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.

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.

This paper concerns the formation problem in multi-USV cluster formation containment tracking tasks with a special topology. A topology reconstruction method was proposed that enables the followers’ formation to be dispersed while achieving the fastest convergence rate for the system. This topology structure is based on tree topology and DAG (directed acyclic graph) local structure stem as prototypes, using the principle of adding reverse edges on the stem to reduce algebraic connectivity. By adding a reverse edge to obtain a more dispersed formation, a method for selecting appropriate reverse edges was achieved. Through relevant theoretical quantitative and qualitative analysis, it was demonstrated that adding this reverse edge can enable the system to achieve the fastest convergence rate. Finally, through simulation experiments, it was verified that the selected reverse edge can optimize the formation of followers and achieve the fastest convergence rate.

Service function chain (SFC) consists of multiple ordered network functions (e.g., firewall, load balancer) and plays an important role in improving network security and ensuring network performance. Offloading SFCs onto programmable switches can bring significant performance improvement, but it suffers from unbearable reconfiguration delays, making it hard to cope with network workload dynamics in a timely manner. To bridge the gap, this paper presents OptRec, an efficient SFC proactive reconfiguration optimization framework based on deep reinforcement learning (DRL). OptRec predicts future traffic and places SFCs on programmable switches in advance to ensure the timeliness of the SFC reconfiguration, which is a proactive approach. However, it is non-trivial to extract effective features from historical traffic information and global network states, while ensuring efficient and stable model training. To this end, OptRec introduces a multi-level feature extraction model for different types of features. Additionally, it combines reinforcement learning and autoregressive learning to enhance model efficiency and stability. Results of in-depth simulations based on real-world datasets show the average prediction error of OptRec is less than 3 can increase the system throughput by up to 69.6 compared with other alternatives.

The successful application cases of Large Language Models (LLMs) and Machine Learning (ML) are driving traditional data centers to transform into intelligent computing data centers characterized by low latency, high bandwidth, high reliability, and zero packet loss. The demand for immense computing and ultra-low latency suggests that in-network computing (INC) may be a viable solution, such as In-network aggregation (INA). INA involves a hierarchical structure of switches and servers to form different Service Function Chains (SFCs) including switches, servers, physical links, and virtual links for accomplishing model training. However, the aggregation of heavy traffic in CTCs tends to a sudden and drastic increase in a specific node, greatly increasing the likelihood of node failure. To detect SFC failure in real time, we propose an in-network SFC failure detection approach based on INC. We introduce digital twins (DT) and propose a collaborative AI framework based on the data plane and control plane to avoid model overfitting. In addition, to reduce the computing consumption, we propose the concept of "multiple SFC chains multiple models" to customize each SFC failure detection model and validate the mechanism on a BMv2-based prototype, which implements a high-accuracy failure detection with minor performance degradation.