Machine generated contents note: pt. I Network Evolution Towards Cloud Networking --
1. Background Introduction --
1.1. Networking Evolution --
1.2. Cloud Computing --
1.2.1. Infrastructure as a Service --
1.2.2. Platform as a Service --
1.2.3. Software as a Service --
1.3. Big Data --
1.3.1. Big Data Batch Processing --
1.3.2. Big Data Stream Processing --
1.4. Summary --
References --
2. Fundamental Concepts --
2.1. Software Defined Networking --
2.1.1. Architecture --
2.1.2. Floodlight --
2.1.3. Open Daylight --
2.1.4. Ryu SDN Framework --
2.2. Network Function Virtualization --
2.2.1. NFV in Data Centers --
2.2.2. NFV in Telecommunications --
2.3. Relationship Between SDN and NFV --
2.4. Big Data Batch Processing --
2.4.1. Hadoop --
2.4.2. DIYAD --
2.4.3. Spark --
2.5. Big Data Stream Processing --
2.5.1. Storm --
2.5.2. HAMR --
2.6. Summary --
References --
3. Cloud Networking --
3.1. Motivation: Fill the Gap Between Application and Network --
3.2. Cloud Networking Architecture --
3.2.1. Parser and Scheduler --
3.2.2. Network Manager --
3.2.3. Cloud Manager --
3.2.4. Monitor --
3.3. Design Issues --
3.3.1. Language Abstractions --
3.3.2. Performance Optimization --
3.3.3. Energy and Cost Optimization --
3.3.4. Flexible Data Management --
3.3.5. Stream Processing Aware Network Resource Management ... --
3.3.6. Security --
3.4. Cloud Networking and Big Data Related Work Review --
3.4.1. Energy and Cost Reduction --
3.4.2. VM Placement --
3.4.3. Big Data Placement --
3.4.4. Big Data Stream Processing --
3.4.5. Big Data Aware Traffic Cost Optimization --
3.4.6. SDN Aware Optimization --
3.4.7. Network Function Virtualization --
3.5. Summary --
References --
pt. II Cost Efficient Big Data Processing in Cloud Networking Enabled Data Centers --
4. Cost Minimization for Big Data Processing in Geo-Distributed Data Centers --
4.1. Motivation and Problem Statement --
4.2. System Model --
4.2.1. Network Model --
4.2.2. Task Model --
4.3. Problem Formulation --
4.3.1. Constraints of Data and Task Placement --
4.3.2. Constraints of Data Loading --
4.3.3. Constraints of QoS Satisfaction --
4.3.4. MINLP Formulation --
4.4. Linearization --
4.5. Performance Evaluation --
4.6. Summary --
References --
5. General Communication Cost Optimization Framework for Big Data Stream Processing in Geo-Distributed Data Centers --
5.1. Motivation and Problem Statement --
5.2. System Model --
5.2.1. Geo-Distributed DCs --
5.2.2. BDSP Task --
5.3. Problem Formulation --
5.3.1. VM Placement Constraints --
5.3.2. Flow Constraints --
5.3.3. Joint MILP Formulation --
5.4. Algorithm Design --
5.5. Performance Evaluation --
5.6. Summary --
References.