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Über dieses Buch

This book introduces two basic big data processing paradigms for batch data and streaming data. Representative programming frameworks are also presented, as well as software defined networking (SDN) and network function virtualization (NFV) technologies as key cloud networking technologies.

The authors illustrate that SDN and NFV can be applied to benefit the big data processing by proposing a cloud networking framework. Based on the framework, two case studies examine how to improve the cost efficiency of big data processing.

Cloud Networking for Big Data

targets professionals and researchers working in big data, networks, wireless communications and information technology. Advanced-level students studying computer science and electrical engineering will also find this book valuable as a study guide.

Inhaltsverzeichnis

Frontmatter

Network Evolution Towards Cloud Networking

Frontmatter

Chapter 1. Background Introduction

Like any other technology, cloud networking is a natural evolution due to the technology development and requirement stimulation. In this chapter, let us first briefly review the networking history to understand how it evolves to cloud networking. We then introduce cloud computing and big data as its enabling technology and driving force, respectively.
Deze Zeng, Lin Gu, Song Guo

Chapter 2. Fundamental Concepts

Today’s computer networking is growing ever larger and more complex everyday. This leads to a critical issue on how to manage and control the network devices. SDN is a dynamic, programmable, and scalable framework proposed to provide effective solutions for network behavior management. SDN decouples data plane and control plane, allowing direct programmable controlling and abstraction of the underlying infrastructures. Due to its many advantages, SDN has drawn significant attention from both academical community [1] and industries. For example, Google is building an SDN-based infrastructure to support their Internet services [2]. Many companies like Huawei have already released their SDN products and solutions.
Deze Zeng, Lin Gu, Song Guo

Chapter 3. Cloud Networking

In the last chapter, we have seen the prosperousness in various big data programming frameworks for either batch data or stream data processing. However, these frameworks still operate on the infrastructure principle in end-to-end fashion evolved from traditional Internet networking where transparent end-to-end transmission services are provided. How data packets are routed in the intermediary devices (e.g., routers) is invisible and uncontrollable.
Deze Zeng, Lin Gu, Song Guo

Cost Efficient Big Data Processing in Cloud Networking Enabled Data Centers

Frontmatter

Chapter 4. Cost Minimization for Big Data Processing in Geo-Distributed Data Centers

As we have known, cloud networking provides the possibility of orchestrating all resources towards different optimisation goals. For data transferring between the storage units and the processing units in big batch data (e.g., credit billing data) processing, SDN enables the programmers to customize the data routing as needed. Communication cost of large volume data transferring is non-ignorable and shall be carefully addressed in the consideration of cost efficiency.
Deze Zeng, Lin Gu, Song Guo

Chapter 5. A General Communication Cost Optimization Framework for Big Data Stream Processing in Geo-Distributed Data Centers

For a big data stream processing (BDSP) application, we may have many different processing units in the form of VMs. These VMs are highly correlated by the data streams as one’s output may be another one’s input. Consequently, the networking shall have a deeper impact to the performance and efficiency of BDSP, compared to batch data processing. Besides, virtualized network functions (VNF), also in the form of VMs, can also be added in stream processing. For example, we may require all the data streams first go through deep packet inspection (DPI) VM before actual processing. How to manage these VMs as well as the communications between them in data centers is critical to the cost-efficiency BDSP.
Deze Zeng, Lin Gu, Song Guo

Chapter 6. Conclusion

Big data is pervasive today and the volume of newly generated data is exploding every day. How to analyze these large data sets (i.e., big data) effectively has become a key issue of business competition, academic research, and industry innovation. The extreme explosion of big data imposes a heavy burden on computation, storage, and networking resources. Cloud, with sufficient resources in large-scale data centers, is widely regarded as an ideal platform for big data processing. How to explore these resources has become the first concern in big data.
Deze Zeng, Lin Gu, Song Guo
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