Novel Multi-Level Dynamic Traffic Load-Balancing Protocol for Data Center

Memon, Sheeba; Huang, Jiawei; Saajid, Hussain; Bux, Naadiya Khuda; Saleem, Arshad; Aljeroudi, Yazan

doi:10.3390/sym11020145

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…In order to satisfy the latency requirement of small flows, the low buffer occupation should be achieved. However, the small buffer queue length will decrease the link utilization, with the result that large flows cannot obtain high throughput [15].…”

Section: Introductionmentioning

confidence: 99%

Fine-Grained Congestion Control for Multipath TCP in Data Center Networks

Feng

Xie

et al. 2019

IEEE Access

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In data center networks, MultiPath TCP (MPTCP) obtains both higher network utilization and fairer allocation of capacity by exploring multiple paths simultaneously. However, MPTCP experiences more queue oscillation in switch buffer with the increasing of the number of subflows. When using explicit congestion notification as the congestion indication to track the queue length in switch buffer, it is difficult for MPTCP to capture the accurate congestion state, resulting in wrong behavior in congestion control. Therefore, we propose an enhanced MPTCP protocol, namely, advanced MPTCP (AMP), which adjusts the time granularity of the congestion detection and control under a different number of subflows. The test results show that compared with Linked Increases Algorithm and eXplicit MultiPath, AMP achieves lower latency for small flows and higher throughput for large flows. INDEX TERMS Data center networks, explicit congestion notification, MultiPath TCP.

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Section: Introductionmentioning

confidence: 99%

Fine-Grained Congestion Control for Multipath TCP in Data Center Networks

Feng

Xie

et al. 2019

IEEE Access

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Hidden Markov Model-based Load Balancing in Data Center Networks

Zhang

Chang

2019

The Computer Journal

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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.

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Dynamic Load Balancing Techniques in the IoT: A Review

Kanellopoulos

Sharma

2022

Symmetry

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The Internet of things (IoT) extends the Internet space by allowing smart things to sense and/or interact with the physical environment and communicate with other physical objects (or things) around us. In IoT, sensors, actuators, smart devices, cameras, protocols, and cloud services are used to support many intelligent applications such as environmental monitoring, traffic monitoring, remote monitoring of patients, security surveillance, and smart home automation. To optimize the usage of an IoT network, certain challenges must be addressed such as energy constraints, scalability, reliability, heterogeneity, security, privacy, routing, quality of service (QoS), and congestion. To avoid congestion in IoT, efficient load balancing (LB) is needed for distributing traffic loads among different routes. To this end, this survey presents the IoT architectures and the networking paradigms (i.e., edge–fog–cloud paradigms) adopted in these architectures. Then, it analyzes and compares previous related surveys on LB in the IoT. It reviews and classifies dynamic LB techniques in the IoT for cloud and edge/fog networks. Lastly, it presents some lessons learned and open research issues.

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Novel Multi-Level Dynamic Traffic Load-Balancing Protocol for Data Center

Cited by 3 publications

References 30 publications

Fine-Grained Congestion Control for Multipath TCP in Data Center Networks

Fine-Grained Congestion Control for Multipath TCP in Data Center Networks

Hidden Markov Model-based Load Balancing in Data Center Networks

Dynamic Load Balancing Techniques in the IoT: A Review

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