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«Integrating Network Management For Cloud Computing Services Peng Sun A Dissertation Presented to the Faculty of Princeton University in Candidacy for ...»

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Integrating Network Management For

Cloud Computing Services

Peng Sun

A Dissertation

Presented to the Faculty

of Princeton University

in Candidacy for the Degree

of Doctor of Philosophy

Recommended for Acceptance

by the Department of

Computer Science

Adviser: Professor Jennifer Rexford

June 2015

c Copyright by Peng Sun, 2015.

All rights reserved.

Abstract

Cloud computing is known to lower costs of corporate IT. Thus enterprises are eager

to move IT applications into public or private cloud. Because of this trend, networks connecting enterprises and cloud providers now play a critical role in delivering highquality cloud applications.

Simply buying better devices is not viable for improving network quality, due to high capital costs. A more attractive approach is to better utilize network resources with proper network management. However, there are two problems with current network management: separately managing network components along the end-toend path, and heavily relying on vendor-specific interfaces with devices.

This dissertation takes a practical approach driven by operational experiences of cloud services to tackle the two problems. With knowledge of real-world challenges, we have designed proper abstractions for low-level device interactions, and have built efficient and scalable systems to integrate the management of various network components. With commercial deployment, our operational experiences feed back into revision of the abstraction and system design.

In this dissertation, we make three major contributions. We first propose to consolidate the traffic and infrastructure management in datacenters. Our system, called Statesman, simplifies management solutions by providing a uniform abstraction to interact with various aspects of devices. Statesman then allows multiple solutions to run together, resolves their conflicts, and prevents network-wide failures caused by their collective actions. Statesman has been operational worldwide in Microsoft’s public cloud offering since October 2013.

The second contribution consists of joining end hosts with networks for cooperative traffic management. Our Hone system brings in the fine-grained knowledge of cloud applications in the hosts, and offers an expressive programming framework with a iii uniform view of both host and network data. Hone has been integrated into Verizon Business Cloud.

The final contribution consists of bridging enterprises and Internet service providers (ISPs) for fine-grained control of inbound traffic from cloud applications.

Our Sprite system enables enterprises to directly decide how traffic enters the enterprise networks via which ISPs, offering expressive interface and scalable execution.

In collaboration with Princeton’s Office of Information Technology, Sprite was tested with campus-network data and live Internet experiments.

–  –  –

I am very grateful to have had Jennifer Rexford as my advisor through the ups and downs in my PhD journey. Jen has been a great advisor on computer-networking research. Her solid expertise and sharp intellect have guided me through the challenges of research, e.g., brainstorming new ideas, clarifying research problems, delving into technical solutions, improving paper writing, making engaging presentation, etc.

Also being a mentor for me, Jen encouraged me to explore different career paths, and help me discover and start the best one for my career development. Thanks to her best imaginable supports, I spent significant portion of my time on industrial research projects seeking both academic and production impacts, and I built my confidence and passion in practically applying technologies from cutting-edge research. I have always been inspired by her thoughtfulness, humility, and kindness to everyone around her, which make her my role model for years to come. My five years at Princeton were one of the best times in my life because of the fortune of working with and learning from Jen.

I am also very fortunate to have worked with Lihua Yuan and Dave Maltz. As mentors of my two-year internship in Microsoft Azure, they put enormous trust on me to drive a complete roadmap of applying my research: from architecting solutions, securing resources from the management layer, to developing and delivering the product. Their rigorous attitude and expert skills in solving practical problems set a great example for my career.

I would like to thank David Walker, Nick Feamster, Margaret Martonosi, and Kai Li for serving on my thesis committee, and giving me valuable feedback on my works.

I also want to thank my colleagues for the papers we co-authored: Ming Zhang, Ratul Mahajan, Ahsan Arefin, David Walker, Minlan Yu, Michael Freedman, and Laurent Vanbever. The major chapters of this dissertation would not be possible without their invaluable helps and contributions. Our collaboration has been a truly v inspiring and rewarding experience. I also would like to thank Chuanxiong Guo, Guohan Lu, Randy Kern, Albert Greenberg, Walter Willinger, and many others for their valuable feedback to my research.

My research at Princeton was supported by the National Science Foundation [34, 52], Intel [33], DARPA [35], and the Office of Naval Research [101]. My research at Microsoft was supported by Microsoft Azure and Microsoft Research. Many thanks to them for making my works possible.





My Princeton experience would have been incomplete without my friends. I beneted a lot from the candid conversations and the fun social activities with the members of our Cabernet group. Minlan Yu, Eric Keller, and Rob Harrison helped me boot the graduate student career in my first year on both research and life. In the same class with me, Srinivas Narayana gave me kindhearted supports in many joys and struggles that we shared in the graduate school. I would also like to thank Xin Jin, Naga Katta, Nanxi Kang, Mojgan Ghasemi, Arpit Gupta, Theophilus Benson, Laurent Vanbever, and Joshua Reich for the fun discussions on my work and life in Princeton.

I also want to thank my friends in the rest of the Computer Science Department for making me feel like home: Jude Nelson, Matt Zoufly, Jeff Terrace, Wyatt Lloyd, David Shue, Erik Nordstrom, Xiaozhou Li, Tianqiang Liu, Yiming Liu, Jingwan Lu, Xiaobai Chen, Tianlong Wang, Sapan Bhatia, Andy Bavier, Christopher Tengi, Yida Wang, Cole Schlesinger, Christopher Monsanto, and many others. I also owe a special thank you to Melissa Lawson, our retired graduate coordinator, for her kind helps on making my extended internship possible. I thank Brian Kernighan for helping me improve the communication and teaching skills.

I also thank my friends in other departments in Princeton (Zhuo Zhang, Zhikai Xu, Shaowei Ke, Xiaochen Feng, Haoshu Tian, Liechao Huang, Pingmei Xu), in Microsoft (George Chen, Huoping Chen, Murat Acikgoz, Kamil Cudnik, Jiaxin Cao, Shikhar vi Suri, Dong Xiang, Chao Zhang, Varugis Kurien, Hongqiang Liu, Yibo Zhu), and too many others to mention for making my life in Princeton and Seattle much more fun.

I thank my parents, Shufang Ma and Shusen Sun, for their enduring love and belief, without which I would not have finished the journey.

Above all, I want to thank my fianc´e, Jiaxuan Li, for her love and unwavering e faith in me. It is the happiness and optimism from her that drives me through the five years of PhD. I dedicate this dissertation to her.

–  –  –

Introduction Cloud computing is reshaping the Information Technology (IT) industry. It offers utility computing by delivering the applications as services over the Internet and providing these services with well-organized hardware and software in datacenters. The utility computing model eliminates the large capital barrier of purchasing hardware for enterprises, and it also lowers the IT operational costs by allowing enterprises to pay for what they actually use. These benefits motivate an ongoing effort of enterprises to move their IT applications into the public cloud and/or build their private cloud [1, 15, 23].

The delivery of the promise of cloud computing depends on the quality of the end-to-end network. As shown in Figure 1.1, the Internet now plays a critical role in carrying the traffic of IT applications between enterprises and datacenters of public/private cloud providers. The performance of cloud-based IT applications depends on not only the application software in the cloud, but also the reliability, efficiency, and performance of the networks in the middle.

Improving the network quality could be achieved by deploying more network devices with higher bandwidth. However, this brute-force approach no longer works due to the high capital costs and the rapidly growing traffic demands [7]. A more

–  –  –

Figure 1.1: End-to-end Structure of Cloud-based Software Services attractive approach is to build proper network management solutions to better utilize the existing network resources.

Yet the current practice of network management has two main problems which have become the main bottlenecks in adopting cloud

computing. The two problems of network management solutions are:

• Disjoint management of the end-to-end components:

Providing an efficient network involves multiple components on the end-to-end path: from the network stack of the datacenter servers’ operating systems, the hardware configuration and the routing control of network devices in datacenters, the traffic exchange of Internet service providers (ISPs), to the setup of enterprise networks. Each component can affect the quality of the networks. Yet these components (e.g., servers, network device hardware, and traffic routing) are managed separately with different systems, since traditionally these components are spread across multiple places. In the cloud era, these components become much more concentrated in the datacenters than before, and the lack of integration among management systems limits the quality improvement of the networks.

• Low-level interfaces for interacting with network devices:

Network devices are heterogeneous with different models from various vendors of varying ages. Interacting with the devices is complicated since the configuration APIs with devices are usually low-level and vendor-specific. Human network operators have to heavily use these low-level APIs in day-to-day operations, and it has been an error-prone process to configure the devices to run the right protocols with the right parameters using the right APIs. Using these APIs also tightly binds the solutions to specific device features by vendors, making it difficult to adapt the solutions to evolving business objectives. In datacenters, the heavy reliance on low-level device interfaces becomes one of the major sources of failures in network operations, especially when the datacenter network is growing in scale and adopting more commodity hardware from multiple vendors [8, 27, 28].

The programmability of management solutions has received much attention in the research community [64]. The concept of Software-Defined Networking (SDN) aims at providing a better way to program traffic management solutions. The literature on SDN, especially the ones surrounding the OpenFlow technology [45, 74, 97], promise to automate managing the traffic routing in networks with high-level programming paradigms [66, 67, 99, 128]. With existing literature focusing on programming traffic management on network devices, two other problems are much less explored beyond just traffic management: disjoint management of network components (e.g., server, device hardware), and low-level device interaction limiting a broader scope of network management (e.g., infrastructure management).

This dissertation focuses on solving these two problems. Rather than proposing clean-slate solutions, the dissertation takes a practical approach driven by operational experiences of cloud services. We identify the real-world opportunities and challenges of integrating the management of different network components on the end-to-end path, and then propose proper abstractions to unify and hide the low-level component interactions. Guided by the abstractions, we design and build systems of integrated management platforms for cloud providers and enterprises that are simple to use, and safe, efficient, and scalable in day-to-day operations [120, 123]. Having deployed the systems in major cloud providers [17, 26], we leverage the operational experiences as feedback to revisit the abstraction and system design.

We first elaborate the problems of network management with examples of management solutions in §1.1. We then discuss the research approach of this dissertation in §1.2, and summarize the major contributions of this dissertation in §1.3.

1.1 Problems of Current Network Management

Current practices of network management have two main problems: 1) disjoint management of the network components, and 2) heavy reliance on the low-level interfaces with devices. These problems become more severe in the era of cloud computing, and have become the main limiting factors in building more reliable and more efficient network management solutions.

Considering the first problem, many components are involved in the end-to-end path of cloud applications. Applications are running on the servers in datacenters, and the servers send traffic to other servers or the outside via datacenter networks. A series of ISPs deliver the application traffic to the enterprise networks, which finally deliver

it to the users. Traditionally, these components are managed as three separate areas:

1) Application and server provisioning for placing the applications and controlling the servers’ operating environments, 2) network infrastructure for configuring and operating the device hardware from power control to topology setup, and 3) traffic engineering for routing the application traffic through the network infrastructure.



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