System Overhead Research Articles

Updates to i-PI package improve performance in widely used atomistic simulation software

Better integration with machine-learning models reduces computational overhead in systems by up to a factor of 10.

Blockchain-Based Cold Chain Traceability with NR-PBFT and IoV-IMS for Marine Fishery Vessels

Due to limited communication, computing resources, and unstable environments, traditional cold chain traceability systems are difficult to apply directly to marine cold chain traceability scenarios. Motivated by these challenges, we construct an improved blockchain-based cold chain traceability system for marine fishery vessels. Firstly, an Internet of Vessels system based on the Iridium Satellites (IoV-IMS) is proposed for marine cold chain monitoring. Aiming at the problems of low throughput, long transaction latency, and high communication overhead in traditional cold chain traceability systems, based on the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm, a Node-grouped and Reputation-evaluated PBFT (NR-PBFT) is proposed to improve the reliability and robustness of blockchain system. In NR-PBFT, an improved node grouping scheme is designed, which introduces a consistent hashing algorithm to divide nodes into consensus and candidate sets, reducing the number of nodes participating in the consensus process, to lower communication overhead and transaction latency. Then, a reputation evaluation model is proposed to improve the node selection mechanism of NR-PBFT. It enhances the enthusiasm of nodes to participate in consensus, which considers the distance between fishery vessels, data size, and refrigeration temperature factors of nodes to increase throughput. Finally, we carried out experiments on marine fishery vessels, and the effectiveness of the cold chain traceability system and NR-PBFT were verified. Compared with PBFT, the transaction latency of NR-PBFT shortened by 81.92%, the throughput increased by 84.21%, and the communication overhead decreased by 89.4%.

Definition and implementation of the Cloud Infrastructure for the integration of the Human Digital Twin in the Social Internet of Things

With the integration of virtualization technologies, the Internet of Things (IoT) is expanding its capabilities and quickly becoming a complex ecosystem of networked devices. The Social Internet of Things (SIoT), where intelligent things include social properties that improve functioning and user engagement, is the result of this progress. The SIoT still has issues with scalability, data management, and user-centric operations, despite tremendous progress. In order to overcome these obstacles, a strong architecture is needed that can handle the enormous number of IoT devices while simultaneously streamlining the user interface.This study provides a unique architecture for the IoT that uses containerization to efficiently deploy and manage services while integrating Virtual Users (VUs) and Social Virtual Objects (SVOs) into a scalable Cloud/Edge infrastructure. These innovative aspects collectively advance previous works presented in literature and focused on novel SIoT architectures and implementations, by addressing key challenges in scalability, efficiency, and automation within the SIoT. The proposed method presents an extensible, modular architecture that lets VUs self-manage IoT services, making user administration easier and improving system security and scalability. Important parts of the design include a host controller for container orchestration, a deployer for automated service deployment, and user clusters for aggregating VUs, SVOs, and apps to provide secured and efficient data sharing. We show through experimental assessment that the architecture can manage high-volume installations and operating needs, exceeding the conventional platform based on Google App Engine in terms of system overhead and deployment timeframes. The obtained results highlight how our suggested architecture, which provides an easy-to-use, scalable, and secure foundation for IoT deployments, has the potential to advance the SIoT landscape.

DBPBFT: A hierarchical PBFT consensus algorithm with dual blockchain for IoT

The Internet of Things (IoT) is composed of smart devices connected to a network that can send and receive large amounts of data with other devices, generating a lot of data for processing and analysis. Due to the fact that every transaction in blockchain is recorded, placed in a data block, and added to an immutable and secure data chain, blockchain is becoming one of the most promising solutions for enhancing IoT security issues. As more devices become intelligent, the scale of IoT systems, including residential IoT and industrial IoT, is on the rise. Consequently, the issue of resource consumption, stemming from the escalating system communication overhead, is becoming more pronounced. In order to improve the efficiency of the consensus process for residential IoT and reduce the overhead caused by the consensus process, this paper proposes a hierarchical PBFT consensus algorithm With Dual Blockchain for IoT (DBPBFT). Compared to industrial IoT, DBPBFT is more suitable for residential IoT with small scope and clear data classification. DBPBFT separates the responsibilities of dual chains, improving system scalability while also enhancing blockchain security. A chain is divided into several small groups, each responsible for a type of data, reducing system overhead and communication overhead. To avoid unnecessary view-change as much as possible, before consensus begins, each group will select the current view primary node based on reputation values. The simulation results show that the DBPBFT algorithm is superior to traditional algorithms. In terms of reducing communication overhead, compared with EPBFT and DPNPBFT, DBPBFT has increased by 73.8% and 53.1%, respectively. In terms of consensus efficiency, DBPBFT has improved by 34% compared to DPNPBFT.

Machine learning‐assisted anomaly detection for power line components: A case study in Pakistan

AbstractA continuous supply of electricity is necessary to maintain an acceptable standard of life, and the power distribution system's overhead line components play a crucial role in this matter. In Pakistan, identifying defective parts often necessitates human involvement. An unmanned aerial vehicle was used to gather a collection of 10,343 photos to automate this procedure. Using supervised and unsupervised machine learning methods, a number of automated anomaly detection systems were created. Support vector machine, random forest, VGG16, and ResNet50 were used as supervised machine learning models, and a convolutional auto‐encoder was used as the unsupervised machine learning model. VGG16 achieved the best accuracy of 99.00% while random forest achieved the worst accuracy of 72.49%. The convolutional auto‐encoder was successful in distinguishing between normal and abnormal components. The aforementioned machine learning models can be put on unmanned aerial vehicles to immediately identify defective parts.

LACT: Liveness-Aware Checkpointing to reduce checkpoint overheads in intermittent systems

Intermittent computing supports execution of the systems experiencing frequent power failures, such as battery-less devices powered by energy-harvesting. In such systems, checkpoint and recovery is a commonly adopted technique, where volatile system states are regularly saved to Non-Volatile Memory (NVM), to preserve computing progress between power cycles. Since checkpoint involves a large number of NVM accesses, which is expensive in terms of both latency and energy, reducing its overhead has been a significant research challenge. In this paper, we present LACT (Liveness-Aware CheckpoinTing), a compiler optimization technique to minimize checkpoint overhead in intermittent systems. At the time of checkpoint execution, there exist dead values in general, which will not be used or overwritten in the future. LACT examines such liveness information, especially in arrays, based on compile-time analysis and excludes the dead values from the checkpoint to reduce required checkpoint data, which is a previously unexplored optimization opportunity. Our evaluation shows that LACT can reduce 46.4% of required checkpoint data without any runtime support, leading to reduction of 31.5% in execution time and a 5.2% decrease in power consumption on average. Our experiments in real energy-harvesting environment demonstrates that such improvement translates to a 31.6% improvement in end-to-end execution time.

RON‐based cross‐chain routing optimization strategy in metaverse

AbstractMetaverse is a new ecology that integrates the digital world with the physical world, generates a mirror image of the real world based on digital twin technology, and guarantees the fairness of the virtual world through the trusted mechanism of blockchain. Cross‐chain communication technology is the key to realizing data circulation and collaborative processing between blockchains, which provides the communication foundation for the massive connection of blockchains in the metaverse. The current cross‐chain communication mode is dominated by direct‐connect routing, leading to network congestion and high propagation delay once the direct‐connect link fails and cannot be recovered quickly. To optimize direct‐connect routing, this paper proposed a cross‐chain routing optimization strategy based on RON (Resilient Overlay Network), that is, Cross‐Chain_RON, which firstly applies RON to reconstruct the direct‐connect routing model, and then selects the optimal link through the shortest‐path algorithm and policy routing, and combines with the RON performance database to improve the data transmission efficiency. Finally, the two communication modes were simulated by simulation tools. The experiments show that Cross‐Chain_RON outperforms the direct‐connect routing in terms of latency, rate of successful data transmission, and throughput in poor network environments, but at the expense of a certain amount of system overhead.

Blockchain-based secure optimized traceable scheme for smart and sustainable food supply chain

A large number of nodes, and significant system overhead are among the challenges. To tackle these problems, a new PBFT technique known as trace-PBFT (t-PBFT) is being contemplated for adoption in the food supply chain. The PBFT algorithm, short for Practical Byzantine Fault Tolerance, serves as the fundamental algorithm. The linkages throughout the supply chain may be categorized into three primary groups. As a response to the continuing discussion over the amount of data, the status of each node is continuously updated in real time. This metric is utilized to evaluate the dependability of the principal node and to analyze the overall performance of the nodes. To minimize the amount of data transmitted between nodes, we optimized the consensus process of the first algorithm. The distinct attributes of offering collaborative food technology were considered throughout this procedure. Experimental evidence indicates that the t-PBFT approach outperforms the PBFT algorithm in terms of throughput, request delay, and information overhead. An architectural paradigm is proposed by integrating the t-PBFT algorithm with a federated network. This model was developed in response to the distinct demands of the food supply chain. This model effectively captures data at each stage of the food supply chain to ensure information tracking while preserving the safety of the food technology flow process.

Integrated Protection Mechanisms for Mitigating Microarchitectural Attacks in Cloud Computing

By utilising the multi-tenancy characteristic, cloud computing promises to reduce expenses through less spending on hardware, infrastructure, and software. Even with all of its advantages, multi-tenancy poses hazards for cloud computing. Without suitable cloud security solutions, security concerns might end up being the main factor delaying adoption. Additionally, multi-tenancy enabled by virtualisation, which is one of the key elements of a cloud, creates significant security vulnerabilities and does not provide adequate isolation between various instances running on the same physical system. The three strategies we suggest to secure shared virtualised systems against microarchitectural attacks are presented in this re- search as a comprehensive solution. This includes experiments for combining the three approaches and assessing them in potential operational contexts. The assessment techniques have used several host systems to assess the system overhead, CPU usage, and protection accuracy. The studies we have conducted on both Debian 10 and Ubuntu 18.04 LTS physical servers utilising the KVM hypervisor demonstrate that our comprehensive protection can identify attacks with about 97% accuracy, and depending on how many mechanisms were used in the various experimental scenario settings, the proportion of CPU consumption has varied significantly. The CPU usage rate in experiments with different scenarios has ranged from 27% to 68%, while the average system load over 5 minutes has ranged from 1.40 to 4.2. This shows our proposed mechanisms are subject to refinement and enhancement, especially in cases that require a high processing load. Note that if we had used servers with more computing power, the results would certainly have been better.

An Analysis on Matching Mechanisms and Token Pruning for Late-interaction Models

With the development of pre-trained language models, the dense retrieval models have become promising alternatives to the traditional retrieval models that rely on exact match and sparse bag-of-words representations. Different from most dense retrieval models using a bi-encoder to encode each query or document into a dense vector, the recently proposed late-interaction multi-vector models (i.e., ColBERT and COIL) achieve state-of-the-art retrieval effectiveness by using all token embeddings to represent documents and queries and modeling their relevance with a sum-of-max operation. However, these fine-grained representations may cause unacceptable storage overhead for practical search systems. In this study, we systematically analyze the matching mechanism of these late-interaction models and show that the sum-of-max operation heavily relies on the co-occurrence signals and some important words in the document. Based on these findings, we then propose several simple document pruning methods to reduce the storage overhead and compare the effectiveness of different pruning methods on different late-interaction models. We also leverage query pruning methods to further reduce the retrieval latency. We conduct extensive experiments on both in-domain and out-domain datasets and show that some of the used pruning methods can significantly improve the efficiency of these late-interaction models without substantially hurting their retrieval effectiveness.

Enhancing Digital Payment Security with Biometric Authentication and AI: A Big Data Approach

The arrival of digital payment technologies has brought unprecedented convenience to businesses and consumers. However, the rapid adoption of digital payments relies on both parties trusting the technology to handle their transactions securely and accurately. Conventional approaches to security issues rely on complex and frequent updates to the software that underpins digital payment systems, as well as cryptographic solutions that are challenging to implement and may, from time to time, require replacement as older systems are hacked. The introduction of biometric data-driven payment security enhances the current security systems, reducing system overheads, and increasing customer confidence in the technology. Our approach is to leverage advances in Artificial Intelligence (AI) systems, driven by the latest developments in big data technology. This novel combination is capable of improving the dynamic three-tier digital payment security model, ultimately reducing both fraud and overheads and increasing trust from both businesses and consumers.

Who Should We Blame for Android App Crashes? An In-Depth Study at Scale and Practical Resolutions

Android system has been widely deployed in energy-constrained IoT devices for many practical applications, such as smart phone, smart home, healthcare, fitness, and beacons. However, Android users oftentimes suffer from app crashes, which directly disrupt user experience and could lead to data loss. Till now, the community have limited understanding of their prevalence, characteristics, and root causes. In this article, we make an in-depth study of the crash events regarding ten very popular apps of different genres, based on fine-grained system-level traces crowd-sourced from 93 million Android devices. We find that app crashes occur prevalently on the various hardware models studied, and better hardware does not seem to essentially relieve the problem. Most importantly, we unravel multi-fold root causes of app crashes, and pinpoint that the most crashes stem from the subtle yet crucial inconsistency between app developers’ supposed memory/process management model and Android’s actual implementations. We design practical approaches to addressing the inconsistency; after large-scale deployment, they reduce 40.4% of the app crashes with negligible system overhead. In addition, we summarize important lessons learned from this study, and have released our measurement code/data to the community.

MADRLOM: A Computation offloading mechanism for software-defined cloud-edge computing power network

Cloud-edge computing power network often exhibits complex and heterogeneous structures, posing several challenges to computation offloading that significantly impact network performance and the efficient utilization of computation resources. In this paper, we propose a cloud-edge computing power network architecture that efficiently integrates cloud and edge computing resources into a single network system using software-defined networking technology to support upper-layer business applications. In this context, existing computation offloading methods struggle with issues like users' personalized requirements for latency and energy consumption, as well as the inability to adapt to dynamically complex environments. To overcome these challenges, we introduce a computation offloading mechanism, MADRLOM, focusing on the network's heterogeneity and modeling the computation offloading problem at the cloud-edge end. We formalize the offloading problem as a Markov process and employ a multi-agent deep reinforcement learning algorithm based on priority experience replay sampling to address the planning problem of computation offloading, allowing user equipment to continuously optimize offloading strategies in response to environmental changes and achieve rational resource allocation. Through dynamic offloading strategies, computation tasks can be intelligently allocated to appropriate computing nodes, thereby achieving optimal resource utilization. We utilized the Mininet simulation platform to construct the experimental environment for the software-defined cloud-edge computing power network and compared it with several representative computation offloading strategies. The experimental results demonstrate that the MADRLOM significantly reduces the total system overhead in the software-defined cloud-edge computing network and shows excellent adaptability to environmental changes.

Taming the Elephants: Affordable Flow Length Prediction in the Data Plane

Machine Learning (ML) shows promising potential for enhancing networking tasks by providing early traffic predictions. However, implementing an ML-enabled system is a challenging task due to network devices limited resources. While previous works have shown the feasibility of running simple ML models in the data plane, integrating them into a practical end-to-end system is not an easy task. It requires addressing issues related to resource management and model maintenance to ensure that the performance improvement justifies the system overhead. In this work, we propose DUMBO, a versatile end-to-end system to generate and exploit early flow size predictions at line rate. Our system seamlessly integrates and maintains a simple ML model that offers early coarse-grain flow size prediction in the data plane. We evaluate the proposed system on flow scheduling, per-flow packet inter-arrival time distribution, and flow size estimation using real traffic traces, and perform experiments using an FPGA prototype running on an AMD(R)-Xilinx(R) Alveo U280 SmartNIC. Our results show that DUMBO outperforms traditional state-of-the-art approaches by equipping network devices data planes with a lightweight ML model. Code is available at https://github.com/cpt-harlock/DUMBO.

SRA-E-ABCO: terminal task offloading for cloud-edge-end environments

AbstractThe rapid development of the Internet technology along with the emergence of intelligent applications has put forward higher requirements for task offloading. In Cloud-Edge-End (CEE) environments, offloading computing tasks of terminal devices to edge and cloud servers can effectively reduce system delay and alleviate network congestion. Designing a reliable task offloading strategy in CEE environments to meet users’ requirements is a challenging issue. To design an effective offloading strategy, a Service Reliability Analysis and Elite-Artificial Bee Colony Offloading model (SRA-E-ABCO) is presented for cloud-edge-end environments. Specifically, a Service Reliability Analysis (SRA) method is proposed to assist in predicting the offloading necessity of terminal tasks and analyzing the attributes of terminal devices and edge nodes. An Elite Artificial Bee Colony Offloading (E-ABCO) method is also proposed, which optimizes the offloading strategy by combining elite populations with improved fitness formulas, position update formulas, and population initialization methods. Simulation results on real datasets validate the efficient performance of the proposed scheme that not only reduces task offloading delay but also optimize system overhead in comparison to baseline schemes.

Optimization Method of RTOS System Delay Scheduling Based on Multi-Core Processor

In order to improve the task execution efficiency of multi-core processor, this paper studies the system delay scheduling optimization method of real-time operating system based on multi-core processor. Firstly, the core structure analysis model of multi-core processor data fusion and data acquisition in RTOS system is established, and then the task information flow of delay scheduling in RTOS system is constructed by using the correlation dimension feature fusion method. Based on the power domain non orthogonal multiple access control method, the data fusion state factor of multi-core processor is calculated for the initial scheduling node, and multi-core processor is used to control load balancing in the delay balancing scheduling process of RTOS system, The delay scheduling and balance control of real-time operating system are realized by multi-core processor. Simulation results show that the algorithm has low bit error rate and low task overhead in RTOS system, and can improve the task execution efficiency of multi-core processor.

Hybrid Privacy Preserving Federated Learning Against Irregular Users in Next-Generation Internet of Things

While federated learning (FL) is a well-known privacy-preserving (PP) solution, recent studies demonstrate that it still has privacy problems and vulnerabilities, particularly in the context of the Next Generation Internet-of-Things (NG-IoT). Attackers on the server can potentially retrieve sensitive information such as data tabs and memberships. Additionally, current FL studies often overlook critical FL issues such as user dropout and low-quality data, which are prevalent in NG-IoT environments. In this article, we propose a privacy-preserving federated model in hybrid terms based on synchronous + asynchronous manner. This federated model removes the fundamental issues of FL-enabled NG-IoT environments while applying Two-Trapdoor Homomorphic Encryption (TTHE) to ensure the privacy and confidentiality of all components in the proposed model. Furthermore, our server protocol removes the effect of irregular users. In this regard, to control the impact of user dropout, we developed the hybrid LEGATO algorithm, which is asynchronous. To tackle users with low-quality data, we utilize the data-sharing method. Security analysis of the proposed model guarantees correctness, auditing, and PP in a federated setup. Also, in the performance evaluation, we achieved a high level of functionality and accuracy compared to peer works, while system overheads are shown to be lower.

Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority

The problem of multiple DAGs sharing satellite constellation resources has gradually attracted widespread attention. Due to the limited computing resources and energy consumption of satellite networks, it is necessary to formulate a reasonable multi-DAG task scheduling scheme to ensure the fairness of each workflow under the premise of considering latency and energy consumption. Therefore, in this paper, we propose a multi-DAG satellite network task scheduling algorithm based on cache-composite priority under the Software-Defined Networking satellite network architecture. The basic idea of this algorithm lies in the DAG selection phase, where not only are task priorities computed but also the concept of fair scheduling is introduced, so as to prevent the excessively delayed scheduling of low-priority DAG tasks. In addition, the concept of public subtasks is introduced to reduce the system overhead caused by repetitive tasks. The experimental results show that the hybrid scheduling strategy proposed in this paper can meet the demand of DAG scheduling and improve the degree of task completion while effectively reducing the task latency and energy consumption.

Design and implementation of user task offloading algorithm

After the service provider temporarily selects the required edge nodes based on social and storage capabilities, application execution causes the edge nodes to cache part of the application data. Therefore, offloading part of the application computing tasks to the selected edge nodes can effectively improve application execution performance. However, in cases where the resources of user’s IoT devices are insufficient, tasks can be further offloaded to traditional edge servers or even to the cloud to maximize application execution efficiency. In this paper, the entire uninstall utility is modeled as a weighted sum of task completion time and energy consumption. Under the premise of considering users’ preferences for completion time and energy consumption, a game-based uninstallation algorithm is proposed. The algorithm performs uninstallation by optimizing the uninstallation decision. Based on user preferences, the total system overhead is relatively small. The subsequent simulation experiments show that the algorithm can reduce system overhead on the basis of satisfying user preferences and has relatively good adaptability.

Attention-based deep learning approach for CSI feedback under 5G TDL channel

In 5G communication systems, accurate channel state information (CSI) is indispensable for signal detection and regulation at the base station side. However, frequent CSI feedback from users leads to excessive system overhead. To tackle this challenge, this paper puts forward a novel deep learning framework - HCNet based on attention mechanism and autoencoder, aiming to efficiently compress and reconstruct the high-dimensional time-varying CSI matrices in an end-to-end approach. The proposed framework incorporates a self-attention module in the encoder to explicitly capture global dependencies within the CSI matrix. Meanwhile, the decoder adopts gated recurrent unit networks to fully exploit inter-feature correlations and redundancy. To evaluate the performance, simulations are conducted using datasets conforming to current 5G time-varying TDL channel models. Results demonstrate superior performance over existing deep learning based feedback networks. Specifically, the proposed framework can reduce the normalized mean square error of CSI reconstruction by 4 dB under various compression ratios which confirms the effectiveness of the attention-enhanced autoencoder structure for compressive CSI sensing and feedback in practical dynamic communication systems.