Network Stack Research Articles

Regulating and Unraveling Electrochemical Behavior of Hierarchically‐Densifying Mesoporous Apocynum Carbon for High performance Supercapacitor

AbstractThe commercial carbon‐based supercapacitor with high power ability (~5 kW kg−1) is still unable to fulfill the superhigh power requirement of specific power‐type equipments (>20 kW kg−1), such as rail transit facilities, electromagnetic and laser equipment. To unravel the structure‐activity relationship and electrochemical behavior of power‐type densifying carbon is a key to overcome the contradiction of the suitable mesoporous ratio and highly‐densifying features toward the superhigh power requirement. Here, we built the hierarchically‐densifying mesoporous apocynum carbon (HDMC) with optimized mesoporous ratio by hierarchical activation method. More importantly, both the isothermal desorption/adsorption and high‐pressure mercury intrusion porosimetry methods were employed to synergistically uncover the microscopic surface carbon network stacking mechanism and the macroscopic carbon skeleton densification assembly mechanism. The highly‐densifying skeleton features and high mesoporous ratio properties were proved to be co‐existed in HDMC, which is in favour of rapidly ion/electron transferring toward electrochemically‐improving power behavior of HDMC. A combination of high tap density (0.387 g cm−3) and ideal microporous‐mesoporous system (23.1 % proportion of mesoporous) have taken this HDMC to provide a super‐high power density (33.5 kW kg−1) and a high volume power density (9.37 kW L−1) for HDMC‐based supercapacitor, more than those of commercial YP‐50F (14.9 kW kg−1 @ 4.63 kW L−1). Therefore, this work provides a synergistic strategy to incorporate the properties of mesoporous and densifying, and reveals its electrochemical behavior toward the further application of power‐type supercapacitors.

Prediction of Multi-Pharmacokinetics Property in Multi-Species: Bayesian Neural Network Stacking Model with Uncertainty.

Pharmacokinetic (PK) properties of a drug are vital attributes influencing its therapeutic effectiveness, playing an important role in the drug development process. Focusing on the difficult task of predicting PK parameters, we compiled an extensive data set comprising parameters across multiple species. Building upon this groundwork, we introduced the PKStack ensemble model to predict PK parameters across diverse species. PKStack integrates a variety of base models and includes uncertainty in its predictions. We also manually collected PK data from animals as an external test set. We predicted a total of 45 tasks for nine PK parameters in five species, and in general, the prediction accuracy was better for intravenous injections, including parameters such as human Vd (R2 = 0.72, RMSE = 0.31), human CL (R2 = 0.52, RMSE = 0.32), and others. In addition to predictive accuracy, we also considered the interpretability of the results and the definition of the model's application domain. Based on the findings, our model has great potential for practical applications in drug discovery.

Active Queue Management in L4S with Asynchronous Advantage Actor-Critic: A FreeBSD Networking Stack Perspective

Bufferbloat is one of the leading causes of high data transmission latency and jitter on the Internet, which severely impacts the performance of low-latency interactive applications such as online streaming, cloud-based gaming/applications, Internet of Things (IoT) applications, voice over IP (VoIP), real-time video conferencing, and so forth. There is currently a pressing need for developing Transmission Control Protocol (TCP) congestion control algorithms and bottleneck queue management schemes that can collaboratively control/reduce end-to-end latency, thus ensuring optimal quality of service (QoS) and quality of experience (QoE) for users. This paper introduces a novel solution by experimentally integrate the low latency, low loss, and scalable throughput (L4S) architecture (specified by the IETF in RFC 9330) in FreeBSD framework with the asynchronous advantage actor-critic (A3C) reinforcement learning algorithm. The first phase involves incorporating a modified dual-queue coupled active queue management (AQM) system for L4S into the FreeBSD networking stack, enhancing queue management and mitigating latency and packet loss. The second phase employs A3C to adjust and fine-tune the system performance dynamically. Finally, we evaluate the proposed solution’s effectiveness through comprehensive experiments, comparing it with traditional AQM-based systems. This paper contributes to the advancement of machine learning (ML) for transport protocol research in the field. The experimental implementation and results presented in this paper are made available through our GitHub repositories.

Efficient and lightweight convolutional neural network architecture search methods for object classification

Determining the architecture of deep learning models is a complex task. Several automated search techniques have been proposed, but these methods typically require high-performance graphics processing units (GPUs), manual parameter adjustments, and specific training approaches. This study introduces an efficient, lightweight convolutional neural network architecture search approach tailored for object classification. It features an optimized search space design and a novel controller design.This study introduces a refined search space design incorporating optimizations in both spatial and operational aspects. The focus is on the synergistic integration of convolutional units, dimension reduction units, and the stacking of Convolutional Neural Network (CNN) architectures. To enhance the search space, ShuffleNet modules are integrated, reducing the number of parameters and training time. Additionally, BlurPool is implemented in the dimension reduction unit operation to achieve translational invariance, alleviate the gradient vanishing problem, and optimize unit compositions. Moreover, an innovative controller model, Stage LSTM, is proposed based on Long Short-Term Memory (LSTM) to generate lightweight architectural sequences. In conclusion, the refined search space design and the Stage LSTM controller model are synergistically combined to establish an efficient and lightweight architecture search technique termed Stage and Lightweight Network Architecture Search (SLNAS).The experimental results highlight the superior performance of the optimized search space design, primarily when implemented with the Stage LSTM controller model. This approach shows significantly improved accuracy and stability compared to random, traditional LSTM, and Genetic Algorithm (GA) controller models, with statistically significant differences. Notably, the Stage LSTM controller excels in accuracy while producing models with fewer parameters within the expanded architecture search space.The study adopts the Stage LSTM controller model due to its ability to approximate optimal sequence structures, particularly when combined with the optimized search space design, referred to as SLNAS. SLNAS's performance is evaluated through experiments and comparisons with other Neural Architecture Search (NAS) and object classification methods from different researchers. These experiments consider model parameters, hardware resources, model stability, and multiple datasets. The results show that SLNAS achieves a low error rate of 2.86 % on the CIFAR-10 dataset after just 0.2 days of architecture search, matching the performance of manually designed models but using only 2 % of the parameters. SLNAS consistently demonstrates robust performance across various image classification domains, with an approximate parameter count 700,000.To summarize, SLNAS emerges as a highly effective automated network architecture search method tailored for image classification. It streamlines the model design process, making it accessible to researchers without specialized knowledge in deep learning. Optimizing this method unlocks the full potential of deep learning across diverse research areas. Interested parties can publicly access the source code and pre-trained models through the following link: https://github.com/huanyu-chen/LNASG-and-SLNAS-model.

Symmetric spatiotemporal learning network with sparse meter graph for short-term energy-consumption prediction in manufacturing systems

Short-term energy-consumption prediction is the basis of anomaly detection, real-time scheduling, and energy-saving control in manufacturing systems. Most existing methods focus on single-node energy-consumption prediction and suffer from difficult parameter collection and modelling. Although several methods have been presented for multinode energy-consumption prediction, their prediction performance needs to be improved owing to a lack of appropriate knowledge guidance and learning networks for complex spatiotemporal relationships. This study presents a symmetric spatiotemporal learning network (SSTLN) with a sparse meter graph (SMG) (SSTLN-SMG) that aims to predict multiple nodes based on energy-consumption time series and general process knowledge. The SMG expresses process knowledge by abstracting production nodes, material flows, and energy usage, and provides initial guidance for the SSTLN to extract spatial features. SSTLN, a symmetrical stack of graph convolutional networks (GCN) and gated linear units (GLU), is devised to achieve a trade-off not only between spatial and temporal feature extraction but also between detail capture and noise suppression. Extensive experiments were performed using datasets from an aluminium profile plant. The experimental results demonstrate that the proposed method allows multinode energy-consumption prediction with less prediction error than state-of-the-art methods, methods with deformed meter graphs, and methods with deformed learning networks.

Kernel vs. User-Level Networking: Don't Throw Out the Stack with the Interrupts

This paper reviews the performance characteristics of network stack processing for communication-heavy server applications. Recent literature often describes kernel-bypass and user-level networking as a silver bullet to attain substantial performance improvements, but without providing a comprehensive understanding of how exactly these improvements come about. We identify and quantify the direct and indirect costs of asynchronous hardware interrupt requests (IRQ) as a major source of overhead. While IRQs and their handling have a substantial impact on the effectiveness of the processor pipeline and thereby the overall processing efficiency, their overhead is difficult to measure directly when serving demanding workloads. This paper presents an indirect methodology to assess IRQ overhead by constructing preliminary approaches to reduce the impact of IRQs. While these approaches are not suitable for general deployment, their corresponding performance observations indirectly confirm the conjecture. Based on these findings, a small modification of a vanilla Linux system is devised that improves the efficiency and performance of traditional kernel-based networking significantly, resulting in up to 45% increased throughput without compromising tail latency. In case of server applications, such as web servers or Memcached, the resulting performance is comparable to using kernel-bypass and user-level networking when using stacks with similar functionality and flexibility.

Design of adaptive hybrid MPPT controllers with universal input voltage DC–DC converter for RES’s

At present, conventional energy production is absent because of the more hazardous gases released into the environment, the high effect on human health, more cost required for maintenance, plus less usefulness for highly populated areas. So, the Renewable Energy Sources are more focused for the present automotive industry application. In this work, the Proton Exchange Membrane Fuel Stack is considered for analyzing the proposed DC–DC converter circuit. The advantages of this fuel stack are high energy density, fast functioning nature, more robustness, and more usefulness for the various water membrane conditions of the fuel stack. However, the disadvantages of the fuel stack are excessive current generation, plus more current conduction losses. So, the wide voltage supply single switch power converter is introduced in this work for optimizing the current production of the fuel stack network. The merits of this converter circuit are high stability, good reliability, low voltage appearing across the switches, plus a uniform power supply. Here, the converter switching pulses are obtained by proposing the Modified Continuous Step Change Adaptive Fuzzy Logic with Grey Wolf Optimization hybrid controller. This controller provides high maximum power extraction efficiency from the fuel stack which is equal to 99.421%. Also, this controller's Maximum Power Point Tracking time is 0.0285 s.

PCNP: A RoCEv2 congestion control using precise CNP

With the development of data centers, the traditional TCP/IP network stacks have become inadequate in addressing the high bandwidth and low latency demands of diverse upper-layer applications. Some cloud providers deploy RoCEv2 (RDMA over Converged Ethernet v2) technology to accelerate network transmission in their data centers, and the reliable network transmission in RoCEv2 relies on congestion control protocols. Currently, traditional congestion control protocols primarily employ heuristic rate adjustment strategies, which often exhibit inadequate performance in terms of convergence, stability, and fairness. Furthermore, the latest congestion control protocols rely on the support of costly switch hardware, which hinders the incremental expansion of data centers. This paper proposes PCNP, a precise congestion control protocol, it extends standard CNP (Congestion Notification Packet) and introduces a bounded rate adjustment strategy that leverages receiver information to achieve precise rate adjustment within existing data centers. Simulation results demonstrate that PCNP effectively decreases the rate fluctuation during the convergence process and enhances fairness. In comparison to DCQCN, HPCC and PCN, PCNP reduces the standard deviation of flow completion time under the incast traffic by 90%, 91% and 91%, respectively. Additionally, it can reduce the tail flow completion time by up to 83%, 40% and 46%.

Simple-Structured Acceptor with Highly Interconnected Electron-Transport Pathway Enables High-Efficiency Organic Solar Cells.

Achieving desirable charge-transport highway is of vital importance for high-performance organic solar cells (OSCs). Here, it is shown how molecular packing arrangements can be regulated via tuning the alkyl-chain topology, thus resulting in a 3D network stacking and highly interconnected pathway for electron transport in a simple-structured nonfused-ring electron acceptor (NFREA) with branched alkyl side-chains. As a result, a record-breaking power conversion efficiency of 17.38% (certificated 16.59%) is achieved for NFREA-based devices, thus providing an opportunity for constructing low-cost and high-efficiency OSCs.

Prediction of permeability in a tight sandstone reservoir using a gated network stacking model driven by data and physical models

Introduction: Permeability is one of the most important parameters for reservoir evaluation. It is commonly measured in laboratories using underground core samples. However, it cannot describe the entire reservoir because of the limited number of cores. Therefore, petrophysicists use well logs to establish empirical equations to estimate permeability. This method has been widely used in conventional sandstone reservoirs, but it is not applicable to tight sandstone reservoirs with low porosity, extremely low permeability, and complex pore structures.Methods: Machine learning models can identify potential relationships between input features and sample labels, making them a good choice for establishing permeability prediction models. A stacking model is an ensemble learning method that aims to train a meta-learner to learn an optimal combination of expert models. However, the meta-learner does not evaluate or control the experts, making it difficult to interpret the contribution of each model. In this study, we design a gate network stacking (GNS) model, which is an algorithm that combines data and model-driven methods. First, an input log combination is selected for each expert model to ensure the best performance of the expert model and selfoptimization of the hyperparameters. Petrophysical constraints are then added to the inputs of the expert model and meta-learner, and weights are dynamically assigned to the output of the expert model. Finally, the overall performance of the model is evaluated iteratively to enhance its interpretability and robustness.Results and discussion: The GNS model is then used to predict the permeability of a tight sandstone reservoir in the Jurassic Ahe Formation in the Tarim Basin. The case study shows that the permeability predicted by the GNS model is more accurate than that of other ensemble models. This study provides a new approach for predicting the parameters of tight sandstone reservoirs.

Detection and isolation of wormhole nodes in wireless ad hoc networks based on post-wormhole actions

The wormhole attack is one of the most treacherous attacks projected at the routing layer that can bypass cryptographic measures and derail the entire communication network. It is too difficult to prevent a priori; all the possible countermeasures are either too expensive or ineffective. Indeed, literature solutions either require expensive hardware (typically UWB or secure GPS transceivers) or pose specific constraints to the adversarial behavior (doing or not doing a suspicious action). The proposed solution belongs to the second category because the adversary is assumed to have done one or more known suspicious actions. In this solution, we adopt a heuristic approach to detect wormholes in ad hoc networks based on the detection of their illicit behaviors. Wormhole and post wormhole attacks are often confused in literature; that’s why we clearly state that our methodology does not provide a defence against wormholes, but rather against the actions that an adversary does after the wormhole, such as packet dropping, tampering with TTL, replaying and looping, etc. In terms of contributions, the proposed solution addresses the knock-out capability of attackers that is less targeted by the researcher’s community. In addition, it neither requires any additional hardware nor a change in it; instead, it is compatible with the existing network stack. The idea is simulated in ns2.30, and the average detection rate of the proposed solution is found to be 98-99%. The theoretical time to detect a wormhole node lies between 0.07-0.71 seconds. But, from the simulation, the average detection and isolation time is 0.67 seconds. In term of packet loss, the proposed solution has a relatively overhead of ≈\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx$$\\end{document} 22%. It works well in static and mobile scenarios, but the frame losses are higher in mobile scenarios as compared to static ones. The computational complexity of the solution is O(n). Simulation results advocate that the solution is effective in terms of memory, processing, bandwidth, and energy cost. The solution is validated using statistical parameters such as Accuracy, Precision, F1-Score and Matthews correlation coefficient (Mcc\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$M_{cc}$$\\end{document}).

Practicality of in-kernel/user-space packet processing empowered by lightweight neural network and decision tree

Integrating machine learning (ML) into kernel packet processing, such as extended Berkeley Packet Filter (eBPF) and eXpress Data Path (XDP), represents a promising strategy for achieving fast and intelligent networking on generic hardware. This includes tasks like automating network operations and discerning traffic classification, exemplified by intrusion detection systems (IDS) combining Decision Tree (DT) and eBPF. However, the potential of ML-empowered packet processing remains to be fully explored. To ensure the integrity and security of kernel operations, eBPF/XDP programs must adhere to stringent constraints such as the maximum number of jump instructions, maximum stack space, and exclusion of floating-point arithmetic. These constraints pose challenges for implementing more intricate ML techniques (e.g., neural networks (NNs)) within eBPF/XDP programs. In such scenarios, AF_XDP provides an alternative solution by allowing XDP programs to redirect packets to user-space applications, bypassing the network stack. This paper initiates an exploration into fast packet classification through two distinct approaches: (1) an in-kernel approach employing eBPF/XDP and (2) a user-space approach assisted by AF_XDP. Specifically, to tackle the eBPF constraints, the in-kernel NN classifier adopts (1) quantization of trained model in the user space, (2) executing the integer-arithmetic-only NN within the kernel space, and (3) sequential layer operations through tail calls. These approaches are evaluated based on factors including packet processing speed, resource efficiency, and detection performance. Notably, our experimental findings demonstrate that (1) Classifiers relying solely on integer arithmetic, such as NN and DT, significantly reduce inference time while maintaining binary classification performance; (2) The lightweight NN classifier can improve the detection performance for most of attacks in case of the multi-class classification compared to the lightweight DT classifier; (3) In single-core scenarios, the DT-empowered in-kernel method can almost achieve the maximum packets per second (pps), i.e., about 800,000pps, whereas the NN-empowered one exhibits lower pps (i.e., about 450,000pps); (4) In multi-core scenarios, the NN-empowered packet processing can almost achieve the maximum pps with two or more cores in the AF_XDP approach and four or more cores in the in-kernel approaches.

Programming Network Stack for Physical Middleboxes and Virtualized Network Functions

Programming Network Stack for Physical Middleboxes and Virtualized Network Functions

Ensemble Prototype Network For Weakly Supervised Temporal Action Localization.

Weakly supervised temporal action localization (TAL) aims to localize the action instances in untrimmed videos using only video-level action labels. Without snippet-level labels, this task should be hard to distinguish all snippets with accurate action/background categories. The main difficulties are the large variations brought by the unconstraint background snippets and multiple subactions in action snippets. The existing prototype model focuses on describing snippets by covering them with clusters (defined as prototypes). In this work, we argue that the clustered prototype covering snippets with simple variations still suffers from the misclassification of the snippets with large variations. We propose an ensemble prototype network (EPNet), which ensembles prototypes learned with consensus-aware clustering. The network stacks a consensus prototype learning (CPL) module and an ensemble snippet weight learning (ESWL) module as one stage and extends one stage to multiple stages in an ensemble learning way. The CPL module learns the consensus matrix by estimating the similarity of clustering labels between two successive clustering generations. The consensus matrix optimizes the clustering to learn consensus prototypes, which can predict the snippets with consensus labels. The ESWL module estimates the weights of the misclassified snippets using the snippet-level loss. The weights update the posterior probabilities of the snippets in the clustering to learn prototypes in the next stage. We use multiple stages to learn multiple prototypes, which can cover the snippets with large variations for accurate snippet classification. Extensive experiments show that our method achieves the state-of-the-art weakly supervised TAL methods on two benchmark datasets, that is, THUMOS'14, ActivityNet v1.2, and ActivityNet v1.3 datasets.

Enhanced Detection of Glass Insulator Defects Using Improved Generative Modeling and Faster RCNN

The precise defect detections for glass insulators are of utmost importance to ensure their safety and functionality. Therefore, this study proposes an improved defect detection algorithm based on the optimized deep learning network. The collected glass insulator data was limited in size. Even after data augmentation, it remained insufficient for the training requirements of deep learning models. In this study, employing the improved Denoising Diffusion Probabilistic Models (DDPM) generative model to expand the glass insulator data. The glass insulator defect images generated by the improved noise-fitting network exhibit enhanced quality and high fidelity. Through manual selection and iterative experiments, a total of 1200 images were curated, constituting the glass insulator defect dataset for training and test of deep learning models. Faster RCNN was chosen as the defect detection model, and its VGG16 feature extraction network was replaced with ResNet50 to address the issue of gradient vanishing caused by excessive network stacking. Additionally, the Feature Pyramid Network (FPN) structure was introduced to enhance semantic extraction for different defect scales. The Kmeans++ algorithm was utilized to improve the proposal box generation parameters in RPN. Compared to the baseline Faster RCNN model's mAP of 72.7%, our improved version achieved a significant increase, reaching a mAP of 85.9%.

Chlorine-mediated strategy for organic photovoltaics.

Organic solar cells (OSCs), a nascent technology in the photovoltaic field, have attracted considerable research interest. Recently, the power conversion efficiency (PCE) of OSCs has significantly improved, thereby demonstrating substantial potential for commercialization. To achieve this, it is crucial to enhance the performance and stability of OSCs, necessitating the development of novel materials and devices. This feature article presents a review of chlorine-mediated photovoltaic materials in our group. By carefully controlling energy levels, molecular stacking and aggregation behavior, significantly improved performance was achieved. Furthermore, single-crystal analysis facilitated a profound comprehension of the influence of chlorine-mediated interactions on molecular stacking. This has enabled the design and synthesis of a series of high-performance non-fullerene acceptors (NFAs) with three-dimensional network stacking structures. Building upon these materials, we developed quasi-planar heterojunction (Q-PHJ) devices with a significant stability advantage. To sum up, the chlorine-mediated materials and the Q-PHJ devices provide valuable guidance and reference for the development of efficient and stable organic solar cells.

Anchor: A Library for Building Secure Persistent Memory Systems

Cloud infrastructure is experiencing a shift towards disaggregated setups, especially with the introduction of the Compute Express Link (CXL) technology, where byte-addressable ersistent memory (PM) is becoming prominent. To fully utilize the potential of such devices, it is a necessity to access them through network stacks with equivalently high levels of performance (e.g., kernel-bypass, RDMA). While, these advancements are enabling the development of high-performance data management systems, their deployment on untrusted cloud environments also increases the security threats. To this end, we present Anchor, a library for building secure PM systems. Anchor provides strong hardware-assisted security properties, while ensuring crash consistency. Anchor exposes APIs for secure data management within the realms of the established PM programming model, targeting byte-addressable storage devices. Anchor leverages trusted execution environments (TEE) and extends their security properties on PM. While TEE's protected memory region provides a strong foundation for building secure systems, the key challenge is that: TEEs are fundamentally incompatible with PM and kernel-bypass networking approaches-in particular, TEEs are neither designed to protect untrusted non-volatile PM, nor the protected region can be accessed via an untrusted DMA connection. To overcome this challenge, we design a PM engine that ensures strong security properties for the PM data, using confidential and authenticated PM data structures, while preserving crash consistency through a secure logging protocol. We further extend the PM engine to provide remote PM data operations via a secure network stack and a formally verified remote attestation protocol to form an end-to-end system. Our evaluation shows that Anchor incurs reasonable overheads, while providing strong security properties.

Kernel vs. User-Level Networking: Don't Throw Out the Stack with the Interrupts

This paper reviews the performance characteristics of network stack processing for communication-heavy server applications. Recent literature often describes kernel-bypass and user-level networking as a silver bullet to attain substantial performance improvements, but without providing a comprehensive understanding of how exactly these improvements come about. We identify and quantify the direct and indirect costs of asynchronous hardware interrupt requests (IRQ) as a major source of overhead. While IRQs and their handling have a substantial impact on the effectiveness of the processor pipeline and thereby the overall processing efficiency, their overhead is difficult to measure directly when serving demanding workloads. This paper presents an indirect methodology to assess IRQ overhead by constructing preliminary approaches to reduce the impact of IRQs. While these approaches are not suitable for general deployment, their corresponding performance observations indirectly confirm the conjecture. Based on these findings, a small modification of a vanilla Linux system is devised that improves the efficiency and performance of traditional kernel-based networking significantly, resulting in up to 45% increased throughput without compromising tail latency. In case of server applications, such as web servers or Memcached, the resulting performance is comparable to using kernel-bypass and user-level networking when using stacks with similar functionality and flexibility.

Embedded software of the KM3NeT central logic board

The KM3NeT Collaboration is building and operating two deep sea neutrino telescopes at the bottom of the Mediterranean Sea. The telescopes consist of latices of photomultiplier tubes housed in pressure-resistant glass spheres, called digital optical modules and arranged in vertical detection units. The two main scientific goals are the determination of the neutrino mass ordering and the discovery and observation of high-energy neutrino sources in the Universe. Neutrinos are detected via the Cherenkov light, which is induced by charged particles originated in neutrino interactions. The photomultiplier tubes convert the Cherenkov light into electrical signals that are acquired and timestamped by the acquisition electronics. Each optical module houses the acquisition electronics for collecting and timestamping the photomultiplier signals with one nanosecond accuracy. Once finished, the two telescopes will have installed more than six thousand optical acquisition nodes, completing one of the more complex networks in the world in terms of operation and synchronization. The embedded software running in the acquisition nodes has been designed to provide a framework that will operate with different hardware versions and functionalities. The hardware will not be accessible once in operation, which complicates the embedded software architecture. The embedded software provides a set of tools to facilitate remote manageability of the deployed hardware, including safe reconfiguration of the firmware. This paper presents the architecture and the techniques, methods and implementation of the embedded software running in the acquisition nodes of the KM3NeT neutrino telescopes. Program summaryProgram title: Embedded software for the KM3NeT CLBCPC Library link to program files:https://doi.org/10.17632/s847hpsns4.1Licensing provisions: GNU General Public License 3Programming language: CNature of problem: The challenge for the embedded software in the KM3NeT neutrino telescope lies in orchestrating the Digital Optical Modules (DOMs) to achieve the synchronized data acquisition of the incoming optical signals. The DOMs are the crucial component responsible for capturing neutrino interactions deep underwater. The embedded software must configure and precisely time the operation of each DOM. Any deviation or timing mismatch could compromise data integrity, undermining the scientific value of the experiment. Therefore, the embedded software plays a critical role in coordinating, synchronizing, and operating these modules, ensuring they work in unison to capture and process neutrino signals accurately, ultimately advancing our understanding of fundamental particles in the Universe.Solution method: The embedded software on the DOMs provides a solution based on a C-based bare-metal application, operating without a real-time embedded OS. It is loaded into the RAM during FPGA configuration, consuming less than 256 kB of RAM. The software architecture comprises two layers: system software and application. The former offers OS-like features, including a multitasking scheduler, firmware updates, peripheral drivers, a UDP-based network stack, and error handling utilities. The application layer contains a state machine ensuring consistent program states. It is navigated via slow control events, including external inputs and autonomous responses. Subsystems within the application code control specific acquisition electronics components via the associated driver abstractions.Additional comments including restrictions and unusual features: Due to the operation conditions of the neutrino telescope, where access is restricted, the embedded software implements a fail-safe procedure to reconfigure the firmware where the embedded software runs.

Network shortcut in data plane of service mesh with eBPF

In recent years, the adoption of the service mesh as a dedicated infrastructure layer to support cloud-native systems has gained significant popularity. Service meshes involve the incorporation of proxies to handle communication between microservices, thereby speeding up the development and deployment of microservice applications. However, the use of service meshes also increases the request latency because they elongate the packet transmission between services. After investigating the transmission path of packets in a representative service mesh Istio, we observed that the service mesh dedicates approximately 25% of its time to packet transmission in the Linux kernel network stack. To shorten this process, we propose a non-intrusive solution that enables packets to bypass the kernel network stack through the implementation of socket redirection and tc (traffic control) redirection with eBPF (extended Berkeley Packet Filter). We also conduct comprehensive experiments on the widely-used Istio. The evaluation results show that our approach can significantly reduce the request latency by up to 21%. Furthermore, our approach decreases CPU usage by 1.73% and reduces memory consumption by approximately 0.98% when compared to the original service mesh implementation.