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.

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function.

Owing to software defined network function virtualization (SDNFV), network services can be implemented as service function chains (SFCs) in SDNFV-enabled Cloud Computing. SFCs consist of a series of ordered virtual network functions (VNFs). Due to the dynamic of underlying network state and the unpredictability of network traffic, the traditional SFC orchestrating (SFCO) approaches based on centralized placement and single-path routing lead to low availability of network resources, making it difficult to effectively manage and utilize complex and heterogeneous network resources. To address the above challenges, we propose a queue-aware SFCs orchestrating and adaptive parallel traffic scheduling optimization approach. First, the SFCO problem is modeled as a stochastic optimization problem, and the Lyapunov optimization theory is used to transform and decompose the SFCO problem to decouple the time coupling of optimal decision-making. An automatic decentralized algorithm based on queue model is proposed to orchestrate SFCs using information of local and its immediate one-hop neighbors. Furthermore, an adaptive parallel traffic scheduling optimization algorithm based on deep reinforcement learning is proposed, according to the decision output of the distributed SFC algorithm and current network state, network traffic is allocated to multiple paths for parallel transmission, which improves the availability of network resources and network performance. Experimental results show that, compared with the benchmarks, the average queue depth of the designed approach is reduced by$42.18\% {\sim} 69.97\%$, the average cost of the designed approach is reduced by$16.1\% {\sim} 55.6\%$, the average throughput is improved by$2.41\%{\sim} 10.07\%$, the average link resource utilization rate is improved by about$6.9\%{\sim} 28.3\%$, the average round-trip delay is shortened by$17.1\%{\sim} 24.1\%$, and the average packet loss rate is reduced by$39.4\%{\sim} 51.7\%$.

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.

Network function virtualization (NFV) is a ground-breaking technology that decouples network functions (NFs) from customized hardware to support more flexible network services and network resource allocation. However, these improvements also lead to an increase in the possibility of service function chain (SFC) failure due to hardware failures, software bugs, or resource contention. This could lead to minor problems or even serious consequences. Unfortunately, the existing failure detection methods have multiple issues, such as small detection range, single detection function, heavy overhead, and low accuracy. Consequently, we propose FullSight, a feasible framework based on deep learning (DL) models that can efficiently integrate both the control plane and programmable data plane for fault detection and classification. This framework obtains the status and indicators of components and network that cause service quality performance degradation through two planes. These indicators are ultimately sent to the knowledge plane for preprocessing, dimensionality reduction, and fault analysis. In addition, we propose two algorithms based on text convolutional neural network (textCNN) and bidirectional encoder representations from transformers (BERT) to classify SFC faults. We implement and evaluate the proposed FullSight prototype extensively on a prototype with thirteen programmable switches and twenty end-hosts. Our experimental results show that FullSight can rapidly and accurately detect and identify eight categories of fine-grained SFC failures, compared with other state-of-the-art methods. Besides, compared with SFC Path Tracer and Pingmesh, our framework can reduce the average bandwidth overhead of the data plane by 57% and 84%, respectively, and achieve detection accuracy of more than 98%.

Network Function Virtualization (NFV) is one of the most promising technologies which decouples Network Functions (NFs) from hardware resources to support more flexible network services and network resource allocation. However, these benefits increase the possibility of Service Function Chain (SFC) failures due to hardware failures, software defects and burst traffic, resulting in serious consequences. Unfortunately, existing failure detection methods have several issues, such as simplification of detection functionality, heavy overhead, and low accuracy. This paper introduces a framework FullSight, in which control plane and the programmable data plane can collaboratively detect failure and Deep Learning (DL) based algorithms are adopted for failure detection. FullSight can achieve an all-round perception of the state of the SFC, in which network information is acquired through the data plane, SFC components' message is obtained through the control plane. In addition, a failure detection model based on DL is established. Compared with the state-of-the-art methods, FullSight can support 8 kinds of the fine-grained failure detection. Our comprehensive evaluation of prototypes and simulations shows that FullSight can realize rapid and accurate detection and classification of diversified failures in SFCs. The bandwidth overhead reduces by 57% compared with the existing methods. Additionally, FullSight has a detection accuracy up to 93.5%.