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

{"title":"Using Deep Reinforcement Learning (DRL) for minimizing power consumption in Video-on-Demand (VoD) storage systems","authors":"Minseok Song,&nbsp;Mingoo Kwon","doi":"10.1016/j.future.2024.107582","DOIUrl":"10.1016/j.future.2024.107582","url":null,"abstract":"<div><div>As video streaming services such as Netflix become popular, resolving the problem of high power consumption arising from both large data size and high bandwidth in video storage systems has become important. However, because various factors, such as the power characteristics of heterogeneous storage devices, variable workloads, and disk array models, influence storage power consumption, reducing power consumption with deterministic policies is ineffective. To address this, we present a new deep reinforcement learning (DRL)-based file placement algorithm for replication-based video storage systems, which aims to minimize overall storage power consumption. We first model the video storage system with time-varying streaming workloads as the DRL environment, in which the agent aims to find power-efficient file placement. We then propose a proximal policy optimization (PPO) algorithm, consisting of (1) an action space that determines the placement of each file; (2) an observation space that allows the agent to learn a power-efficient placement based on the current I/O bandwidth utilization; (3) a reward model that assigns a greater penalty for increased power consumption for each action; and (4) an action masking model that supports effective learning by preventing agents from selecting unnecessary actions. Extensive simulations were performed to evaluate the proposed scheme under various solid-state disk (SSD) models and replication configurations. Results show that our scheme reduces storage power consumption by 5% to 25.8% (average 12%) compared to existing benchmark methods known to be effective for file placement.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107582"},"PeriodicalIF":6.2,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651662","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":"NARA: Network-Aware Resource Allocation mechanism for minimizing quality-of-service impact while dealing with energy consumption in volunteer networks","authors":"Sergio Gonzalo San José ,&nbsp;Joan Manuel Marquès ,&nbsp;Javier Panadero ,&nbsp;Laura Calvet","doi":"10.1016/j.future.2024.107593","DOIUrl":"10.1016/j.future.2024.107593","url":null,"abstract":"<div><div>A large-scale volunteer computing system is a type of distributed system in which contributors volunteer their computing resources, such as personal computers or mobile devices, to contribute to a larger computing effort. Volunteer resources are connected over the Internet and together form a powerful computing system capable of providing a service without depending on a service provider. Volunteer network resource allocation is the process of assigning computing tasks or services to a network of volunteer resources. The allocation process includes identifying the needed resources, selecting appropriate volunteers, and assigning tasks or services based on their capabilities. Volunteer computing systems consist of a large number of heterogeneous resources - in terms of processing power, storage, and availability - belonging to different authorities - users or organizations - and exhibiting uncertain behavior in terms of connection, disconnection, capacity, and failure. All of this makes resource allocation a challenging task in terms of ensuring a minimum quality of service, requiring complex algorithms and optimization techniques to ensure that services are efficiently allocated while respecting the constraints of the available resource. This paper introduces the Network-Aware Resource Allocation mechanism, which leverages the location, connectivity, and network latency of volunteer nodes to minimize the time a service runs with degraded quality of service and aims to deal with the energy consumption resulting from data replication requirements. This resource allocation mechanism applies to both the initial deployment of the service in the network and to the reallocation of nodes in the event that one of the allocated nodes fails or becomes unavailable. Our method has been validated in a simulation environment of a realistic volunteer system. The analysis of the results shows how our mechanism meets the quality requirements of users while minimizing the synchronization and replication times of service data replicas, as well as the time that services run with degraded quality of service, reducing the times by more than 70% in the service deployment phase and by more than 60% in the service execution phase. It also helps to reduce overall energy consumption.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107593"},"PeriodicalIF":6.2,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651751","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":"Special Issue on Intelligent Architectures and Platforms for Private Edge Cloud Systems","authors":"Sayed Chhattan Shah,&nbsp;Taehong Kim,&nbsp;Blesson Varghese,&nbsp;Zhi Wang","doi":"10.1016/j.future.2024.107605","DOIUrl":"10.1016/j.future.2024.107605","url":null,"abstract":"","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107605"},"PeriodicalIF":6.2,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651756","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":"Reducing the cost of cold start time in serverless function executions using granularity trees","authors":"Mahrad Hanaforoosh,&nbsp;Mohammad Abdollahi Azgomi,&nbsp;Mehrdad Ashtiani","doi":"10.1016/j.future.2024.107604","DOIUrl":"10.1016/j.future.2024.107604","url":null,"abstract":"<div><div>In serverless computing, cold starts significantly impede performance. This paper presents a granularity tree-based scheduling strategy, dynamically adjusting serverless function deployment by package dependencies to mitigate cold starts and optimize resource usage. This approach notably reduces cold start and response times. Empirical results from evaluating functions across various datasets show the strategy outperforms existing methods. Specifically, it consistently delivers lower response times and decreases resource consumption, demonstrating its effectiveness in managing computational resources while ensuring swift function invocation. In particular scenarios, the proposed scheduler impressively reduced response times from 8134.1 ms to 392.8 ms and idle memory usage from 15.2 GB to 11.2 GB per machine. In other scenarios, it reduced response times from 12,152.7 ms to 504.2 ms while maintaining a 100% function execution percentage. These quantified improvements underscore the significant enhancements in cold start mitigation and overall system performance, highlighting the potential of granularity tree-based scheduling in enhancing serverless computing architectures by effectively balancing rapid response with reduced resource usage.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107604"},"PeriodicalIF":6.2,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651666","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":"Competitive cost-effective memory access predictor through short-term online SVM and dynamic vocabularies","authors":"Pablo Sanchez-Cuevas ,&nbsp;Fernando Diaz-del-Rio ,&nbsp;Daniel Casanueva-Morato ,&nbsp;Antonio Rios-Navarro","doi":"10.1016/j.future.2024.107592","DOIUrl":"10.1016/j.future.2024.107592","url":null,"abstract":"<div><div>In recent years, there has been a significant increase in the processing of massive amounts of data, driven by the growing demands of mobile systems, parallel and distributed architectures, and real-time systems. This applies to various types of platforms, both specific and general-purpose. Despite numerous advancements in Computer Systems, a critical challenge remains: the efficiency and speed of memory access. This bottleneck is being addressed through cache prefetching, that is, by predicting the next memory address to be accessed and then by having always prefetched in the cache system those data to be used shortly by the processor. This paper explores established intelligent techniques for address prediction, examining their limitations and analyzing the memory access patterns of popular software applications. Building on the successes of previous intelligent predictors based on Machine and Deep Learning models, we introduce a new predictor, SVM4AP (Support Vector Machine For Address Prediction), designed to overcome the identified drawbacks of its predecessors. The architecture of SVM4AP improves the trade-off between performance and cost, compared to those previous proposals in the literature, achieving high accuracy through short-term learning. Comparisons are made with two prominent predictors from the literature: the classical DFCM (Differential Finite Context Method) and the contemporary Deep Learning-based DCLSTM (Doubly Compressed Long-Short Term Memory). The results demonstrate that SVM4AP achieves superior cost-effectiveness across various configurations. Simulations reveal that SVM4AP configurations dominate both DFCM and DCLSTM counterparts, forming the majority of the first Paretto front. Particularly noteworthy is the significant advantage of our proposal for small-size predictors. Furthermore, we release an open-source tool enabling the scientific community to reproduce the results presented in this paper using a set of benchmark traces.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107592"},"PeriodicalIF":6.2,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651743","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":"SecDefender: Detecting low-quality models in multidomain federated learning systems","authors":"Sameera K.M. ,&nbsp;Arnaldo Sgueglia ,&nbsp;Vinod P. ,&nbsp;Rafidha Rehiman K.A. ,&nbsp;Corrado Aaron Visaggio ,&nbsp;Andrea Di Sorbo ,&nbsp;Mauro Conti","doi":"10.1016/j.future.2024.107587","DOIUrl":"10.1016/j.future.2024.107587","url":null,"abstract":"<div><div>Federated learning (FL) is an innovative distributed learning paradigm that permits multiple parties to train models collaboratively while protecting individual privacy. However, it encounters security challenges, making it vulnerable to several adversarial attacks and leading to compromising model performance. Existing research on FL poisoning attacks and defense techniques tends to be application-specific, primarily emphasizing attack capabilities. However, it fails to consider inherent vulnerabilities in FL and the impact of attack intensity. To our knowledge, no existing work has delved into these issues within a multi-domain FL environment. This paper addresses these concerns by investigating the consequences of targeted label-flipping attacks within FL systems and comprehensively examining the effects of the attacks in single-label, double-label, and triple-label scenarios with different levels of poisoning intensities. Additionally, we investigate the influence of a temporal label-flipping attack, where we study the impact of adversaries available only for specific federated training rounds. Moreover, we propose a novel server-based defense mechanism called SecDefender to detect low-quality models in both IID and Non-IID settings of multi-domain environments. Our approach is rigorously evaluated against state-of-the-art alternatives using six benchmark datasets: CIC-Darknet2020, Fashion-MNIST, FEDMNIST, GTSR, HAR, and MNIST. Extensive experiments demonstrate that our proposed SecDefender significantly enhances its performance by over 65% in terms of source class recall, maintaining a low attack success rate. Consequently, there is a 1% to 2% enhancement in global model accuracy compared to existing approaches.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107587"},"PeriodicalIF":6.2,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651750","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 blockchain-assisted privacy-preserving signature scheme using quantum teleportation for metaverse environment in Web 3.0","authors":"Sunil Prajapat ,&nbsp;Garima Thakur ,&nbsp;Pankaj Kumar ,&nbsp;Ashok Kumar Das ,&nbsp;M. Shamim Hossain","doi":"10.1016/j.future.2024.107581","DOIUrl":"10.1016/j.future.2024.107581","url":null,"abstract":"<div><div>At the forefront of next-generation internet technology, the concept of the metaverse is gaining traction. It unifies the virtual and physical worlds into a single virtual realm and has the potential to revolutionize social networks, gaming, healthcare and education. Quantum teleportation, renowned for its ability to ensure secure and reliable communications, is set to transform interactions within this immersive digital realm. Nevertheless, the conventional cryptosystems that typically rely on mathematical complexity will no longer be dependable and secure with the advancement of quantum computing. Applied to blockchain architecture, existing post-quantum cryptography technologies, which raise the time complexity of the algorithm, will result in relatively low efficiency and substantial resource overhead. In contrast, quantum cryptography has the ability to improve blockchain’s security and efficiency. Moreover, the reliance on centralized authorities in current metaverse platforms highlights the need for decentralization to improve interoperability and security. Web 3.0 technologies offer a solution by enabling a decentralized metaverse ecosystem. Therefore, the proposed approach leverages quantum teleportation to achieve secure communication and the integration of quantum cryptography and Web 3.0 enhances the efficiency, security and realism of metaverse environments. Through rigorous safety and efficiency analysis, we demonstrate the protocol’s robustness and performance, ensuring adherence to fundamental security properties such as unforgeability, undeniability, verifiability, and traceability.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107581"},"PeriodicalIF":6.2,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651753","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":"SWIM: Sliding-Window Model contrast for federated learning","authors":"Heng-Ru Zhang ,&nbsp;Rui Chen ,&nbsp;Shi-Huai Wen ,&nbsp;Xiao-Qiang Bian","doi":"10.1016/j.future.2024.107590","DOIUrl":"10.1016/j.future.2024.107590","url":null,"abstract":"<div><div>In federated learning, data heterogeneity leads to significant differences in the local models learned by the clients, thereby affecting the performance of the global model. To address this issue, contrast federated learning algorithms increase the comparison of positive and negative samples on the clients, bringing the local models closer to the global model. However, existing methods take the global model as the positive sample and the previous round of local models as the negative sample, resulting in insufficient utilization of historical local models. In this paper, we propose SWIM: Sliding-WIndow Model contrast method, which introduces more rounds of local models. First, we design and utilize a sliding window mechanism for collecting client representations of historical local models. Subsequently, we employ the cosine distance function as a discriminator to distinguish them into positive and negative samples. In addition, we introduce a dynamic coefficient that balances the federated classification learning and feature learning tasks. By adjusting the dynamic coefficient at different training rounds, the global model becomes more focused on feature learning in the early stages and classification learning in the later stages. Experiments are compared with four state-of-the-art federated learning algorithms on three datasets. The results show that the proposed algorithm outperforms the four state-of-the-art algorithms in terms of accuracy. Source code is available at <span><span>https://github.com/zhanghrswpu/SWIM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107590"},"PeriodicalIF":6.2,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593354","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":"Special Issue on integration of machine learning and edge computing for next generation of smart wearable systems","authors":"Paolo Gastaldo ,&nbsp;Edoardo Ragusa ,&nbsp;Strahinja Dosen ,&nbsp;Francesco Palmieri","doi":"10.1016/j.future.2024.107574","DOIUrl":"10.1016/j.future.2024.107574","url":null,"abstract":"<div><div>Machine learning (ML) provides an enabling technology for the development of the next generation of smart devices. However, the integration of ML and edge computing faces major challenges. While powerful models can tackle difficult tasks such as visual recognition or natural language processing, the constrained resources of embedded systems might prevent direct deployment of the designed inference function into an edge device. This Special Issue collects manuscripts describing methodologies and systems that tackle the integration of ML into embedded systems. The focus is on solutions that can stimulate significant improvements across different domains.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107574"},"PeriodicalIF":6.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651755","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":"Heterogeneous system list scheduling algorithm based on improved optimistic cost matrix","authors":"Min Wang ,&nbsp;Haoyuan Wang ,&nbsp;Sibo Qiao ,&nbsp;Jiawang Chen ,&nbsp;Qin Xie ,&nbsp;Cuijuan Guo","doi":"10.1016/j.future.2024.107576","DOIUrl":"10.1016/j.future.2024.107576","url":null,"abstract":"<div><div>In heterogeneous computing systems, efficient task-scheduling methods are paramount for enhancing computational performance. However, the existing algorithm exhibits certain deficiencies, notably its oversight of load balancing concerns and inadequate emphasis on the out-degree property of tasks. To address these issues, a novel list scheduling algorithm is proposed, Average Earliest Finish Time (AEFT), which proficiently allocates task flows onto heterogeneous processors. The AEFT algorithm primarily consists of two key stages: (1) prioritizing tasks to determine the distribution of task priorities and (2) assigning optimal processors for tasks with given priorities. By leveraging its specific topology, the AEFT algorithm minimizes the scheduling length of task flows. Simultaneously, a prediction mechanism in determining task prioritization and selecting processors stages is proposed to reduce the scheduling time of task flows. In addition, in the processor selection stage, AEFT algorithm considers the out-degree characteristics of tasks, ameliorating situations of processor load imbalance. The AEFT algorithm demonstrates superior performance compared to prior list scheduling algorithms concerning makespan, speedup, and the percentage of occurrences of better solutions, as evidenced by experiments conducted on randomly generated and real-application graphs. Specifically, for <span><math><mi>t</mi></math></span> tasks and <span><math><mi>p</mi></math></span> processors, the AEFT algorithm achieves a time complexity of <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>t</mi></mrow><mrow><mn>2</mn></mrow></msup><mi>p</mi><mo>)</mo></mrow></mrow></math></span>.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"164 ","pages":"Article 107576"},"PeriodicalIF":6.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573349","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}