IEEE Transactions on Services Computing最新文献_第5页

Prediction of Heterogeneous Device Task Runtime Based on Edge Server-Oriented Deep Neuro-Fuzzy System 基于边缘服务器的深度神经模糊系统异构设备任务运行时预测

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-12-23 DOI: 10.1109/TSC.2024.3520869

Haijie Wu;Weiwei Lin;Wangbo Shen;Xiumin Wang;C. L. Philip Chen;Keqin Li

{"title":"Prediction of Heterogeneous Device Task Runtime Based on Edge Server-Oriented Deep Neuro-Fuzzy System","authors":"Haijie Wu;Weiwei Lin;Wangbo Shen;Xiumin Wang;C. L. Philip Chen;Keqin Li","doi":"10.1109/TSC.2024.3520869","DOIUrl":"10.1109/TSC.2024.3520869","url":null,"abstract":"Predicting the runtime of tasks is of great significance as it can help users better understand the future runtime consumption of the tasks and make decisions for their heterogeneous devices, or be applied to task scheduling. Learning features from user task history data for predicting task runtime is a mainstream method. However, this method faces many challenges when applied to edge intelligence. In the Big Data era, user devices and data features are constantly evolving, necessitating frequent model retrains. Meanwhile, the noisy data from these devices requires robust methods for valuable insight extraction. In this paper, we propose an edge server-oriented deep neuro-fuzzy system (ESODNFS) that can be trained and inferred on edge servers, for providing users with task runtime prediction services. We divided the dataset and trained it on multiple improved adaptive-network-based fuzzy inference system units (ANFISU), and finally conducted joint training on a deep neural network (DNN). By partitioning the dataset, we reduced the number of parameters for each ANFISU, and at the same time, multiple units can be trained in parallel, supporting fast training and iteration. Additionally, the application of fuzzy inference can effectively learn the features in noisy data and make accurate predictions. The experimental results show that ESODNFS can accurately predict the runtime of real tasks. Compared with other DNN and DNFS, it can achieve good prediction results while reducing training time by over 35%.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"372-384"},"PeriodicalIF":5.5,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879581","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}

引用次数: 0

VPFLI: Verifiable Privacy-Preserving Federated Learning With Irregular Users Based on Single Server VPFLI：基于单服务器的不规则用户可验证隐私保护联邦学习

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-12-23 DOI: 10.1109/tsc.2024.3520867

Yanli Ren, Yerong Li, Guorui Feng, Xinpeng Zhang

引用次数: 0

Crowdsourcing Home Healthcare Service: Matching Caretakers With Caregivers for Jointly Rostering and Routing 众包家庭医疗保健服务：将护理人员与护理人员配对，共同登记和路由

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-12-17 DOI: 10.1109/TSC.2024.3517344

Chun-Cheng Lin;Yi-Chun Peng;Zhen-Yin Annie Chen;Pei-Yu Liu

{"title":"Crowdsourcing Home Healthcare Service: Matching Caretakers With Caregivers for Jointly Rostering and Routing","authors":"Chun-Cheng Lin;Yi-Chun Peng;Zhen-Yin Annie Chen;Pei-Yu Liu","doi":"10.1109/TSC.2024.3517344","DOIUrl":"10.1109/TSC.2024.3517344","url":null,"abstract":"In this work, we introduce crowdsourcing home healthcare service (CHHS) systems, where caregivers (including nurses, personal care attendants, and housekeepers) from different locations (rather than centralized institutions) offer diverse home healthcare services to caretakers at home. Powered by cloud computing, the CHHS system enables real-time, dynamic, and large-scale matching between caretakers and caregivers based on their preferences and constraints, and determines caregivers’ rostering and routing plans, involving the NP-hard nurse rostering problem (NRP) and the vehicle routing problem (VRP). This work firstly creates a mathematical programming model to jointly roster and route for the CHHS, maximizing the matching scores of caregivers and caretakers based on the preferred features through analytic hierarchy process (AHP), and minimizing caregivers’ overtime and routing costs, under constraints of caregiver skills, regulations, and vehicle routing. The proposed matching score mechanism assigns weights to caretaker preferences, enhancing pairing with preferred caregivers and reducing dissatisfaction. This work proposes a hybrid genetic algorithm with variable neighborhood search (GAVNS), respectively tailored to handle the rostering and routing aspects of CHHS. Simulation indicates that the GAVNS lowers costs by approximately 38% and 26% in rural and city cases, respectively, and outperforms standalone GA and VNS, achieving a 3% additional cost reduction and consistently yielding feasible solutions.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"126-139"},"PeriodicalIF":5.5,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840882","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}

引用次数: 0

A Game-Based Computation Offloading With Imperfect Information in Multi-Edge Environments 多边缘环境中基于游戏的不完全信息计算卸载

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-12-16 DOI: 10.1109/TSC.2024.3517336

Bing Lin;Jie Weng;Xing Chen;Yun Ma;Ching-Hsien Hsu

{"title":"A Game-Based Computation Offloading With Imperfect Information in Multi-Edge Environments","authors":"Bing Lin;Jie Weng;Xing Chen;Yun Ma;Ching-Hsien Hsu","doi":"10.1109/TSC.2024.3517336","DOIUrl":"10.1109/TSC.2024.3517336","url":null,"abstract":"Mobile Edge Computing (MEC) can augment the capability of Internet of Things (IoT) mobile devices (MDs) through offloading the computation-intensive tasks to their adjacent servers. Synergistic computation offloading among MEC servers is one possible solution to reduce the completion time of system during peak hours. However, due to the large number of servers and the long distance between base stations (BSs), synchronizing the information of all servers takes a long time, which is not applicable to the fluctuant environments. Meanwhile, each server from different BSs is typically selfish and rational, and can only obtain the imperfect information from its adjacent servers, which is a challenge for computation offloading among servers from a global perspective. This article proposes a game-based computation offloading scheme with imperfect information in multi-edge environments. First, a non-cooperative game with imperfect information is designed to analyze the complex interactions during synergistic computation offloading among MEC servers. Second, a Synergistic Balancing Offloading Algorithm (SBOA) through distributed decision-making manner to obtain the optimal offloading decision is proposed, which guarantees that the game converges to a Nash Equilibrium (NE) point. Extensive simulation results reveal the fast convergence of SBOA. As the percentage of high-load servers rises and the number of heavy tasks increases, SBOA performs better than other benchmark algorithms in terms of timeliness, effectiveness, and system completion time.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"1-14"},"PeriodicalIF":5.5,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142832324","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}

引用次数: 0

When Differential Privacy Meets Query Control: a Hybrid Framework for Practical Range Query Leakage Quantification and Mitigation 当差异隐私遇到查询控制：实用范围查询泄漏量化和缓解的混合框架

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-12-16 DOI: 10.1109/tsc.2024.3517316

Xinyan Li, Yuefeng Du, Cong Wang

引用次数: 0

TFEGRU: Time-Frequency Enhanced Gated Recurrent Unit With Attention for Cloud Workload Prediction TFEGRU：用于云计算工作量预测的时频增强型门控注意递归单元

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-12-13 DOI: 10.1109/TSC.2024.3517324

Feiyu Zhao;Weiwei Lin;Shengsheng Lin;Haocheng Zhong;Keqin Li

{"title":"TFEGRU: Time-Frequency Enhanced Gated Recurrent Unit With Attention for Cloud Workload Prediction","authors":"Feiyu Zhao;Weiwei Lin;Shengsheng Lin;Haocheng Zhong;Keqin Li","doi":"10.1109/TSC.2024.3517324","DOIUrl":"10.1109/TSC.2024.3517324","url":null,"abstract":"Accurate prediction of cloud workload is crucial for effective resource allocation in cloud computing. However, due to the complexity and high dimensionality of workloads in the cloud environment, achieving precise workload prediction is a complex and challenging problem. Current approaches to cloud workload prediction mainly rely on deep learning methods based on the Recurrent Neural Network (RNN), which struggle to capture the long-term dependencies inherent in workloads effectively. To tackle these challenges and overcome the limitations of existing methods, we propose an effective approach Time-Frequency Enhanced Gated Recurrent Unit with Attention (TFEGRU) for cloud workload prediction. First, we design a Time-Frequency Enhanced Block (TFEB) to capture complex workload patterns and extract features from both the frequency and temporal domains. Next, we integrate channel independent strategy and channel embedding into the model to adapt to high-dimensional workloads and enhance predictive performance. Finally, we apply a Gated Recurrent Unit (GRU) in conjunction with a multi-head self-attention mechanism to achieve accurate workload prediction. To validate the effectiveness of TFEGRU, comprehensive experiments are conducted using real-world traces from Google and Alibaba cloud data centers. The experimental results demonstrate that TFEGRU achieves accurate and efficient predictions across diverse cloud workloads, outperforming existing state-of-the-art methods.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"467-478"},"PeriodicalIF":5.5,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142832325","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}

引用次数: 0

PetTC: Pairwise Joint Embedding Based Contrastive Tensor Completion for Network Traffic Monitoring Services PetTC：基于成对联合嵌入的对比张量完成法，用于网络流量监测服务

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-12-12 DOI: 10.1109/TSC.2024.3517331

Haoxuan Wang;Kun Xie;Jigang Wen;Guangxing Zhang;Wei Liang;Gaogang Xie;Kenli Li

{"title":"PetTC: Pairwise Joint Embedding Based Contrastive Tensor Completion for Network Traffic Monitoring Services","authors":"Haoxuan Wang;Kun Xie;Jigang Wen;Guangxing Zhang;Wei Liang;Gaogang Xie;Kenli Li","doi":"10.1109/TSC.2024.3517331","DOIUrl":"10.1109/TSC.2024.3517331","url":null,"abstract":"Network traffic matrices often suffer from incompleteness and sparsity due to various factors, including network device policies and system limitations. The incompleteness can undermine the reliability and accuracy of network traffic monitoring services, negatively impacting downstream tasks such as network planning and fault diagnosis. Our focus is on network traffic data recovery, intending to infer missing traffic data from partial measurements accurately. Although tensor completion algorithms are quite effective in recovering traffic data, existing models often overlook cross-domain traffic relationships and fail to account for the order and distribution of traffic, leading to reduced recovery accuracy. To overcome these limitations, we propose a new contrastive tensor completion model that utilizes pairwise joint embedding. This model employs innovative techniques, including a cross-domain embedding module to avoid information homogeneity and enhance model expressiveness, a contrastive module to preserve the order and distribution of traffic volumes, and an injective interaction module to map entry embeddings into the numerical space, ensuring convergence and retaining the original numerical distribution. Experiments on three real-world network traffic datasets show that our model significantly reduces the error in missing traffic data recovery compared to other existing models while maintaining traffic order and distribution.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"357-371"},"PeriodicalIF":5.5,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815742","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}

引用次数: 0

MEC-Enabled Task Replication With Resource Allocation for Reliability-Sensitive Services in 5G mMTC Networks 5g mMTC网络中支持mec的任务复制和资源分配

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-12-12 DOI: 10.1109/TSC.2024.3517341

Rui Huang;Wushao Wen;Zhi Zhou;Chongwu Dong;Xu Chen

{"title":"MEC-Enabled Task Replication With Resource Allocation for Reliability-Sensitive Services in 5G mMTC Networks","authors":"Rui Huang;Wushao Wen;Zhi Zhou;Chongwu Dong;Xu Chen","doi":"10.1109/TSC.2024.3517341","DOIUrl":"10.1109/TSC.2024.3517341","url":null,"abstract":"The increasing demand for connectivity in 5G networks has led to a focus on massive machine-type communication (mMTC) in mobile edge computing (MEC) for IoTs. However, the proliferation of IoT devices has resulted in densely deployed networks and led to a high volume of task offloading to the same edge servers simultaneously. As a consequence, mMTC applications may experience service congestion, negatively impacting service reliability. To enhance the service reliability of latency-sensitive applications, task replication with resource allocation is proposed in MEC, in which a task can be sent simultaneously to multiple computing nodes. Task replication can reduce task latency and improve service reliability at the cost of consuming more computation resources. However, unconstrained task replication may result in too many uploading links, leading to severe costs in network operation. To handle the above challenge, we propose a constrained stochastic optimization problem by task replication with wireless resource block (RB) allocation and edge server queue management. To ensure queue stability while minimizing cost, we design one strategy based on the Lyapunov optimization framework. Accordingly, we further model RB allocation as a mean-field game (MFG) due to the intensive coupling of the RB pool for massive users. Tractable partial differential equations are used to analyze MFG equilibrium, and we derive the optimal edge server queue management based on a given task replication strategy and RB allocation scheme. Our theoretical analysis demonstrates that our algorithm closely approaches the optimal overall costs within a small gap, and simulation results show that our strategy generates a significantly lower cumulative cost than other alternative strategies.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"253-269"},"PeriodicalIF":5.5,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815743","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}

引用次数: 0

FeDistSlice: Federated Policy Distillation for Collaborative Intelligence in Multi-Tenant RAN Slicing FeDistSlice：多租户RAN切片中协同智能的联邦策略蒸馏

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-12-12 DOI: 10.1109/TSC.2024.3517334

Guolin Sun;Daniel Ayepah-Mensah;Huan Chen;Gordon Owusu Boateng;Guisong Liu

{"title":"FeDistSlice: Federated Policy Distillation for Collaborative Intelligence in Multi-Tenant RAN Slicing","authors":"Guolin Sun;Daniel Ayepah-Mensah;Huan Chen;Gordon Owusu Boateng;Guisong Liu","doi":"10.1109/TSC.2024.3517334","DOIUrl":"10.1109/TSC.2024.3517334","url":null,"abstract":"Federated Deep Reinforcement Learning (FDRL) for Radio Access Network (RAN) Slicing offers a promising approach for optimizing resource allocation and network performance, while also preserving data privacy for multiple tenants. However, the inherently non-independent and identically distributed (non-IID) nature of data, stemming from the diverse services and unique characteristics of RAN slices, poses significant challenges. This heterogeneity can disrupt the standard assumptions FDRL makes, leading to model training inefficiencies and potentially suboptimal slicing decisions. Addressing this non-IID challenge is imperative to harness the full potential of FDRL in RAN slicing and to ensure seamless, adaptive, and efficient resource sharing among the tenants. Hence, we propose FeDistSlice, a federated distillation slicing framework wherein multiple decision agents collaborate in real time, optimizing resource allocation tailored to each tenant's specific characteristics. Motivated by collaborative intelligence, we introduced a customized mutual policy distillation (MPD) strategy to foster collaboration across multiple tenants. This innovation allows for the creating of personalized models tailored to each agent's unique requirements and context. Through MPD, these models can collaboratively learn and refine their policies by leveraging insights from other agents within the network. Simulation results show that FeDistSlice converges more effectively and achieves increased robustness to non-IID data.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"184-197"},"PeriodicalIF":5.5,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815737","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}

引用次数: 0

Task Offloading and Resource Pricing Based on Game Theory in UAV-Assisted Edge Computing 基于博弈论的无人机辅助边缘计算任务卸载与资源定价

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-12-09 DOI: 10.1109/TSC.2024.3512936

Zhuoyue Chen;Yaozong Yang;Jiajie Xu;Ying Chen;Jiwei Huang

{"title":"Task Offloading and Resource Pricing Based on Game Theory in UAV-Assisted Edge Computing","authors":"Zhuoyue Chen;Yaozong Yang;Jiajie Xu;Ying Chen;Jiwei Huang","doi":"10.1109/TSC.2024.3512936","DOIUrl":"10.1109/TSC.2024.3512936","url":null,"abstract":"Due to the limited battery capacity and computational resources of mobile devices, computation-intensive tasks generated by mobile devices can be offloaded to edge servers for processing. This paper investigates the multi-user task offloading and resource pricing issues in Autonomous aerial vehicle (AAV)-assisted Multi-Access Edge Computing (MEC) systems. The optimization objectives is optimizing the utility of the server and the utility of the Edge Users (EUs), with decision variables encompassing the offloading strategies of EUs and the pricing strategies of the server. We divide the entire optimization problem into two parts. When optimizing the server's utility, server energy consumption is a crucial metric; hence, in the first part, we formulate the user allocation problem with the goal of minimizing the server's overall energy consumption. Utilizing game theory, we transform the user allocation problem into a multi-user non-cooperative game and prove the existence of a Nash Equilibrium (NE). The Game-based User Allocation (GBUA) algorithm is proposed to obtain the user allocation strategy. After addressing the user allocation problem, we consider the simultaneous optimization of both server and EUs utility. Therefore, in the second part, we model the server and EUs's engagement using the Stackelberg game model and employ backward induction to verify the presence of a Stackelberg Equilibrium (SE). Additionally, we propose the Resource Pricing and Task Offloading (RPATO) algorithm, based on game theory, to obtain the SE solution. Finally, extensive experiments are conducted to validate the effectiveness of the proposed algorithms, and numerous comparative algorithms are tested to prove the advancement and innovation of our proposed algorithms.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"440-452"},"PeriodicalIF":5.5,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804516","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}

引用次数: 0