IEEE Transactions on Sustainable Computing最新文献_第6页

A Safe Virtual Machine Scheduling Strategy for Energy Conservation and Privacy Protection of Server Clusters in Cloud Data Centers 云数据中心服务器集群节能与隐私保护的安全虚拟机调度策略

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-09-01 DOI: 10.1109/TSUSC.2023.3303637

Xiaoyun Han;Chaoxu Mu;Jiebei Zhu;Hongjie Jia

{"title":"A Safe Virtual Machine Scheduling Strategy for Energy Conservation and Privacy Protection of Server Clusters in Cloud Data Centers","authors":"Xiaoyun Han;Chaoxu Mu;Jiebei Zhu;Hongjie Jia","doi":"10.1109/TSUSC.2023.3303637","DOIUrl":"10.1109/TSUSC.2023.3303637","url":null,"abstract":"With the increasing scale of cloud data centers (CDCs), the energy consumption of CDCs is sharply increasing. In this article, an efficient energy-saving strategy is proposed for CDCs. The greedy virtual machine (VM) deployment strategy is obtained by using the least number of servers, the heuristic VM migration strategy is obtained by using the improved double threshold algorithm, and the comprehensive VM scheduling strategy of severs is obtained by combining deployment and migration strategies. Furthermore, for the privacy security of VM scheduling, a safety-oriented energy-saving scheme based on information difference is proposed to ensure the dataset availability under privacy protection, comparing with \u0000<inline-formula><tex-math>$varepsilon$</tex-math></inline-formula>\u0000-differential privacy algorithm and \u0000<inline-formula><tex-math>$(varepsilon, delta)$</tex-math></inline-formula>\u0000-differential privacy algorithm. Simulation results show that the safe energy-saving strategy can significantly reduce the energy consumption in CDCs with guaranteeing the security and availability of the important datasets.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 1","pages":"46-60"},"PeriodicalIF":3.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83911053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sustainable Serverless Computing With Cold-Start Optimization and Automatic Workflow Resource Scheduling 利用冷启动优化和自动工作流资源调度实现可持续的无服务器计算

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-09-01 DOI: 10.1109/TSUSC.2023.3311197

Shanxing Pan;Hongyu Zhao;Zinuo Cai;Dongmei Li;Ruhui Ma;Haibing Guan

{"title":"Sustainable Serverless Computing With Cold-Start Optimization and Automatic Workflow Resource Scheduling","authors":"Shanxing Pan;Hongyu Zhao;Zinuo Cai;Dongmei Li;Ruhui Ma;Haibing Guan","doi":"10.1109/TSUSC.2023.3311197","DOIUrl":"10.1109/TSUSC.2023.3311197","url":null,"abstract":"In recent years, serverless computing has garnered significant attention owing to its high scalability, pay-as-you-go billing model, and efficient resource management provided by cloud service providers. Optimal resource scheduling of serverless computing has become imperative to reduce energy consumption and enable sustainable computing. However, existing serverless platforms encounter two significant challenges: the cold-start problem of containers and the absence of an effective resource allocation strategy for serverless workflows. Existing pre-warm strategies are associated with high computational overhead, while current resource scheduling techniques inadequately account for the intricate structure of serverless workflows. To address these challenges, we present SSC, a pre-warming and automatic resource allocation framework designed explicitly for serverless workflows. We introduce an innovative gradient-based algorithm for pre-warming containers, significantly reducing cold start hit rates. Moreover, leveraging a critical path and priority queue-based algorithm, SSC enables efficient allocation of resources for serverless workflows. In our experimental evaluation, SSC reduces the cold start hit rate by nearly 50% and achieves substantial cost savings of approximately 30%.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"329-340"},"PeriodicalIF":3.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72796524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

bSlight: Battery-Less Energy Autonomous Street Light Management System for Smart City bSlight：用于智慧城市的无电池能源自主路灯管理系统

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-08-31 DOI: 10.1109/TSUSC.2023.3310884

Prajnyajit Mohanty;Umesh C. Pati;Kamalakanta Mahapatra;Saraju P. Mohanty

{"title":"bSlight: Battery-Less Energy Autonomous Street Light Management System for Smart City","authors":"Prajnyajit Mohanty;Umesh C. Pati;Kamalakanta Mahapatra;Saraju P. Mohanty","doi":"10.1109/TSUSC.2023.3310884","DOIUrl":"10.1109/TSUSC.2023.3310884","url":null,"abstract":"Public lighting is a ubiquitous utility in cities to ensure the safety of people. In addition to playing a significant role in amending the comfort and safety of cities, street lighting causes substantial financial burden on governments to maintain its operation. Smart Light Emitting Diode (LED) street light system has become a prominent alternative to conventional street lighting systems with the involvement of Internet of Things (IoT). In this manuscript, a supercapacitor based smart street management system with energy autonomous capability has been proposed. It works in real-time and as an energy-saving alternative to prevent unnecessary electricity consumption of the street light. The average current consumption and power consumption of the system are 619.14 \u0000<inline-formula><tex-math>$mu$</tex-math></inline-formula>\u0000A and 2.022 mW, respectively. Three charging schemes have been investigated to find the optimized topology to harvest energy. The proposed device harvests energy from ambient sunlight and artificial light using a solar cell of 64 mm x 37 mm x 0.22 mm with maximum output power of 66 mW. LoRaWAN has been incorporated for communication, with a communication range of 761 m in real-world testbed. The operation characteristics and performance evaluation has been done based on implementing the system in field to ensure seamless operation.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 1","pages":"100-114"},"PeriodicalIF":3.9,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72516185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Supervised Representation Learning for Network Traffic With Cluster Compression 利用集群压缩对网络流量进行有监督的表征学习

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-08-28 DOI: 10.1109/TSUSC.2023.3292404

Xiaojuan Wang;Yu Zhang;Mingshu He;Shize Guo;Liu Yang

{"title":"Supervised Representation Learning for Network Traffic With Cluster Compression","authors":"Xiaojuan Wang;Yu Zhang;Mingshu He;Shize Guo;Liu Yang","doi":"10.1109/TSUSC.2023.3292404","DOIUrl":"10.1109/TSUSC.2023.3292404","url":null,"abstract":"In the face of increasing network traffic, network security issues have gained significant attention. Existing network intrusion detection models often improve the ability to distinguish network behaviors by optimizing the model structure, while ignoring the expressiveness of network traffic at the data level. Visual analysis of network behavior through representation learning can provide a new perspective for network intrusion detection. Unfortunately, representation learning based on machine learning and deep learning often suffer from scalability and interpretability limitations. In this article, we establish an interpretable multi-layer mapping model to enhance the expressiveness of network traffic data. Moreover, the unsupervised method is used to extract the internal distribution characteristics of the data before the model to enhance the data. What’s more, we analyze the feasibility of the proposed flow spectrum theory on the UNSW-NB15 dataset. Experimental results demonstrate that the flow spectrum exhibits significant advantages in characterizing network behavior compared to the original network traffic features, underscoring its practical application value. Finally, we conduct an application analysis using multiple datasets (CICIDS2017 and CICIDS2018), revealing the model’s strong universality and adaptability across different datasets.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 1","pages":"1-13"},"PeriodicalIF":3.9,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76492870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Sustainable Trust: A Practical SGX Aided Anonymous Reputation System 实现可持续信任：实用的 SGX 匿名声誉辅助系统

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-08-24 DOI: 10.1109/TSUSC.2023.3308081

Xu Yang;Xuechao Yang;Junwei Luo;Xun Yi;Ibrahim Khalil;Shangqi Lai;Wei Wu;Albert Y. Zomaya

引用次数: 0

Renewable Energy in Data Centers: The Dilemma of Electrical Grid Dependency and Autonomy Costs 数据中心的可再生能源：电网依赖与自主成本的两难选择

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-08-23 DOI: 10.1109/TSUSC.2023.3307790

Wedan Emmanuel Gnibga;Anne Blavette;Anne-Cécile Orgerie

{"title":"Renewable Energy in Data Centers: The Dilemma of Electrical Grid Dependency and Autonomy Costs","authors":"Wedan Emmanuel Gnibga;Anne Blavette;Anne-Cécile Orgerie","doi":"10.1109/TSUSC.2023.3307790","DOIUrl":"10.1109/TSUSC.2023.3307790","url":null,"abstract":"Integrating larger shares of renewables in data centers’ electrical mix is mandatory to reduce their carbon footprint. However, as they are intermittent and fluctuating, renewable energies alone cannot provide a 24/7 supply and should be combined with a secondary source. Finding the optimal infrastructure configuration for both renewable production and financial costs remains difficult. In this article, we examine three scenarios with on-site renewable energy sources combined respectively with the electrical grid, batteries alone and batteries with hydrogen storage systems. The objectives are first, to size optimally the electric infrastructure using combinations of standard microgrids approaches, second to quantify the level of grid utilization when data centers consume/ export electricity from/to the grid, to determine the level of effort required from the grid operator, and finally to analyze the cost of 100% autonomy provided by the battery-based configurations and to discuss their economical viability. Our results show that in the grid-dependent mode, 63.1% of the generated electricity has to be injected into the grid and retrieved later. In the autonomous configurations, the cheapest one including hydrogen storage leads to a unit cost significantly more expensive than the electricity supplied from a national power system in many countries.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"315-328"},"PeriodicalIF":3.9,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87444789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy Allocation for Vehicle-to-Grid Settings: A Low-Cost Proposal Combining DRL and VNE 车辆到电网设置的能源分配：结合 DRL 和 VNE 的低成本建议

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-08-22 DOI: 10.1109/TSUSC.2023.3307551

Peiying Zhang;Ning Chen;Neeraj Kumar;Laith Abualigah;Mohsen Guizani;Youxiang Duan;Jian Wang;Sheng Wu

{"title":"Energy Allocation for Vehicle-to-Grid Settings: A Low-Cost Proposal Combining DRL and VNE","authors":"Peiying Zhang;Ning Chen;Neeraj Kumar;Laith Abualigah;Mohsen Guizani;Youxiang Duan;Jian Wang;Sheng Wu","doi":"10.1109/TSUSC.2023.3307551","DOIUrl":"10.1109/TSUSC.2023.3307551","url":null,"abstract":"As electric vehicle (EV) ownership becomes more commonplace, partly due to government incentives, there is a need also to design solutions such as energy allocation strategies to more effectively support sustainable vehicle-to-grid (V2G) applications. Therefore, this work proposes an energy allocation strategy, designed to minimize the electricity cost while improving the operating revenue. Specifically, V2G is abstracted as a three-domain network architecture to facilitate flexible, intelligent, and scalable energy allocation decision-making. Furthermore, this work combines virtual network embedding (VNE) and deep reinforcement learning (DRL) algorithms, where a DRL-based agent model is proposed, to adaptively perceives environmental features and extracts the feature matrix as input. In particular, the agent consists of a four-layer architecture for node and link embedding, and jointly optimizes the decision-making through a reward mechanism and gradient back-propagation. Finally, the effectiveness of the proposed strategy is demonstrated through simulation case studies. Specifically, compared to the used benchmarks, it improves the VNR acceptance ratio, Long-term average revenue, and Long-term average revenue-cost ratio indicators by an average of 3.17%, 191.36, and 2.04%, respectively. To the best of our knowledge, this is one of the first attempts combining VNE and DRL to provide an energy allocation strategy for V2G.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 1","pages":"75-87"},"PeriodicalIF":3.9,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77214489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Deep Reinforcement Learning-Based Preemptive Approach for Cost-Aware Cloud Job Scheduling 基于深度强化学习的成本感知云作业调度抢占式方法

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-08-10 DOI: 10.1109/TSUSC.2023.3303898

Long Cheng;Yue Wang;Feng Cheng;Cheng Liu;Zhiming Zhao;Ying Wang

{"title":"A Deep Reinforcement Learning-Based Preemptive Approach for Cost-Aware Cloud Job Scheduling","authors":"Long Cheng;Yue Wang;Feng Cheng;Cheng Liu;Zhiming Zhao;Ying Wang","doi":"10.1109/TSUSC.2023.3303898","DOIUrl":"10.1109/TSUSC.2023.3303898","url":null,"abstract":"With some specific characteristics such as elastics and scalability, cloud computing has become the most promising technology for online business nowadays. However, how to efficiently perform real-time job scheduling in cloud still poses significant challenges. The reason is that those jobs are highly dynamic and complex, and it is always hard to allocate them to computing resources in an optimal way, such as to meet the requirements from both service providers and users. In recent years, various works demonstrate that deep reinforcement learning (DRL) can handle real-time cloud jobs well in scheduling. However, to our knowledge, none of them has ever considered extra optimization opportunities for the allocated jobs in their scheduling frameworks. Given this fact, in this work, we introduce a novel DRL-based preemptive method for further improve the performance of the current studies. Specifically, we try to improve the training of scheduling policy with effective job preemptive mechanisms, and on that basis to optimize job execution cost while meeting users’ expected response time. We introduce the detailed design of our method, and our evaluations demonstrate that our approach can achieve better performance than other scheduling algorithms under different real-time workloads, including the DRL approach.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"422-432"},"PeriodicalIF":3.9,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91223232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Intrusion Detection and Identification System for Internet of Things Networks Using a Hybrid Ensemble Deep Learning Framework 使用混合集合深度学习框架的物联网网络入侵检测和识别系统

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-08-09 DOI: 10.1109/TSUSC.2023.3303422

Yanika Kongsorot;Pakarat Musikawan;Phet Aimtongkham;Ilsun You;Abderrahim Benslimane;Chakchai So-In

{"title":"An Intrusion Detection and Identification System for Internet of Things Networks Using a Hybrid Ensemble Deep Learning Framework","authors":"Yanika Kongsorot;Pakarat Musikawan;Phet Aimtongkham;Ilsun You;Abderrahim Benslimane;Chakchai So-In","doi":"10.1109/TSUSC.2023.3303422","DOIUrl":"10.1109/TSUSC.2023.3303422","url":null,"abstract":"Owing to the exponential proliferation of internet services and the sophistication of intrusions, traditional intrusion detection algorithms are unable to handle complex invasions due to their limited representation capabilities and the unbalanced nature of Internet of Things (IoT)-related data in terms of both telemetry and network traffic. Drawing inspiration from deep learning achievements in feature extraction and representation learning, in this study, we propose an accurate hybrid ensemble deep learning framework (HEDLF) to protect against obfuscated cyber-attacks on IoT networks. To address complex features and alleviate the imbalance problem, the proposed HEDLF includes three key components: 1) a hierarchical feature representation technique based on deep learning, which aims to extract specific information by supervising the loss of gradient information; 2) a balanced rotated feature extractor that simultaneously encourages the individual accuracy and diversity of the ensemble classifier; and 3) a meta-classifier acting as an aggregation method, which leverages a semisparse group regularizer to analyze the base classifiers’ outputs. Additionally, these improvements take class imbalance into account. The experimental results show that when compared against state-of-the-art techniques in terms of accuracy, precision, recall, and F1-score, the proposed HEDLF can achieve promising results on both telemetry and network traffic data.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"8 4","pages":"596-613"},"PeriodicalIF":3.9,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77175733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Privacy Evaluation of Blockchain Based Privacy Cryptocurrencies: A Comparative Analysis of Dash, Monero, Verge, Zcash, and Grin 基于区块链的隐私加密货币的隐私评估：Dash、Monero、Verge、Zcash 和 Grin 的比较分析

IF 3.9 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2023-08-08 DOI: 10.1109/TSUSC.2023.3303180

Tao Zhang

引用次数: 0