The rapid growth of large-scale Industrial Internet of Things (IIoT) drives the deployment of time-sensitive applications requiring stringent deterministic latency guarantees and high-throughput communication. Consequently, Large-Scale Deterministic Networking (LDN) and its variant, Advanced-LDN (A-LDN), have been proposed to achieve deterministic transmission by partitioning the timeline into fixed-length cycles and allocating sufficient resources for time-sensitive (TS) flows within each cycle. To reduce the complexity of resource reservation decisions, LDN adopts a uniform resource reservation pattern (RRP) that allocates identical resources in each cycle, while A-LDN uses a periodic RRP that reserves resources at fixed cycle intervals. However, these simplified RRPs lead to excessive over-provisioning and fail to meet the high-throughput demands of IIoT applications. To address this, we propose a flexible LDN (Flex-LDN) mechanism that supports arbitrary RRPs and minimizes resource reservation for TS flows. First, due to the lack of an analytical method to establish upper bounds of shaping delays for arbitrary RRPs, Flex-LDN constructs a service curve model and calculates delay bounds using network calculus. Furthermore, since shaping delay budgets vary for different paths of a TS flow, the required resource reservation to ensure deterministic latency and jitter bounds also differs. Therefore, we design a load-balancing-based RRP decision algorithm and a transmission strategy space generation algorithm to assign RRPs to each path, thereby constructing a transmission strategy space that consists of RRPs and paths for each TS flow. The RRPs generated by these algorithms minimize resource reservation while satisfying the shaping delay budgets. This approach ensures minimal resource allocation during TS flow scheduling. Finally, to efficiently solve the TS flow scheduling problem, we model it as an Exact Potential Game (EPG) and propose a distributed Ordered Best-Response (OBR) algorithm that can converge to a Nash equilibrium in polynomial time. Simulation results show that Flex-LDN improves the TS flow scheduling success ratio by 62.2% and 24.5% over LDN and A-LDN, respectively, while reducing scheduling time by 72.5% compared to A-LDN.

The rapid growth of network traffic has resulted in a substantial increase in log data, creating significant challenges for storage and processing. Although general-purpose compression algorithms are widely used, they often underperform on network logs due to their inability to exploit inherent structural characteristics. While advanced compression techniques can offer better performance, they typically require extensive system modifications and add deployment complexity. This paper presents CFseq, a lightweight and efficient framework designed to construct compression-friendly field sequences that improve the compressibility of network logs. CFseq is founded on two key observations: first, some fields exhibit high redundancy; second, others contain shared prefixes or suffixes that are well suited to compression algorithms. The framework comprises two modules: the Text Similarity Enhancement module, which ranks fields based on information entropy, and the Brute-Force Search module, which identifies the optimal field order for compression. CFseq operates without modifying existing compression or decompression pipelines, allowing for seamless and low-cost integration. Experimental results show that CFseq improves the compression ratios of general-purpose compressors by up to 32 % and enhances the performance of the state-of-theart advanced compressor Denum by up to 20 %.

With the rise of autonomous driving, the performance requirements for In-vehicular networks are continuously increasing. Existing research leverages Frame Replication and Elimination for Reliability (FRER) and Time-Aware Shaper (TAS) mechanisms in Time-Sensitive Networking (TSN) to achieve deterministic transmission. FRER requires transmitting flows over multiple disjoint paths. However, FRER lacks redundancy degree selection strategies, and the delay differences between redundant paths lead to packet disorder, which increases network resource overhead and compromises traffic QoS. In this paper, we propose a Bandwidth-Aware with Frame Replication and Elimination for Reliability (BA-FRER) algorithm dynamically selects redundancy degree based on the current network bandwidth resources, delay, and reliability utility function. Additionally, we propose a Redundant-Aware Order Preservation (RAOP) algorithm configures time slots for each flow based on the TAS mechanism to align the delays of redundant paths. The evaluation results show that the BA-FRER algorithm improves the utilization of network bandwidth resources, the flow access rate, and the reliability, while the RAOP algorithm reduces the probability of packet disorder.

Due to the low entropy traffic characteristics of LLM (Large Language Model) training, existing load balancing mechanisms such as Equal-Cost Multi-Path (ECMP) fail to fully utilize the redundant bandwidth between computing nodes in RDMA over Converged Ethernet (RoCE). Packet spraying mechanism has become a typical solution to the load balancing problem in RoCEs. However, it has a negative effect on congestion control mechanisms and suffers severe out-of-order problems.In this paper, we propose Mercury, an host-driven spraying scheme that synergizes congestion feedback and reordering control. Mercury selects paths by leveraging ECN, RTT, and reordering metrics, adjusts rates via multi-metric window. It also employs receiver-side buffers with priority-based dropping to mitigate out-of-order penalties. Evaluations in ns-3 under AllReduce/All-to-All traffic show Mercury reduces maximum flow completion time (Max FCT) by 40%-63% compared to ECMP-based DCQCN/TIMELY/HPCC. It also achieves at least 10%-20% improvement against switch-based spraying.

Domain Name System (DNS) logs capture detailed records of the queries and responses exchanged between DNS servers and clients, playing a crucial role in applications such as cybersecurity monitoring and regulatory compliance, which often require long-term data retention. With the rapid growth of Internet traffic, the volume of DNS logs has surged, presenting significant storage challenges. Although many DNS operators use general-purpose compression algorithms to reduce storage costs, these solutions fail to fully exploit the unique characteristics of DNS data, leading to inefficiencies and rising storage demands.

The emergence of new time-sensitive networking (TSN) technologies empowers almost-deterministic ultra-reliable low-latency communications for industrial cloud-fog automation paradigms. However, current heterogeneous networks struggle to balance time-sensitivity and flexibility, particularly in mixed-flow scenarios. Existing scheduling approaches within 5G-TSN integration model either suffer from quality of service (QoS) flow mismatch or microbursts. This paper proposes a class-aware mixed-flow scheduling (CAMFS) strategy to enable domain-specific resource allocation in a hybrid wired-wireless TSN, while meeting the differentiated time-sensitive (TS) requirements. A time-triggered multiple cyclic-queuing (TTMCQ) shaper is designed to effectively align the classified and regulated mixed flows from TSN domains to 5G QoS flows through class-aware mapping. We also introduce an anti-starvation resource optimization method that minimizes the total average idle resources resulting from temporal-spatial resource over-provisioning within reserved TS windows (RTWs). Additionally, we present a CAMFS algorithm aimed at enhancing schedulability and resource utilization by sorting mixed flows based on a combination of flow features. Finally, simulation results show that CAMFS exhibits outstanding scheduling performance in terms of end-to-end service latency, scheduling success ratio, and normalized resource distribution compared to other state-of-the-art methods.

In the context of smart transportation, the collaborative processing of video data sourced from multiple cameras plays a pivotal role in promoting efficient traffic management and augmenting safety measures. Nevertheless, the exponential surge in surveillance cameras deployment has concurrently engendered a rapid increase in the magnitude of video analysis tasks and data volume. To address these challenges, we propose a comprehensive framework for collaborative video processing. Primarily, a collaborative content analysis approach is proposed, and which employs a Transformer-based ReID (Re-identification) algorithm to construct key stickers. These key stickers are optimized with cross-cameras correlations and serve as the foundational structure for subsequent online video compression. Subsequently, we propose a collaborative resource allocation approach, and which involves the formulation of a queue model designed for the orchestration of online camera analysis tasks. In addition, we have devised an enhanced deep reinforcement learning algorithm to fine-tune the task scheduling configuration of multiple cameras, with guidance from the queue model. Extensive experiments and simulations were conducted to evaluate the proposed framework. The results demonstrate its effectiveness in achieving accurate and real-time analysis of video data in smart transportation scenarios.

The integration of time-sensitive networking (TSN) and fifth-generation (5G) offers a promising solution for real-time and reliable data transmission in the Industrial Internet of Things (IIoT). However, current research focuses on traffic scheduling in TSN-5G networks to support low latency. New challenges arise when TSN-5G networks leverage time-aware shaper (TAS) and frame replication and elimination for reliability (FRER) to achieve low latency and high reliability. Simply combining TAS and FRER (SCTF) requires scheduling all time-triggered (TT) flows and their replica flows, which substantially increases the computational complexity of gate control lists (GCLs) and severely weakens scheduling capabilities. Moreover, the packet elimination function (PEF) in FRER may induce packet misordering. In this paper, we propose an efficient and fault-tolerant time-aware shaper (EF-TAS) mechanism for TSN-5G networks. EF-TAS only allocates timeslots for TT flows, while replica TT (RT) flows are delivered using a best-effort strategy. Due to the potential violation of deadlines in RT flows, we design an adaptive cyclic GCL window (ACGW)-based hybrid scheduling (AHS) algorithm to schedule TT and RT flows differentially. The AHS algorithm utilizes network calculus to ensure the timely arrival of RT flows without affecting the deterministic transmission of TT flows. In particular, we provide upper bounds on the amount of reordering to quantify the disorder caused by PEF and analyze the impact of introducing the packet ordering function (POF) on EF-TAS performance. The evaluation results show that EF-TAS not only meets the reliability and deadline requirements but also significantly reduces the total number of GCL entries and the computation time of GCLs compared to state-of-the-art methods.

Congestion control plays a crucial role in ensuring the performance of data center networks. However, mainstream RDMA congestion control algorithms still face challenges such as slow congestion response and poor deployability. In this paper, we propose a novel fast-convergence congestion control algorithm, FCC, to address these shortcomings. FCC leverages Explicit Congestion Notification (ECN) and Round-Trip Time (RTT) signals, utilizing the gradient of RTT to enhance response speed and employing a Sigmoid curve for rate increment. Through experiments, we demonstrate that compared to existing state-of-the-art algorithms, FCC achieves superior performance in terms of convergence speed, fairness, and small flow latency metrics.

With the rapid growth of network speed, datacenter applications have increasing demands on networks for high throughput and ultra-low latency. RDMA is widely deployed due to its high performance. Most existing RDMA congestion control schemes employ end-to-end architecture with inherent feedback delays of at least one Round-Trip Time (RTT).To overcome these limitations, we propose RACC, a rapid and accurate INT-Based RDMA congestion control scheme. The switches directly provide INT feedback, reducing the feedback signal latency. In addition, the adaptive rate update mechanism is adopted at the sender, which enhances the rapid response to network congestion and the stable control of in-flight bytes. We conduct simulation experiments based on the Fat-Tree topology to analyze the requirements for datacenter performance metrics including convergence, fairness, and dynamic queues. Our evaluations show that the peak queue lengths are reduced by up to 80% compared to HPCC and PowerTCP, and the convergence time after congestion is reduced by half.

Deterministic IP (DIP) networking is a time division duplex technique providing delay-bounded transmissions in large-scale networks. Dedicated resources are allocated to each time-sensitive (TS) flow, reducing queuing delay uncertainties. However, most research relies on offline scheduling, unfit for dynamic networks. Some online heuristics prioritize early flows, risking later traffic access issues. To tackle this, we propose an online algorithm using historical data for better foresight. We develop and analyze a competitive algorithm considering historical data, demonstrating its effectiveness through simulations.

With the continuous innovation of network technology, emerging network service models have begun to be proposed, which also puts forward higher requirements for business settlement. Business settlement, as the foundation of network services, involves the interests of users and service providers. The traditional centralized settlement schemes no longer respond to the needs of both parties in terms of transparency, security, and fairness. Therefore, some blockchain-based settlement solutions have been proposed to address these issues. However, due to the additional overhead brought by blockchain, it is difficult for these solutions to ensure both security and efficiency. In this paper, we propose a state channels-based business settlement scheme (SCBS) to complete network service settlement securely and efficiently. In SCBS, the settlement process can be effectively carried out off-chain. When non-cooperative behavior occurs, both parties can create, resolve, and refute the dispute through the state channel to assure safety. Finally, the test results from Hyperledger Fabric platform demonstrate the feasibility and effectiveness of our SCBS.

Time-Sensitive networking (TSN) has put forward a series of standards, such as cyclic queuing and forwarding (CQF) and frame replication and elimination for reliability (FRER), to achieve deterministic latency and high reliability. However, most work studies these two mechanisms separately, while directly combining CQF and FRER (DCCF) will inevitably introduce distinct multiple-path delays, seriously impair scheduling capa-bilities and result in a large jitter. In this paper, we propose a Multi-path CQF (MCQF) mecha-nism. Firstly, MCQF enables flexible end-to-end delay calculation by extending the ping-pong queues of CQF to multi-queues. Then, we formulate a joint routing and scheduling mathematical model to maximize the number of schedulable flows and satisfy diverse latency and reliability requirements. Moreover, a hop-by-hop offset scheduling (HOS) algorithm is designed to achieve low jitter by aligning the packet delays on multiple disjoint paths. Evaluation results show that MCQF performs better than CQF on reliability. Compared to DCCF, MCQF greatly reduces the jitter and improves the schedulable flow number by about 31.9 %.

With the emergence of time-sensitive IIoT applications, such as remote operation and industrial control, a long-distance deterministic forwarding service is highly desirable. However, most of the existing research is limited to local area networks, or requires costly replacement of core network devices. Enabling incremental deterministic networks based on off-the-shelf technologies is a significant challenge. This paper designs a core-agnostic and cost-effective solution named Hirail to achieve the smooth evolution of long-distance deterministic networks. Firstly, we investigate that a time-discrete shaper (TDS) can be deployed at the ingress node to enable millisecond-level bounded delay. TDS functions similarly to the concept of buying time-stamped tickets for each flow prior to getting on a high-speed rail, thus avoiding the expensive modification of core devices. Then, to alleviate the flow aggregation problem under long-distance links, we utilize the inband network telemetry to construct the delay-aware network map and conduct adaptive source routing based on the map. Finally, an adjustable buffer at the last hop is devised for jitter reduction. Evaluation results show that Hirail can meet the bounded delay and jitter demands, and outperforms other solutions in terms of performance and overhead.

The emerging space-terrestrial integrated network (STIN) assumes a pivotal role within the 6G vision, promising to deliver seamless global coverage and connectivity. Achieving advanced, high-reliability, and time-sensitive (TS) services in a resource-constrained and failure-prone space environment is critical, but also presents challenges. Existing space-terrestrial communication approaches either suffer from temporary link failures with unstable reliability, or intolerable service latency due to the extensive coverage and uneven traffic distribution. This paper presents FastTS, a heuristic resilient and performant scheduling strategy to achieve fault-tolerant and time-sensitive scheduling in futuristic STINs. First, we model the high-dynamic and failure-prone topology in space, and formulate the scheduling problem as a mixed non-linear problem with the objective of minimizing the average task completion time. To approach the optimal solution, joint time-variant routing and frame replication and elimination for reliability (FRER) redundancy under resource constraints are formally considered in our design. During the path-stable duration, FastTS prioritizes the multipath selection with higher redundancy scores, all while ensuring a bounded low latency for TS services based on time-sensitive networking (TSN) techniques. Specifically, our FastTS is divided into three phases: time-sensitive multipath generation (TMG), series-parallel redundancy scoring (SPRS), and SPRS-based time-variant routing (STR). Finally, simulation results show that FastTS exhibits outstanding performance improvements in terms of packet delay, scheduling success ratio, task completion time and packet loss rate, when compared to other state-of-the-art methods.

As the Internet of Things gathers pace and popularity, more and more data is collected at the edge. To unleash the value of data and make it tradable, data markets have been proposed. However, existing data markets generally depend on broker or blockchain, which inevitably raises concerns about one or more aspects of fairness, security, or efficiency. In addition, to promote data trading in the data market, a data trading incentive mechanism is also essential. In this paper, we propose a novel data trading scheme based on state channels and Stackelberg game. First, we propose aStateChannels-basedDataTrading (SCDT) framework to support non-repudiable and efficient data trading. The framework can arbitrate disputes arising from off-chain data trading through state channels, enabling traders to conduct efficient transactions off-chain without worrying about security issues. Second, we propose an optimal incentive mechanism to solve the pricing and purchasing problems. The tripartite interactions among the data seller, resource seller, and user service platform are formulated as a Stackelberg game to maximize the profits of all participants. Finally, we implement the data trading framework and analyze the incentive mechanism, which reveals the feasibility of the framework and the rationality of the incentive mechanism.

Recent studies leverage time-aware shaper (TAS) and frame replication and elimination for reliability (FRER) techniques to achieve deterministic latency and high reliability for time-triggered (TT) flows. FRER requires transmitting TT flows on$k$disjoint paths to tolerate transient and permanent failures. However, directly allocating timeslot resources for all$k$TT flows will dramatically increase the computational complexity of gate control lists (GCLs), seriously impair scheduling capabilities, and result in a wastage of bandwidth. In this paper, we propose an ultra-reliable time-aware shaper (uTAS). uTAS only allocates timeslots for one TT flow to ensure deterministic transmission. The$k - 1$replica TT (RT) flows are delivered using a best-effort strategy. On this basis, we propose an adaptive window scheduling (AWS) algorithm based on network calculus, which aims to guarantee that RT flows reach their destinations within the deadline. Evaluation results show that uTAS can meet the flow reliability and deadline requirements. Compared to directly combining TAS and FRER (DCTF), uTAS reduces the total number of GCLs by approximately 72.9%.

The emerging time-critical applications pose intense demands for enabling large-scale deterministic networks. In this paper, we propose a new Programmable Cycle-Specified Queue (PCSQ) for wide-area deterministic packet scheduling. We implement the first end-to-end high-precision rotation dequeuing, which enables microsecond-level time slot resource reservation (noted as T) and especially jitter control of up to 2T. We prototype the PCSQ scheduler on an FPGA. The PCSQ-enabled switches can guarantee bounded delay and jitter transmission on a realistic testbed.

Blockchain has been widely used to solve data privacy and security issues in edge computing scenarios. However, the blockchain based on edge computing still has some performance problems, such as insufficient scalability, difficulty in balancing security and edge device power consumption, and inability to simultaneously meet low latency, high throughput, high security and privacy issues, etc. In order to solve these problems, this paper proposes a generally improved Byzantine consensus mechanism based on the K-medoids clustering algorithm - FIBFT. Considering the different performance characteristics of each node in the network, the node’s state is first abstracted into a multi-dimensional state space containing eigenvalues, and then the nodes are divided into subnets by the efficient K-medoids clustering algorithm. Each subnet uses a Byzantine consensus mechanism based on arbitration for consensus and data interaction, and the consensus data could be exchanged between the subnets without interfering with the consensus process. The research results show that FIBFT has better scalability and throughput while ensuring high security compared with the traditional Byzantine consensus algorithm.

With the development of edge computing technology, the resource-limited Internet of Things (IoT) devices can offload computation-intensive artificial intelligence tasks, such as model training and inference to edge servers through resource trading. However, due to the increase in the number of intelligent applications and the rise of peer-to-peer (P2P) resource trading, the existing resource trading schemes based on the blockchain can no longer meet the needs of efficiency and security at the same time. In this article, a new state channels-based resource trading scheme is proposed for IoT, which can improve scalability without sacrificing security and fairness. In our scheme, most of the trading process could be completed off-chain, and the blockchain is used as an adjudicator to determine malicious behavior according to the users’ actions. Moreover, a method without being reliant on support from third parties is presented to defend against execution forks that must be faced when using the state channels. Finally, the feasibility and efficiency of our scheme are experimentally verified in the realistic testbed.

Time-Sensitive Networking (TSN) is emerging to support critical real-time applications in Industry 4.0. Recent proposals leverage Cyclic Queuing and Forwarding (CQF) to achieve bounded-delay transmission for cyclic flows in TSN. However, the CQF is not flexible enough in two aspects. First, it cannot achieve zero jitter. The Ping-Pong queue-based model in CQF will introduce the jitter of two cycles, which is inapplicable to industrial automation scenarios where isochronous flows require zero jitter. Second, it may require setting the maximum queue length to a fixed value in advance and scheduling the flows offline, which is challenging for dynamic traffic scheduling. In this paper, we firstly present a time-aware cyclic-queuing (TACQ) mechanism to enable zero jitter for CQF. TACQ consists of a novel no-wait shaper (NWS) and a cyclic-queuing shaper (CQS). The NWS handles isochronous flows by strictly limiting the transmission time of flows that do not overlap on each output port and each period. The CQS is extended from CQF to schedule cyclic flows. Then, we propose a variable time slot mechanism and a novel incremental routing and scheduling (IRAS) algorithm based on software-defined networking (SDN) to online schedule dynamic flows. Simulation results show that TACQ significantly reduces the delay of isochronous flows and achieves zero jitter compared with CQF. And the IRAS algorithm approaches 96.1% of the optimal solution in scheduling 2000 flows with a feasible per-flow computational time.

The virtual cloud network has been the first choice for most enterprises to expand local networks due to its convenience, flexibility, and elasticity. Cloud gateways are the key to steering traffic among these virtual networks, which require high throughput and low delay, usually in Tbps and microseconds (us), to improve the performance of cloud regions. However, as cloud traffic growth far exceeds Moore's law, previous software cloud gateways are facing the performance bottleneck that they are vulnerable to the attack of heavy-hitter flows. In this paper, we propose a high-performance and low-cost software cloud gateway for accelerating virtual cloud networks. By revisiting the Vector Packet Processing (VPP) framework, we design a custom control plane to enable various network functions of the cloud gateway, which enhances the routing and forwarding actions in the data plane. And we also provide a common interface for users to flexibly configure and manage the gateway. This pure software architecture has a low deployment cost. Compared with other software gateways with roughly the same price, the processing performance is close to the NIC's line speed, which is suitable for high concurrency cloud scenarios.

Recent proposals leverage emerging programmable switches to implement high-throughput and low-latency load balancers in the datacenter. However, most of them store per-connection states in programmable switches to ensure consistent load balancing decisions, which is costly due to the limited on-chip memory. Other proposals avoid storing per-connection states but have difficulty in large-scale deployment due to the modification of running applications. We present CDLB, a cost-effective and deployment-friendly Layer-4 load balancer based on programmable switches in the datacenter. To be cost-effective, CDLB leverages an improved hash-based algorithm that can maintain per-connection consistency in a static environment. To be easier in deployment, we introduce a small state table and design a protocol between load balancers and the controller to maintain per-connection consistency in a dynamic environment without modifying running applications. The state table stores small amounts of connection states temporarily to ensure consistent load balancing decisions under device variations. We implemented CDLB with bmv2 in the mininet environment. The evaluation results show that CDLB greatly reduces overhead and achieves better performance compared with stateful load balancers which store per-connection states in programmable switches, and CDLB maintains per-connection consistency well in a dynamic environment.

IEEE 802.1Qbv (TAS) is the most widely used technique in Time-Sensitive Networking (TSN) which aims to provide bounded transmission delays and ultra-low jitters in industrial local area networks. With the development of emerging technologies (e.g., cloud computing), many wide-range time-sensitive network services emerge, such as factory automation, connected vehicles, and smart grids. Nevertheless, TAS is a Layer 2 technique for local networks, and cannot provide large-scale deterministic transmission. To tackle this problem, this paper proposes a hierarchical network containing access networks and a core network. Access networks perform TAS to aggregate time-sensitive traffic. In the core network, we exploit DIP (a well-known deterministic networking mechanism for backbone networks) to achieve long-distance deterministic transmission. Due to the differences between TAS and DIP, we design cross-domain transmission mechanisms at the edge of access networks and the core network to achieve seamless deterministic transmission. We also formulate the end-to-end scheduling to maximize the amount of accepted time-sensitive traffic. Experimental simulations show that the proposed network can achieve end-to-end deterministic transmission even in high-loaded scenarios.

Deterministic IP (DIP) networking is a promising technique that can provide delay-bounded transmission in large-scale networks. Nevertheless, DIP faces several challenges in the mixed traffic scenarios, including (i) the capability of ultralow latency communications, (ii) the simultaneous satisfaction of diverse QoS requirements, and (iii) the network efficiency. The problems are more formidable in the dynamic surroundings without prior knowledge of traffic demands. To address the above-mentioned issues, this paper designs a flexible DIP (FDIP) network. In the proposed network, we classify the queues at the output port into multiple groups. Each group operates with different cycle lengths. FDIP can assign the time-sensitive flows with different groups, hence delivering diverse QoS requirements, simultaneously. The ultra-low latency communication can be achieved by specific groups with short cycle lengths. Moreover, the flexible scheduling with diverse cycle lengths improves resource utilization, hence increasing the throughput (i.e., the number of acceptable time-sensitive flows). We formulate a throughput maximization problem that jointly considers the admission control, transmission path selection, and cycle length assignment A branch and bound (BnB)-based heuristic is developed. Simulation results show that the proposed FDIP significantly outperforms the standard DIP in terms of both the throughput and the latency guarantees.

In recent years, with the development and widespread use of blockchain, many projects have introduced blockchain technology to solve the increasingly serious security problems of the Industrial Internet of Things (IIoT). However, due to the conflict between the operational performance and security of the blockchain system, the conflict between transparency and privacy, and the compatibility issues with a large number of IIoT devices running together, the mainstream blockchain system cannot be applied to IIoT scenarios. In order to solve these problems, in this article, we propose an IIoT distributed data system based on blockchain technology. We provide a novel system architecture for different IIoT devices to deploy high-performance blockchain systems in many scenarios, such as smart factory networks. To improve the performance of the blockchain network, we adopt the sharding hashgraph consensus mechanism and introduce a node evaluation mechanism based on the state of the node, which is applied to divide a large number of nodes into many shards dynamically. We abstract the node sharding problem as a joint optimization problem and use deep reinforcement learning to solve it. Finally, we compared with asynchronous Byzantine consensus algorithms, such as HoneybadgerBFT and BEAT, which validated the performance of this system architecture.

Recent proposals leverage Cyclic-Queuing and Forwarding (CQF) to achieve bounded-delay transmission for cyclic flows in Time-Sensitive Networking (TSN). However, the Ping-Pong queue-based model in CQF will introduce the jitter of two cycles, which is inapplicable to industrial automation scenarios where isochronous flows such as synchronized frames and motor control loops require zero jitter.We present TACQ, a first time-aware cyclic-queuing mechanism to support the co-transmission of cyclic flows and isochronous flows. Our key insight is that isochronous flows should enable zero-jitter while minimally impacting the bounded-delay of cyclic flows. To achieve this goal, we design a novel no-wait shaper (NWS) that handles isochronous flows with as little time-slot as possible. For cyclic flows, we extend the CQF to schedule flows with a double-closed state on the Tx-gate and compute the open time on the Rx-gate. Simulation results show that TACQ significantly reduces the delay of isochronous flows by 81.3% and achieves zero jitter compared with CQF. And the NWS effectively schedules 87.2% flows at 500 flows level in feasible execution time.

Recent proposals leverage Time-Aware Shaper (TAS) to achieve precise transmission in Time-Sensitive Networking (TSN). However, most of the proposals require the information of all time-triggered flows to be known in advance and synthesize the gate control list of each switch offline, making the mechanisms they designed inapplicable to industrial automation scenarios where the devices are changed dynamically and the flows should be scheduled online. In this paper, we propose an online routing and scheduling mechanism of TAS for time-sensitive networks. In order to maximize the number of schedulable flows and reduce bandwidth waste, we devise the variable time slot mechanism and minimize the sending start time of each flow. Based on these mechanisms, a novel incremental routing and scheduling (IRAS) algorithm is designed to achieve per-flow deployment, with a pre-routing algorithm to reduce synthesis time. The evaluations show that the IRAS algorithm approaches 96.5 % of the optimal solution in scheduling 2000 flows, and has a feasible per-flow computational time from sub-seconds to less than ten seconds.

Traditional networks face many challenges due to the diversity of applications, such as cloud computing, Internet of Things, and the industrial Internet. Future Internet needs to address these challenges to improve network scalability, security, mobility, and quality of service. In this work, we survey the recently proposed architectures and the emerging technologies that meet these new demands. Some cases for these architectures and technologies are also presented. We propose an integrated framework called the service customized network which combines the strength of current architectures, and discuss some of the open challenges and opportunities for future Internet. We hope that this work can help readers quickly understand the problems and challenges in the current research and serves as a guide and motivation for future network research.

Recently, designing information-agnostic coflow scheduling mechanisms attracts much attention since by leveraging priority queues, they could reduce coflow completion time in data-parallel clusters without a priori knowledge, such as flow size, coflow size. However, existing information-agnostic mechanisms generally schedule coflows only according to the sent data size of different coflows and ignore other useful coflow-level attributes like width, length and communication patterns. In this paper, we investigate that the coflow completion time could be further decreased by jointly leveraging multiple coflow-level attributes. Based on this investigation, we present a Multiple-attributes-based Coflow Scheduling (MCS) mechanism to reduce the coflow completion time. In MCS, a Shortest and Narrowest Coflow First (SNCF) algorithm is designed to separate coflows based on their widths and estimated lengths at the start of a coflow. During the transmission of coflows, one type of demotion thresholds employed in previous coflow scheduling mechanisms is too crude for various coflows. Therefore, we proposed a double-threshold scheme to adjust the priorities of narrow (small coflow width) and wide (large coflow width) coflows according to different thresholds. Trace-driven simulations with production workloads show that MCS outperforms the previous information-agnostic scheduler Aalo, and reduces the coflow completion time of small coflows.

ECN thresholds have limited operational range and very strict scope. Lower thresholds exacerbate the queue underflow while higher thresholds increase the queueing delays. In this paper, an Adaptive ECN (A-ECN) marking scheme is proposed to enhance the performance of ECN. A-ECN can adaptively adjust ECN marking thresholds in different scenarios to achieve good generality. Therefore, network operators can directly deploy A-ECN in various environments regardless of underlying queue types and bandwidth.

Data transfer duration accounts for a great proportion of job completion time in big-data systems. To reduce the time spent on data transfer, some traffic scheduling mechanisms at coflow-level are proposed recently. Most of them abstract datacenter networks as an ideal non-blocking big-switch, and the bottleneck is located at egress or ingress ports of end-hosts instead of in networks. Thus, they mainly focus on how to allocate port capacities of end-hosts to jobs without considering innetwork congestion. However, link congestion frequently occurs in datacenter networks due to network oversubscription and load imbalance. When link congestion occurs, bottleneck locations will move from the ports of end-hosts to network links. In this paper, we design and implement SkipL, a congestionaware coflow scheduler which could detect congestion and schedules coflows at end-hosts to effectively reduce coflow completion time. In addition, to be easily deployed in cloud environments, SkipL does not require to control flow routes. SkipL prototype system is implemented in Linux. The results of experiments conducted in a real small testbed and simulations conducted in the flow-level simulator show that SkipL reduces the average Coflow Completion Time(CCT) compared to the per-flow fair sharing scheduling method and Varys.

In today’s networks, load balancing and priority queues in switches are used to support various quality-of-service (QoS) features and provide preferential treatment to certain types of traffic. Traditionally, network operators use ‘traceroute’ and ‘ping’ to troubleshoot load balancing and QoS problems. However, these tools are not supported by the common OpenFlow-based switches in software-defined networking (SDN). In addition, traceroute and ping have potential problems. Because load balancing mechanisms balance flows to different paths, it is impossible for these tools to send a single type of probe packet to find the forwarding paths of flows and measure latencies. Therefore, tracing flows’ real forwarding paths is needed before measuring their latencies, and path tracing and latency measurement should be jointly considered. To this end, FlowTrace is proposed to find arbitrary flow paths and measure flow latencies in OpenFlow networks. FlowTrace collects all flow entries and calculates flow paths according to the collected flow entries. However, polling flow entries from switches will induce high overhead in the control plane of SDN. Therefore, a passive flow table collecting method with zero control plane overhead is proposed to address this problem. After finding flows’ real forwarding paths, FlowTrace uses a new measurement method to measure the latencies of different flows. Results of experiments conducted in Mininet indicate that FlowTrace can correctly find flow paths and accurately measure the latencies of flows in different priority classes.

We present FDALB, a flow distribution aware load balancing mechanism aimed at reducing flow collisions and achieving high scalability. FDALB, like the most of centralized methods, uses a centralized controller to get the view of networks and congestion information. However, FDALB classifies flows into short flows and long flows. The paths of short flows and long flows are controlled by distributed switches and the centralized controller respectively. Thus, the controller handles only a small part of flows to achieve high scalability. To further reduce the controller's overhead, FDALB leverages end-hosts to tag long flows, thus switches can easily determine long flows by inspecting the tag. Besides, FDALB can adaptively adjust the threshold at each end-host to keep up with the flow distribution dynamics.