Abstract High-performance computing utilizes powerful processor clusters to parallel process big data and solve complex problems at extremely high speeds, relying significantly on interconnection networks. As networks grow in scale and complexity, failures become unavoidable. Interconnection networks demand consistent operation and efficient routing algorithms to enable smooth data transmission among processors. Fault-tolerant routing is essential for assessing network reliability. The application of independent spanning trees (ISTs) is an effective method to enhance network fault tolerance. Regarded as a significant extension of the hypercube, the $3$-ary $n$-cube network $(Q^{3}_{n})$ boasts many advantageous such as low vertex degree, regularity, and straightforward implementation. In this paper, we introduce parallel algorithms for generating $2n$ ISTs on $Q^{3}_{n}$, where $2n$ represents the maximum achievable number, enhancing the efficiency and obtaining additional sets of ISTs and disjoint paths. Building upon previously constructed ISTs, a fault-tolerant routing system is developed, utilizing them as the routing table. Subsequently, the effectiveness of this mechanism is assessed through simulated data, showing an increment in transmission success rates as dimensionality grows, nearing near-perfection at almost $100\%$. These results also reveal that the algorithm we proposed demonstrates better performance than traditional classical algorithms.

As a data center network (DCN) constructed using recursive modules, BCube enables efficient communication for decentralized machine learning systems. Its various variants, such as RCube and RRect, outperform BCube in certain performance metrics. To unify related research, BCube and its variants are integrated into a unified framework known as BCube-based DCNs (BDCN). In practical DCN deployments, efficient fault diagnosis is essential for reliability and stability. However, intermittent faults are more challenging to diagnose than permanent ones due to their randomness and uncertainty. Moreover, existing intermittent fault diagnosis algorithms generally rely on searching for the largest component, which leads to high time complexity. To address this issue, this paper systematically analyzes the intermittent fault diagnosability of BDCN under the PMC model, and proposes a fast intermittent fault diagnosis algorithm (FIFDA). The proposed algorithm significantly improves diagnosis efficiency by avoiding the need to search for the largest component. Extensive experimental results verify the applicability of FIFDA in both BDCN and other high-performance DCNs. Comparative analyses with existing algorithms show that FIFDA achieves higher diagnostic speed. Moreover, under comparable diagnosis times, FIFDA demonstrates superior diagnostic performance. In addition, simulation results demonstrate that FIFDA maintains outstanding performance in large-scale DCNs and under varying noise levels and fault probabilities, fully showcasing its efficiency and scalability in practical DCN environments.

With the rapid advancement of digital technologies such as cloud computing, big data, and artificial intelligence, large-scale data centers have become critical infrastructure supporting these technologies, imposing increasingly high demands on data center networks (DCNs). Traditional server-centric DCNs face challenges in large-scale distributed systems, such as difficulty in balancing bandwidth and latency, high expansion costs, and conflicts between fault tolerance and communication efficiency. To address these issues, this paper proposes ECQDC, a novel server-centric DCN based on exchanged crossed cube. Specifically, we present its logical structure ECD(s, t) and study the connectivity and edge connectivity of ECD(s, t). Furthermore,we develop efficient fault-free routing algorithm and faulttolerant routing algorithm for the ECD(s, t). The experimental results demonstrate that, compared with Dijkstra and BFS, the proposed ECDR and ECDFTR algorithms reduce the average running time by over 50% and cut the average path length by approximately 20% relative to BFS, while keeping path lengths close to Dijkstra’s optimal performance. Moreover, it exhibits excellent performance in scalability, fault tolerance, and communication efficiency, making it an ideal network topology for large-scale data center deployment.

Data centers rely on Priority-based Flow Control (PFC) to achieve lossless data transmission in Ethernet networks. To avoid buffer overflow, PFC pauses flows in a coarse-grained manner, which brings potential problems, e.g., Head-of-Line (HoL) blocking, and PFC deadlock. Although the state-of-the-art approach BFC with per-flow backpressure tackles some of the limitations of PFC, it faces implementation challenges due to the need for a large number of queues. In this paper, we present F-PFC, a fine-grained flow control scheme that only leverages a small amount of queues to address the limitations of PFC. Specifically, F-PFC first designs a fine-grained flow backpressure scheme to adjust the intensity of flow control adaptively. With different levels of flow backpressure, F-PFC ensures high throughput and low latency simultaneously. Then, F-PFC presents an accurate flow identification scheme to locate flows that really contribute to congestion. Finally, F-PFC presents a dynamic queue assignment and scheduling scheme to isolate congestion flows with limited queues. We theoretically analyze the performance of F-PFC and present the implementation of F-PFC. Extensive testbed experiments and large-scale simulations verify the performance of F-PFC. The experimental results show that F-PFC reduces tail latency by at least 33% and queue occupancy by 46% compared with state-of-the-art approaches.

Bare-metal cloud services provide direct access to dedicated physical hardware, significantly enhancing computational power, disk I/O, and network I/O performance. To effectively manage physical resources, bare-metal typically relies on specialized cloud management platforms. However, the existing management architecture still faces significant bottlenecks. These bottlenecks include slow and cumbersome deployment processes, inadequate security isolation that exposes the system to potential vulnerabilities, and limited scalability that fails to meet dynamic and evolving demand. Therefore, optimizing the management architecture to improve deployment efficiency, security, and flexibility has become a key challenge for bare-metal cloud services. This paper proposesNBBM(NebulaMatrix Bare Metal), an innovative bare-metal cloud management platform architecture designed to restructure the management of bare-metal servers in OpenStack. To simplify the complexity of bare-metal cloud management and significantly improve the overall system efficiency,NBBMadopts the following technologies: an architecture that thoroughly decouples compute and storage, a distributed management system based on SmartNIC technology, and a high-performance cloud storage interconnect solution relying on SmartNICs. These technological innovations enable theNBBMarchitecture to provide a more secure and efficient cloud service management solution. Extensive experimental results demonstrate that theNBBMplatform achieves minute-level deployment and delivers at least a 12× speedup (approximately 92% reduction) over widely used methods, while ensuring secure and flexible access to storage resources without compromising performance.

The rapid growth of cloud computing, large-scale distributed systems, and AI-driven applications has placed stringent demands on the performance and reliability of data center networks (DCNs). As DCNs scale in size and structural complexity, they become increasingly vulnerable to multiple concurrent node and/or link failures, which can lead to severe service disruptions and significant performance degradation. Existing data transmission approaches typically address node and link failures in isolation, frequently mitigating one type while overlooking the other, and thus fall short in effectively handling complex multi-failure scenarios. This paper presents a novel and efficient data transmission scheme designed to ensure robust communication under multiple node and/or link failures in DCNs. The proposed solution integrates a proactive path redundancy mechanism with a failure-aware routing strategy to enable rapid identification and avoidance of faulty components. We adopt the generalized hypercube network (GHN), a regular and scalable topology, as the underlying network model. Firstly, leveraging the method of Yang and Chang [44], we construct multiple independent spanning trees (ISTs) in GHNs, which provide structural path diversity and fault isolation. Building upon these ISTs, we propose GFP-IST, an optimized routing algorithm with a time complexity ofO(NlogN), whereNdenotes the number of nodes. GFP-IST enables efficient route computation and resilient packet forwarding in the presence of multiple simultaneous failures. Extensive simulation results demonstrate that our approach outperforms several fault-tolerant routing schemes in terms of average path length, path construction time, and fault recovery success rate, especially in large-scale and high-failure-rate network environments.

In the realm of interconnection networks, reliability analysis is of utmost importance, especially considering the increasing vulnerability of components as the network scales. Fault tolerance is a key aspect in this regard, and extra connectivity and component connectivity are two crucial metrics for its assessment. In this paper, we establish a theoretical framework for multi-component fault tolerance in the augmentedk-aryn-cubeAQn,k, a hypercube-derived interconnection network commonly used in distributed-memory architectures. We derive a general result for ther-component connectivity ofAQn,kas$4n(n - 1) - \lfloor{\frac{{5{{(r - 1)}^2}}}{2}}\rfloor$(n≥ 4,k≥ 4, and 2 ≤r≤n). Furthermore, we extend the result to explore theh-extrar-component connectivity ofAQn,kas (8n− 10)(r−1) −2(r−2) (n≥ 4,k≥ 4,h= 1 and 2≤r≤n). Based on these theoretical results, we propose a novel fault-tolerant path algorithm forAQn,kthat handlesh-extrar-component faults. The algorithm first preprocesses and classifies fault-free components, which efficiently determines whether two fault-free nodes belong to the same component, thereby avoiding ineffective path searches. When two fault-free nodes are in the same component, we employ a hybrid greedy-BFS algorithm to construct fault-free paths between them. To validate the algorithm, we conduct comprehensive simulations onAQn,kwith varying parameters. The experimental results demonstrate that the proposed algorithm achieves constant-time path existence queries after preprocessing, significantly reduces path discovery time in multi-query scenarios compared to conventional methods, and maintains near-optimal path lengths while exhibiting superior scalability as network dimensions increase. Furthermore, the algorithm demonstrates robust and highly efficient performance even under fault conditions significantly exceeding theoretical connectivity limits. Additionally, the greedy strategy effectively resolves the vast majority of pathfinding scenarios, confirming its effectiveness underh-extrar-component fault conditions.

With the rapid advancement of big data processing and artificial intelligence (AI), data center networks (DCNs) must deliver more efficient resource management and data transmission mechanisms. Unfortunately, due to the significant differences in bandwidth requirements, transmission patterns, and temporal characteristics across various traffic types in DCNs (such as short flows, long flows, and bursty flows), traditional buffer allocation strategies fail to adapt flexibly to these disparities. In this paper, we propose Enhanced Buffer Management (EBM), a novel buffer-sharing scheme designed for scenarios that require higher performance from DCNs. Unlike prior approaches, EBM employs a multi-level flow identification and adaptive threshold adjustment mechanism to enhance the flexibility and efficiency of buffer management under varying traffic conditions. Specifically, EBM first performs coarse-grained and fine-grained classification of traffic based on packet size, inter-arrival interval, and other flow characteristics. It then applies an improved threshold computation function to allocate buffer space differentially across traffic classes while maintaining allocation smoothness. Our evaluation results demonstrate that EBM significantly improves performance under realistic workloads. For instance, it reduces the 99th percentile Flow Completion Time (FCT) slowdown by 32.7% for short flows in the web-search workload and by 45.1% for incast flows in the hadoop workload, all without sacrificing overall throughput.

With the accelerated development of large models and distributed training, the explosive growth of data volume has brought huge challenges to traditional data processing and machine learning algorithms. The inconsistency and accuracy of the data processing process will directly affect the analysis and decision-making effect of the data. In this paper, we investigate a data fusion framework based on a trustworthiness metric model, aiming to incorporate a credibility evaluation mechanism of data sources into the data fusion process. Firstly, we propose a trust measurement model based on dynamic Bayesian networks that is correlated with time factors, taking into account the impact of network interaction behavior on trust measurement. Secondly, we design a data security aggregation mechanism based on the trustworthy measurement model, which combines credibility measurement with the minimum spanning tree (MST) protocol to improve the network's perception performance. Finally, we conduct simulation experiments and real experimental bed tests separately, and the results showed that the proposed model has continuous trustworthiness measurement ability in dynamic uncertain network environments. On a large benchmark dataset, the proposed algorithm performs better than state-of-the-art methods in statistics, with a significant reduction rate of 36.6% in the computational cost of the control center, and a reduction rate of 23.8% and 34.7% in the communication and storage costs of the system, respectively.

As distributed applications increasingly span geographically distributed data centers, the demand for high-performance, long-distance transmission has been continuously growing. While intra-data-center networks have employed techniques like remote direct memory access (RDMA) to meet these design goals, extending these techniques toWANs presents unique challenges. WANs notably suffer from inherent packet losses due to buffer overflows in routers and switches, leading to decreased throughput and making distributed applications barely usable. This paper proposes Lossless Transmission (LLT), a novel buffer management scheme for enabling lossless WAN transport. LLT intelligently integrates on-chip switch buffers with an off-chip caching system to absorb traffic bursts that would otherwise cause packet loss. Its data plane logic uses a multi-level threshold system to selectively offload only critical flows during congestion. A closed-loop control protocol, managed by a stateful flow table, ensures these offloaded packets are later re-injected with guaranteed lossless and in-order delivery, effectively protecting latency-sensitive applications from retransmission overhead. We evaluate LLT using both ns-3 simulations and P4-programmable devices. The experimental results show that in typical use cases (RTT > 30ms), LLT improves link bandwidth utilization by 1.9% to 29.5% and reduces the P99 percentile tail latency by 17% to 66% in WANs compared to the state-of-the-art solutions. Overall, LLT provides a scalable, efficient, and reliable framework for long-distance data transmission, addressing critical challenges in WANs. Additionally, LLT eliminates the need for expensive WAN infrastructure modifications.

Different interconnection structures exert a significant impact on network communication ability, directly influencing system performance. The half hypercube Network has an excellent topology that can provide high network fault tolerance and communication efficiency while maintaining a low node degree. In this paper, we investigate efficient and reliable communication algorithms for half hypercube networks in distributed system. Firstly, we design a disjoint path construction algorithm for a half hypercube, which enables reliable communication of the optimal number of disjoint paths between any two nodes in the network. Secondly, we present a fault-tolerant path embedding algorithm for a half hypercube. When the number of faulty nodes does not exceed ⌈n/2⌉, this algorithm can obtain a fault-tolerant unicast path between any two non-faulty nodes in ann-dimensional half hypercube network. Finally, we evaluate the performance of communication algorithms through simulation experiments and real testbed. Experimental results demonstrate that the efficiency and buffer utilization rate of the proposed algorithms can be improved by at least 21.8% and 15.6%, respectively. Testbed results show that the data delivery rate increased by 21.8%, and the path interference degree decreased by 32.5%.

The dramatic rise in encrypted traffic brings huge challenges to traditional traffic classification methods. Deep learning-based traffic classification methods have been demonstrated to significantly improve performance. However, the following limitations remain: i) It is challenging to concurrently focus on both global and local information in traffic flows, resulting in the absence of important information. ii) The existing methods relying on temporal information suffer from low robustness in case of packet disordering or loss. iii) The use of multi-layer encryption and random routing in Tor technology poses more challenges for traffic identification. In this paper, we propose a novel ViT-based model for more accurate encrypted traffic classification, called SRViT to overcome the above challenges. Firstly, SRViT proposes a novel mechanism of multi-size patch division to learn comprehensive hidden knowledge and dependencies between packets. Secondly, we propose a self-attention operation with a relative position bias to learn the relative position relationship. After that, an incremental update mechanism is proposed to adapt to dynamic changes in the real traffic environment. At last, the comprehensive experiments on 5 real-world encrypted traffic datasets are carried out. The experimental results indicate that SRViT outperforms the state-of-the-art methods with an average accuracy improvement of 24.62% while keeping higher robustness and execution efficiency.

As the demand for cloud services and data-intensive applications continues to surge, the design of efficient and reliable data center network (DCN) topologies has become increasingly critical. However, traditional DCNs often face challenges of limited scalability, insufficient fault tolerance, and high communication latency. To address these issues, we introduce SFDC, a novel recursive and modular server-centric network topology. SFDC is built on a hierarchical element-layer structure that enables the construction of highly scalable and fault-tolerant networks. The modular design of SFDC supports flexible expansion, allowing for the integration of servers with varying network interface card (NIC) configurations without requiring significant redesigns. Furthermore, SFDC’s design effectively mitigates the growth of network diameter, ensuring low latency even at massive scales. We also propose a routing algorithm, SFRouting, which leverages SFDC’s hierarchical structure to efficiently compute unicast paths, while minimizing routing complexity and enhancing data transmission efficiency. Additionally, we present a multipath routing scheme based on disjoint path construction, which ensures robust communication by providing alternative paths in case of node or link failures, thus enhancing network fault tolerance. Experimental results demonstrate that SFDC outperforms existing DCN topologies such as BCube, DCell, and HS-DCell, exhibiting superior scalability, reduced network diameter, and enhanced fault tolerance while maintaining low latency and stable performance.

Fault diagnosis is crucial for ensuring the reliability of interconnection networks. Traditional diagnostic models usually assume that edges connected to faulty nodes are fault-free, which is unrealistic in practice where both node and edge failures can occur simultaneously. The recently proposed HPMC* diagnostic model provides a more realistic framework by considering both node and edge failures simultaneously, but existing diagnostic approaches under this model have significant limitations in handling complex fault scenarios. This paper proposes HYBRID-GNN, the first graph neural network-based approach for hybrid fault diagnosis under the HPMC* model. HYBRID-GNN employs an edge-enhanced GraphSAGE with comprehensive feature engineering that extracts diagnostic characteristics from HPMC* syndrome data and enables joint training for node and edge fault prediction. HYBRID-GNN learns complex fault patterns from syndrome data, overcoming traditional diagnosability constraints. Experiments on multiple interconnection network topologies show that HYBRID-GNN matches the traditional algorithm in node fault diagnosis (achieving over 99% accuracy within the hybrid diagnosability bound), while delivering substantially higher performance in link fault diagnosis (with accuracy above 97%). Even beyond the diagnosability bound, HYBRID-GNN remains robust, maintaining over 98% node accuracy and over 83% link precision under high fault rates. Furthermore, results on real-world networks further validate its practical effectiveness, achieving over 99% node accuracy and over 95% link accuracy.

Centralized training for Human Activity Recognition (HAR) typically relies heavily on vast amounts of aggregated data, compromising user privacy. Federated learning (FL) for HAR offers a solution to protect local data privacy. However, existing FL methodologies often fail to fully capture the heterogeneity of user data and the latent correlations among user models, resulting in suboptimal performance and limited robustness. This paper proposes a Correlation-Aware Multi-Similarty Learning Method for Federated HAR, namely MultiSim. Our approach enhances model accuracy with an effective inter-user knowledge learning while protecting data privacy. MultiSim first constructs multiple similarity metrics, and then makes model feature fusion cunningly by the above metrics to learn inherent user similarity profiles. Additionally, we introduce a novel clustering-based FL framework by isolating malicious nodes, thereby mitigating the impact of adversarial attacks. Extensive evaluations on two realworld HAR datasets demonstrate the superiority of MultiSim over other state-of-the-art FL methods under accuracy and robustness. These findings demonstrate MultiSim's potential as a robust and effective solution for HAR.

Abstract With the development of the fourth industrial revolution, the importance of data centers has significantly increased. Data centers are widely used in many fields due to their ability to provide efficient, secure, and reliable data storage and processing services. However, with the increasing amount of data, traditional data center networks (DCNs) are currently facing various challenges, prompting academia and industry to propose new DCN architectures. As a dual-port server-based DCN, DPCell has excellent scalability and bisection width, enabling it to meet the demands of large-scale data storage, processing, and computation in the digital revolution. In order to ensure the secure and reliable data communication in the DPCell, this paper designs a disjoint paths communication scheme based on the actual DCN routing requirements. This scheme constructs the optimal number of disjoint paths in DPCell, with a maximum path length of $2^{k}+3$, where $k$ represents the dimension of the DPCell. Furthermore, experiments have verified that the time complexity of this scheme is sublinear, making it more efficient than the current optimal maximum flow algorithm. To a certain extent, this scheme provides DPCell with the required high bandwidth, fault tolerance, and security for data communication.

Abstract Data center networks (DCNs) provide critical data storage and computing services for cloud computing. The continuous increase in demand for cloud computing has led to a surge in data volume, necessitating the continual expansion of DCNs. However, this expansion also heightens the risk of device failures. Therefore, it is particularly important to study the fault tolerance of DCNs, which refers to their ability to ensure reliable communication even in the presence of device failures. Among DCNs constructed using dual-port servers, DPCell achieves higher scalability and bisection width while maintaining a smaller diameter. This paper assesses the fault tolerance of DPCell using two metrics: connectivity and diagnosability. Recognizing the limitations of traditional connectivity and diagnosability, we investigate the connectivity and diagnosability of DPCell under the condition that each fault-free node in the network has at least $g$ fault-free neighbors. The results indicate that, under this condition, the connectivity and diagnosability of DPCell exceed its traditional metrics by more than $g$ times.

ABSTRACT With the increasingly widespread application of multiprocessor systems, some processors in multiprocessor systems are inevitably prone to malfunctions. The reliability and effectiveness of the system are key issues. As a standard for measuring system fault tolerance, connectivity, and edge connectivity have many drawbacks. Therefore, Haray proposed conditional connectivity by restricting the connected components in disconnected subgraphs to satisfy certain properties, where and represent the interconnection network and its set of faulty vertices, respectively. Restricted connectivity is a special type of conditional connectivity. Exchanged crossed cube, as a deformation of hypercube, has more favorable properties, such as smaller diameter, smaller link size, and lower cost. We prove that the 2‐restricted connectivity of the exchanged crossed cubes is for .

With technological advancements, real-time applications have permeated various aspects of human life, relying on fast, reliable, and low-latency data transmission for seamless user experiences. The development of data center networks (DCNs) has greatly advanced real-time applications, with network reliability being a key factor in ensuring high-quality network services. As a switch-centric DCN, DPCell has good scalability and the ability to achieve load balancing at different traffic levels. With the increasing demand for high availability, fault tolerance, and efficient data transmission, highly reliable communication for DPCell is essential. Completely independent spanning trees (CISTs) play a significant role in enhancing reliable communication performance in networks. This paper proposes an algorithm for constructing CISTs in DPCell, which has relatively low time and space consumption compared to other CISTs construction algorithms in DCNs, offering an efficiency advantage. Communication simulations validate the effectiveness of using paths provided by CISTs in DPCell for data transmission. Furthermore, experimental results show that a multi-protection routing scheme configured with multiple CISTs significantly enhances fault tolerance in DPCell.

Multi-path transmission enables load balancing and improves network performance in data center networks (DCNs). It increases the possibility of network congestion and makes traditional network traffic engineering methods inefficient due to the uneven distribution of network traffic in data centers. In this paper, we present a reliable and efficient Disjoint paths based Multi-Path Transmission scheme (DMPT) that selects distributed requests through topology awareness. Firstly, we propose disjoint path construction algorithms through rigorous theoretical proof, aiming at the different transmission requirements of DCNs. Secondly, we offer an optimal solution to the disjoint multi-path selection problem, which is aimed at the trade-off between link load and transmission time. Furthermore,DMPTcan split the flow over multiple transmission paths based on the link status. Finally, extensive experiments are executed forDMPTon a novel EHDC of DCN that is based on exchanged hypercube. The experimental results show thatDMPTcan reduce the average running time by 18.6%, and the average path length is close to the optimal path. Furthermore, it achieves significant improvements in balancing network link traffic and facilitating deployment, which also reflects the advantages of topology aware multiplexing in practice.

Optical Data Center Networks (ODCNs) are high-performance interconnect architectures in parallel and distributed computing, providing higher bandwidth and lower power consumption. However, current optical DCNs struggle to achieve both high scalability and incremental scalability simultaneously. In this paper, we propose an extendedExchanged hyperCube, denoted by ExCube, which is a highly scalable network architecture for optical data centers. Firstly, we detail the address scheme and constructing method for ExCube, including exponential, linear, and composite scalability, which can adapt to different scalability requirements. ExCube boasts flexible scalability modes, including exponential, linear, and composite scalability, meeting diverse scalability requirements. In particular, the diameter of ExCube remains unchanged as its size increases linearly, indicating superior incremental scalability. Secondly, an efficient routing algorithm with linear time complexity is presented to determine the shortest path between any two different ToRs in ExCube. Additionally, we propose a per-flow scheduling algorithm based on the disjoint paths to enhance the performance of ExCube. The optical devices in ExCube are identical to those in existing optical DCNs, such as WaveCube and OSA, facilitating its construction. Experimental results demonstrate that ExCube outperforms WaveCube in terms of throughput and reduces data transmission time by 5%-35%. Further analysis reveals that ExCube maintains comparable performance to WaveCube across several critical metrics, including low diameter and link complexity. Compared with advanced networks, the overall cost-effectiveness and energy efficiency of ExCube have been reduced by 36.7% and 46.5%, respectively.

The design and optimization of network topologies play a critical role in ensuring the performance and efficiency of high-performance computing (HPC) systems. Traditional topology designs often fall short in satisfying the stringent requirements of HPC environments, particularly with respect to fault tolerance, latency, and bandwidth. To address these limitations, we propose a novel class of hierarchical networks, termed Hypercube-Structured Hierarchical Networks (HHNs). This architecture generalizes and extends existing architectures such as half hypercube networks and complete cubic networks, while also introducing previously unexplored hierarchical designs. HHNs exhibit several advantages, particularly in high-performance computing. Most notably, their high connectivity enables efficient parallel data processing, and their hierarchical structure supports scalability to accommodate growing computational demands. Furthermore, we present a unicast routing strategy and a broadcast algorithm for HHNs. A fault-tolerant algorithm is also designed based on the construction of disjoint paths. Experimental evaluations demonstrate that HHNs consistently outperform mainstream architectures in critical performance metrics, including scalability, latency, and robustness to failures.

Detecting anomalous BGP (Border Gateway Protocol) messages is critical for securing inter-domain routing systems over autonomous system (AS)-level networks. The dynamic nature of routing policies, massive scale of global routes, and incomplete global topology visibility make BGP anomalies exceptionally challenging to identify—let alone trace back to malicious or misconfigured ASes. To effectively overcome these barriers, this paper proposesGraphBGP, a novel BGP anomaly detection method that dynamically constructs real-time AS-level topologies, achieves precise anomaly detection and classification, and accurately traces malicious or misconfigured ASes. Specifically, to address the evolving nature of BGP routing status,GraphBGPconstructs an attributed AS-level graph that dynamically integrates node and edge attributes. It intelligently tracks BGP updates to refresh this graph efficiently. Leveraging this enriched, up-to-date representation,GraphBGPemploys tailored detection and tracing models grounded in graph convolutional networks (GCNs), enabling precise anomaly identification and source tracing. Comprehensive experiments with real-world and synthetic datasets demonstrate thatGraphBGPachieves state-of-the-art anomaly detection accuracy while significantly reducing inference time, even under partial BGP network visibility. Furthermore,GraphBGPprecisely traces malicious or misconfigured ASes within a short time period of 7 milliseconds after anomaly detection, enabling rapid mitigation.

This paper focuses on developing a reliable physical layer authentication (PLA) scheme in an millimeter wave (mmWave) communication system. To this end, we first derive the statistical quantities of the radiation pattern with random array errors in terms of gain, phase and position, and demonstrate that both Beckmann distribution and Rice distribution can effectively characterize the distorted radiation pattern. We then design a highly reliable PLA scheme, which combines three array error features to increase the distinguishability of the radiation pattern fused these array errors, as well as creates constructive two-beam pattern transmission that can not only resist to occasional blockages of few constituent beams but also enhance the reliability of the PLA. Applying the principles of statistical signal processing and composite hypothesis testing, a theoretical framework modeling of the typical performance metrics is also established to assess the performance of the proposed novel authentication scheme, under Rice distribution approximation model for radiation pattern statistics. Finally, performance evaluation is verified the reliability, effectiveness of the proposed authentication scheme with various settings in the presence of the identity-based impersonate attack, and performance comparison is also provided to highlight performance gain using the three array errors and two-beam pattern transmission.

In mobile edge computing (MEC), edge servers and mobile terminals use federated learning distributed architecture to build a deep model, so that terminals can cooperate in training without sharing data. Distributed training requires network virtualization to provide high bandwidth and low latency characteristics to support large-scale parallel computing. Traditional virtual network embedding (VNE) relies on a static network topology, which lacks flexibility and incurs high resource costs during model training. To improve the efficiency of embedding distributed training tasks, we propose a novel Node Selection and Dynamic Topology resource allocation scheme for VNE of distributed training, NSDT-VNE, based on reconfigurable network topology. This algorithm divides the underlying network into static and dynamic topologies, enhancing low latency for small flows while providing high bandwidth for large flows as needed. Additionally, we introduce a two-phase coordinated alternating optimization algorithm that optimizes embedding decisions at both computational and topological levels, ensuring optimal node selection. Overall, NSDT-VNE follows demand-aware network design principles, allowing continuous optimization of the underlying topology. Compared to state-of-the-art heuristic and reinforcement learning-based virtual network algorithms, NSDT-VNE achieves superior performance, with request acceptance rates improving by 6.67% to 25.68% and embedding revenue increasing by approximately 7% to 32%.

With the rapid increase in user demand for business traffic, the deployment and migration of virtual service function chains (VSFC) considering network load balancing has become a fascinating research topic. In this paper, we delve into the cascading failures in virtual network function (VNF) migration and design a topology-awareness fault-tolerant migration mechanism for VSFC. Firstly, we design a measurement model for the evolution of network nodes under cascading faults, which provides an evaluation method for node importance. Secondly, we present aTopology andResourceAware VNF fault-tolerant migration framework (TRA) under cascading faults. Finally, we investigate a topology-awareness energy consumption optimization algorithm based on the cascading failure. The proposed algorithm can reduce network energy consumption and alleviate network link congestion while reasonably migrating virtual machines to meet system performance requirements. Through extensive simulation experiments and real testbed evaluations,TRAreduced migration time and average latency by 28.6% and 24.7% compared with the average performance of VVi, TeaVisor, and MiOvnm, respectively. In addition,TRAhas improved revenue to expense ratio and load balancing index by 19.6% and 26.5%, respectively.

The state-of-the-art storage structure relies on the NVMe devices and SmartNICs to provide high IO performance and low CPU overhead. In data centers, the existing data transmission control and storage methods are not ideal, resulting in long flow completion time, especially for small IO, which directly affects the performance of disaggregated storage systems. In this paper, we present Thunder, a disaggregated storage solution designed to minimize tail latency. Firstly, Thunder achieves the minimum I/O tail latency for disaggregated storage via packet-level write-through, and has an ingenious mechanism for precise semantic conversion from message level to packet level. It refers to the process of converting message level data into packet level data and ensuring the integrity and reliability of data transmission. This process involves steps such as message segmentation, addressing, acknowledgment, and reassembly. Secondly, we present a novel optimization approach for end-to-end and information transmission processes, aiming to address a range of issues such as user usage, congestion control, and system compatibility. Finally, we conducted both testbed and large-scale simulations to verify the performance of Thunder. The results show that Thunder reduced the average latency and tail latency by 71.6% and 59.7%, respectively compared to Gimbal and Timely. Furthermore, it effectively avoids queue head blocking and congestion diffusion in PFC, increasing throughput by 2.5X and reducing tail latency by an average of 49.7%.

Designing a data center network (DCN) architecture capable of accommodating and managing thousands of servers is crucial for optimizing computing services in fields, such as Big Data, cloud computing, and artificial intelligence. This article introduces a novel class of DCN architecture, termed hypercube-like data center network (HLDN), which extends existing frameworks like high scalability data center network and crossed cube-based scalability data center network, while exploring previously unexamined designs. As the scale of DCNs grows and the number of servers increases, the incidence of network failures also rises, impacting network reliability. To address this challenge, we propose the first adaptive diagnostic schemes specifically designed for DCNs. We investigate the Hamiltonian properties of HLDN and, based on this analysis, present parallel adaptive diagnostic algorithms under the Preparata, Metze, and Chien (PMC) and comparison (COM) models. Simulation experiments validate the effectiveness of these algorithms, showing that the PMC model-based approach significantly outperforms the COM model in terms of diagnostic performance under identical conditions. Furthermore, comparative analyses with existing methods highlight the enhanced diagnostic accuracy of our proposed algorithms. This work not only provides an innovative solution for improving the reliability of DCNs, but also offers valuable theoretical and practical insights for fault detection and management in large-scale network systems.

The design of interconnection network topologies significantly impacts the performance and reliability of parallel systems. Enhanced incremental scalability enables networks to expand with reduced hardware overhead. In practice, rather than always adding many nodes at once, a small number of nodes are occasionally added as needed. However, existing topologies struggle to achieve effective incremental scalability. To address this, we propose the incremental scalability exchanged hypercube (ISEH), a novel interconnection network for parallel computing. The significant advantages of ISEH include improved incremental scalability and interconnection flexibility, while maintaining low interconnection complexity. Its diameter remains unchanged as the network size increases linearly and does not exceed the diameter of the exchanged hypercube. First, we present the topological properties of ISEH, including isomorphism, incremental scalability, and diameter. Next, we design an efficient communication method for ISEH to ensure low communication overhead. To support reliable communication, we design algorithms to construct disjoint paths between any two distinct nodes. Furthermore, based on generalized exchangedX-cubes, we propose the incremental scalability generalized exchangedX-cubes, offering better incremental scalability. Finally, we compare the performance of ISEH with other interconnection networks and evaluate the proposed algorithms. The results demonstrate that ISEH achieves a favorable balance among incremental scalability, diameter, and flexibility compared to existing networks.

With the increasing network scale, hardware faults are inevitable. Therefore, research on network reliability under the condition of hardware failure is an important subject. In this article, we investigate$h$-extra connectivity,$h$-extra diagnosability under the PMC and MM$^{*}$models, and$t/m$-diagnosability under the PMC model of recursive networks based on complete graphs (RNCGs) that include not only interconnection network Dragonfly but also data center networks—DCell, generalized DCell, and other unknown networks. In addition, we propose the fault-tolerant routing (F-TR) algorithm, FTPath, which constructs a F-TR in the largest component when the number of faulty nodes is less than the$h$-extra connectivity. Moreover, we evaluate the performance of RNCGs. First, we compare the fault-tolerant performance of RNCGs through experiments. The results show that the average path length constructed by the FTPath algorithm is close to that of the breadth-first search algorithm, and shorter than that of the depth-first search algorithm. Second, we evaluate the performance of this network for different parameters:$h$-extra connectivity, diagnosability under different strategies. The results show that the network has good fault tolerance and fault diagnosis capabilities.

In the application of the Internet of Things (IoT), existing cloud edge collaboration technologies face the problem of poor coordination of heterogeneous resources. In this article, we proposeCFEMC, which is a novelCloud-Fog-EdgeMulti-layerCollaboration resource scheduling framework for IoT. First, we design a collaborative resource scheduling framework based on semi-distributed artificial intelligence. It can achieve collaborative optimization of cloud/edge computing resource allocation under the constraints of high reliability and low latency. Second, we present a workflow applications scheduling strategy based on the proposed collaborative resource scheduling framework. This can solve the problem of unstable computing performance and transmission bandwidth during the scheduling process. Finally, the extensive and real data supported simulation results show thatCFEMChas advantages in terms of energy consumption, delay and throughput compared with other benchmark strategies. Against CEC Hu et al. 2023 and PSO Zeng et al. 2022, the average throughput increases by 16.37% and 24.21%, and the total queuing delay decreases by 54.23% and 58.12%, respectively.

Mobile edge computing (MEC) can improve execution efficiency and reduce overhead for offloading computing tasks to edge servers with more resources. In the microservice system, the current research only considers the cross segment communication cost of computing tasks, does not consider the case of the same end, and ignores the discovery and invocation optimization of associated services. In this paper, we proposeCACO, which is a novel content-aware classification offloading framework for MEC based on correlation matrix.CACOfirst designs an adaptive service discovery model, which can make timely response and adjustment to the changes of the external environment. It then investigates an efficient affinity matrix based service discovery algorithm, which expresses the association relationship between services by constructing a service association matrix. In addition,CACOconstructs a relational model by giving different weight coefficients to the delay and energy loss, which improves the delay and energy loss of message processing in a satisfying manner. Simulation results indicate thatCACOreduces the total traffic of redundant messages by 46.2%$\sim$76.5%, respectively compared with state-of-the-art solutions. Testbed benchmarks show that it can also improve the stability by reducing control overhead by 34.5%$\sim$81.6% .

Malicious attackers frequently aim to partition the network into disjointed segments to facilitate specific attacks. Consequently, enhancing network reliability stands as an effective preventive measure. Connectivity serves as a crucial metric for gauging network reliability, yet classical connectivity inadequately captures a network's fault tolerance in the face of such attacks. To address this, cluster connectivity has been proposed, considering the faults within clusters to improve fault tolerance assessment. In this paper, we establish the cluster connectivity of the half hypercube network HHn. In detail, we show that the K1,1-cluster connectivity of HHnis $\left\lfloor {n/2} \right\rfloor + 1$, where n ≥ 3, and the K1,r- cluster connectivity of HHnis $\left\lceil {\frac{{\left\lceil {n/2} \right\rceil }}{2}} \right\rceil + 1$, where n ≥ 5 and 2 ≤ r ≤ 4, which is almost r times the classical connectivity. This indicates that the network possesses an enhanced capacity to accommodate a greater number of faulty nodes, potentially enabling more effective orchestration of attacks.

As a centralized location for computer systems, data centers provide high-performance computing hardware, storage devices, and network facilities for collaborative computing. The node-disjoint paths can be used to implement multi-path transmission in data center networks, which provide multiple high-quality transmission paths and improve the performance of the network. Moreover, the disjoint paths can also provide redundant transmission paths, which enhance the fault tolerance of the network. The EHDC network is a novel server-centric and highly scalable data center network based on exchanged hypercube, and its logical structure is ED(s, t). In this paper, we propose the algorithm NDPath to construct the node-disjoint paths between two distinct nodes when the two nodes are in the same EDs in ED(s, t). Moreover, we analyze the maximum length of the disjoint paths. Experimental results show that our proposed algorithm performs better than the classical algorithm Dijkstra in the Average Running Time (ART) and is very close to that in the Average Path Length (APL).

In the era of information explosion, the scale of data center networks (DCNs) has expanded exponentially, consequently leading to an inevitable increase in server failures. Therefore, how to ensure the efficient and secure operation of the network has emerged as a critically important research topic. WaveCube is a scalable, fault-tolerant, high-performance optical DCN architecture. In this paper, we first propose a local secure model (LS model) of WaveCube. This model segments fault-free nodes within sub-Wavecube by imposing specific constraints, thereby adeptly circumventing potential communication impediments that could arise due to faulty nodes. Secondly, based on this model, we design a protection routing with secure mechanism to ensure stable communication within WaveCube. Finally, we perform a series of experiments, and the results show that when the number of faulty nodes is less than half of the number of total nodes, the hit rate can reach nearly 100%, while the shortest path rate can achieve up to 90%.

With the development of wireless communication technology and the Industrial Internet, Software Defined Network (SDN) technology has been introduced to wireless sensor networks due to its agility and flexibility. This meets the potential scalability and flexibility requirements of the Internet of Things. Thus, a new Industrial Internet architecture, called SDN-WSN, was formed. As the scale of SDN-WSN increases, efficient routing protocols with low latency and high security are required, while the standard routing protocol of SDN is still vulnerable to dynamic changes in traffic control rules, especially when the network is under attack. To address the above issues, a network node credibility evaluation model based on D-S evidence theory was constructed to evaluate the trust value of wireless sensor network nodes. A trustworthy secure routing algorithm based on Q-learning (QTSRA) was proposed. This method extracts knowledge from historical traffic demands by interacting with the underlying network environment to evaluate the trustworthiness of network nodes. Simultaneously, it implements dynamic optimising routing strategies based on deep reinforcement learning algorithms. We conducted simulation experiments for several network performance metrics, and the results showed that the proposed QTSRA routing algorithm exhibited good performance. In most of the cases, the QTSRA had an improved relative performance gain as compared to the traditional AODV and OLSR routing algorithms.

Abstract Connectivity is a meaningful metric parameter and indicator for estimating network reliability and evaluating network fault tolerance. However, the traditional connectivity and current conditional connectivity do not take into account the association between a certain node and its neighboring nodes. In fact, adjacent nodes are easily influenced by each other so that the failing probability of adjacent nodes around a faulty node is high. Therefore, cluster and super cluster connectivities are proposed to more intuitively measure the fault tolerance of the network. In this paper, we mainly explore the cluster connectivity and super cluster connectivity of the hierarchical cubic network $HCN_{n}$. In detail, we show that $\kappa (HCN_{n}\mid K_{1, 0}(K_{1, 0}^{*}))=n+1$, $\kappa (HCN_{n}\mid K_{1, 1}(K_{1, 1}^{*}))=\kappa ^{\prime}(HCN_{n}\mid K_{1, 1}(K_{1, 1}^{*}))=n+1$, $\kappa (HCN_{n}\mid K_{1, m}(K_{1, m}^{*}))=\lceil n/2\rceil +1$ ($2\leq m\leq 4$), $\kappa ^{\prime}(HCN_{n}\mid K_{1, 0}(K_{1, 0}^{*}))=2n$, and $\kappa ^{\prime}(HCN_{n}\mid K_{1, m}(K_{1, m}^{*}))=n+1$ ($2\leq m\leq 3$) if $n$ is odd and $\kappa ^{\prime}(HCN_{n}\mid K_{1, m}(K_{1, m}^{*}))=n$ ($2\leq m\leq 3$) if $n$ is even, where $n\geq 4$.

Summary With the continuous expansion scale of data center networks (DCNs), the probability of network failures becomes high. Trustworthy fault‐tolerant routing is extremely significant for reliable communication in data centers. In this article, we tackle the challenge by proposing a novel fault‐tolerant routing scheme for a torus‐based DCN. First, we present a multipath information transmission model based on the trust degree of reachable paths and propose a novel Hamiltonian odd–even turning model without deadlock. Second, we design an efficient deadlock‐free fault‐routing algorithm by constructing the longest fault‐free path between any two fault‐free nodes in DCN. Extensive simulation results show that the proposed fault‐tolerant routing outperforms the previous algorithms. Compared with the most advanced fault‐tolerant routing algorithms, the proposed algorithm has a 21.5% to 25.3% increase in throughput and packet arrival rate. Moreover, it can reduce the average delay of 18.6% and the maximum delay of 23.7% in the network respectively.

3D tori are significant interconnection architectures in building supercomputers and parallel computing systems. Due to the rapid growth of edge faults and the crucial role of path structures in large-scale distributed systems, fault-tolerant path embedding and correlated issues have drawn widespread researches. However, existing path embedding methods are based on traditional fault models, allowing all faults to be near the same node, so they usually only focus on theoretical proof and generate linear fault-tolerance related to dimension$n$. In order to improve the fault-tolerance of 3D torus, we first propose a novel conditional fault model called the Locally Faulty Block model (LFB model). On the basis of this model, the Hamiltonian paths with large-scale edge defects in torus are investigated. After that, we construct an Hamiltonian path embedding algorithm HP-LFB into torus with$O(N)$under the LFB model, where$N$is the number of nodes in torus. Furthermore, we present an adaptive routing algorithm HoeFA, which is based on the method of distance vector to limit the use of virtual channels (VCs). We also make a comparison with state-of-the-art schemes, indicating that our scheme enhance other comprehensive results. The experiment indicated that HP-LFB can sustain the dynamic degradation of the batting average of establishing Hamiltonian paths, with the added faulty edges exceeding fault-tolerance.

Recently, Hyun et al. have utilized simplicial complexes to construct several infinite families of binary minimal and optimal linear codes. Building upon their work, we draw inspiration and extend their research by constructing codes over the ring$\mathbb {Z}_{4}$with the aid of simplicial complexes. In this paper, we present two infinite families of quaternary codes, one of which is linear while the other is nonlinear. We analyze the Lee weight distributions of the resulting quaternary codes and compare them with the existing database of$\mathbb {Z}_{4}$codes. Our findings reveal the discovery of several new quaternary codes. Furthermore, we also provide two classes of binary codes that can be obtained from these quaternary codes using the Gray map.

The mixed flow in multi-tenant data centers presents a challenge for priority flow scheduling due to the coexistence of various requirements such as latency and throughput. To address this issue, we propose Key Flow First (KFF), a balanced scheduling algorithm suitable for mixed flows in multi-tenant data centers. Firstly, KFF categorizes flows into Latency-Sensitive Flows (LS Flow) and Throughput-Demanding Flows (TD Flow) based on the Quality of Service (QoS) of their application sources. Secondly, it further differentiates flows into Mice Flows and Elephants Flows based on the amount of already sent bytes. Thirdly, KFF employs the Multi-Level Feedback Queue (MLFQ) threshold update algorithm and a priority-based strict forwarding mechanism. By avoiding reliance on complex flow priors, KFF consistently maintains reasonable scheduling of mixed flows under different load scenarios. Experimental results demonstrate that KFF effectively reduces the real-time load on the network and achieves good performance in terms of MAX (Shortest Job First (SJF), Earliest Deadline First (EDF)) performance under diverse load conditions. Compared to PIAS, KFF reduces the FCT slow down of deadline flows by nearly 60% under high TD loads; compared to Karuma and Time Deadline Aware pFabric (TDA-pFabric), KFF reduces the flow completion time (FCT) slow down of non-deadline Mice flows by over 90% under high LS loads and meanwhile guaranteeing nearly 0 deadline miss rate.

As the core of cloud computing, the data center network (DCN) provides services and decision support for smart cities by providing powerful data storage and computing capabilities. As a server-centric DCN, the high scalability data center network architecture (HSDC) can cope with the rapid growth of data volume and provide an effective service foundation for smart cities. However, the rapid development of smart cities requires that DCNs can still operate reliably in the presence of faulty while expanding its scale. Therefore, it is very important to design reliable fault-tolerant communication algorithms in DCNs. This article investigates the fault-tolerant communication algorithm in HSDC. Initially, we propose an$O(n^{2})$algorithm to establish$n$disjoint paths between any pair of nodes in the$n$-dimension HSDC. Simulation experiments show that the disjoint paths generated by our algorithm in HSDC have a maximum length only 3 longer than the diameter, which guarantees a small communication delay in the worst case. In addition, we propose an$O(n)$algorithm to establish a fault-tolerant unicast path between any pair of fault-free nodes in the$n$-dimension HSDC. Moreover, simulation experiments indicate that as the scale of HSDC increases, the algorithm performs well in both running efficiency and the length of constructed paths.

The efficiency of cloud computing is significantly impacted by the capabilities of data center networks (DCNs). However, with the increasing demands of applications, the number of servers in DCNs is growing exponentially. In general, networks become more vulnerable as they expand in size. Consequently, it is essential to assess the network's reliability. Subsystem reliability, a crucial parameter of network reliability, is the likelihood that a faultless subsystem with a specific size would function normally. Even though several networks have the same order, their subsystem reliability may differ. In this article, we compare two distinct subsystems of$m$-port$k$-dimensional DCNs BCube that have the same number of vertices and ascertain the critical time so that the reliability bound is robust. In addition, we discover that, when comparing two subsystems of BCube that have the same number of vertices, the smaller the dimension$k$, the greater the subsystem reliability of BCube will be for$m\geq 2$and$k\geq 1$. This offers a theoretical foundation that allows us to choose a more reliable system in BCube that have the same number of vertices. Finally, we use numerical experiments to verify our findings.

In this poster, we propose a new transaction forwarding strategy for PCN, named PNSFF. Then we perform several experiments to study the effectiveness of the proposed strategy. Experimental results conclude that PNSFF achieves more incentivizing and higher security than previous similar works.

In this study, we propose a unified framework for designing a class of server-centric network topologies for DML by adopting top-down design method and combinatorial design theory. Simulation results show that this flexible framework is capable of effectively supporting various DML tasks. Our framework can generate compatible topologies that meet various resource constraints and different DML tasks.

Tensors are naturally suitable for representing high-dimensional data. Tensor train decomposition is an effective data processing method to cope with high-dimensional tensors. It is widely used in many fields, such as recommendation system, data completion and dimension reduction. However, experiments show that the traditional tensor decomposition method is only suitable for processing small-scale data. With the increase of the amount of data, the traditional algorithm will not be able to meet the efficiency of processing data. Therefore, this paper improves the traditional tensor train decomposition algorithm. Based on the most crucial step—SVD in the algorithm process, we first divide the matrix into column blocks, and then, considering the storage characteristics of cache, we call multiple threads to process different submatrix blocks. Each thread calls the one-sided Jacobi algorithm respectively to parallelize the SVD process of the matrix. In this paper, performance comparison experiments are carried out on simulated tensor data. The experimental results demonstrate that this method shows good scalability and can greatly improve the speed of tensor train decomposition.

BCube is a promising structure of data center network, as it can significantly improve the performance of typical applications. With the expansion of network scale and increasement of complexity, reliability and stability of networks have become more essential. In this paper, we study the fault-tolerant routings in BCube. First, we design a fault-tolerant routing algorithm based on node disjoint multi-paths. The proposed multi-path routing has stronger fault tolerance, since each path has no other common nodes except the source node and the destination node. Second, we investigate an effective fault-tolerant routing based on routing capabilities algorithm for BCube. The proposed algorithm has higher fault tolerance and success rate of finding feasible routes, since it does not limit the faults number. Third, we present an adaptive path finding algorithm for establishing virtual links between any two nodes in BCube, which can shorten the diameter of BCube. Extensive simulation results show that the proposed routing scheme outperforms the existing popular algorithms. Compared with the state-of-the-art fault-tolerant routing algorithms, the proposed algorithm has a 21.5% to 25.3% improvement on both throughput and packet arrival rate. Meanwhile, it reduces the average latency of 18.6% and the maximum latency of 23.7% in networks.

With the increasing scale of parallel computer interconnection network, the possibility of processor failure or link failure between processors in the network is also increasing. In the design of supercomputers, not only link overhead and communication delay should be taken into account, but also fault-tolerant performance of networks should be emphasized. Locally exchanged twisted cube ($LeTQ$) is a newly proposed interconnection network with lower link overhead and shorter diameter. With the increasing scale of supercomputers, fault-tolerant routing is indispensable. In this article, we propose a new load balancing fault-tolerant routing algorithm based on node contraction for$LeTQ$networks. The proposed algorithm uses the node shrinkage method to evaluate the priority of nodes. The sending node adaptively adjusts the probability of forwarding packets to the neighbor node according to the priority of the neighbor node and the state of the network. The path can be adapted to the load state of the network. The simulation results show that the fault-tolerant routing algorithm has good performance in throughput and delay.

The commoditization of private data has become an attractive research topic with the emergence of Big Data era. In this paper, we study the trading of high-dimensional private data with differential privacy guarantee. We proposeCheap, which is a novel Correlated data trading framework for High-dimEnsionAl Private data.Cheapfirst models data correlations among high-dimensional user attributes, and builds an initial attribute clustering scheme. Combined with this scheme,Cheapdevises a novel data perturbation mechanism by solving optimal attribute clustering (OAC) problem, in order to improve data utility of traded data and further generate a privacy-preserving high-dimensional dataset with close joint distribution with the original one. It then quantifies privacy loss based on near-optimal attribute cluster scheme due to the NP-hardness of theOACproblem, and further compensates data owners by running auction in a cost-effective way. We evaluate the performance ofCheaponUserBehaviordataset andObesitydataset, respectively. Our evaluation and analysis demonstrate thatCheapwell balances data utility and privacy protection, and achieves all desired economic properties of budget balance, individual rationality and truthfulness.

Network virtualization (NV) has extensive and significant applications in cloud computing and parallel and distributed systems. Virtual network embedding (VNE) is a key issue in NV, which is an effective means to advance systems’ performance. While existing VNE research lacks resource allocation coordination between mappings of different virtual network requests, resulting in insufficient resource utilization and high overhead. In this article, we propose a novel node essentiality evaluation model for data center networks (DCNs), and design an efficient distributed collaborative virtual network embedding. Firstly, we propose a node essentiality evaluation scheme based on dynamic model, which combines the characteristics of network topology and nodes to make the evaluation results more comprehensive. Secondly, we establish the two-stage node importance evaluation criteria for the deviation mean of the data center dynamic model and the variance based on the deviation mean. Furthermore, we investigate a nodal importance assessment method based on the data center dynamic model for perturbation testing. Finally, we design a distributed coordinated VNE algorithm (CNI-VNE) which calculates the importance index of physical nodes through topology awareness. The proposed algorithm can increase the coordination between different request mappings, thereby reducing the mapping cost of physical node resources and minimizing the cost of VNE. We use the real Fat-tree DCN of 128 servers and 80 switches as testbed, and evaluate them from indicators such as average reliability, average bandwidth consumption, average energy consumption, and average mapping time. Massive simulation results in different scenarios show that our algorithm achieves the best performance on most indicators compared with the existing state-of-the-art proposals, mapping acceptance and average revenue increased by 19.4% and 21.3%, respectively, and DCN reduced bandwidth consumption by about 30%.

With the continuous attention to the parallel computing system, the reliability of the system, which is mainly measured by two parameters, connectivity and diagnosability, needs to be constantly studied and improved. At present, the component connectivities of some networks have been extensively studied, while the component diagnosabilities of these networks have rarely involved in. In this article, some networks with common characteristics are summarized as a class of regular networks. The definition of this kind of networks is given, and its reliability based on component failures is determined. To be specific, we prove that$c\kappa _{m+1}(G)=m(k-1)-\binom{m}{2}+1$for$1\leq m\leq k-2$and$ct_{m+1}(G)=(m+1)k-\binom{m}{2}-2\ m$for$1\leq m\leq k-2$under the PMC model, where$c\kappa _{m+1}(G)$and$ct_{m+1}(G)$represent the$(m+1)$-component connectivity and the$(m+1)$-component diagnosability of such networks$G$, respectively. Based on this, we design a low time complexity component diagnosis algorithm for this kind of networks. As applications, the above two component reliability parameters of many famous networks are explored. Furthermore, the proposed diagnosis algorithm is simulated on these networks, and the results show that the algorithm has high diagnosis accuracy for various networks.

The fault tolerance of a network can be measured by many parameters. Connectivity is a classic measurement parameter for evaluating the fault tolerance of a network. g-extra connectivity and g-restricted connectivity are generalizations of connectivity, which can better reflect the fault tolerance of a network. Specifically, the g-extra connectivity $\kappa_{g}(G)$ of a graph G is the minimum number of nodes whose removal will disconnect G, and each remaining component has no less than $g+1$ nodes. Furthermore, the g-restricted connectivity $\kappa^{g}(G)$ of G is the minimum number of nodes whose deletion results in a graph being disconnected and the minimum degree of each remaining component is at least g. In general, g-restricted connectivity is not equal to g-extra connectivity of a network. Therefore, many scholars often discuss g-restricted connectivity and g-extra connectivity with regard to different networks separately. In this paper, we show that g-restricted connectivity is equal to g-extra connectivity under some conditions. Then, the relationship we derived can be applied to some known networks such as the data center networks DCell and BCDC, multiprocessor network $(n,k)$-star. In addition, we construct a new network $H(G_{0},G_{1},G_{2};\mathbb{M})$ and prove that our result can be applied to it. In detail, we prove $\kappa^{g}(H(G_{0},G_{1},G_{2};\mathbb{M}))=\kappa_{g}(H(G_{0},G_{1},G_{2};\mathbb{M}))=n+g+1$ for any integers $n\geq 3$ and $ g\displaystyle \leq\lfloor\frac{n-2}{2}\rfloor$.

In the author list, "Ferry Sansoto" should be Ferry Susanto.• To reflect more accurately the contribution of the article, the title should be changed to "A permutation and equinumerosity based polynomial-time algorithm for simple undirected graph isomorphism."• In the abstract, the "Pythagorean Triples Theorem" should be removed.• In the abstract, "squared sums of elements" should be "nth power sums."• In Section 2.2, "and the sum of the individual squared elements.By checking two sums," should be ", the sum of the individual squared elements and until the sum of the nth power of the nth element in the array.By checking these sums,"• In Section 2.2, "For both vertex and edge arrays of row/column sum based on the vertex and edge adjacency matrices, if and only if one array is a permutation of another one, the corresponding two graphs are isomorphic."should be "For both the vertex and edge arrays of row/column sum based on the vertex and edge adjacency matrices, if and only if one array is a permutation of another one and the corresponding edge and vertex's adjacent relationship has been preserved, the corresponding two graphs are isomorphic."

Abstract The secrecy outage performance for a cooperative cognitive radio energy‐harvesting network is analyzed. The cognitive network is composed of an energy‐constrained cognitive source (CS), multiple energy‐constrained cognitive relays (CRs) and a cognitive destination (CD) as well as an eavesdropper (E) coexists with a primary network consisting of a primary transmitter (PT) and a primary receiver (PR). The CS and CRs are equipped with energy harvesters for collecting energy from the radio frequency signal from PT and their transmit powers are limited by the interference threshold at PR. To prevent confidential information leaking to E, an optimal relay selection (ORS) scheme and a suboptimal relay selection (SRS) scheme are proposed. In ORS scheme, the whole channels state information (CSI) of wireless links is available to CRs while SRS only needs to know the CSI of main channels from CRs to CD. Moreover, the closed‐form expressions of secrecy outage probabilities for both ORS and SRS schemes are derived. For the purpose of comparison, the classical round‐robin relay selection (RRRS) is also analyzed in terms of secrecy outage probability. Furthermore, the numerical results show that ORS achieves the best performance and RRRS performs the worst in terms of secrecy outage probability.

Data center networks (DCNs) always strive to ensure high reliability and fault tolerance when Big Data processing and cloud computing are carried out. One effective method is to conduct the fault diagnosis based on subsystem structures (establish the subsystem-based reliability), which can be transformed into solving the problem of the probability that there exists at least one fault-free subnetwork in one network. In this article, we establish the subsystem-based reliability of BCube, which is the first time that fault diagnosis is carried out in the subsystem structures of the DCN. More specifically, we compute the upper and lower bounds on the subsystem-based reliability of BCube and determine the approximation on the subsystem-based reliability of BCube. Furthermore, we conduct some numerical simulations to validate the established analytical formulation. Our results show that the precise value of subsystem-based reliability of BCube can be basically represented by the approximation value of subsystem-based reliability of BCube, which means that it is much easier to evaluate the reliability of the network even when the network scale is sufficient large. Although the analysis is done for a particular network (BCube), the outcome can serve as a useful reference, and can shed light on the effectiveness of the fault diagnosis for other DCNs.

Since Deep Convolutional Neural Network (DCNN) training involves complex computations and data transmissions, the previous DCNN processors hard to achieve ideal energy efficiency. This paper proposed a DCNN processor supports both inference and training for the embedded system. The processor contains three heterogeneous cores to provide distinct computation patterns and dataflow for different training phases. In addition, since inference takes up more than 90% of the workload of the DCNN application, the three cores of the processor can be reconfigured to efficiently support the inference to achieve leading resources Utilization. The processor is fabricated in 55nm CMOS technology, post-layout simulation shows the processor achieving 1.36 Tops/w energy efficiency for training and 2.44 Tops/w for the inference.

Summary The graph isomorphism problem is to determine two finite graphs that are isomorphic which is not known with a polynomial‐time solution. This paper solves the simple undirected graph isomorphism problem with an algorithmic approach as NP=P and proposes a polynomial‐time solution to check if two simple undirected graphs are isomorphic or not. Three new representation methods of a graph as vertex/edge adjacency matrix and triple tuple are proposed. A duality of edge and vertex and a reflexivity between vertex adjacency matrix and edge adjacency matrix were first introduced to present the core idea. Beyond this, the mathematical approval is based on an equivalence between permutation and bijection. Because only addition and multiplication operations satisfy the commutative law, we propose a permutation theorem to check fast whether one of two sets of arrays is a permutation of another or not. The permutation theorem was mathematically approved by Integer Factorization Theory, Pythagorean Triples Theorem, and Fundamental Theorem of Arithmetic. For each of two n ‐ary arrays, the linear and squared sums of elements were respectively calculated to produce the results.

The demand for data center bandwidth has exploded due to the continuous development of cloud computing, causing the use of network resources close to saturation. Optical network has become an encouraging technology for many burgeoning networks and parallel/distributed computing applications because of its huge bandwidth. This article focuses on efficient embedding of data centers into optical networks, which aims to reduce complexity of the network topology by using the parallel transmission characteristics of optical fiber. We first present a novel virtual network embedding (VNE) mathematical model used for optical data center networks. Then we derive a priority of location VNE algorithm according to node proximity sensing and path comprehensive evaluation. Furthermore, we propose routing and wavelength assignment for DCNs into optical networks, and identify the lower bound of the required number of wavelengths. Extensive evaluations show that the proposed embedding algorithm can reduce the average waiting time of virtual network requests by 20 percent, increase the request acceptance rate and revenue-overhead ratio by 13 percent, as compared to the latest VNE algorithm.

The topological distance is to measure the structural difference between two graphs in a metric space. Graphs are ubiquitous, and topological measurements over graphs arise in diverse areas, including, e.g. COVID-19 structural analysis, DNA/RNA alignment, discovering the Isomers, checking the code plagiarism. Unfortunately, popular distance scores used in these applications, that scale over large graphs, are not metrics, and the computation usually becomes NP-hard. While, fuzzy measurement is an uncertain representation to apply for a polynomial-time solution for undirected multigraph isomorphism. But the graph isomorphism problem is to determine two finite graphs that are isomorphic, which is not known with a polynomial-time solution. This paper solves the undirected multigraph isomorphism problem with an algorithmic approach as NP=P and proposes a polynomial-time solution to check if two undirected multigraphs are isomorphic or not. Based on the solution, we define a new fuzzy measurement based on graph isomorphism for topological distance/structural similarity between two graphs. Thus, this paper proposed a fuzzy measure of the topological distance between two undirected multigraphs. If two graphs are isomorphic, the topological distance is 0; if not, we will calculate the Euclidean distance among eight extracted features and provide the fuzzy distance. The fuzzy measurement executes more efficiently and accurately than the current methods.

Summary Network‐on‐chip (NoC) is a new design method of system‐on‐chip used in very large scale integrated circuit (VLSI) systems. It is an important issue for choosing the appropriate topology for NoC. Wirelength and layout area are significant parameters affecting NoC due to the restriction of chip area. In this paper, we propose a new interconnection network called the incomplete ternary n‐cube for parallel computing systems. Then, a linear algorithm is proposed to layout incomplete ternary n‐cube network onto torus NoC. Furthermore, the failure of interconnection network is also taken into account, and a fault‐tolerant layout of incomplete ternary n‐cube with faulty edges into torus NoC is verified. Theoretical analysis demonstrates that the proposed algorithm can reduce the network cost and wirelength, which be conducive to estimate the wire length and chip area.

Fault-tolerance is an important parameter to measure the performance of a network. However, most works only consider the fault of single vertex and ignore the structure-fault of a network. The generalized hypercube G(mr,mr−1,…,m1) is one key interconnection network with excellent topological properties. In this paper, we study the H-structure fault-tolerance of G(mr,mr−1,…,m1) network by studying its H-structure connectivity and H-substructure connectivity for H∈{K1,M,C3,C4,K4}⁠. Since the generalized hypercube network can be used to construct some data center networks, such as BCube, HyperX, and FBFLY, the results in this paper can be applied not only to interconnection networks but also to data center networks.

Fog computing is a distributed computing model as the middle layer between the cloud data center and the IoT device/sensor. It provides computing, network, and storage devices so that cloud based services can be closer to IOT devices and sensors. Cloud computing requires a lot of bandwidth, and the bandwidth of the wireless network is limited. In contrast, the amount of bandwidth required for “fog computing” is much less. In this paper, we improved a new protocol Peer Assistant UDT‐Based Data Transfer Protocol (PaUDT), applied to Iot‐Cloud computing. Furthermore, we compared the efficiency of the congestion control algorithm of UDT with the Adobe’s Secure Real‐Time Media Flow Protocol (RTMFP), based on UDP completely at the transport layer. At last, we built an evaluation model of UDT in RTT and bit error ratio which describes the performance. The theoretical analysis and experiment result have shown that UDT has good performance in IoT‐Cloud computing.