IEEE Transactions on Services Computing最新文献

{"title":"A Resource-Efficient Multiple Recognition Services Framework for IoT Devices","authors":"Chuntao Ding;Zhuo Liu;Ao Zhou;Jinhui Yu;Yidong Li;Shangguang Wang","doi":"10.1109/TSC.2024.3512949","DOIUrl":"10.1109/TSC.2024.3512949","url":null,"abstract":"Deploying the convolutional neural network (CNN) model on Internet of Things (IoT) devices to provide diverse recognition services has received increasing attention. Due to the limited storage, computing, and other resources of IoT devices, it has become mainstream to first train the CNN model on the edge/cloud server and then send the trained CNN to the IoT device. However, most existing related methods suffer from two limitations, (i) low performance due to service interference or insufficient mutual assistance, and (ii) large memory resources and switching resource overhead. To this end, this article proposes a resource-efficient multiple recognition services framework for IoT devices. The proposed framework is based on the edge server-assisted IoT device training of the CNN model, and the framework includes a deeper weight adaptation (DeepWAdapt) algorithm to mitigate service interference. The DeepWAdapt algorithm consists of a set of learnable masks, and by inserting these masks into the appropriate layers of the CNN model, it mitigates mutual interference between services caused by training a single CNN model for multiple services. Each service has a specific set of masks. These learnable masks work like keys for each service, selecting appropriate and specific features for each service from a shared feature set. Experimental results demonstrate that the DeepWAdapt outperforms other state-of-the-art methods on image-level classification services and pixel-level dense prediction services. Specifically, when executing 40 services based on ResNet18, the proposed DeepWAdapt achieves 66.82% F1-score on the CelebA dataset, which is +2.61% F1-score than the previous state-of-the-art result. In addition, compared with the routing method, our proposed DeepWAdapt also reduces network transmission traffic by approximately 35%.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"29-42"},"PeriodicalIF":5.5,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797067","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":"ObliuSky: Oblivious User-Defined Skyline Query Processing in the Cloud","authors":"Yifeng Zheng;Weibo Wang;Songlei Wang;Zhongyun Hua;Yansong Gao","doi":"10.1109/TSC.2024.3512945","DOIUrl":"10.1109/TSC.2024.3512945","url":null,"abstract":"The proliferation of cloud computing has spurred the popularity of storing and querying databases in the cloud. Among others, skyline queries play an important role in the database field due to its usefulness in multi-criteria decision support systems. To accommodate the tailored needs of users, user-defined skyline query has recently emerged, allowing users to define custom preferences in their skyline queries. However, user-defined skyline query services, if deployed in the cloud, may raise critical privacy concerns as the outsourced databases and skyline queries may contain proprietary/privacy-sensitive information. In light of the above, this paper presents ObliuSky, a new solution enabling oblivious user-defined skyline query processing in the cloud. ObliuSky departs from prior work by not only providing confidentiality protection for the content of the outsourced database, the user-defined skyline queries, and the query results, but also hiding the data patterns (e.g., user-defined dominance relations among database points and search access patterns) which may indirectly cause data leakages. We formally analyze the security guarantees and conduct extensive performance evaluations. The results show that while achieving much stronger security guarantees than the state-of-the-art prior work, ObliuSky is superior in database and query encryption efficiency, and scalable in oblivious query processing.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"314-327"},"PeriodicalIF":5.5,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797066","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":"Privacy-Preserving Competitive Detour Tasking in Spatial Crowdsourcing","authors":"Yifeng Zheng;Menglun Zhou;Songlei Wang;Zhongyun Hua;Jinghua Jiang;Yansong Gao","doi":"10.1109/TSC.2024.3511992","DOIUrl":"10.1109/TSC.2024.3511992","url":null,"abstract":"Spatial crowdsourcing (SC) has recently emerged as a new crowdsourcing service paradigm, where workers move physically to designated locations to perform tasks. Most SC systems perform task assignment based on the spatial proximity between task locations and worker locations. Under such a strategy, workers can only perform tasks near them, which may result in low social welfare (i.e., the total profit of the platform and workers). In contrast, the newly emerging strategy of competitive task assignment (CTA) stimulates workers to compete for their preferred tasks, allowing optimization of the overall profit of SC systems. Among others, one novel CTA setting is competitive detour tasking, which allows workers to compete for tasks that need them to make detours from their original travel paths. However, it requires collecting each worker’s bidding profile which may expose private information. In light of this, in this article, we design, implement, and evaluate PrivCO, a new system framework enabling privacy-preserving competitive detour tasking services in SC. PrivCO delicately bridges state-of-the-art competitive detour tasking algorithms with lightweight cryptography, providing strong protections for workers’ bidding profiles. Extensive experiments over real-world datasets demonstrate that while offering strong security guarantees, PrivCO achieves social welfare comparable to the plaintext domain.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"385-398"},"PeriodicalIF":5.5,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142782800","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":"Telemedicine Monitoring System Based on Fog/Edge Computing: A Survey","authors":"Qiang He;Zhaolin Xi;Zheng Feng;Yueyang Teng;Lianbo Ma;Yuliang Cai;Keping Yu","doi":"10.1109/TSC.2024.3506473","DOIUrl":"10.1109/TSC.2024.3506473","url":null,"abstract":"Telemedicine Monitoring (TM) integrates mobile communication technology and Internet of Things (IoT) technology for health monitoring and data management. Amidst the escalating demand for telemedicine, traditional cloud computing struggles to guarantee real-time performance and data privacy. To address these challenges, we systematically survey the application of fog and edge computing technologies in TM systems. We focus on the following key aspects: (1) We delve into the theoretical foundations of fog and edge computing, underscoring their salient advantages including low latency, location awareness, high mobility, and more. (2) We elaborate on the architecture of a TM system hinged on fog and edge computing. (3) We outline key challenges facing fog/edge computing-based TM systems, including bandwidth limitations, low latency, data security, privacy, heterogeneity, and reliability. (4) We discuss the need for future advancements in the realms of security defense capability, system adaptability, and convergence of scheduling algorithms to refine the construction of the TM system and stimulate the development of telemedicine.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"479-498"},"PeriodicalIF":5.5,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142760612","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":"Content-Specific and Buffer-Based Migration Schemes for Fog Computing","authors":"Mohammed A. Jasim","doi":"10.1109/TSC.2024.3506474","DOIUrl":"10.1109/TSC.2024.3506474","url":null,"abstract":"Fog computing and network function virtualization (NFV) technologies reduce latency and provide scalable services at the network edge. However, the limited resources of edge nodes pose a challenge in handling high traffic volumes from requests that demand high computation, extended lifetime, and low latency. To address this challenge, dynamic load migration schemes for NFV-based fog paradigms are proposed here. First, a content-specific scheme that diffuses excess loads to nearby locations hosting relevant virtual network functions (VNFs) of the requests. Second, a buffer scheme reserves a dedicated node to absorb loads from saturated nodes in the proximity of terminals. Third, a hybrid scheme integrates both strategies by initially migrating loads to the pre-allocated buffer to provide immediate relief without searching for candidate nodes. Upon the buffer saturation, it switches to the content-specific phase to distribute loads to the proximate nodes. The network introduces a novel request model comprised of dependent and independent VNFs of varying resource demands. Dependent VNFs are collectively mapped on a primary node while distributing independent VNFs across neighboring secondary nodes, forming a structured ring topology mapping method. These schemes enhance migration success rates, and reduce migration iterations, service downtime, and cost, as compared to prominent solutions.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"98-111"},"PeriodicalIF":5.5,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142718347","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":"Deep Reinforcement Learning With Entropy and Attention Mechanism for D2D-Assisted Task Offloading in Edge Computing","authors":"Cong Wang;Xiaojuan Chai;Sancheng Peng;Ying Yuan;Guorui Li","doi":"10.1109/TSC.2024.3495503","DOIUrl":"10.1109/TSC.2024.3495503","url":null,"abstract":"The rapid development of edge computing and the Industrial Internet of Things have facilitated near real-time optimization of compute-intensive industrial tasks. Mobile edge computing (MEC) and device-to-device (D2D) offloading are promising ways to achieve near-real-time optimization. In this article, We propose a D2D-assisted MEC computing offloading framework by using deep reinforcement Learning (DRL) with entropy and attention mechanism (DMOEA). DMOEA considers interactions among related entities, including horizontal device-to-device collaboration and vertical device-to-edge offloading. Then, a DRL-based model with multi-actor single-critic structure is designed to solve the offloading strategy. In addition, to further improve efficiency, an attention mechanism is introduced to adapt dynamic changes in network and enhance the exploration ability. The experimental results show that the proposed framework can obtain a fast convergence rate and small oscillation amplitude and also can effectively reduce latency.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3317-3329"},"PeriodicalIF":5.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142712796","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":"Task Allocation With Geography-Context-Capacity Awareness in Distributed Burstable Billing Edge-Cloud Systems","authors":"Shihao Shen;Chenfei Gu;Yuanze Li;Chao Qiu;Xiaofei Wang;Rui Tan;Cheng Zhang;Wenyu Wang","doi":"10.1109/TSC.2024.3506475","DOIUrl":"10.1109/TSC.2024.3506475","url":null,"abstract":"The new real-time interactive services, such as virtual and augmented reality, demand significantly higher network bandwidth and quality, which the traditional centralized cloud struggles to meet. In addition, centralized optimization management becomes inefficient as the scale of the scene continues to expand. In response, edge cloud systems have emerged, but distributed geographic locations, burstable billing business models, and large numbers of servers in large-scale scenarios pose new challenges for resource management. In this article, we propose <italic>GeoCC</i>, a novel strategy to save bandwidth overhead in burstable billing edge cloud systems. <italic>GeoCC</i> addresses challenges through a dual approach. First, a geography-aware graph construction and partitioning algorithm is used to organize server resources, and a large number of servers are reasonably divided into multiple server pools for parallel processing. Second, it introduces an enhanced burstable billing optimization mechanism that considers contextual factors and adaptive bandwidth capacity. Experiments based on real data from an edge cloud operator demonstrate the effectiveness of <italic>GeoCC</i>. Compared with the baseline, <italic>GeoCC</i> can effectively reduce bandwidth peaks, decreasing bandwidth costs by an average of 28.30% and up to 81.83% at the 95th percentile billing.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"427-439"},"PeriodicalIF":5.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142712596","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":"Participation-Dependent Privacy Preservation in Cross-Silo Federated Learning","authors":"Yanling Qin;Xiangping Zheng;Qian Ma;Guocheng Liao;Xu Chen","doi":"10.1109/TSC.2024.3506479","DOIUrl":"10.1109/TSC.2024.3506479","url":null,"abstract":"In cross-silo federated learning (FL), clients of common interest cooperatively train a global model without sharing local sensitive data, but they still face potential privacy leakage due to privacy threats from malicious attackers. Although some articles have proposed effective privacy-preserving mechanisms for FL (such as differential privacy (DP)), clients in cross-silo FL are usually different companies or organizations who may behave selfishly to optimize their own benefits. In this article, we study DP-based cross-silo FL where clients selfishly decide their participation levels (i.e., data sizes for model trainings) and privacy leakage tolerance levels to trade off between model accuracy loss and privacy loss, and we model clients’ interactions as a participation-dependent privacy preservation game. It is challenging to analyze the game since the comprehensive impact of participation levels and privacy leakage tolerance levels on model accuracy is unclear and the behaviors of heterogeneous clients are coupled in a highly complex manner. To capture the impact of participation and privacy preservation behaviors, we first characterize the optimality gap of DP-based cross-silo FL for both convex and non-convex models, where the privacy leakage tolerance levels and the participation levels are coupled nonlinearly. We model clients’ costs based on the optimality gap, and prove that clients’ selfish participation-dependent privacy preservation game is a potential game. To analyze the optimal strategies of heterogeneous clients in a stable state, we derive the closed-form expression for the unique Nash equilibrium (NE), where clients may choose full participation or partial participation, and the equilibrium privacy preservation strategy depends on clients’ accuracy-privacy preference ratios. We analyze the social efficiency of the NE by calculating the price of anarchy (PoA) and show that the PoA increases with the number of clients and the heterogeneity of clients’ model accuracy preferences. To improve the social efficiency achieved at equilibrium, we design a socially efficient incentive mechanism that allows clients with large model accuracy preferences to compensate clients with small model accuracy preferences. Extensive experiments verify our theoretical results for both the convex and non-convex models as well as both the i.i.d. data distribution case and the non-i.i.d. data distribution case.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"342-356"},"PeriodicalIF":5.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142712524","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":"Efficient Parameter Synchronization for Peer-to-Peer Distributed Learning With Selective Multicast","authors":"Shouxi Luo;Pingzhi Fan;Ke Li;Huanlai Xing;Long Luo;Hongfang Yu","doi":"10.1109/TSC.2024.3506480","DOIUrl":"10.1109/TSC.2024.3506480","url":null,"abstract":"Recent advances in distributed machine learning show theoretically and empirically that, for many models, provided that workers will eventually participate in the synchronizations, <inline-formula><tex-math>$i)$</tex-math></inline-formula> the training still converges, even if only <inline-formula><tex-math>$p$</tex-math></inline-formula> workers take part in each round of synchronization, and <inline-formula><tex-math>$ii)$</tex-math></inline-formula> a larger <inline-formula><tex-math>$p$</tex-math></inline-formula> generally leads to a faster rate of convergence. These findings shed light on eliminating the bottleneck effects of parameter synchronization in large-scale data-parallel distributed training and have motivated several optimization designs. In this paper, we focus on optimizing the parameter synchronization for <i>peer-to-peer</i> distributed learning, where workers broadcast or multicast their updated parameters to others for synchronization, and propose <small>SelMcast</small>, a suite of expressive and efficient multicast receiver selection algorithms, to achieve the goal. Compared with the state-of-the-art (SOTA) design, which randomly selects exactly <inline-formula><tex-math>$p$</tex-math></inline-formula> receivers for each worker’s multicast in a bandwidth-agnostic way, <small>SelMcast</small> chooses receivers based on the global view of their available bandwidth and loads, yielding two advantages, i.e., accelerated parameter synchronization for higher utilization of computing resources and enlarged average <inline-formula><tex-math>$p$</tex-math></inline-formula> values for faster convergence. Comprehensive evaluations show that <small>SelMcast</small> is efficient for both peer-to-peer Bulk Synchronous Parallel (BSP) and Stale Synchronous Parallel (SSP) distributed training, outperforming the SOTA solution significantly.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"156-168"},"PeriodicalIF":5.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142712798","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":"Enhanced Tube-Based Sampling for Accurate Network Distance Measurement with Minimal Sampling Scheduling Overhead","authors":"Jiazheng Tian;Cheng Wang;Kun Xie;Jigang Wen;Gaogang Xie;Kenli Li;Wei Liang","doi":"10.1109/TSC.2024.3506477","DOIUrl":"10.1109/TSC.2024.3506477","url":null,"abstract":"The surge in demand for latency-sensitive services has propelled network distance measurement to the forefront of networking research. Utilizing the low-rank structure of full network data, the tensor completion method can efficiently estimate network distance from partially sampled distance data measured from a small set of node pairs. However, its performance is affected by sampling algorithm limitations, including unreliability and high overhead in dynamic networks. To tackle these challenges, we propose tube-based sampling as an alternative to point-based sampling, utilizing a partition-based algorithm to incorporate randomness for improved reliability. Additionally, we introduce a Tube Length Identification Algorithm to dynamically adjust tube length based on network status, balancing scheduling overhead reduction with estimation accuracy. Experimental results on three real network distance datasets, compared against 13 baseline algorithms, demonstrate the high accuracy and low scheduling overhead of our approach.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"169-183"},"PeriodicalIF":5.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142712797","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}