scholarly journals Load Balancing Oriented Predictive Routing Algorithm for Data Center Networks

2021 ◽  
Vol 13 (2) ◽  
pp. 54
Author(s):  
Yazhi Liu ◽  
Jiye Zhang ◽  
Wei Li ◽  
Qianqian Wu ◽  
Pengmiao Li
Keyword(s):  
Load Balancing ◽  
Data Center ◽  
Load Balance ◽  
Routing Algorithm ◽  
Round Robin ◽  
Data Center Networks ◽  
Bandwidth Utilization ◽  
Time Flow ◽  
Data Flows ◽  
Network Links

A data center undertakes increasing background services of various applications, and the data flows transmitted between the nodes in data center networks (DCNs) are consequently increased. At the same time, the traffic of each link in a DCN changes dynamically over time. Flow scheduling algorithms can improve the distribution of data flows among the network links so as to improve the balance of link loads in a DCN. However, most current load balancing works achieve flow scheduling decisions to the current links on the basis of past link flow conditions. This situation impedes the existing link scheduling methods from implementing optimal decisions for scheduling data flows among the network links in a DCN. This paper proposes a predictive link load balance routing algorithm for a DCN based on residual networks (ResNet), i.e., the link load balance route (LLBR) algorithm. The LLBR algorithm predicts the occupancy of the network links in the next duty cycle, according to the ResNet architecture, and then the optimal traffic route is selected according to the predictive network environment. The LLBR algorithm, round-robin scheduling (RRS), and weighted round-robin scheduling (WRRS) are used in the same experimental environment. Experimental results show that compared with the WRRS and RRS, the LLBR algorithm can reduce the transmission time by approximately 50%, reduce the packet loss rate from 0.05% to 0.02%, and improve the bandwidth utilization by 30%.

Hidden Markov Model-based Load Balancing in Data Center Networks

2019 ◽  
Vol 63 (10) ◽  
pp. 1449-1462
Author(s):  
Binjie He ◽  
Dong Zhang ◽  
Chang Zhao
Keyword(s):  
Load Balancing ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Data Center ◽  
Data Centers ◽  
Hidden Markov ◽  
Path Selection ◽  
Data Center Networks ◽  
Data Flows ◽  
Load Balancing Algorithm

Abstract Modern data centers provide multiple parallel paths for end-to-end communications. Recent studies have been done on how to allocate rational paths for data flows to increase the throughput of data center networks. A centralized load balancing algorithm can improve the rationality of the path selection by using path bandwidth information. However, to ensure the accuracy of the information, current centralized load balancing algorithms monitor all the link bandwidth information in the path to determine the path bandwidth. Due to the excessive link bandwidth information monitored by the controller, however, much time is consumed, which is unacceptable for modern data centers. This paper proposes an algorithm called hidden Markov Model-based Load Balancing (HMMLB). HMMLB utilizes the hidden Markov Model (HMM) to select paths for data flows with fewer monitored links, less time cost, and approximate the same network throughput rate as a traditional centralized load balancing algorithm. To generate HMMLB, this research first turns the problem of path selection into an HMM problem. Secondly, deploying traditional centralized load balancing algorithms in the data center topology to collect training data. Finally, training the HMM with the collected data. Through simulation experiments, this paper verifies HMMLB’s effectiveness.

Load balancing with traffic isolation in data center networks

2021 ◽  
Author(s):  
Tao Zhang ◽  
Qianqiang Zhang ◽  
Yasi Lei ◽  
Shaojun Zou ◽  
Juan Huang ◽  
...  
Keyword(s):  
Load Balancing ◽  
Data Center ◽  
Data Center Networks

scholarly journals Load Balance in Data Center SDN Networks

2018 ◽  
Vol 8 (5) ◽  
pp. 3084 ◽  
Author(s):  
Tariq Emad Ali ◽  
Ameer Hussein Morad ◽  
Mohammed A. Abdala
Keyword(s):  
Load Balancing ◽  
Data Center ◽  
Network Architecture ◽  
Current Data ◽  
Traffic Load ◽  
Network Architectures ◽  
Data Center Networks ◽  
Network Resources ◽  
Cloud Network ◽  
And Storage

<span>In the last two decades, networks had been changed according to the rapid changing in its requirements.  The current Data Center Networks have large number of hosts (tens or thousands) with special needs of bandwidth as the cloud network and the multimedia content computing is increased. The conventional Data Center Networks (DCNs) are highlighted by the increased number of users and bandwidth requirements which in turn have many implementation limitations.  The current networking devices with its control and forwarding planes coupling result in network architectures are not suitable for dynamic computing and storage needs.  Software Defined networking (SDN) is introduced to change this notion of traditional networks by decoupling control and forwarding planes. So, due to the rapid increase in the number of applications, websites, storage space, and some of the network resources are being underutilized due to static routing mechanisms. To overcome these limitations, a Software Defined Network based Openflow Data Center network architecture is used to obtain better performance parameters and implementing traffic load balancing function. The load balancing distributes the traffic requests over the connected servers, to diminish network congestions, and reduce underutilization problem of servers. As a result, SDN is developed to afford more effective configuration, enhanced performance, and more flexibility to deal with huge network designs</span>

Analisis Mirror Server menggunakan Load Balancing pada Jaringan Area Lokal

2020 ◽  
Vol 10 (4) ◽  
pp. 173-178
Author(s):  
Alfian Nurdiansyah ◽  
Nugroho Suharto ◽  
Hudiono Hudiono
Keyword(s):  
Load Balancing ◽  
Load Balance ◽  
Round Robin ◽  
Data Set ◽  
Load Balancer

Server merupakan serbuah sistem yang memberikan layanan tertentu pada suatu jaringan komputer. Server mempunyai sistem operasi sendiri yang disebut sistem operasi jaringan. Server juga mengontrol semua akses terhadap jaringan yang ada didalamnya.  Agar membantu tugas server, dibuatlah sistem mirroring server dimana server tersebut menduplikasi sebuah data set atau tiruan persis dari sebuah server yang menyediakan berbagai informasi. Mirror server atau disebut juga sinkronisasi server merupakan duplikat dari suatu server. Untuk menambah kinerja dari server maka dibutuhkan load balancer. Load balancing adalah teknik untuk mendistribusikan internet dua jalur koneksi secara seimbang. Dengan penerapan load balancing trafik akan berjalan lebih optimal, memaksimalkan throughput dan menghindari overload pada jalur koneksi. Iptables digunakan untuk memfilter IP sehigga client mengakses server sesuai dengan zona server yang paling dekat. Sehingga load balance yang dipadukan dengan iptables dapat membuat kinerja server menjadi lebih ringan. Masalah yang sering terjadi adalah ketika banyaknya client yang mengakses sebuah server maka server akan overload dan mengakibatkan kinerja server menjadi berat karena padatnya trafik. Client yang mengakses juga mendapatkan efek dari hal tersebut yaitu akses yang lama. Dari hasil penelitian tentang perpaduan antara load balance dan iptables didapati bahwa load balance dengan algoritma round robin rata-rata delay yang didapatkan untuk server1 yaitu 0,149 detik dan 0,19122. Server2 rata-rata delay yang didapatkan 0,161 detik dan 0,012 detik.

Challenges in Future Intra-Data-Center Networks and Possible Solutions

2020 ◽  
pp. 157-199
Author(s):  
Muhammad Ishaq ◽  
Mohammad Kaleem ◽  
Numan Kifayat
Keyword(s):  
Optical Networks ◽  
Data Center ◽  
Network Architecture ◽  
Power Efficiency ◽  
Current Data ◽  
Network Architectures ◽  
Data Center Network ◽  
Data Center Networks ◽  
Bandwidth Utilization ◽  
Transparent Optical Networks

This chapter briefly introduces the data center network and reviews the challenges for future intra-data-center networks in terms of scalability, cost effectiveness, power efficiency, upgrade cost, and bandwidth utilization. Current data center network architecture is discussed in detail and the drawbacks are pointed out in terms of the above-mentioned parameters. A detailed background is provided that how the technology moved from opaque to transparent optical networks. Additionally, it includes different data center network architectures proposed so far by different researchers/team/companies in order to address the current problems and meet the demands of future intra-data-center networks.

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