Management of Data Center Networks

Nadjib Aitsaadi, PhD, is a Full Professor in Networks and Telecommunications at UVSQ Paris-Saclay University in France. He is a member of DAVID Laboratory and leads the Next Generation Networks Team. Prof. Aitsaadi earned a PhD in 2010 from Sorbonne University in Networks & Telecommunications and graduated in 2016 with a “Habilitation” diploma from University Paris Est (UPE). His main research fields are the security and QoS optimization of cellular networks (5G, 6G, HAP), IoT, DCN, V2X, MEC, NFV/SDN, and more. The results have been published in many major journals such as IEEE JSAC, IEEE TVT, Elsevier ComNet, Elsevier ComCom, etc. and major conferences such IEEE SECON, IEEE LCN, IEEE MASS, ACM MSWiM, IEEE/IFIP NOMS/IM, IEEE ICC, IEEE GLOBECOM, etc. Prof. Aitsaadi chairs many tracks in IEEE/IFIP conferences such as IEEE GLOBECOM, IEEE/IFIP IM, IEEE/IFIP CIoT, etc. and he is very active in IEEE Technical Committees TCIIN.

• About the Editor xiContributors xiiiAcronyms xvIntroduction xvii1 Architectures of Data Center Networks: Overview 1Boutheina Dab, Ilhem Fajjari, Dallal Belabed, and Nadjib Aitsaadi1.1 Taxonomy of DCN Architectures 11.1.1 Classification of DCN Architectures 21.1.2 Switch-Centric DCN Architectures Overview 31.1.2.1 Tree-Based DCN 31.1.2.2 Hierarchical DCN Architecture 41.1.2.3 Flat DCN Architecture 61.1.3 Server-Centric DCN Architectures Overview 71.1.4 Enhanced DCN Architectures Overview 101.1.4.1 Optical DCN Architecture 101.1.4.2 Wireless DCN Architecture 121.2 Comparison Between DCN Architectures 151.3 Proposed HDCN Architecture 151.3.1 HDCN Architecture Based on MSDC Model 191.3.1.1 ECMP Protocol 191.3.2 60GHz Technology in HDCN 201.3.3 Beamforming Technique in HDCN 211.4 Conclusion 23References 232 Data Center Optimization Techniques 29Dallal Belabed2.1 Ethernet Switching and Routing 292.2 Data Center Optimization Techniques 382.2.1 Virtual Network Embedding 382.2.2 Server Consolidation 402.2.3 Traffic Engineering 432.2.3.1 Link-State Traffic Engineering 442.2.3.2 MPLS Traffic Engineering 442.2.3.3 TCP Proportional Fairness Model 462.3 Conclusion 49Bibliography 513 Resource Management in Hybrid (Wired/Wireless) Data Center Networks 57Boutheina Dab, Ilhem Fajjari, and Nadjib Aitsaadi3.1 Routing and Wireless Channel Allocation Problematic in HDCN 583.1.1 Routing and Wireless Channel Assignment Challenges in HDCN 593.1.2 Routing and Wireless Channel Assignment Criteria in HDCN 613.2 Wireless Channel Allocation Strategies for One-Hop Communications in HDCN 623.2.1 Channel Allocation Problem in Wireless Networks 633.2.2 Omni-Directional Antennas Based Strategies 633.2.3 Beamforming-Based Strategies 673.3 Online Joint Routing and Wireless Channel Allocation Strategies in HDCN 693.3.1 Joint Routing and Channel Assignment in Mesh Networks 703.3.2 Online Joint Routing and Channel Assignment Strategies in HDCN 713.4 Joint Batch Routing and Channel Allocation Strategies in HDCN 753.5 Joint Batch Routing and Channel Allocation Strategies in HDCN 753.6 Summary 773.7 Conclusion 80References 804 Inter-Data Center Networks: Routing and Reliability in Virtual Network Backbone 85Oussama Soualah, Ilhem Fajjari, and Nadjib Aitsaadi4.1 Overview of Basic Virtual Network Embedding Without Reliability Constraint 854.1.1 Online Approaches 864.1.2 Batch Approaches 874.2 Overview of Virtual Network Embedding with Reliability Constraint 894.2.1 Distributed Approaches 894.2.2 Centralized Approaches 914.2.2.1 Substrate Router Failures 914.2.2.2 Substrate Link Failures 924.2.2.3 Substrate Router and Link Failures 944.2.2.4 Regional Failures 954.2.3 Summary 1014.3 Conclusion 101References 1015 An Evaluation Method of Optimal Cost Saving in a Data Center with Proactive Management 105Ruben Milocco, Pascale Minet, Éric Renault, and Selma Boumerdassi5.1 Introduction 1065.2 RelatedWork 1085.3 Framework for DC Modeling 1115.3.1 Notations and Assumptions 1115.3.2 Energy Computation 1115.3.2.1 Single-Resource Case 1115.3.2.2 Extension to the Multi-resource Case 1145.4 Cost Formulation 1145.4.1 Example 1155.4.2 Methodology 1165.4.3 Relative Energy Cost Saving 1165.4.4 Upper Bound Computation 1185.5 Application to a Real DC 1185.5.1 Generalities 1195.5.1.1 Selection of the Sampling Interval 1195.5.1.2 Selection of Possible Values for the Costs 1195.5.1.3 Dynamic Capacity Provisioning Based on Energy Prediction 1195.5.2 Application to a Google Dataset 1205.5.2.1 Energy Computation 1205.5.2.2 Evaluation of the Upper Bound 1225.5.2.3 Computation of the Relative Energy Cost Saving 1235.5.2.4 Discussion of Results 1245.6 Conclusion 124References 125Index 129