scholarly journals Artificial Intelligence Based Reliable Load Balancing Framework in Software-Defined Networks

2022 ◽  
Vol 70 (1) ◽  
pp. 251-266
Author(s):  
Mohammad Riyaz Belgaum ◽  
Fuead Ali ◽  
Zainab Alansari ◽  
Shahrulniza Musa ◽  
Muhammad Mansoor Alam ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Load Balancing ◽  
Software Defined Networks

scholarly journals Study of Multimedia Delivery over Software Defined Networks

2016 ◽  
Vol 7 (4) ◽  
pp. 37 ◽  
Author(s):  
Jose Miguel Jimenez ◽  
Oscar Romero ◽  
Albert Rego ◽  
Avinash Dilendra ◽  
Jaime Lloret
Keyword(s):  
Artificial Intelligence ◽  
Personal Computer ◽  
Network Protocols ◽  
Performance Comparison ◽  
Distributed Artificial Intelligence ◽  
Software Defined Networks ◽  
Multimedia Delivery ◽  
Real Network ◽  
Multimedia Streams ◽  
The Difference

Software Defined Networks (SDN) have become a new way to make dynamic topologies. They have great potential in both the creation and development of new network protocols and the inclusion of distributed artificial intelligence in the network. There are few emulators, like Mininet, that allow emulating a SDN in a single personal computer, but there is lack of works showing its performance and how it performs compared with real cases. This paper shows a performance comparison between Mininet and a real network when multimedia streams are being delivered. We are going to compare them in terms of consumed bandwidth (throughput), delay and jitter. Our study shows that there are some important differences when these parameters are compared. We hope that this research will be the basis to show the difference with real deployments when Mininet is used.

scholarly journals Research on load balancing technology for microservice architecture

2021 ◽  
Vol 336 ◽  
pp. 08002
Author(s):  
Hao Wang ◽  
Yong Wang ◽  
Guanying Liang ◽  
Yunfan Gao ◽  
Weijian Gao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Load Balancing ◽  
High Availability ◽  
Software Architectures ◽  
Delayed Response ◽  
Urgent Problem ◽  
Service Load ◽  
Development Direction ◽  
Service Capability ◽  
Microservice Architecture

With the emergence and development of new software architectures such as microservices, how to effectively handle the service load and ensure the service capability of the system has become an urgent problem to be solved. Load balancing technology needs to achieve high availability of microservices without affecting the delayed response of requests. According to different principles of adoption, mainstream load balancing technologies have emerged, such as polling methods, hash algorithms, and artificial intelligence technologies. This article categorizes and summarizes load balancing technologies for microservice architecture, and elaborates the methods and characteristics of current mainstream load balancing technologies. Based on the comparative analysis of existing technologies, this paper summarizes and points out the future development direction of load balancing technology.

Control Link Load Balancing and Low Delay Route Deployment for Software Defined Networks

2017 ◽  
Vol 35 (11) ◽  
pp. 2446-2456 ◽  
Author(s):  
Pengzhan Wang ◽  
Hongli Xu ◽  
Liusheng Huang ◽  
Jie He ◽  
Zeyu Meng
Keyword(s):  
Load Balancing ◽  
Software Defined Networks ◽  
Low Delay

DPLBAnt: Improved load balancing technique based on detection and rerouting of elephant flows in software-defined networks

2021 ◽  
Author(s):  
Mosab Hamdan ◽  
Suleman Khan ◽  
Ahmed Abdelaziz ◽  
Shahidatul Sadiah ◽  
Nasir Shaikh-Husin ◽  
...  
Keyword(s):  
Load Balancing ◽  
Software Defined Networks

Cloud Load Balancing and Reinforcement Learning

2018 ◽  
pp. 266-291
Author(s):  
Abdelghafour Harraz ◽  
Mostapha Zbakh
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Load Balancing ◽  
Decision Process ◽  
Cloud System ◽  
Human Intervention ◽  
Q Learning ◽  
State Action ◽  
Learning Techniques ◽  
Markov Decision

Artificial Intelligence allows to create engines that are able to explore, learn environments and therefore create policies that permit to control them in real time with no human intervention. It can be applied, through its Reinforcement Learning techniques component, using frameworks such as temporal differences, State-Action-Reward-State-Action (SARSA), Q Learning to name a few, to systems that are be perceived as a Markov Decision Process, this opens door in front of applying Reinforcement Learning to Cloud Load Balancing to be able to dispatch load dynamically to a given Cloud System. The authors will describe different techniques that can used to implement a Reinforcement Learning based engine in a cloud system.

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