamlines NFs' lifecycle management and lowers the cost of Operation Administration and Maintenance. However, these advantages cause Service Function Chain (SFC) failure to grow in both scope and dimensionality, making it difficult to establish a model to locate the failure effectively. In this paper, we propose a complete analysis scheme DTFL (Digital Twin (DT) based for SFC failure localization (FL)) through the following two steps: one is classifying and locating failures, and the other is conducting root cause analysis. We propose transGNN based on the Graph Neural Network and improved graph search model to achieve the classification and location for SFC failures. On this basis, the FNSG-RCA algorithm (failure based graph model) is proposed to analyze failures. We build a prototype based on the cloud platform and experimental results show that this scheme can achieve an accuracy rate of over 98% in fine-grained classification of 49 failure types. In addition, DTFL delivers desirable performance in RCA, approximately 13% more accurate than SOTA, the state-of-the-art approach. DTFL improves both RCA accuracy and model deployment efficiency compared with the non-DT approaches.

A zero-carbon port microgrid that integrates carbon capture power plants is proposed to build the green port and promote the achievement of the dual-carbon goal. To achieve the optimal economic operation of the port microgrid and reduce carbon emissions, an energy management model considering carbon trading mechanisms is established. Furthermore, a distributed energy management method is proposed for the zero-carbon port based on the alternating direction method of multipliers (ADMMs). The simulation results prove the effectiveness and accuracy of the proposed method, which can effectively improve the economy of the port microgrid and reduce the carbon emission of the port.

Motivated by the need to reduce vendor lock-in and address concerns regarding dedicated hardware availability, cloud applications have increasingly adopted a multi-cloud deployment strategy, in which cloud applications are deployed in different zones associated with various cloud service providers. When deploying cloud applications in multi-clouds, there are three crucial and coupled metrics: deployment cost, access delay and traffic demand. Unfortunately, existing works overlook either the data transfer cost in deployment cost or the access delay and traffic demand requirements, resulting in high operating costs or low user QoS. To bridge this gap, this paper proposes the Cost-Efficient Application Deployment Framework (CADER) with tenant requirement guarantee in multi-clouds environment. However, due to the challenges of service price heterogeneity, transfer cost diversity, and resource limitation, achieving cost-efficient cloud application deployment while satisfying all tenant requirements is not an easy task. To tackle this issue, we design an approximate algorithm based on the random rounding method and prove that its approximate ratio is $O(\log g)$, where $g$ is the number of cloud zones. Results of in-depth simulations indicate that CADER can reduce the application deployment cost ranging from 16% to 38% compared to commonly used alternatives while ensuring the satisfaction of tenant requirements.

In the emerging Industrial Internet of Things (IIoT), authentication problems have become an urgent issue for massive resource-constrained devices because traditional costly security mechanisms are not suitable for them. The security protocol designed for resource-constrained systems should not only be secure but also efficient in terms of usage of energy, storage, and processing. Although recently many lightweight schemes have been proposed, to the best of our knowledge, they are unable to address the problem of privacy preservation with the resistance of Denial of Service (DoS) attacks in a practical way. In this paper, we propose a lightweight authentication protocol based on the Physically Unclonable Function (PUF) to overcome the limitations of existing schemes. The protocol provides an ingenious authentication and synchronization mechanism to solve the contradictions amount forward secrecy, DoS attacks, and resource-constrained. The performance analysis and comparison show that the proposed scheme can better improve the authentication security and efficiency for resource-constrained systems in IIoT.