Data Center Networks: Topologies, Architectures and Fault-Tolerance Characteristics (SpringerBriefs in Computer Science) - Softcover

Liu, Yang; Muppala, Jogesh K.; Veeraraghavan, Malathi; Lin, Dong; Hamdi, Mounir

Softcover

ISBN 10: 3319019481 ISBN 13: 9783319019482

Verlag: Springer, 2013

Inhaltsangabe

This SpringerBrief presents a survey of data center network designs and topologies and compares several properties in order to highlight their advantages and disadvantages. The brief also explores several routing protocols designed for these topologies and compares the basic algorithms to establish connections, the techniques used to gain better performance, and the mechanisms for fault-tolerance. Readers will be equipped to understand how current research on data center networks enables the design of future architectures that can improve performance and dependability of data centers. This concise brief is designed for researchers and practitioners working on data center networks, comparative topologies, fault tolerance routing, and data center management systems. The context provided and information on future directions will also prove valuable for students interested in these topics.

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Über die Autorin bzw. den Autor

Prof. Liang Lin is a world-renowned scholar in the field of artificial intelligence and a Fellow of IEEE, IAPR, and IET. He currently serves as the Director of the Institute of Multi-Agent and Embodied Intelligence at Peng Cheng Laboratory, a Distinguished Professor at Sun Yat-sen University. He previously held the position of Executive Dean at the SenseTime Research Institute. He was a recipient of the National Science Fund for Distinguished Young Scholars, and the Chief Scientist of China’s National Major Project on Artificial Intelligence. His research has led to a series of pioneering contributions in multimodal representation learning, causal inference, and embodied intelligence. As of October 2024, he has published more than 400 papers, which have been cited over 45,000 times according to Google Scholar. He has received five Best Paper or Outstanding Paper Awards at leading international conferences and journals, including ACL, ICCV, ICME, and Pattern Recognition. As the first contributor, he has been awarded CCF-ACM Award for Artificial Intelligence in 2025, the First Prize of the Guangdong Provincial Science and Technology Progress Award in 2024, the Wu Wenjun Artificial Intelligence Award in 2018, and the First Prize of the Science and Technology Award of the China Society of Image and Graphics in 2019. He has supervised and mentored a number of outstanding PhD students who received prestigious honors such as the CCF Outstanding Doctoral Dissertation Award, the ACM China Doctoral Dissertation Award, and the CAAI Outstanding Doctoral Dissertation Award. Yang Liu is an associate professor at the School of Computer Science, Sun Yat-sen University, and a key member of the Human-Cyber-Physical Intelligence Integration Laboratory (HCP-Lab) at Sun Yat-sen University. His primary research interests include embodied intelligence, multimodal spatial perception and reasoning, and causal inference. He has published over 40 papers in prestigious journals and conferences such as TPAMI, TIP, TMECH, TKDE, CVPR, ICCV, ACM MM, and NeurIPS. Among these, four conference papers were selected as Oral/Highlight presentations, and four journal papers have been recognized as ESI Highly Cited Papers. He has led more than 10 research projects, including the National Natural Science Foundation of China (General Program, Youth Program, and Key Program as Project Lead) and the Pengcheng Laboratory "Open Challenge" program. He served as Co-Chair for the AIGC and Multi-Agent Parallel Computing Track at ICPADS 2025 and the Multimodal Mathematical Reasoning Workshop at ICDAR 2025. He won the Excellence Award at the 2023 China Software Conference for the Robotic Large Model and Embodied Intelligence Challenge, and the First Prize at the 2023 Guangdong Province Third Youth Academic Showcase in Computer Science.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2013
• Sprache: Englisch
• ISBN 10: 3319019481
• ISBN 13: 9783319019482
• Einband: Taschenbuch
• Anzahl der Seiten: 80
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