IEEE Transactions on Network and Service Management最新文献_第10页

Delay and Load Fairness Optimization With Queuing Model in Multi-AAV Assisted MEC: A Deep Reinforcement Learning Approach 多 AAV 辅助 MEC 中队列模型的延迟和负载公平性优化：一种深度强化学习方法

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-12-19 DOI: 10.1109/TNSM.2024.3520632

Qiang Tang;Bao Li;Halvin H. Yang;Yan Li;Shiming He;Kun Yang

{"title":"Delay and Load Fairness Optimization With Queuing Model in Multi-AAV Assisted MEC: A Deep Reinforcement Learning Approach","authors":"Qiang Tang;Bao Li;Halvin H. Yang;Yan Li;Shiming He;Kun Yang","doi":"10.1109/TNSM.2024.3520632","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3520632","url":null,"abstract":"Autonomous aerial vehicles (AAV) can alleviate the computational burden on edge devices through assisted computing. However, with the increase in the number of Internet of Things Devices (IoTDs), it is essential to establish a task queue on the AAV to schedule computing tasks from IoTDs. In addition, the load fairness of AAVs should be optimized to fully utilize the computing resources. Therefore, a multi-AAV-assisted mobile edge computing (MEC) network framework based on the queuing model is proposed, which aims at optimizing the average delay of all user devices and the load fairness of AAVs. Firstly, we prove that the arrangement of tasks with different computing delays on the AAV queue can affect the user’s average delay, so a short-job-first (SJF) queuing model is proposed to minimize the average delay of users. On this basis, a joint optimization problem related to the AAV’s three-dimensional trajectory and user connection scheduling is formulated. A SJF based low-complexity connection scheduling algorithm is proposed and combined in a deep reinforcement learning (DRL) to solve this NP-hard problem. To evaluate the performance of the proposed algorithm, we compare it with deep deterministic policy gradient (DDPG), particle swarm optimization (PSO), random moving (RM), and local computing (LC). Simulation results show that our algorithm effectively reduces user average delay and enhances AAV load fairness. Finally, SJF is compared with the traditional first-come-first-served (FCFS) queuing model on different algorithms. The results indicate that the average delay of SJF is significantly lower than that of FCFS.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1247-1258"},"PeriodicalIF":4.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860977","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

Deploying Disaster-Resilient Service Function Chains Using Adaptive Multi-Path Routing 使用自适应多路径路由部署容灾业务功能链

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-12-19 DOI: 10.1109/TNSM.2024.3520392

Mohamed Abderrahmane Madani;Fen Zhou;Ahmed Meddahi

{"title":"Deploying Disaster-Resilient Service Function Chains Using Adaptive Multi-Path Routing","authors":"Mohamed Abderrahmane Madani;Fen Zhou;Ahmed Meddahi","doi":"10.1109/TNSM.2024.3520392","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3520392","url":null,"abstract":"Network Function Virtualization (NFV) is a new technology that deploys network services and functions as software components in data centers and cloud environments. One of its key applications is Service Function Chain (SFC), which chains a set of Virtual Network Functions (VNFs) in a specific order to deliver a desired service. However, deploying NFV and SFC networks faces challenges, particularly in terms of disaster resiliency. This encompasses natural disasters and hardware failures, which can disrupt network operations and lead to service interruption or degradation across an entire disaster zone (DZ). Therefore, designing NFV and SFC networks that can withstand disasters while providing high levels of service availability and reliability is important. This paper presents a new method for protecting SFCs using adaptive multi-path routing. The proposed Multi-path Protection (MP) method has the advantage of reducing the amount of reserved bandwidth on backup paths by distributing SFC traffic over multiple DZ-disjoint working paths. The problem being addressed involves VNFs placement, routing SFCs, and implementing protection mechanisms. The objective is to minimize network resource consumption, including both the bandwidth used by request routing paths and the computing resources for VNF execution. To solve this multi-dimensional optimization problem, a path-adaptive and flow-based integer linear program (ILP) is proposed to provide the optimal solution in sall-size network settings. We also propose a heuristic approach that offers the near-optimal solution in a time-efficient way. Comprehensive simulation results show that the proposed MP strategy outperforms traditional Dedicated Protection (DP) in terms of bandwidth and processing resource consumption, resulting in a significant gain up to 20%.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1825-1840"},"PeriodicalIF":4.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860755","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

UaaS-SFL: Unlearning as a Service for Safeguarding Federated Learning UaaS-SFL：为保障联合学习而提供的 "非学习即服务"（Unlearning as a Service for Safeguarding Federated Learning

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-12-19 DOI: 10.1109/TNSM.2024.3520109

Wathsara Daluwatta;Ibrahim Khalil;Shehan Edirimannage;Mohammed Atiquzzaman

{"title":"UaaS-SFL: Unlearning as a Service for Safeguarding Federated Learning","authors":"Wathsara Daluwatta;Ibrahim Khalil;Shehan Edirimannage;Mohammed Atiquzzaman","doi":"10.1109/TNSM.2024.3520109","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3520109","url":null,"abstract":"The rapid expansion of the Internet of Things (IoT) and network services has revolutionized technology, enabling numerous intelligent applications. However, this interconnected environment also introduces significant security challenges, particularly the susceptibility of federated learning (FL) systems to poisoning attacks. Such attacks compromise the integrity of the global model by injecting malicious data, leading to inaccurate predictions and potentially endangering system reliability and user safety. While traditional approaches, such as early detection and secure aggregation methods, aim to prevent the aggregation of malicious updates, they are ineffective in addressing threats within systems that have already been compromised and did not initially implement these safeguards. This gap highlights the urgent need for robust post-compromise mitigation strategies in FL security. To address this challenge, we introduce “Unlearning as a Service for Safeguarding Federated Learning” (UaaS-SFL), a novel service designed to seamlessly integrate with any FL management system to remove the impact of poisoning clients and restore the integrity of the global model. UaaS-SFL effectively unlearns the contributions of malicious clients, ensuring both model security and system reliability. Our empirical evaluations, conducted in a simulated IoT environment, demonstrate that our service maintains model accuracy with less than a 10% deviation from the baseline achieved through retraining from scratch, underscoring the efficacy of our methodology in safeguarding FL systems. These results highlight UaaS-SFL as a critical service for securing FL management systems, providing a robust foundation for the continued growth of secure and intelligent IoT applications.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1029-1045"},"PeriodicalIF":4.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860981","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 Network Connectivity-Aware Reinforcement Learning Method for Task Exploration and Allocation 用于任务探索和分配的网络连接感知强化学习方法

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-12-18 DOI: 10.1109/TNSM.2024.3514894

Xingyu He;Xiankai Li;Guisong Yang;Shi Chang;Jiehan Zhou

{"title":"A Network Connectivity-Aware Reinforcement Learning Method for Task Exploration and Allocation","authors":"Xingyu He;Xiankai Li;Guisong Yang;Shi Chang;Jiehan Zhou","doi":"10.1109/TNSM.2024.3514894","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3514894","url":null,"abstract":"For a limited scale self-organized multi-agent system operating in environments with unknown task distributions, one challenge is to reduce the task response time via efficiently combining task exploration and allocation, another challenge is to improve the task completion rate via unlocking the potential of network cooperation in task allocation. However, in the existing studies, task allocation is generally regarded as an independent issue for known task distribution environments, rarely combined with task exploration, also hardly solving the conflict between the multi-hop network cooperation and mobility flexibility of agents. In view of this, this paper proposes a network connectivity-aware deep reinforcement learning method for task exploration and allocation in limited scale multi-agent systems (NCADRL4TEA). This method divides the task environment into regions and integrates task exploration with task allocation via two policies: a leaving policy to guide global task exploration among regions according to the distribution of agents and tasks, and a stay policy to guide local task allocation within each region according to the multi-hop network cooperation performance between agents. Further, in the stay policy, a network connectivity-aware task allocation optimization model is provided, which leads agents in the same region to cooperate with each other via multi-hop intermittent network connectivity and flexibly adjust their locations until the optimal multi-hop network cooperation performance is achieved. The experimental results verify that NCADRL4TEA can reduce the task response time in combination of task exploration and allocation, and improve the task completion rate in network cooperation.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1160-1173"},"PeriodicalIF":4.7,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860876","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 Novel E2E Path Selection Algorithm for Superior QoS and QoE for 6G Services 一种新的实现6G业务优质QoS和QoE的端到端路径选择算法

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-12-18 DOI: 10.1109/TNSM.2024.3519707

Yogita Pimpalkar;Sharvari Ravindran;Jyotsna Bapat;Debabrata Das

{"title":"A Novel E2E Path Selection Algorithm for Superior QoS and QoE for 6G Services","authors":"Yogita Pimpalkar;Sharvari Ravindran;Jyotsna Bapat;Debabrata Das","doi":"10.1109/TNSM.2024.3519707","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3519707","url":null,"abstract":"The progression of Sixth-Generation (6G) services requires networks to meet stringent Quality of Service (QoS) and Quality of Experience (QoE) standards, demanding superior performance from all network segments. Selecting the most suitable path that meets application demands across multiple network segments is a key strategy for achieving these objectives. Traditional and modern path selection methods focus on QoS metrics such as bandwidth and latency to ensure efficient and reliable data delivery. However, as 6G introduces more interactive applications like Virtual Reality (VR) and Augmented Reality (AR), prioritizing user experience alongside QoS metrics becomes more crucial. This paper presents a Service-Aware Optimal Path Selection (SOPS) algorithm designed to select an optimal End-to-End (E2E) path for each application by minimizing a global cost function that incorporates both QoS and QoE parameters. The global optimality of the selected E2E path is proven by demonstrating the closed and convex nature of the underlying cost functions. Moreover, our proposed distributed cost function ensures optimal E2E paths formed from selected network segment-specific paths. Extensive simulations show that SOPS improves QoS and QoE when compared with other state-of-the-art routing algorithms. SOPS enhances QoS by improving reliability by 0.84% and reducing packet delay by 5.55%. An improvement of up to 99.65% is seen in the connection acceptance ratio, particularly for diverse application requirements. Significant improvements in QoE are observed across Hypertext Transfer Protocol (HTTP) and video applications, with throughput increases of 10.04% and 7.99%, jitter reductions of 86.25% and 2.49%, and delay improvements of 77.08% and 0.69%, respectively.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1174-1187"},"PeriodicalIF":4.7,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860775","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

An Extended Bandit-Based Game Scheme for Distributed Joint Resource Allocation in Underwater Acoustic Communication Networks 水下声学通信网络中分布式联合资源分配的基于 Bandit 的扩展博弈方案

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-12-17 DOI: 10.1109/TNSM.2024.3519152

Jun Dai;Xinbin Li;Song Han;Junzhi Yu;Zhixin Liu;Tongwei Zhang

{"title":"An Extended Bandit-Based Game Scheme for Distributed Joint Resource Allocation in Underwater Acoustic Communication Networks","authors":"Jun Dai;Xinbin Li;Song Han;Junzhi Yu;Zhixin Liu;Tongwei Zhang","doi":"10.1109/TNSM.2024.3519152","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3519152","url":null,"abstract":"This paper investigates a joint discrete-channel and continuous-power allocation problem for multi-user underwater acoustic communication networks. The unknown underwater acoustic Channel State Information (CSI) and the distributed optimization requirement make the proposed hybrid discrete-continuous optimization problem full of challenges. Firstly, an adversarial multi-player bandit game model is formulated, which enables each user to independently optimize its own strategy, thereby achieving the distributed decision. In the strategic game, the Multi-armed Bandit (MAB) learning theory is exploited to achieve the best response strategy of independent user without prior CSI. Secondly, an evolutive finite discrete strategy pool learning structure is proposed to achieve an efficient search for the hybrid discrete-continuous space. The constant evolvement of strategy pool endows the proposed MAB-based algorithm with the ability to search the whole continuous power space, thereby avoiding missing the superior strategy caused by the discretization of continuous space. Thirdly, a selection probability setting rule is proposed, which promotes the exploration-exploitation balance for the dynamic strategy pool, thereby improving the learning efficiency. Finally, simulation results demonstrate the superiority of the proposed algorithm.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1205-1218"},"PeriodicalIF":4.7,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860880","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

Cyber Attacks Prevention Toward Prosumer-Based EV Charging Stations: An Edge-Assisted Federated Prototype Knowledge Distillation Approach 针对产消型电动汽车充电站的网络攻击预防：一种边缘辅助联邦原型知识蒸馏方法

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-12-16 DOI: 10.1109/TNSM.2024.3517621

Luyao Zou;Quang Hieu Vo;Kitae Kim;Huy Q. Le;Chu Myaet Thwal;Chaoning Zhang;Choong Seon Hong

{"title":"Cyber Attacks Prevention Toward Prosumer-Based EV Charging Stations: An Edge-Assisted Federated Prototype Knowledge Distillation Approach","authors":"Luyao Zou;Quang Hieu Vo;Kitae Kim;Huy Q. Le;Chu Myaet Thwal;Chaoning Zhang;Choong Seon Hong","doi":"10.1109/TNSM.2024.3517621","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3517621","url":null,"abstract":"In this paper, cyber-attack prevention for the prosumer-based electric vehicle (EV) charging stations (EVCSs) is investigated, which covers two aspects: 1) cyber-attack detection on prosumers’ network traffic (NT) data, and 2) cyber-attack intervention. To establish an effective prevention mechanism, several challenges need to be tackled, for instance, the NT data per prosumer may be non-independent and identically distributed (non-IID), and the boundary between benign and malicious traffic becomes blurred. To this end, we propose an edge-assisted federated prototype knowledge distillation (E-FPKD) approach, where each client is deployed on a dedicated local edge server (DLES) and can report its availability for joining the federated learning (FL) process. Prior to the E-FPKD approach, to enhance accuracy, the Pearson Correlation Coefficient is adopted for feature selection. Regarding the proposed E-FPKD approach, we integrate the knowledge distillation and prototype aggregation technique into FL to deal with the non-IID challenge. To address the boundary issue, instead of directly calculating the distance between benign and malicious traffic, we consider maximizing the overall detection correctness of all prosumers (ODC), which can mitigate the computational cost compared with the former way. After detection, a rule-based method will be triggered at each DLES for cyber-attack intervention. Experimental analysis demonstrates that the proposed E-FPKD can achieve the largest ODC on NSL-KDD, UNSW-NB15, and IoTID20 datasets in both binary and multi-class classification, compared with baselines. For instance, the ODC for IoTID20 obtained via the proposed method is separately 0.3782% and 4.4471% greater than FedProto and FedAU in multi-class classification.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1972-1999"},"PeriodicalIF":4.7,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860955","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

OTA-Key: Over-the-Air Key Management for Flexible and Reliable IoT Device Provision OTA-Key：无线密钥管理，实现灵活可靠的物联网设备配置

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-12-11 DOI: 10.1109/TNSM.2024.3515212

Qian Zhang;Yi He;Yue Xiao;Xiaoli Zhang;Chunhua Song

{"title":"OTA-Key: Over-the-Air Key Management for Flexible and Reliable IoT Device Provision","authors":"Qian Zhang;Yi He;Yue Xiao;Xiaoli Zhang;Chunhua Song","doi":"10.1109/TNSM.2024.3515212","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3515212","url":null,"abstract":"As the Internet of Things (IoT) industry advances, the imperative to secure IoT devices has become increasingly critical. Current practices in both industry and academia advocate for the enhancement of device security through key installation. However, it has been observed that, in practice, IoT vendors frequently assign shared keys to batches of devices. This practice can expose devices to risks, such as data theft by attackers or large-scale Distributed Denial of Service (DDoS) attacks. To address this issue, our intuition is to assign a unique key to each device. Unfortunately, this strategy proves to be highly complex within the IoT context, as existing keys are typically hardcoded into the firmware, necessitating the creation of bespoke firmware for each device. Furthermore, correct pairing of device keys with their respective devices is crucial. Errors in this pairing process would incur substantial human and temporal resources to rectify and require extensive communication between IoT vendors, device manufacturers, and cloud platforms, leading to significant communication overhead. To overcome these challenges, we propose the OTA-Key scheme. This approach fundamentally decouples device keys from the firmware features stored in flash memory, utilizing an intermediary server to allocate unique device keys in two distinct stages and update keys. We conducted a formal security verification of our scheme using ProVerif and assessed its performance through a series of evaluations. The results demonstrate that our scheme is secure and effectively manages the large-scale distribution and updating of unique device keys. Additionally, it achieves significantly lower update times and data transfer volumes compared to other schemes.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"2106-2119"},"PeriodicalIF":4.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860881","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

Web API Recommendation via Exploring Textual and Structural Semantics With Contrastive Learning and Joint Training 通过对比学习和联合训练探索文本和结构语义的Web API推荐

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-12-11 DOI: 10.1109/TNSM.2024.3515103

Guosheng Kang;Hongshuai Ren;Wanjun Chen;Jianxun Liu;Buqing Cao;Yu Xu

{"title":"Web API Recommendation via Exploring Textual and Structural Semantics With Contrastive Learning and Joint Training","authors":"Guosheng Kang;Hongshuai Ren;Wanjun Chen;Jianxun Liu;Buqing Cao;Yu Xu","doi":"10.1109/TNSM.2024.3515103","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3515103","url":null,"abstract":"With the advancement of service computing technology, software developers tend to consume a variety of Web APIs (Application Programming Interfaces, also named Web services) from Web API markets to create feature-rich Mashup applications to save time and cost. Under such a background, the ever-increasing number of Web APIs makes the service discovery become a challenge. Thus, Web API recommendation becomes an effective means for service discovery. However, the existing approaches to Web API recommendation still have limitations in extracting rich semantics sufficiently from functional description documents and service networks, resulting in a limited recommendation performance. To further improve the recommendation performance, this paper proposes an effective Web API recommendation approach via exploring textual and structural semantics with contrastive learning and joint training, named CLJT. On one side, discriminative feature representations from textual and structural semantics could be derived by contrastive learning with information correlation across views. On the other side, the derived representations could be applicable to Web API recommendation by joint training of the representation tasks and the recommendation task. Extensive experiments are conducted over a real-world dataset crawled from ProgrammableWeb.com. The experimental results demonstrate the superiority of the proposed approach compared to the baseline methods.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"1558-1568"},"PeriodicalIF":4.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870949","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

Robust Deployment Model for Parallelized Service Function Chains Against Uncertain Traffic Arrival Rates 不确定流量到达率下并行化业务功能链的鲁棒部署模型

IF 4.7 2区计算机科学

IEEE Transactions on Network and Service Management Pub Date : 2024-12-11 DOI: 10.1109/TNSM.2024.3515078

Chenlu Zhang;Takehiro Sato;Eiji Oki

{"title":"Robust Deployment Model for Parallelized Service Function Chains Against Uncertain Traffic Arrival Rates","authors":"Chenlu Zhang;Takehiro Sato;Eiji Oki","doi":"10.1109/TNSM.2024.3515078","DOIUrl":"https://doi.org/10.1109/TNSM.2024.3515078","url":null,"abstract":"In network function virtualization, a network service is provided by a service function chain (SFC), which consists of a chain of virtual network functions (VNFs) within a specific order. SFC parallelism allows parallel processing among VNFs to reduce the end-to-end service delay. Existing works handle the service delay without considering traffic uncertainty, which leads to degraded performance on parallel structure balancing and deployment cost saving in the parallelized SFC deployment problem. This paper proposes a robust deployment model for parallelized SFCs against traffic uncertainty that satisfies the requirement of balanced parallel structures and minimizes the deployment cost. We define a traffic uncertainty set that handles both the variation of service traffic arrival rates and the fluctuation of parallel structures. We apply VNF sharing to improve the efficiency of resource allocation. We formulate the proposed model as a mixed integer second-order cone programming (MISOCP) problem. We introduce a heuristic algorithm to handle larger-size problems, where the MISOCP approach is intractable to obtain a solution in a practical time. Numerical results show the advantages of the proposed model in terms of deployment cost over the baseline models.","PeriodicalId":13423,"journal":{"name":"IEEE Transactions on Network and Service Management","volume":"22 2","pages":"2156-2180"},"PeriodicalIF":4.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860824","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