What is it about?
This article file discusses how software-defined networking (SDN) can be used to optimize the performance of data centers. It explains the challenges faced by large-scale data center servers due to the overload phenomenon and how the proposed framework based on SDN can help address these challenges. The document also highlights the potential benefits of implementing load-balanced data centers in terms of performance and efficiency.
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Why is it important?
This publication is unique in its focus on proposing a framework for data centers based on software-defined networking (SDN) technology to balance server loads and prevent overloading. It addresses the timely and crucial need for comprehensive approaches to combine data center server load balancing with quality of service (QoS) mechanisms. By offering practical insights and real testbed implementations, this work aims to make a significant difference in optimizing data center performance and efficiency, thus increasing its relevance and readership.
Perspectives
As an expert in the field of data center technologies, I find this publication on software-defined load-balanced data centers to be highly informative and relevant. The focus on utilizing software-defined networking (SDN) to optimize data center performance and prevent server overloading is a crucial step towards enhancing the efficiency and scalability of modern data centers. The practical implementation of the proposed framework in real testbed scenarios adds credibility to the research findings and demonstrates the potential impact of SDN technology in improving data center operations. Overall, this publication provides valuable insights into the evolving landscape of data center design and management, highlighting the importance of innovative solutions in addressing the challenges faced by large-scale data centers.
AhmadReza Montazerolghaem
University of Isfahan
Read the Original
This page is a summary of: Software-defined load-balanced data center: design, implementation and performance analysis, Cluster Computing, July 2020, Springer Science + Business Media,
DOI: 10.1007/s10586-020-03134-x.
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