An efficient priority-driven congestion control algorithm for data center networks

Zhu, Jiahua; Jiang, Xianliang; Yu, Yang; Jin, Guang; Chen, Haiming; Li, Xiaohui; Qu, Long

doi:10.23919/jcc.2020.06.004

Cited by 8 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Padahal di satu sisi, setiap packet-data yang didistribusikan hanya berasal dari empat klasifikasi data, yakni text, audio, image, dan video; dan masing-masing jenis data tersebut memiliki karakteristik yang berjenjang untuk proses loading dan storing pada sistem komputer [9]. Berdasar [7], [8], dan [11] bahwa beban pemrosesan data video lebih besar dibandingkan image, data image lebih besar dibandingkan audio, dan data audio lebih besar dibandingkan data text. Dengan demikian dapat diasumsikan bahwa parameter delay akan lebih besar untuk jenis data video pada kanal data yang sedang berjalan.…”

Section: Hipotesisunclassified

“…Parameter-parameter pengukuran Quality of Service (QoS) diamati pada nilai throughtput, kapasitasdata, delay, latency, dan variance [6][7] [11]. QoS didefinisikan sebagai ukuran sebuah parameter untuk menilai performansi layanan-data pada sebuah kanal komunikasi-data saat melewati berbagai gangguan yang muncul saat sedang bekerja atau berjalan [10][11] [12].…”

Section: Dasar Teoriunclassified

See 1 more Smart Citation

Analisis QoS Pada Layanan Streaming Untuk Konfigurasi Jaringan Wireless

Simamora¹

2022

JNKTI

View full text Add to dashboard Cite

Abstrak - Kebutuhan masyarakat atas penggunaan layanan-data seiring dengan kemajuan teknologi informasi dan komunikasi, atau umum diistilahkan dengan telematika; cukup memicu dorongan perubahan dan perbaikan metode serta teknik komunikasi-data. Terlebih lagi keinginan yang lebih untuk menyajikan bentuk komunikasi dua-arah yang interaktif untuk tujuan kejelasan informasi yang dibutuhkan. Bisnis dan teknologi cukup erat saling membutuhkan seperti sebuah simbiosis mutulisme yang saling mengisi kekurangan untuk perbaikan lebih baik demi sebuah tujuan kelancaran aktivitas yang padat dan just-in-time. Persoalan atas kebutuhan penyajian informasi yang cepat dan akurat serta dapat dipercaya adalah faktor kunci utama terselenggaranya dukungan telematika dalam kegiatan bisnis dan organisasi. Dan saat ini layanan-streaming cukup mendorong minat end-user memanfaatkannya dalam kegiatan bisnis dan organisasi. Pada penelitian ini telah dilakukan serangkaian pengujian secara observasi dengan dukungan tools dan infrastruktur jaringan komunikasi data, yakni private dan public. Adapun parameter Quality of Service sebagai acuan pengukuran performansi pada kanal-data dari layanan-streaming, dimana diamati pada ukuran latency, delay, dan throughput. Skenario yang ditetapkan berupa pembagian alokasi bandwidth dari kanal-data yang dibangun. Hasilnya menunjukan adanya pengaruh signifikan antara konfigurasi jaringan, teknik alokasi bandwidth, dan ratio bandwidth terhadap throughput yang diperoleh.Kata kunci: latency, quality of services, manajemen bandwidth, layanan-streaming, kanal-data

show abstract

Section: Hipotesisunclassified

Section: Dasar Teoriunclassified

Analisis QoS Pada Layanan Streaming Untuk Konfigurasi Jaringan Wireless

Simamora¹

2022

JNKTI

View full text Add to dashboard Cite

show abstract

“…The priority transmission method ensures the optimal transmission of important data, (16) but when the system maintains multiple remote technical support groups, the single available bandwidth b will be reduced, resulting in increased transmission delay and even congestion, which has a particularly significant impact on low-priority data. To complete the transmission of all data within the time slot T, the following formula must be satisfied:…”

Section: Design Of Adaptive Transmission Algorithmmentioning

confidence: 99%

“…Comparison of transmission success rates. Solution parametersUnenabled method(16) Enabled method (proposed)…”

mentioning

confidence: 99%

Route Planning and Monitoring Design of Unmanned Aerial Vehicles Based on Global Particle Algorithm

Ding¹,

Li²

2021

Sensors and Materials

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs), possessing the characteristics of a sensor and a sensing function, are in great demand in emergency rescue, agricultural plant protection, police and law enforcement, geological exploration, environmental monitoring, film and television entertainment, and other industries. However, the UAV industry is faced with various bottlenecks, such as the low intelligence level of UAVs, the high technical threshold for completing complex tasks, the lack of autonomous cooperative ability between the formation of UAVs, and the lack of an intelligent operating system. To achieve the route planning and monitoring design of UAVs, we first designed a remote intelligent monitoring system for a UAV based on multimode communication and cloud computing, which consisted of an intelligent control module, a coordination module for the multimode communication and autonomous formation of UAVs, a UAV cloud operating system, and panoramic image dynamic fusion technology. Then, we presented an adaptive data transmission method based on priority in the multimode communication system. Finally, a route planning and monitoring design based on a global particle algorithm was designed for the remote intelligent monitoring system of the UAV. The simulation results show that the remote security system operates normally and meets urgent communication needs. At the same time, both data and video image transmission can be achieved, and the route of the UAV can be effectively planned.

show abstract

“…In the context of distributed data centers, high bandwidth costs and suboptimal resource utilization are common issues in inter-domain networks [3]. The complexity of inter-domain traffic scheduling in data centers stems from diverse application and service requirements, each with unique and dynamic use of network resources [4]. Therefore, the implementation of more intelligent traffic engineering is crucial for effective scheduling of inter-domain network traffic.…”

Section: Introductionmentioning

confidence: 99%

A Reinforcement Learning-Based Traffic Engineering Algorithm for Enterprise Network Backbone Links

Cheng,

Luo,

Zhang

et al. 2024

Electronics

View full text Add to dashboard Cite

Large enterprise networks typically rely on expensive, high-speed backbone links to connect multiple campuses across diverse regions. As the volume of traffic traversing these backbone links increases, traffic engineering techniques are employed to filter or redirect traffic flows. Nevertheless, simple rerouting strategies can introduce business disruptions such as packet reordering, which significantly impact the user experience. To address this issue, we introduce an enhanced traffic scheduling algorithm named Critical Flow Rerouting with Weight- Reinforcement Learning(CFRW-RL), which builds upon the critical flow rerouting-reinforcement learning (CFR-RL) algorithm. CFRW-RL incorporates the principles of reinforcement learning, accounting for both the weights and classifications of data flows. This approach enables the algorithm to prioritize flows with lower weights for rerouting. The simulation results demonstrate that CFRW-RL significantly minimizes the rerouting of high-priority business flows and reduces business interference compared with the CFR-RL algorithm and that it maintains a similar computational complexity.

show abstract

An efficient priority-driven congestion control algorithm for data center networks

Cited by 8 publications

References 10 publications

Analisis QoS Pada Layanan Streaming Untuk Konfigurasi Jaringan Wireless

Analisis QoS Pada Layanan Streaming Untuk Konfigurasi Jaringan Wireless

Route Planning and Monitoring Design of Unmanned Aerial Vehicles Based on Global Particle Algorithm

A Reinforcement Learning-Based Traffic Engineering Algorithm for Enterprise Network Backbone Links

Contact Info

Product

Resources

About