Heterogeneous Cloud Systems Based on Broadband Embedded Computing

Computing systems continue to evolve from homogeneous systems of commodity-based servers within a single data-center towards modern Cloud systems that consist of numerous data-center clusters virtualized at the infrastructure and application layers to provide scalable, cost-effective and elastic services to devices connected over the Internet. There is an emerging trend towards heterogeneous Cloud systems driven from growth in wired as well as wireless devices that incorporate the potential of millions, and soon billions, of embedded devices enabling new forms of computation and service delivery. Service providers such as broadband cable operators continue to contribute towards this expansion with growing Cloud system infrastructures combined with deployments of increasingly powerful embedded devices across broadband networks. Broadband networks enable access to service provider Cloud data-centers and the Internet from numerous devices. These include home computers, smart-phones, tablets, game-consoles, sensor-networks, and set-top box devices. With these trends in mind, I propose the concept of broadband embedded computing as the utilization of a broadband network of embedded devices for collective computation in conjunction with centralized Cloud infrastructures. I claim that this form of distributed computing results in a new class of heterogeneous Cloud systems, service delivery and application enablement. To support these claims, I present a collection of research contributions in adapting distributed software platforms that include MPI and MapReduce to support simultaneous application execution across centralized data-center blade servers and resource-constrained embedded devices. Leveraging these contributions, I develop two complete prototype system implementations to demonstrate an architecture for heterogeneous Cloud systems based on broadband embedded computing. Each system is validated by executing experiments with applications taken from bioinformatics and image processing as well as communication and computational benchmarks. This vision, however, is not without challenges. The questions on how to adapt standard distributed computing paradigms such as MPI and MapReduce for implementation on potentially resource-constrained embedded devices, and how to adapt cluster computing runtime environments to enable heterogeneous process execution across millions of devices remain open-ended. This dissertation presents methods to begin addressing these open-ended questions through the development and testing of both experimental broadband embedded computing systems and in-depth characterization of broadband network behavior. I present experimental results and comparative analysis that offer potential solutions for optimal scalability and performance for constructing broadband embedded computing systems. I also present a number of contributions enabling practical implementation of both heterogeneous Cloud systems and novel application services based on broadband embedded computing

A Robust Wireless Mesh Access Environment For Mobile Video Users

The rapid advances in networking technology have enabled large-scale deployments of online video streaming services in today\u27s Internet. In particular, wireless Internet access technology has been one of the most transforming and empowering technologies in recent years. We have witnessed a dramatic increase in the number of mobile users who access online video services through wireless access networks, such as wireless mesh networks and 3G cellular networks. Unlike in wired environment, using a dedicated stream for each video service request is very expensive for wireless networks. This simple strategy also has limited scalability when popular content is demanded by a large number of users. It is desirable to have a robust wireless access environment that can sustain a sudden spurt of interest for certain videos due to, say a current event. Moreover, due to the mobility of the video users, smooth streaming performance during the handoff is a key requirement to the robustness of the wireless access networks for mobile video users. In this dissertation, the author focuses on the robustness of the wireless mesh access (WMA) environment for mobile video users. Novel video sharing techniques are proposed to reduce the burden of video streaming in different WMA environments. The author proposes a cross-layer framework for scalable Video-on-Demand (VOD) service in multi-hop WiMax mesh networks. The author also studies the optimization problems for video multicast in a general wireless mesh networks. The WMA environment is modeled as a connected graph with a video source in one of the nodes and the video requests randomly generated from other nodes in the graph. The optimal video multicast problem in such environment is formulated as two sub-problems. The proposed solutions of the sub-problems are justified using simulation and numerical study. In the case of online video streaming, online video server does not cooperate with the access networks. In this case, the centralized data sharing technique fails since they assume the cooperation between the video server and the network. To tackle this problem, a novel distributed video sharing technique called Dynamic Stream Merging (DSM) is proposed. DSM improves the robustness of the WMA environment without the cooperation from the online video server. It optimizes the per link sharing performance with small time complexity and message complexity. The performance of DSM has been studied using simulations in Network Simulator 2 (NS2) as well as real experiments in a wireless mesh testbed. The Mobile YouTube website (http://m.youtube.com) is used as the online video website in the experiment. Last but not the least; a cross-layer scheme is proposed to avoid the degradation on the video quality during the handoff in the WMA environment. Novel video quality related triggers and the routing metrics at the mesh routers are utilized in the handoff decision making process. A redirection scheme is also proposed to eliminate packet loss caused by the handoff

Multihop packet radio networks: design alogorithms and protocols.

Hung, Kwok-Wah.Thesis (Ph.D.)--Chinese University of Hong Kong, 1991.Bibliography: leaves 109-111.ACKNOWLEDGEMENTSABSTRACTChapter CHAPTER 1 --- Overview of Packet Radio Networks --- p.1Chapter 1.1 --- Introduction --- p.2Chapter 1.2 --- Network Structure --- p.3Chapter 1.3 --- Channel Access Protocol --- p.3Chapter 1.4 --- Spatial Reuse --- p.5Chapter 1.5 --- Spread Spectrum --- p.6Chapter 1.6 --- Thesis Introduction --- p.8Chapter CHAPTER 2 --- Design Algorithms for Networks with Directional Antennas --- p.12Chapter 2.1 --- Introduction --- p.13Chapter 2.2 --- Problems in The MTCD/MDA Protocol --- p.14Chapter 2.3 --- The Simple Tone Sense (STS) Protocol --- p.15Chapter 2.4 --- The Variable Power Tone Sense (YPTS) Protocol --- p.18Chapter 2.5 --- Network Design Algorithms --- p.19Chapter 2.6 --- Network Design Example --- p.25Chapter 2.7 --- Simulation Results --- p.28Chapter 2.8 --- Chapter Summary --- p.31Chapter CHAPTER 3 --- The Coded Tone Sense Protocol --- p.44Chapter 3.1 --- Introduction … --- p.45Chapter 3.2 --- System Model and Code Assignment Algorithm --- p.46Chapter 3.3 --- Protocol Description --- p.48Chapter 3.4 --- Simulation Results --- p.49Chapter 3.5 --- Chapter Summary --- p.51Chapter CHAPTER 4 --- An Efficient Spreading Code Assignment Algorithm --- p.54Chapter 4.1 --- Introduction … --- p.55Chapter 4.2 --- Code Assignment and Graph Coloring --- p.55Chapter 4.3 --- Algorithm Description --- p.57Chapter 4.4 --- Results and Discussion --- p.59Chapter 4.5 --- Chapter Summary --- p.60Chapter CHAPTER 5 --- Fair and Efficient Transmission Scheduling --- p.64Chapter 5.1 --- Introduction --- p.65Chapter 5.2 --- The Scheduling Problem --- p.67Chapter 5.3 --- The Scheduling Algorithm --- p.68Chapter 5.4 --- Performance Analysis --- p.70Chapter 5.5 --- Results and Discussion --- p.72Chapter 5.6 --- Chapter Summary --- p.74Chapter CHAPTER 6 --- Staggered Multicast Protocol with Collision-Free Acknowledgement --- p.79Chapter 6.1 --- Introduction --- p.80Chapter 6.2 --- System Model --- p.83Chapter 6.3 --- Protocol Description --- p.84Chapter 6.4 --- Staggered Relay Broadcasting --- p.90Chapter 6.5 --- Simulation Results --- p.91Chapter 6.6 --- Chapter Summary --- p.92Chapter CHAPTER 7 --- Conclusion --- p.104Chapter 7.1 --- Summary --- p.105Chapter 7.2 --- Topics for Future Research --- p.107REFERENCES --- p.10