Concurrency and Computation-Practice & Experience最新文献

{"title":"Optimizing Resource Estimation for Scientific Workflows in HPC Environments: A Layered-Bucket Heuristic Approach","authors":"Luis C. R. Alvarenga,&nbsp;Yuri Frota,&nbsp;Daniel de Oliveira,&nbsp;Rafaelli Coutinho","doi":"10.1002/cpe.8381","DOIUrl":"https://doi.org/10.1002/cpe.8381","url":null,"abstract":"<div>\u0000 \u0000 <p>As computational simulations become complex and the amount of processed data grows, executing scientific workflows in High-Performance Computing (HPC) environments is increasingly essential. However, accurately estimating the required computational resources for such executions presents a significant challenge, requiring a thorough examination of the workflow structure and the characteristics of the computational environment. This manuscript introduces the <span>GraspCC-LB</span> heuristic, based on the Greedy Randomized Adaptive Search Procedure (GRASP), for estimating the necessary resources for executing scientific workflows in HPC environments. Unlike existing methods, <span>GraspCC-LB</span> incorporates the layered structure of workflows into its estimation process. The proposed approach was evaluated using real traces of workflows from the fields of bioinformatics and astronomy. The resource estimations produced by <span>GraspCC-LB</span> were compared against the actual resource usage in a real-world HPC environment to evaluate its effectiveness. The results demonstrate the effectiveness of <span>GraspCC-LB</span> as a robust approach for resource optimization in the context of large-scale scientific workflows that require HPC capabilities.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Cold Start Prediction and Provisioning Optimization in Serverless Computing Using Deep Learning","authors":"N. Saravana Kumar,&nbsp;S. Selvakumara Samy","doi":"10.1002/cpe.8392","DOIUrl":"https://doi.org/10.1002/cpe.8392","url":null,"abstract":"<div>\u0000 \u0000 <p>Serverless computing has emerged as a significant framework for application development, offering benefits such as simplified deployment and enhanced developer productivity. Serverless designs accelerate app development, but user experience and performance are put at risk through the delay during a cold start. In this paper, an optimized concurrent provisioning methodology for the AWS Lambda environment has been proposed along with a cold start prediction technique based on deep learning. Employing historical data and real-time features like timestamp, invoke frequency, cold start indicator, duration, previous cold starts, event type, historical cold starts, time since the last cold start, and consecutive cold starts, an attention-based bi-directional gated recurrent unit (ABiGRU) is used to predict the cold start occurrences with high precision. In the AWS Lambda environment, the proposed DL model was able to predict the cold start likelihood for incoming Lambda invocations very accurately. In addition, the performance of the ABiGRU model is enhanced by hyperparameter tuning using the RMSProp optimizer. The findings of the experiments establish the proposed DL model to perform in the reduction of cold starts compared to the existing approach. Further, the ODL-CSP technique achieves an accuracy of 90.36%, a precision of 91.87%, a recall of 90.42%, an F1_score of 90.28%, and an MCC of 82.28% when applied to the testing dataset. Additionally, the proposed paradigm optimizes Lambdas using provisioned concurrency, similar to a function warmer. The proposed DL paradigm will eliminate cold start times by early deployment of Lambdas so that the ice age of the serverless architecture is eliminated.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143389266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Convolutional Neural Networks for Imbalanced Advanced Security Network Metrics and Non-Payload-Based Obfuscations Dataset to Detect Intrusion","authors":"Neda Matin,&nbsp;Mina Zolfy Lighvan,&nbsp;Najibeh Farzi-Veijouyeh","doi":"10.1002/cpe.8377","DOIUrl":"https://doi.org/10.1002/cpe.8377","url":null,"abstract":"<div>\u0000 \u0000 <p>Network Intrusion Detection Systems (NIDSs) are essential for identifying and preventing cyber threats in modern networks. However, improving their adaptability and responsiveness to unforeseen threats remains a significant challenge. Among the various datasets available for NIDS, the ASNM dataset holds particular significance due to its rich diversity of network traffic traces, including detailed labels for legitimate traffic, direct attacks, and obfuscated network attacks. Despite its potential, the ASNM dataset has been relatively underexplored in existing research, presenting an opportunity for further investigation and application in the development and benchmarking of advanced NIDS models. In this paper, we introduce a convolutional neural network (CNN) architecture designed for network intrusion detection, which autonomously learns patterns and anomalies directly from raw network data. Unlike traditional methods that rely on handcrafted features or predefined signatures, the proposed CNN dynamically adapts to detect both known and obfuscated attacks. The quality and balance of the dataset used to train NIDSs are critical, as imbalanced data can skew detection results and significantly impact overall system performance. To address this, we introduce an innovative preprocessing technique to mitigate class imbalances, ensuring more accurate classification across all categories, including under-represented attack types. The proposed CNN architecture was rigorously tested against decision trees, neural networks, and k-nearest neighbor classifiers, demonstrating superior performance. The model achieved a True Positive Rate (TPR) of 99.43% and an average recall of 99.26% on a balanced dataset, significantly outperforming traditional models. Furthermore, the preprocessing method improved the TPR by 62% and 83% on datasets with and without obfuscated samples, respectively, highlighting its effectiveness in addressing dataset imbalances and improving detection accuracy. In conclusion, the combination of the ASNM dataset's comprehensive attack scenarios and the dynamic feature-learning capabilities of the proposed CNN represents a significant advancement in intrusion detection technology.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 New Multi-Objective Binary Bat Algorithm for Feature Selection in Intrusion Detection Systems","authors":"Mohamed Amine Laamari,&nbsp;Nadjet Kamel","doi":"10.1002/cpe.70000","DOIUrl":"https://doi.org/10.1002/cpe.70000","url":null,"abstract":"<div>\u0000 \u0000 <p>Monitoring network traffic and detecting security threats is a vital task in today's world, and intrusion detection systems (IDS) have become an essential tool for this purpose. However, IDSs have to analyze large volumes of data, which often contain irrelevant and redundant features. This makes the job of IDSs more challenging, as they must sift through all available features to identify attack patterns, leading to longer processing time and reduced detection accuracy. To address this, we propose a new wrapper approach for solving the feature selection (FS) problem. Our proposed approach uses a novel multi-objective binary bat algorithm (MBBA-FS) with a decision tree classifier. The MBBA-FS aims to produce a set of non-dominated solutions that minimize the number of features used while maintaining a high detection accuracy. Then, we use a frequency ranking method to identify a single subset of relevant features from the resulting set of non-dominated solutions. We tested the feasibility and performance of our approach against other leading FS methods using various datasets, including KDD CUP 1999, NLS-KDD, UNSW-NB15, and several synthetic benchmarks. The experimental results show that MBBA-FS outperforms existing FS approaches in terms of classification accuracy and number of selected features.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"ONOS Flood Defender: A Real-Time Flood Attacks Detection and Mitigation System in SDN Networks","authors":"Hussein Younis,&nbsp;Mohammad M. N. Hamarsheh","doi":"10.1002/cpe.8388","DOIUrl":"https://doi.org/10.1002/cpe.8388","url":null,"abstract":"<div>\u0000 \u0000 <p>Cybercriminals are constantly developing new and sophisticated methods for exploiting network vulnerabilities. Software-defined networking (SDN) faces security challenges more than other traditional networks because the controller is a bottleneck device. This necessitates the implementation of robust security systems, including intrusion detection to mitigate the effect of attacks. Distributed denial of service (DDoS) attacks targeting the centralized controller of an SDN network can disrupt the entire network. If the controller becomes unavailable due to an attack, flow rules (FRs) cannot be deployed at the network switches, affecting data forwarding and network management. This study focuses on the detection and mitigation of synchronized (SYN) and normal transmission control protocol (TCP) DDoS flood attacks. It introduces two enhanced statistical detection and mitigation algorithms that work seamlessly with the open network operating system (ONOS) SDN controller, and sFlow-RT engine in real-time. Through a comprehensive set of experiments, our empirical findings demonstrate that the proposed algorithms efficiently detect and mitigate attacks with minimal average detection time, and negligible impact on resource consumption. By utilizing tuned threshold values based on network traffic volume, TCP flood attack detection (TFAD) algorithm and the synchronized TCP flood attack detection (STFAD) Algorithm achieved a minimal average detection time, of 4.032 and 3.430 s, respectively. These algorithms also have high detection accuracy in distinguishing normal traffic when appropriate threshold values are applied. Overall, this research significantly contributes to fortifying SDN networks with robust security measures, enhancing their resilience against evolving cyber threats.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Dynamic Multi-Objective Workflow Scheduling Model in Cloud Environment Based on Adaptive Mutation Strategy","authors":"Tao Ye,&nbsp;Zhihua Cui","doi":"10.1002/cpe.8363","DOIUrl":"https://doi.org/10.1002/cpe.8363","url":null,"abstract":"<div>\u0000 \u0000 <p>In the cloud computing environment, workflow scheduling presents a significant challenge due to the unpredictable and dynamic nature of user demands and cloud resources. To address the complexities of workflow scheduling, this paper introduces a dynamic multi-objective workflow scheduling model that comprehensively considers task completion time, load balancing, as well as dynamic changes in power consumption and cost in real-world scenarios. To effectively solve this model and better adapt to dynamic multi-objective optimization problems, we propose a dynamic reference vector guided evolutionary algorithm (DRVEA). The proposed algorithm incorporates an adaptive random mutation strategy, which dynamically adjusts the evolutionary process based on changing optimization goals, thereby enhancing convergence and solution diversity. Experimental results, obtained from both workflow scheduling simulations and standard multi-objective test environments, demonstrate that the proposed algorithm outperforms existing methods, achieving superior results in both solution quality and adaptability.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Robust Watermarking for Camera-Captured Images Using Few-Shot Learning and Simulated Noise Layer","authors":"Guoquan Yuan,&nbsp;Xinjian Zhao,&nbsp;Shuaiqi Zhang,&nbsp;Shi Chen,&nbsp;Shanming Wei","doi":"10.1002/cpe.8389","DOIUrl":"https://doi.org/10.1002/cpe.8389","url":null,"abstract":"<div>\u0000 \u0000 <p>With the rise of social media and the spread of a large number of pictures on the Internet, protecting data privacy and verifying copyright has become hot research. A common method is to use digital watermarking. However, the existing blind watermarking methods only consider embedding the watermark in the image itself and ignore the fact that the attacker can remove the watermark through other shooting devices. Therefore, to solve this problem, we propose an image watermarking method based on few-shot learning. We use an autoencoder to learn the embedding and extraction of watermarks. Then, we propose a framework named Simulated Candid Shooting Layer (SCSL). The SCSL simulates a variety of candid scenes as noise data using meta-learning and enhances the robustness of image watermarking. Experiments show that the proposed method is superior to the state of the art in watermarking technologies in both robustness and invisibility of watermarking. Specifically, it achieved an improvement of over 8% in evaluation metrics against JPEG attacks. The proposed SCSL framework further enhanced these metrics by more than 5%.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"On Dynamics of Basic Network Creation Games With Non-Uniform Communication Interest","authors":"Maxime Dresler,&nbsp;Sanaï Mansour,&nbsp;Safaâ Talhaoui,&nbsp;Yukiko Yamauchi,&nbsp;Sébastien Tixeuil","doi":"10.1002/cpe.8361","DOIUrl":"https://doi.org/10.1002/cpe.8361","url":null,"abstract":"<div>\u0000 \u0000 <p>We consider the network construction process by selfish players. Each player is associated with a vertex of a communication graph and can simultaneously remove one incident edge and add a new incident edge. Each player is interested in a subset of players and the goal of each player is to minimize the average or maximum distance to these players. Starting from a given initial communication graph, a sequence of selfish edge swaps generates an evolution of the communication graph. Due to non-uniform communication interest, this game may converge to a disconnected Nash equilibrium, which may attain infinite social costs. In this paper, we focus on the dynamics of this game. We first give theoretical analysis such as the existence of a best response cycle and a sufficient condition for keeping connectivity in dynamics. We then present simulation results to show the ratio of Nash equilibria with infinite cost, diameters of Nash equilibria, social cost, price of anarchy, price of stability, and convergence time.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"PM2.5 Concentration Prediction Using CNN-LSTM Model Based on Multi-Feature Fusion","authors":"Zhiwen Wang,&nbsp;Jiexia Huang,&nbsp;Junlin Huang,&nbsp;Yuhang Wang,&nbsp;Canlong Zhang","doi":"10.1002/cpe.8391","DOIUrl":"https://doi.org/10.1002/cpe.8391","url":null,"abstract":"<div>\u0000 \u0000 <p>In order to solve the problem that the existing PM_2.5 concentration prediction methods ignore the spatial and temporal influencing factors of PM_2.5 concentration, this paper constructs a spatial characteristic factor of PM_2.5 concentration based on the maximum information coefficient, and proposes a CNN-LSTM combined prediction model based on multi-feature fusion, which transforms the abstract spatial and temporal influencing factors into quantifiable features. The model has good feature extraction ability and strong ability to capture short-term transient information and long-range dependent information in time series data, which improves the prediction performance of the model. The experimental results show that the prediction accuracy of CNN-LSTM model based on multi-feature fusion is 87.21%, and MAPE is 6.25, 4.84, and 1.29 less than BP, SVR, and LightGBM, and 1.91 and 7.04 less than CNN and LSTM.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Multiobjective Phase-Wise Teaching Learning–Based Optimization With No-Wait Time Heuristic for Job Shop Scheduling Problem","authors":"Remya Kommadath,&nbsp;Debasis Maharana,&nbsp;Prakash Kotecha","doi":"10.1002/cpe.8380","DOIUrl":"https://doi.org/10.1002/cpe.8380","url":null,"abstract":"<div>\u0000 \u0000 <p>This study proposes a multiobjective variant of the phase-wise teaching learning–based optimization algorithm, namely Non-dominated Sorting phase-wise Teaching Learning–Based Optimization (NSpTLBO), for solving the multiobjective job shop scheduling problems with unrelated parallel machines. The proposed technique is integrated with a no-wait time heuristic mechanism that reschedules the jobs assigned to the machines so as to minimize the constraint violation. Such an approach is implemented to facilitate the determination of feasible solutions in the earlier iterations of the metaheuristic technique. The efficacy of the proposed multiobjective technique is tested on job shop scheduling problems having jobs with release and due time. The performance of the proposed NSpTLBO and the hybrid NSpTLBO heuristic mechanism is compared against five multiobjective metaheuristic and hybrid metaheuristic-heuristic techniques. Based on the hypervolume and coverage metric, the performance of NSpTLBO with the heuristic mechanism is observed to be competitive compared with other algorithms as well as with its stand-alone version. This study has found the presence of the heuristic mechanism to be beneficial to all the multiobjective metaheuristic techniques in determining better-converged and diversified solutions against their stand-alone algorithms as hybrid techniques have provided a better hypervolume ratio than their stand-alone counterpart.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 4-5","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143389265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}