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

{"title":"15+ years of joint parallel application performance analysis/tools training with Scalasca/Score-P and Paraver/Extrae toolsets","authors":"","doi":"10.1016/j.future.2024.07.050","DOIUrl":"10.1016/j.future.2024.07.050","url":null,"abstract":"<div><p>The diverse landscape of distributed heterogeneous computer systems currently available and being created to address computational challenges with the highest performance requirements presents daunting complexity for application developers. They must effectively decompose and distribute their application functionality and data, efficiently orchestrating the associated communication and synchronisation, on multi/manycore CPU processors with multiple attached acceleration devices structured within compute nodes with interconnection networks of various topologies.</p><p>Sophisticated compilers, runtime systems and libraries are (loosely) matched with debugging, performance measurement and analysis tools, with proprietary versions by integrators/vendors provided exclusively for their systems complemented by portable (primarily) open-source equivalents developed and supported by the international research community over many years. The <em>Scalasca</em> and <em>Paraver</em> toolsets are two widely employed examples of the latter, installed on personal notebook computers through to the largest leadership HPC systems. Over more than fifteen years their developers have worked closely together in numerous collaborative projects culminating in the creation of a universal parallel performance assessment and optimisation methodology focused on application execution efficiency and scalability, and the associated training and coaching of application developers (often in teams) in its productive use, reviewed in this article with lessons learnt therefrom.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167739X24004187/pdfft?md5=6d95fa0157a348afe6a2f74eeb9b1f7c&pid=1-s2.0-S0167739X24004187-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141915232","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":"A cross-modal high-resolution image generation approach based on cloud-terminal collaboration for low-altitude intelligent network","authors":"","doi":"10.1016/j.future.2024.07.054","DOIUrl":"10.1016/j.future.2024.07.054","url":null,"abstract":"<div><p>The advancement of digitization and automation in Low Altitude Intelligent Networking (LAIN) is constrained by limited computational resources and the absence of a dedicated modal transformation mechanism, affecting the performance of latency-sensitive missions. This study addresses these challenges by proposing a Downscaling Reconstruction Multi-scale Locally Focused Generative Adversarial Network (DR-MFGAN) with Federated Learning (FL). This integration employs wavelet transform downscaling and zero-shot residual learning techniques to create noise-suppressed image pairs, ultimately facilitating high-quality image reconstruction. The core network structure is composed of multidimensional residual blocks and generative confrontation network, and feature extraction is further enhanced through cross channel attention mechanism. Finally, distributed training based on Federated Learning ensures the training effectiveness of nodes with small data volumes.Experimental results demonstrate significant improvements: an 18.18% reduction in Mean Squared Error (MSE), a 33.52% increase in Peak Signal to Noise Ratio (PSNR), and a 39.54% improvement in Learned Perceptual Image Patch Similarity (LPIPS). The edge terminal can provide high-resolution imagery with limited data, achieving precise cross-modal transformations. This approach enhances LAIN capabilities, addressing computational and transformation challenges to support critical latency-sensitive missions.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141915070","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":"Self-adaptive asynchronous federated optimizer with adversarial sharpness-aware minimization","authors":"","doi":"10.1016/j.future.2024.07.045","DOIUrl":"10.1016/j.future.2024.07.045","url":null,"abstract":"<div><p>The past years have witnessed the success of a distributed learning system called Federated Learning (FL). Recently, asynchronous FL (AFL) has demonstrated its potential in concurrency compared to mainstream synchronous FL. However, the inherent systematic and statistical heterogeneity has presented several impediments to AFL: On the client side, the discrepancies in trips and local model drift impede global performance enhancement; On the server side, dynamic communication leads to significant fluctuations in gradient arrival time, while asynchronous arrival gradients with ambiguous value are not fully leveraged. In this paper, we propose an adaptive AFL framework, ARDAGH, which systematically addresses the aforementioned challenges: Firstly, to address the discrepancies in client trips, ARDAGH ensures their convergence by incorporating only 1-bit feedback information into the downlink. Secondly, to counter the drift of clients, ARDAGH generalizes the local models by employing our novel adversarial sharpness-aware minimization, which does not necessitate reliance on additional global variables. Thirdly, in the face of gradient latency issues, ARDAGH employs a communication-aware dropout strategy to adaptively compress gradients to ensure similar transmission times. Finally, to fully unleash the potential of each gradient, we establish a consistent optimal direction by conceptualizing the aggregation as an optimizer with successive momentum. In light of the comprehensive solution provided by ARDAGH, an algorithm named FedAMO is derived, and its superiority is confirmed by experimental results obtained under challenging prototype and simulation settings. Particularly in typical sentiment analysis tasks, FedAMO demonstrates an improvement of up to 5.351% with a 20.056-fold acceleration compared to conventional asynchronous methods.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141915134","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":"A study on characterizing energy, latency and security for Intrusion Detection Systems on heterogeneous embedded platforms","authors":"","doi":"10.1016/j.future.2024.07.051","DOIUrl":"10.1016/j.future.2024.07.051","url":null,"abstract":"<div><p>Drone swarms are increasingly being used for critical missions and need to be protected against malicious users. Intrusion Detection Systems (IDS) are used to analyze network traffic in order to detect possible threats. Modern IDSs rely on machine learning models for this purpose. Optimizing the execution of resource-hungry IDS algorithms on resource-constrained drone devices, in terms of energy consumption, response time, memory footprint and guaranteed level of security, allows to extend the duration of missions. In addition, the embedded platforms used in drones often incorporate heterogeneous computing platforms on which IDSs could be executed. In this paper, we present a methodology and results about characterizing the execution of different IDS models on various processing elements, namely, Central Processing Units (CPU), Graphical Processing Units (GPU), Deep Learning Accelerators (DLA) and Field-Programmable Gate Array (FPGA). In effect, drones operate in different mission contexts in terms of criticality level, energy and memory budgets, and traffic load, so it is important to identify which IDS model to run on which processing element in a given context. For this sake, we evaluated several metrics on different platforms: energy and resource consumption, accuracy for malicious traffic detection and response time. Different models, namely Random Forests (RF), Convolutional Neural Networks (CNN) and Dense Neural Networks (DNN), have been implemented and characterized on different processing elements/platforms. This study has shown that relating the chosen implementation to the resources available on the drone is a judicious strategy to work on. It highlights the disparity between IDS implementations characteristics. For example, the inference time ranges from <span><math><mrow><mn>1</mn><mo>.</mo><mn>27</mn><mspace></mspace><mi>μ</mi><mi>s</mi></mrow></math></span> to 30 ms, the energy consumption per inference is between <span><math><mrow><mn>10</mn><mo>.</mo><mn>7</mn><mspace></mspace><mi>μ</mi><mi>J</mi></mrow></math></span> and 70 mJ, and the accuracy of the IDS models is between 65.73% and 81.59%. In addition, we develop a set of guidelines for choosing the best IDS model given a mission context.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979544","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":"Harnessing federated learning for anomaly detection in supercomputer nodes","authors":"","doi":"10.1016/j.future.2024.07.052","DOIUrl":"10.1016/j.future.2024.07.052","url":null,"abstract":"<div><p>High-performance computing (HPC) systems are a crucial component of modern society, with a significant impact in areas ranging from economics to scientific research, thanks to their unrivaled computational capabilities. For this reason, the worldwide HPC installation is steeply trending upwards, with no sign of slowing down. However, these machines are both complex, comprising millions of heterogeneous components, hard to effectively manage, and very costly (both in terms of economic investment and of energy consumption). Therefore, maximizing their productivity is of paramount importance. For instance, anomalies and faults can generate significant downtime due to the difficulty of promptly detecting them, as there are potentially many sources of issues preventing the correct functioning of computing nodes.</p><p>In recent years, several data-driven methods have been proposed to automatically detect anomalies in HPC systems, exploiting the fact that modern supercomputers are typically endowed with fine-grained monitoring infrastructures, collecting data that can be used to characterize the system behavior. Thus, it is possible to teach Machine Learning (ML) models to distinguish normal and anomalous states automatically. In this paper, we contribute to this line of research with a novel intuition, namely exploiting Federated Learning (FL) to improve the accuracy of anomaly detection models for HPC nodes. Although FL is not typically exploited in the HPC context, we show that FL can boost several types of underlying ML models, from supervised to unsupervised ones. We demonstrate our approach on a production Tier-0 supercomputer hosted in Italy. Applying FL to anomaly detection improves the average f-score from 0.46 to 0.87. Our research also shows FL can reduce the data collection time required to develop a representation data set, facilitating faster deployment of anomaly detection models. ML models need 5 months of training data for efficient anomaly detection performance while using FL reduces the training set by 15 times to 1.25 weeks.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141915220","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":"PoAh 2.0: AI-empowered dynamic authentication based adaptive blockchain consensus for IoMT-edge workflow","authors":"","doi":"10.1016/j.future.2024.07.048","DOIUrl":"10.1016/j.future.2024.07.048","url":null,"abstract":"<div><p>This paper introduces a significant advancement in the Proof of Authentication (PoAh) consensus algorithm, designed specifically for resource-constrained Internet of Things (IoT) devices. Building upon the foundations of PoAh consensus, this enhanced iteration, known as PoAh 2.0, integrates Artificial Intelligence (AI) at the block creator node level. This novel approach allows for the generation of block transactions embedded with AI-determined sensitivity and other applicable transaction-related metadata, a pioneering concept in this domain. The verifier node, a trusted entity, is tasked with verifying incoming blocks, utilizing the block header and its metadata information to determine authenticity while preserving the privacy of the content of the block’s data. A core innovation of PoAh 2.0 is its dynamic authentication mechanism, which adapts to the sensitivity level of the data within each block, behaving in an adaptive way based on the situation. AI plays a crucial role in this process, ensuring the block’s integrity and security are maintained. To demonstrate the efficacy of this advanced AI-enabled PoAh 2.0 consensus, we conducted a case study in an Internet of Medical Things (IoMT)-based eHealth scenario. The results from this study reveal that our developed dynamic authentication technique not only significantly enhances the original PoAh version but also establishes a new benchmark in block validation and security for eHealth applications. The integration of AI and improved dynamic authentication, calibrated to the security needs of each block, marks a novel and significant stride in blockchain research. This development not only enriches the current understanding of blockchain applications in IoT, but also sets a new direction for future research in secure and efficient blockchain implementations in the IoMT-Edge centric eHealth landscape.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167739X24004229/pdfft?md5=ab53936c53084f645fdff43a06a5a7c7&pid=1-s2.0-S0167739X24004229-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141915071","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":"Multi-objective federated learning: Balancing global performance and individual fairness","authors":"","doi":"10.1016/j.future.2024.07.046","DOIUrl":"10.1016/j.future.2024.07.046","url":null,"abstract":"<div><p>In federated learning, non-iid data not only diminishes the performance of the global model but also gives rise to the fairness problem which manifests as an increase in the variance of the global model’s accuracy across clients. Fairness issues can result in the global model performing poorly or even failing on certain clients. Existing methods addressing the fairness problem in federated learning tend to neglect the comprehensive improvement of both the average performance and fairness of the global model. In addressing it, the multi-objective optimization method for fine-tuning global gradients, FedMC algorithm is introduced in this paper. The primary objective is the average loss function of all clients, and the sub-objective involves fine-tuning the global gradient by reducing the gradient conflict between the global gradient and the local gradients. Specifically, we refine the global gradient by incorporating a sub-optimization objective aimed at alleviating conflicts between the global gradient and the local gradient with the largest deviation, denoted as FedMC. FedMC can enhance the performance and convergence rate of clients with initially poor performance, albeit at the cost of the earlier convergence rate of clients with initially good performance. Nevertheless, it enables the latter to reach the accuracy level achieved before fine-tuning. In addition, we also propose FedMC+ algorithm, owning three additional optimization mechanisms built upon the FedMC optimization objective which includes the decay of hyperparameter, the sliding window mechanism, and data-balanced client selection. Besides, we present a theoretical analysis of the convergence rate of FedMC, demonstrating its convergence to a Pareto stationary solution. Our combined experimental results confirm that FedMC+ achieves an average 4.5% improvement in accuracy and a 22% reduction in the degree of dispersion compared to state-of-the-art federated learning (FL) methods.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979555","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":"Speeding up the communications on a cluster using MPI by means of Software Defined Networks","authors":"","doi":"10.1016/j.future.2024.07.047","DOIUrl":"10.1016/j.future.2024.07.047","url":null,"abstract":"<div><p>The Open MPI library is widely employed for implementing the message-passing programming model on parallel applications running on distributed memory computer systems, such as large data centers. These applications aim to utilize the highest amount of resources required by High Performance Computing (HPC). The interconnection network is an essential part of the HPC environment, as processes on parallel applications are constantly communicating and sharing data. Software Defined Networking (SDN) is a different networking approach that separates the control plane from the data forwarding plane, which can be configured depending on the network status or specific requirements of parallel application communications. Given that the communication time significantly contributes to the overall execution time of a parallel program and considering the elapsed time during Open MPI initialization of TCP connections between processes in Ethernet networks, this paper proposes the integration of a software defined networking environment into the Open MPI library. The primary objective of our contribution is to provide the network controller with information about Open MPI processes, in order to configure the network during the initialization procedure of the Open MPI library. This may facilitate the development of SDN-based routing techniques that reduce communication times, and thus execution times, using application information, such as the Open MPI endpoints participating in a parallel program execution. To demonstrate the utility of the information provided by Open MPI processes, we have implemented a routing algorithm that will calculate the optimal paths between processes based on the weighted Dijkstra algorithm, using the number of flows traversing the topology links. The evaluation of the proposed mechanism utilizing a 2-stage fat tree topology and two parallel applications - a matrix product and the Model for Prediction Across Scales (MPAS) - showed significant improvements in execution time, with reductions of up to 2.5 times for a 4096 × 4096 matrix product and 1.3 times for an 8192 × 8192 matrix product, as well as a 1.5 times reduction for MPAS in the worst network occupancy scenario. This demonstrates the improvements in communication and therefore execution time.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167739X24004217/pdfft?md5=879ad982dcda72cf4341e57ad5bcfe85&pid=1-s2.0-S0167739X24004217-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141915133","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":"Deadline-constrained security-aware workflow scheduling in hybrid cloud architecture","authors":"","doi":"10.1016/j.future.2024.07.044","DOIUrl":"10.1016/j.future.2024.07.044","url":null,"abstract":"<div><p>A hybrid cloud is an efficient solution to deal with the problem of insufficient resources of a private cloud when computing demands increase beyond its resource capacities. Cost-efficient workflow scheduling, considering security requirements and data dependency among tasks, is a prominent issue in the hybrid cloud. To address this problem, we propose a mathematical model that minimizes the monetary cost of executing a workflow and satisfies the security requirements of tasks under a deadline. The proposed model fulfills data dependency among tasks, and data transmission time is formulated with exact mathematical expressions. The derived model is a Mixed-integer linear programming problem. We evaluate the proposed model with real-world workflows over changes in the input variables of the model, such as the deadline and security requirements. This paper also presents a post-optimality analysis that investigates the stability of the assignment problem. The experimental results show that the proposed model minimizes the cost by decreasing inter-cloud communications for dependent tasks. However, the optimal solutions are affected by the limitations that are imposed by the problem constraints.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167739X24004163/pdfft?md5=c48372b68cff4a3fe9055bfe40ee4ce5&pid=1-s2.0-S0167739X24004163-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979543","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":"Serverless computing in the cloud-to-edge continuum","authors":"","doi":"10.1016/j.future.2024.07.043","DOIUrl":"10.1016/j.future.2024.07.043","url":null,"abstract":"<div><p>Serverless computing is establishing itself as a way to efficiently run cloud applications while abstracting the underlying infrastructure complexity away from application developers. At the same time, edge computing provides cloud-like facilities toward the edge of the network, in closer proximity to client devices. Integration of serverless and edge computing is promising. However, many research challenges still need to be solved to let this integration unleash its full potential. This special issue brings together works making high-quality and original contributions in this field, either proposing innovative strategies, implementing and testing novel solutions, or making new serverless datasets available to the community.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":null,"pages":null},"PeriodicalIF":6.2,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141892051","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}