IEEE Networking Letters最新文献_第3页

Performance Analysis of Slotted-Aloha With Mandatory Access and Retries in a Finite Frame 有限框架中带有强制访问和重试功能的插槽-Aloha 性能分析

IEEE Networking Letters Pub Date : 2024-04-04 DOI: 10.1109/LNET.2024.3385661

Diogo Pereira;Rodolfo Oliveira;Daniel Benevides da Costa;Hyong Kim

{"title":"Performance Analysis of Slotted-Aloha With Mandatory Access and Retries in a Finite Frame","authors":"Diogo Pereira;Rodolfo Oliveira;Daniel Benevides da Costa;Hyong Kim","doi":"10.1109/LNET.2024.3385661","DOIUrl":"https://doi.org/10.1109/LNET.2024.3385661","url":null,"abstract":"In recent medium access control (MAC) protocols, such as the ones adopted in IEEE 802.11ad and IEEE 802.11ay, stations mandatorily access one of the \u0000<inline-formula> <tex-math>$N_{S} gt 0 $ </tex-math></inline-formula>\u0000 consecutive slots forming a frame. This can be seen as a variant of the traditional slotted-aloha (SA), where instead of accessing a slot with probability p and not accessing in the finite frame with probability \u0000<inline-formula> <tex-math>$(1-p)^{N_{S}}$ </tex-math></inline-formula>\u0000, a station always accesses at least once in a frame, i.e., stations mandatorily access in a frame’s slot. Additionally, a station can also perform multiple retries in the frame when the previous attempt did not succeed. Given the lack of modeling efforts and performance evaluation for SA schemes with mandatory access and retries (SAMAR), in this letter, we evaluate the expected number of stations that can successfully access the channel in a frame composed of a finite number of slots. Contrarily to SA, the analysis of SAMAR performance is challenging due to its enumerative nature. We propose an innovative recursive model of SAMAR performance, showing that SAMAR can achieve higher performance than that of SA parameterized with asymptotically optimal access probabilities. A comparative analysis shows the operational region where SAMAR overpasses SA’s performance, which is of crucial importance to define the number of slots of the SAMAR frame.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 2","pages":"101-105"},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparing HAProxy Scheduling Algorithms During the DDoS Attacks 比较 DDoS 攻击期间的 HAProxy 调度算法

IEEE Networking Letters Pub Date : 2024-04-01 DOI: 10.1109/LNET.2024.3383601

Anmol Kumar;Gaurav Somani;Mayank Agarwal

引用次数: 0

Necessary and Sufficient Condition for Triggering ECN Before PFC in Shared Memory Switches 共享内存开关中 PFC 前触发 ECN 的必要条件和充分条件

IEEE Networking Letters Pub Date : 2024-03-28 DOI: 10.1109/LNET.2024.3382955

Natchanon Luangsomboon;Jörg Liebeherr

引用次数: 0

DDoS Mitigation in IoT Using Machine Learning and Blockchain Integration 利用机器学习和区块链集成缓解物联网中的 DDoS

IEEE Networking Letters Pub Date : 2024-03-18 DOI: 10.1109/LNET.2024.3377355

Ammar Ibrahim El Sayed;Mahmoud Abdelaziz;Mohamed Hussein;Ashraf D. Elbayoumy

引用次数: 0

Blockchain-Enabled Variational Information Bottleneck for IoT Networks 物联网网络的区块链变异信息瓶颈

IEEE Networking Letters Pub Date : 2024-03-18 DOI: 10.1109/LNET.2024.3376435

Qiong Wu;Le Kuai;Pingyi Fan;Qiang Fan;Junhui Zhao;Jiangzhou Wang

引用次数: 0

Unlocking Reconfigurability for Deep Reinforcement Learning in SFC Provisioning 在 SFC 配置中释放深度强化学习的可重构性

IEEE Networking Letters Pub Date : 2024-03-13 DOI: 10.1109/LNET.2024.3400764

Murat Arda Onsu;Poonam Lohan;Burak Kantarci;Emil Janulewicz;Sergio Slobodrian

{"title":"Unlocking Reconfigurability for Deep Reinforcement Learning in SFC Provisioning","authors":"Murat Arda Onsu;Poonam Lohan;Burak Kantarci;Emil Janulewicz;Sergio Slobodrian","doi":"10.1109/LNET.2024.3400764","DOIUrl":"https://doi.org/10.1109/LNET.2024.3400764","url":null,"abstract":"Network function virtualization (NFV) is a key foundational technology for 5G and beyond networks, wherein to offer network services, execution of Virtual Network Functions (VNFs) in a defined sequence is crucial for high-quality Service Function Chaining (SFC) provisioning. To provide fast, reliable, and automatic VNFs placement, Machine Learning (ML) algorithms such as Deep Reinforcement Learning (DRL) are widely being investigated. However, due to the requirement of fixed-size inputs in DRL models, these algorithms are highly dependent on network configuration such as the number of data centers (DCs) where VNFs can be placed and the logical connections among DCs. In this letter, a novel approach using the DRL technique is proposed for SFC provisioning which unlocks the reconfigurability of the networks, i.e., the same proposed model can be applied in different network configurations without additional training. Moreover, an advanced Deep Neural Network (DNN) architecture is constructed for DRL with an attention layer that improves the performance of SFC provisioning while considering the efficient resource utilization and the End-to-End (E2E) delay of SFC requests by looking up their priority points. Numerical results demonstrate that the proposed model surpasses the baseline heuristic method with an increase in the overall SFC acceptance ratio by 20.3% and a reduction in resource consumption and E2E delay by 50% and 42.65%, respectively.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"6 3","pages":"193-197"},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142517983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE COMMUNICATIONS SOCIETY IEEE 通信学会

IEEE Networking Letters Pub Date : 2024-03-05 DOI: 10.1109/LNET.2024.3365241