Utilization Of Computing Resources Research Articles

Research on the Transport of Typical Pollutants in the Yellow Sea with Flow and Wind Fields

In this study, we developed a transport model for typical pollutants in the Yellow Sea using the Lagrangian particle tracking method to analyze the trajectories of fish feed, a common pollutant in the Yellow Sea. The model incorporates the influence of ocean currents and surface winds on pollutant transport and utilizes a series of numerical experiments to simulate pollutant transport. Through statistical analysis of the numerical experiment results, we identified characteristic circles that represent the pollutant distribution patterns. Furthermore, based on the current and wind information within these characteristic circles, we derived an empirical formula to describe pollutant distribution. This formula enables us to predict the spatial distribution of pollutants using available current and wind data. Using this empirical formula, we designed an effective path to avoid pollutant contamination. This approach not only optimizes the utilization of computational resources within the study area but also contributes to the rational planning of navigation routes for aquaculture vessels. Overall, our study provides valuable insights into the transport behavior of fish feed pollutants in the Yellow Sea. The establishment of the empirical formula and the design of effective routes to avoid pollution contribute to the efficient management of pollution and facilitate the planning of marine activities in the region.

Evaluating Structural Details' Influence on Elastic Wave Propagation for Composite Structures via Ray Tracing.

This study presents a novel method based on ray tracing for analyzing wave propagation in composites specifically tailored for structural health monitoring applications. This method offers distinct advantages over the commonly used finite element method mainly in computational resource utilization, which has become a limiting factor for these kinds of analyses. The ray tracing method is evaluated against a number of example cases representing structural details such as thickness changes, stringers, or simulated damage, and the significance of ray tracing to study wave propagation under these conditions and how it can serve as a valuable tool for structural health monitoring are highlighted. This model has been developed as part of a complete SHM framework with the intention of being an efficient and simple way to calculate wave propagation and therefore it could be used as a way to determine relevant damage indicators or train an artificial intelligence model.

Partition-Tolerant and Byzantine-Tolerant Decision Making for Distributed Robotic Systems With IOTA and ROS2

With the increasing ubiquity of autonomous robotic solutions, the interest in their connectivity and in the cooperation within multi-robot systems is rising. Two aspects that are a matter of current research are robot security and secure multirobot collaboration robust to byzantine agents. Blockchain and other distributed ledger technologies (DLTs) have been proposed to address the challenges in both domains. Nonetheless, some key challenges include scalability and deployment within realworld networks. This paper presents an approach to integrating IOTA and ROS 2 for more scalable DLT-based robotic systems while allowing for network partition tolerance after deployment. This is, to the best of our knowledge, the first implementation of IOTA smart contracts for robotic systems, and the first integrated design with ROS 2. This is in comparison to the vast majority of the literature which relies on Ethereum. We present a general IOTA+ROS 2 architecture leading to partitiontolerant decision-making processes that also inherit byzantine tolerance properties from the embedded blockchain structures. We demonstrate the effectiveness of the proposed framework for a cooperative mapping application in a system with intermittent network connectivity. We show both superior performance with respect to Ethereum in the presence of network partitions, and a low impact in terms of computational resource utilization. These results open the path for wider integration of blockchain solutions in distributed robotic systems with less stringent connectivity and computational requirements.

A novel data augmentation approach for enhancement of ECG signal classification

The use of deep learning models in the classification of medical diseases has evolved drastically in recent years. One such prominent application was in the classification of ECG data with the goal of achieving high accuracy and computational efficiency. However, several challenges remain to be resolved to achieve this goal, such as finding a generalized model, data imbalance, and preserving patient privacy. This research aims to overcome the first two challenges. The novelty of the proposed work is to introduce unique data augmentation for time series ECG signal data based on simple segmentation and then re-arrangement of them. Simple in nature makes it more convenient to use minimal memory for the processing and allows to obtain well-distinguished synthetic signals. Additionally, a less computationally expensive four layers convolutional neural network (CNN) model has also been proposed. The original ECG signal wrapped in a Numpy array was divided into segments of identical length. During segment re-organization, a few section values are investigated, crucial for joining the segments back into a proper signal structure. The proposed algorithm has been evaluated in two ways, including similarity & features measure and classification accuracy through customized CNN & transfer learning model with five-fold cross-validation. The study results revealed that the proposed augmentation algorithm achieved a validation accuracy of 89.87% with 88.99% recall and 0.291 loss by the four layers CNN model. The presented work can be implemented in two ways, i.e., ECG signals dataset enhancement and better utilization of computational resources through the simple CNN model architecture.

Pendampingan Operator Komputer Pada Bagian Kepegawaian Kantor Camat Praya Lombok Tengah

Developments in the field of technology at this time provide a significant contribution and influence to the world of government. Management of government data such as data on employees, communities and other elements so that the process of conveying information becomes faster and more structured. With the assistance of optimizing observation activities carried out with students who get higher knowledge. The implementation method goes through several stages starting with the observation or observation stage, the activity implementation stage and the evaluation or monitoring stage. The obstacle experienced was that the participants just found out about the report, and they did not understand employee data reports. Because they are not used to only recording incoming and outgoing data that is used to achieve the many planned activities, the utilization of computer operator assistance in the personnel department is good so that it is very easy to receive important information and programs. Services are carried out informally and flexibly. Giving material begins with introducing recording and bookkeeping to participants, then also explaining the importance of ability or knowledge in implementing counselling activities. Increasing the level of understanding of computer operators about the importance of recording employee data reports in the office, utilization and use of computer resources is carried out according to the method that has been planned, namely through the stages of data collection, implementation of activities and evaluation. Participants gave positive responses to the activities that had been carried out.

A context-aware multi-agent reasoning based intelligent assistive formalism

In recent years, the rapid proliferation of intelligent smart resource-bounded devices and autonomous software technologies has significantly influenced human life and made human life much smarter, more efficient, flexible, comfortable, and secure but device dependent. These intelligent devices are equipped with the feature of context awareness that often run in a highly decentralized environment with minimal and/or without human intervention. As these systems are often distributed in nature, they exhibit complex adaptive behavior by exchanging contextualized information among different resource-bounded devices and/or systems. However, due to the limitation of resources (computational memory), there are still many challenges for a real-time deployment. In this paper, we present a resource-bounded multi-agent reasoning-based context-aware model that assists users intelligently to take the right action autonomously whenever and wherever needed. The core emphasis is given to the lightweight, efficient reasoning mechanism to manipulate rapidly triggering contextual information with the least utilization of computational resources. We develop algorithms along with the complexity factors to evaluate the system’s efficacy in computational time and memory utilization. To suitably formalize the specification and model the proposed formalism using UPPAAL model checker, we develop the case study of a context-aware intelligent assistant along with its rule-based reasoning strategies and priorities for the execution of prioritized tasks according to the customized needs of the user and verify the correctness properties.

PIRAP: AASM: An Advanced Attribute-Based Security Model for Enhancing Trustworthy Data Sharing in Cloud Computing

In today’s world, cloud computing is widely used in various applications and services, which refers to the utilization of computational resources as per user requirements through the Internet. However, despite the numerous advancements in cloud services and applications, there are various security threats associated with it, mainly due to data outsourcing to third-party controlled data centers. In this context, this paper introduces a new model called Attribute-based Advanced Security Model (AASM) for Reliable Data Sharing in the Cloud. This model combines the Advanced Encryption Technique (AET) with Attribute-Based Signature (ABS) to ensure secure data sharing in the Cloud while efficiently controlling data access. The model enables encrypted access control from the data owner’s side with advanced access privileges, ensuring user privacy with an anonymous authentication model using ABS. By implementing these measures, the model provides security for cloud providers and users while safeguarding against malicious attacks. The effectiveness of the proposed model is evaluated based on factors such as time complexities, security and accountability.

Advanced data analytics using three-stage intelligent model pipelining for containerized microservices in 5G networks and beyond

The recent rapid advancement of cloud-native networking infrastructure has leveraged the resource virtualization technology of containers to realize diverse microservice-based applications in 5G/6G networks and clouds. Containers drastically enhance the efficiency of computational resource allocation and utilization as compared to the related virtualization technology of Virtual Machines (VMs). The networking environment leveraging both VM and container mixed virtualization technologies makes the most use of them to realize a computational platform whose resources can be dynamically adjusted to a fine granularity. To continuously meet the required levels of quality of services in 5G/6G networks and clouds in that platform, an agile and autonomous data analytics system in the control plane is essential for the accurate prediction of server workloads and dynamic allocation of enough amount of computational resource. In this paper, we introduce a framework, which complies with Recommendation ITU-T Y.3177, for autonomous computational resource control and management. The framework consists of an advanced data analytics system and a resource control system. We propose an architecture for the advanced data analytics system consisting of learning and prediction components. The learning component includes a three-stage intelligent model pipelining with three cascaded machine learning models, nonlinear regression, clustering, and multiple regression. These models determine the fluctuation trends in CPU utilization, classify services with similarities in the trends, and predict the peak CPU utilization of each containerized microservice. We evaluate the proposed models through experiments and numerical analysis. The results prove that the models support agile data analytics, which can complete data processing in the time granularity of seconds and achieve higher prediction accuracy of CPU utilization.

Task Containerization and Container Placement Optimization for MEC: A Joint Communication and Computing Perspective

Containers are used by an increasing number of Internet service providers to deploy their applications in multi-access edge computing (MEC) systems. Although container-based virtualization technologies significantly increase application availability, they may suffer expensive communication overhead and resource use imbalances. However, so far there has been a scarcity of studies to conquer these difficulties. In this paper, we design a workflow-based mathematical model for applications built upon interdependent multitasking composition, formulate a multi-objective combinatorial optimization problem composed of two subproblems—graph partitioning and multi-choice vector bin packing, and propose several joint task-containerization-and -container-placement methods to reduce communication overhead and balance multi-type computing resource utilization. The performance superiority of the proposed algorithms is demonstrated by comparison with the state-of-the-art task and container scheduling schemes.

Human Activity Recognition in a Realistic and Multiview Environment Based on Two-Dimensional Convolutional Neural Network

Recognition of human activity based on convolutional neural network has received the interest of researchers in recent years due to its significant improvement in accuracy. A large number of algorithms based on the deep learning approach have been proposed for activity recognition purpose. However, with the increasing advancements in technologies having limited computational resources, it needs to design an efficient deep learning-based approaches with improved utilization of computational resources. This paper presents a simple and efficient 2-Dimensional convolutional neural network (2-D CNN) architecture with very small size convolutional kernel for human activity recognition. The merit of the proposed CNN architecture over standard deep learning architectures is fewer trainable parameters and lesser memory requirement which enables it to train the proposed CNN architecture on low GPU memory-based devices and also works well with smaller as well as larger size datasets. The proposed approach consists of mainly four stages: namely (1) creation of dataset and data augmentation, (2) designing 2-D convolutional neural network (CNN) architecture, (3) the proposed 2-D CNN architecture trained from scratch up to optimum stage, and (4) evaluation of the trained 2-D CNN architecture. To illustrate the effectiveness of the proposed architecture several extensive experiments are conducted on three publicly available datasets, namely IXMAS, YouTube, and UCF101 dataset. The results of the proposed method and its comparison with other state-of-the-art methods [8-12,14,18-26,29-33] demonstrate the usefulness of the proposed method.

Modeling and optimization of collaborative computing in regional multi-energy systems for energy Internet

Multi-energy systems (MES) exploit advanced physical information technology and innovative management pattern to achieve collaborative control of multiple heterogeneous energy. The refined control of MES bursts massive delay-sensitive computing tasks, requiring precise system modeling and collaborative computing strategies. However, the unbalanced spatial and temporal distribution of resources and real-time requirements of tasks further increase the difficulty of computing. To address these challenges, a precise modeling and optimization method of collaborative computing in MES is proposed. First, an integrated system framework for regional multi-energy systems (RMES) in northeastern China is designed. Then, the collaborative computing problem with energy and delay constraints in RMES is modeled. It is proved that the problem can be transformed into a joint convex and nonconvex optimization problem. Moreover, an improved simulated annealing-based joint resources optimization (ISA-JRO) scheme is proposed. Finally, extensive simulation results show that ISA-JRO significantly improves the computational resource utilization ratio and reduces the total cost in MES. ISA-JRO reduces at least 25 % of the total cost compared with the traditional methods.

Merak: An Efficient Distributed DNN Training Framework With Automated 3D Parallelism for Giant Foundation Models

Foundation models are in the process of becoming the dominant deep learning technology. Pretraining a foundation model is always time-consuming due to the large scale of both the model parameter and training dataset. Besides being computing-intensive, the pretraining process is extremely memory- and communication-intensive. These challenges make it necessary to apply 3D parallelism, which integrates data parallelism, pipeline model parallelism, and tensor model parallelism, to achieve high training efficiency. However, current 3D parallelism frameworks still encounter two issues: i) they are not transparent to model developers, requiring manual model modification to parallelize training, and ii) their utilization of computation resources, GPU memory, and network bandwidth is insufficient. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Merak</i> , an automated 3D parallelism deep learning training framework with high resource utilization. Merak automatically deploys 3D parallelism with an automatic model partitioner, which includes a graph-sharding algorithm and proxy node-based model graph. Merak also offers a non-intrusive API to scale out foundation model training with minimal code modification. In addition, we design a high-performance 3D parallel runtime engine that employs several techniques to exploit available training resources, including a shifted critical path pipeline schedule that increases computation utilization, stage-aware recomputation that makes use of idle worker memory, and sub-pipelined tensor model parallelism that overlaps communication and computation. Experiments on 64 GPUs demonstrate Merak's capability to speed up training performance over state-of-the-art 3D parallelism frameworks of models with 1.5, 2.5, 8.3, and 20 billion parameters by up to 1.42, 1.39, 1.43, and 1.61×, respectively.

BV-ICVs: A privacy-preserving and verifiable federated learning framework for V2X environments using blockchain and zkSNARKs

As part of vehicle to everything (V2X) environments, intelligent connected vehicles (ICVs) generate a large amount of data, which can be exploited securely and effectively through decentralized techniques such as federated learning (FL). Existing FL systems, however, are vulnerable to attacks and barely meet the security requirements for real-world applications. If malicious or compromised ICVs upload inaccurate or low-quality local model updates to the central aggregator, they may reduce the accuracy of the global model, thereby reducing drivers safety and efficiency. This paper aims to alleviate these concerns by presenting BV-ICVs, a blockchain-enabled and privacy-preserving FL framework for ICVs in an edge-envisioned V2X environment. This system uses Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zkSNARKs) verification that is compiled as smart contracts to prevent malicious, compromised or even rational ICVs from uploading unreliable, erroneous or low-quality model updates. The verification process is embedded within the consensus of the underlying permissioned blockchain, which maximizes both the efficiency of the process and the utilization of computer resources. As demonstrated by discussions, security analysis, and numerical results, BV-ICVs reduced data poisoning attacks and increased the privacy protection and accuracy of FL.

An ALBERT-based TextCNN-Hatt hybrid model enhanced with topic knowledge for sentiment analysis of sudden-onset disasters

Sudden-onset disasters put forward new requirements for on the state authorities’ ability to analyze public opinion sentiment. However, traditional sentiment analysis methods ignore the contextual semantic relationships and out-of-vocabulary words, and their computational resource utilization is excessive compared to their expected accuracy. In this paper, an ALBERT-based model combined with a text convolution neural network, a hierarchical attention mechanism and the latent Dirichlet allocation is proposed to create a hybrid model enhanced with topic knowledge for sentiment analysis of sudden-onset disasters. Weibo text data from a rainstorm disaster in China are used to evaluate the model’s performance. Compared with the XLNet, DistilBERT and RoBERTa models, the experimental results demonstrate that the proposed approach is capable of achieving better performance by incorporating external topic knowledge into the language representation model to compensate for the limited vocabulary data.

Dynamic event‐triggered leader‐following consensus of nonlinear multi‐agent systems with measurement noises

AbstractThis paper investigates a leader‐following consensus problem for nonlinear multi‐agent systems (MASs) with measurement noises under fixed and Markovian switching topologies, respectively. Noises are considered when each agent measures the states of its neighbours, where intensities of noises are vector functions of relative states. To alleviate the utilization of communication and computation resources, a dynamic event‐triggered consensus protocol is designed, where the coupling strength is restricted in a given interval. By using the stochastic stability theorem, it is shown that leader‐following consensus is achieved under fixed topology, where an estimation of the convergence rate is given. Moreover, the problem is studied under switching topologies subjecting to the Markovian process, which is applicable to practical situations with a time‐varying communication environment. Finally, simulation examples are given to show the correctness of the proposed results.

A High-Performance FPGA-Based Depthwise Separable Convolution Accelerator

Depthwise separable convolution (DSC) significantly reduces parameter and floating operations with an acceptable loss of accuracy and has been widely used in various lightweight convolutional neural network (CNN) models. In practical applications, however, DSC accelerators based on graphics processing units (GPUs) cannot fully exploit the performance of DSC and are unsuitable for mobile application scenarios. Moreover, low resource utilization due to idle engines is a common problem in DSC accelerator design. In this paper, a high-performance DSC hardware accelerator based on field-programmable gate arrays (FPGAs) is proposed. A highly reusable and scalable multiplication and accumulation engine is proposed to improve the utilization of computational resources. An efficient convolution algorithm is proposed for depthwise convolution (DWC) and pointwise convolution (PWC), respectively, to reduce the on-chip memory occupancy. Meanwhile, the proposed convolution algorithms achieve partial fusion between PWC and DWC, and improve the off-chip memory access efficiency. To maximise bandwidth utilization and reduce latency when reading feature maps, an address mapping method for off-chip accesses is proposed. The performance of the proposed accelerator is demonstrated by implementing MobileNetV2 on an Intel Arria 10 GX660 FPGA by using Verilog HDL. The experimental results show that the proposed DSC accelerator achieves a performance of 205.1 FPS, 128.8 GFLOPS, and 0.24 GOPS/DSP for input images of size 224×224×3.

Observer-based event-triggered adaptive neural control for time-delay nonlinear systems with input saturation and external disturbances

In this article, the issue of adaptive event-triggered tracking control is investigated for time-delay nonlinear systems with input saturation and external disturbances. In the whole process of control design, the radial basis function neural networks are utilized to approximate uncertain nonlinearities. To estimate the unknown states, a neural network-based observer is constructed. Pade approximation method is adopted to eliminate the effect of input delay. A smooth non-affine function is introduced to replace input saturation, and an auxiliary variable is employed to obtain the actual control input. An event-triggered strategy is designed to reduce the utilization of communication and computation resources. Moreover, the command filtering technique is applied to handle the issue of “explosion of complexity” in the conventional backstepping method. The designed controller can assure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Therefore, the proposed event-triggered neural control scheme can not only save network resources, but also improve the robustness of the system by dealing with input constraints and external disturbances. Finally, two simulation examples are given to verify the availability and feasibility of the designed controller.

Computation Offloading Method for Large-Scale Factory Access in Edge-Edge Collaboration Mode

Large-scale manufacturing enterprises have complex business processes in their production workshops, and the edge-edge collaborative business model cannot adapt to the traditional computation offloading methods, which leads to the problem of load imbalance. For this problem, a computation offloading algorithm based on edge-edge collaboration mode for large-scale factory access is proposed, called the edge and edge collaborative computation offloading (EECCO) algorithm. First, the method partitions the directed acyclic graphs (DAGs) on edge server and terminal industrial equipment, then updates the tasks using a synchronization policy based on set theory to improve the accuracy effectively, and finally achieves load balancing through processor allocation. The experimental results show that the method shortens the processing time by improving computational resource utilization and employs a heterogeneous distributed system to achieve high computing performance when processing large-scale task sets.

Edge-Cloud Continuum Solutions for Urban Mobility Prediction and Planning

In recent years, there has been an increase in the use of edge-cloud continuum solutions to efficiently collect and analyze data generated by IoT devices. In this paper, we investigate to what extent these solutions can manage tasks related to urban mobility, by combining real-time and low latency analysis offered by the edge with large computing and storage resources provided by the cloud. Our proposal is organized into three parts. The first part focuses on defining three application scenarios in which geotagged data generated by IoT objects, such as taxis, cars, and smartphones, are collected and analyzed through machine learning algorithms (i.e., next location prediction, location-based advertising, and points of interest recommendation). The second part is dedicated to modeling an edge-cloud continuum architecture capable of managing a large number of IoT devices and executing machine learning algorithms to analyze the data they generate. The third part analyzes the experimental results in which different design choices were evaluated, such as the number of devices and orchestration policies, to improve the performance of machine learning algorithms in terms of processing time, network delay, task failure, and computational resource utilization. The results highlight the potential benefits of edge and cloud cooperation in the three application scenarios, demonstrating that it significantly improves resource utilization and reduces the task failure rate compared to other widely adopted architectures, such as edge- or cloud-only architectures.

Joint Online Optimization of Task Rescheduling and Data Redistribution

&lt;p&gt;Wide-area distributed computing environment is the main platform for storing large amounts of data and conducting wide-area computing. Tasks and data are jointly scheduled among multiple computing platforms to improve system efficiency. However, large network latency and limited bandwidth in wide-area networks may cause a large delay in scheduling information and data migration, which brings low task execution efficiency and a long time waiting for data. Traditional works mainly focus on allocating tasks based on data locality or distributing data replications, but optimizing task allocation or data placement alone is insufficient from a global perspective. To mitigate the impact of large network latency and limited bandwidth on system performance, joint online optimization of task rescheduling and data redistribution is proposed in this study. The task allocation and data placement can be adjusted collaboratively during the system running process through the task stealing and backfilling mechanism and the data replication placement mechanism. The experimental results indicate that compared with the state-of-the-art method, the proposed method improves the system throughput and computing resource utilization by 20.67% and 20.26% respectively, and can significantly reduce the global data migration costs.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;