Future Generation Computer Systems-The International Journal of Escience最新文献

{"title":"Analyzing inference workloads for spatiotemporal modeling","authors":"","doi":"10.1016/j.future.2024.107513","DOIUrl":"10.1016/j.future.2024.107513","url":null,"abstract":"<div><p>Ensuring power grid resiliency, forecasting climate conditions, and optimization of transportation infrastructure are some of the many application areas where data is collected in both space and time. Spatiotemporal modeling is about modeling those patterns for forecasting future trends and carrying out critical decision-making by leveraging machine learning/deep learning. Once trained offline, field deployment of trained models for near real-time inference could be challenging because performance can vary significantly depending on the environment, available compute resources and tolerance to ambiguity in results. Users deploying spatiotemporal models for solving complex problems can benefit from analytical studies considering a plethora of system adaptations to understand the associated performance-quality trade-offs.</p><p>To facilitate the co-design of next-generation hardware architectures for field deployment of trained models, it is critical to characterize the workloads of these deep learning (DL) applications during inference and assess their computational patterns at different levels of the execution stack. In this paper, we develop several variants of deep learning applications that use spatiotemporal data from dynamical systems. We study the associated computational patterns for inference workloads at different levels, considering relevant models (Long short-term Memory, Convolutional Neural Network and Spatio-Temporal Graph Convolution Network), DL frameworks (Tensorflow and PyTorch), precision (FP16, FP32, AMP, INT16 and INT8), inference runtime (ONNX and AI Template), post-training quantization (TensorRT) and platforms (Nvidia DGX A100 and Sambanova SN10 RDU).</p><p>Overall, our findings indicate that although there is potential in mixed-precision models and post-training quantization for spatiotemporal modeling, extracting efficiency from contemporary GPU systems might be challenging. Instead, co-designing custom accelerators by leveraging optimized High Level Synthesis frameworks (such as SODA High-Level Synthesizer for customized FPGA/ASIC targets) can make workload-specific adjustments to enhance the efficiency.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain-based conditional privacy-preserving authentication scheme using PUF for vehicular ad hoc networks","authors":"","doi":"10.1016/j.future.2024.107530","DOIUrl":"10.1016/j.future.2024.107530","url":null,"abstract":"<div><p>Vehicular ad hoc networks (VANET) have been the key indispensable module of the future intelligent transportation system. Security and privacy are two essential attributes that protect the safe driving of vehicles. Over the last two decades, numerous conditional privacy-preserving authentication schemes have been presented for the VANET environment. However, existing schemes have various limitations, including security issues, high storage overhead, and frequent interactions. In order to bridge these difficulties, this work combines physically unclonable function and blockchain technology to construct a conditional privacy-preserving authentication scheme for the VANET environment. Specifically, we combine physical unclonable function and dynamic pseudonym techniques to generate unique pseudonym IDs dynamically and private keys using physical unclonable function to enhance privacy protection and resist physical attack. To reduce the number of communication rounds during the verification process, we deployed lightweight blockchain nodes to avoid direct communication between the receiver and the blockchain network. The proposed scheme demonstrates resilience against various potential attacks through comprehensive security analysis and proof. Furthermore, performance metrics indicate that our scheme outperforms similar schemes, making it suitable for resource-constrained VANET.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative adversarial networks to detect intrusion and anomaly in IP flow-based networks","authors":"","doi":"10.1016/j.future.2024.107531","DOIUrl":"10.1016/j.future.2024.107531","url":null,"abstract":"<div><p>Computer networks facilitate regular human tasks, providing services like data streaming, online shopping, and digital communications. These applications require more and more network capacity and dynamicity to accomplish their goals. The networks may be targeted by attacks and intrusions that compromise the applications that rely on them and lead to potential losses. We propose a semi-supervised systematic methodology for developing a detection system for traffic volume anomalies in IP flow-based networks. The system is implemented with a vanilla Generative Adversarial Network (GAN). The mitigation module is triggered whenever an anomaly is detected, automatically blocking the suspect IPs and restoring the correct network functioning. We implemented three versions of the proposed solution by incorporating Long Short-Term Memory (LSTM), 1D-Convolutional Neural Network (1D-CNN), and Temporal Convolutional Network (TCN) into the GAN internal structure. The experiments are conducted on three public benchmark datasets: Orion, CIC-DDoS2019, and CIC-IDS2017. The results show that the three considered deep learning models have distinct impacts on the GAN model and, consequently, on the overall system performance. The 1D-CNN-based GAN implementation is the best since it reasonably solves the mode collapse problem, has the most efficient computational complexity, and achieves competitive Matthews Correlation Coefficient scores for the anomaly detection task. Also, the mitigation module can drop most anomalous flows, blocking only a slight portion of legitimate traffic. For comparison with state-of-the-art models, we implemented 1D-CNN, LSTM, and TCN separately from the GAN. The generative networks show improved overall results in the considered performance metrics compared to the other models.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An efficient federated learning solution for the artificial intelligence of things","authors":"","doi":"10.1016/j.future.2024.107533","DOIUrl":"10.1016/j.future.2024.107533","url":null,"abstract":"<div><p>Federated Learning (FL) has gained popularity due to its advantages over centralized learning. However, existing FL research has primarily focused on unconstrained wired networks, neglecting the challenges posed by wireless Internet of Things (IoT) environments. The successful integration of FL into IoT networks requires tailored adaptations to address unique constraints, especially in computation and communication. This paper introduces Communication-Aware Federated Averaging (CAFA), a novel algorithm designed to enhance FL operations in wireless IoT networks with shared communication channels. CAFA primarily leverages the latent computational capacities during the communication phase for local training and aggregation. Through extensive and realistic evaluations in dedicated FL-IoT framework, our method demonstrates significant advantages over state-of-the-art approaches. Indeed, CAFA achieves up to a 4x reduction in communication costs and accelerates FL training by as much as 70%, while preserving model accuracy. These achievements position CAFA as a promising solution for the efficient implementation of FL in constrained wireless networks.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MobFedLS: A framework to provide federated learning for mobile nodes in V2X environments","authors":"","doi":"10.1016/j.future.2024.107514","DOIUrl":"10.1016/j.future.2024.107514","url":null,"abstract":"<div><p>Federated Learning (FL) is a promising approach for parameter normalisation in Machine Learning (ML) models, especially when data privacy and computing distribution are crucial. However, there are significant constraints in FL solutions, particularly concerning the handling of the mobility of participating nodes in the parameter aggregation processes, with a substantial impact on Vehicle to Everything (V2X) scenarios within the scope of smart cities. To address this challenge, we propose Mobile Federated Learning System (MobFedLS), a lightweight microservices-based framework capable of operating on various types of devices (mobile and non-mobile). MobFedLS features an interface to integrate ML models to cooperate in the FL process without intrusion between the parties. MobFedLS manages the entire federation process, from instantiating services on mobile nodes to the final parameter updates in the involved ML models and the release of resources used in all participating nodes. Additionally, MobFedLS handles node mobility and ensures the proper execution of federated processes, even with nodes entering and leaving at any stage of the aggregation process. To demonstrate the capabilities of MobFedLS, we use data collected through the city-scale infrastructure of Aveiro Tech City Living Lab (ATCLL), specifically the position of vehicles during their movement through the city. In the tests, we evaluate all phases of the aggregation process for mobile nodes. The results show that, even with intermittent connectivity to the city-infrastructure ATCLL, the MobFedLS system manages the node mobility and effectively handles node availability during the aggregation of ML model parameters.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167739X24004783/pdfft?md5=3c92be13be4749855108227401af7549&pid=1-s2.0-S0167739X24004783-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UAV-IRS-assisted energy harvesting for edge computing based on deep reinforcement learning","authors":"","doi":"10.1016/j.future.2024.107527","DOIUrl":"10.1016/j.future.2024.107527","url":null,"abstract":"<div><p>In the internet of everything (IoE) era, the proliferation of internet of things (IoT) devices is accelerating rapidly. Particularly, smaller devices are increasingly constrained by hardware limitations that impact their computational capacity, communication bandwidth, and battery longevity. Our research explores a multi-device, multi-access edge computing (MEC) environment within small cells to address the challenges posed by the hardware limitations of IoT devices in this environment. We employ wireless power transfer (WPT) to ensure these IoT devices have sufficient energy for task processing. We propose a system architecture in which an intelligent reflective surface (IRS) is carried by an unmanned aerial vehicle (UAV) to enhance communication conditions. For sustainable energy harvesting (EH), we integrate a normal distribution into the objective function. We utilize a softmax deep double deterministic policy gradients (SD3) algorithm, based on deep reinforcement learning (DRL), to optimize the computational and communication capabilities of IoT devices. Simulation experiments demonstrate that our SD3-based EH edge computing (EHEC-SD3) algorithm surpasses existing DRL algorithms in our explored environments, achieving more than 90% in overall optimization and EH performance.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142229598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Split ways: Using GAN watermarking for digital image protection with privacy-preserving split model training","authors":"","doi":"10.1016/j.future.2024.107523","DOIUrl":"10.1016/j.future.2024.107523","url":null,"abstract":"<div><p>In recent years, the significant importance of digital data in the Industrial Internet of Things (IIoT) is receiving more and more attention, followed by more copyright violation challenges to the transmission and storage of sensitive data. To address this issue, we propose a generative adversarial network (GAN)-based image watermarking in privacy-preserving split model training. In the first stage, we trained our model in split ways without the client sharing raw data to reduce privacy leakage, if any. In the second stage, we designed a GAN-based watermarking embedder and extraction network to imperceptibly embed sensitive information while enhancing robustness. Moreover, the sensitive mark is jointly encrypted and compressed before sending it to the server, thus protecting user confidentiality while reducing the bandwidth and storage demand. We tested our proposed scheme using multiple standard datasets such as div2k, CelebA, and Flickr. The results on the div2k datasets showed that the proposed method surpassed several state-of-the-art methods, with average PSNR and NC increasing by 47.75% and 26.72% respectively. Our joint encryption and compression method also achieved superior performance compared with other methods with an average NPCR and UACI increasing by 18.25% and 16.87% respectively. To the best of our knowledge, we are the first to explore a GAN-based watermarking in split learning ways for digital images.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acceleration offloading for differential privacy protection based on federated learning in edge intelligent controllers","authors":"","doi":"10.1016/j.future.2024.107526","DOIUrl":"10.1016/j.future.2024.107526","url":null,"abstract":"<div><div>When implementing Federated Learning (FL) on Edge Intelligence Controllers (EIC) in the Industrial Internet of Things (IIoT), it is important to consider the limitations of the EICs’ computational capabilities and to address potential privacy concerns. For the efficient and secure implementation of FL on EICs, three key issues require attention: (i) efficient deployment on EICs with limited computational capacity, (ii) avoiding privacy issues that arise from offloading strategies when using offloading to accelerate, and (iii) mitigating privacy leaks that may result from disclosed parameters. To address the aforementioned concerns, this paper proposes a task offloading model called <em>FedOffloading</em>. Employing Deep Reinforcement Learning (DRL) techniques, <em>FedOffloading</em> accelerates EIC training by offloading the training tasks of the model to the Edge servers (ES). It utilizes the Laplace distribution to safeguard the privacy of the offloading strategies. Meanwhile, to prevent privacy breaches caused by disclosed parameters, <em>FedOffloading</em> allows EICs to inject different levels of artificial noise before transmitting training data. Experimental studies conducted on a test platform reveal that, compared to classical FL, <em>FedOffloading</em> can reduce training time by 54.70%, and even up to 78.06% when training larger models. The <em>Security Module</em> effectively protects the offloading strategies, meeting privacy requirements while also minimizing training time. In addition, to prevent privacy leakage caused by EICs, we introduce noise in the parameters disclosed during training, and show that the intermediate activation data is more susceptible to noise.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain based computing power sharing in urban rail transit: System design and performance improvement","authors":"","doi":"10.1016/j.future.2024.06.021","DOIUrl":"10.1016/j.future.2024.06.021","url":null,"abstract":"<div><p>With the development of urban rail transit (URT), many latency-sensitive and computationally intensive tasks arise. Edge computing can provide low-latency computing service in URT systems. Edge servers cannot always process all incoming computing tasks in a timely manner when operating independently due to limited computing power resources. They need to collaborate frequently through peer-to-peer offloads. However, it is challenging for the server to select the appropriate computing power resources and corresponding network connections to fulfill its performance and cost requirement. More importantly, edge servers are deployed and managed by different computing departments, putting the task offload process at risk. We propose a blockchain-based computing power sharing system to achieve secure and efficient computing power sharing in URT systems. The blockchain provides auditing and checking functions to guarantee the security of computing power resource sharing. We further propose a method to optimize the computing power sharing strategy and node selection strategy in the computing power sharing workflow. The numerical findings reveal that the proposed scheme provides significant improvements in both departmental utility and business processing capability.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CG-Kit: Code Generation Toolkit for performant and maintainable variants of source code applied to Flash-X hydrodynamics simulations","authors":"","doi":"10.1016/j.future.2024.107511","DOIUrl":"10.1016/j.future.2024.107511","url":null,"abstract":"<div><p>CG-Kit is a new Code Generation tool-Kit that we have developed as a part of the solution for portability and maintainability for multiphysics computing applications. The development of CG-Kit is rooted in the urgent need created by the shifting landscape of high-performance computing platforms and the algorithmic complexities of a particular large-scale multiphysics application: Flash-X. To efficiently use computing resources on a heterogeneous node, an application must have a map of computation to resources and a mechanism to move the data and computation to the resources according to the map. Most existing performance portability solutions are focussed on abstracting the expression of computations so that a unified source code can be specialized to run on different resources. However, such an approach is insufficient for a code like Flash-X, which has a multitude of code components that can be assembled in various permutations and combinations to form different instances of applications. Similar challenges apply to any code that has composability, where a single specified way of apportioning work among devices may not be optimal. Additionally, use cases arise where the optimal control flow of computation may differ for different devices while the underlying numerics remain identical. This combination leads to unique challenges including handling an existing large code base in Fortran and/or C/C++, subdivision of code into a great variety of units supporting a wide range of physics and numerical methods, different parallelization techniques for distributed and shared memory systems and accelerator devices, and heterogeneity of computing platforms requiring coexisting variants of parallel algorithms. All of these challenges demand that scientific software developers apply existing knowledge about domain applications, algorithms, and computing platforms to determine custom abstractions and granularity for code generation. There is a critical lack of tools to tackle those problems. CG-Kit is designed to fill this gap by providing a user with the ability to express their desired control flow and computation-to-resource map in the form a pseudocode-like recipe. It consists of standalone tools that can be combined into highly specific and, we argue, highly effective portability and maintainability toolchains. Here we present the design of our new tools: parametrized source trees, control flow graphs, and recipes. The tools are implemented in Python. They are agnostic to the programming language of the source code targeted for code generation. We demonstrate the capabilities of the toolkit with two examples, first, multithreaded variants of the basic AXPY operation, and second, variants of parallel algorithms within a hydrodynamics solver, called Spark, from Flash-X that operates on block-structured adaptive meshes.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}