Browsing by Subject "IP network"
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Item A Multi-FPGA Networking Architecture and Its Implementation(2015-05-12) Knezek, Gabriel SFPGAs show great promise in accelerating compute-bound parallelizable applications by offloading kernels into programmable logic. However, currently FPGAs present significant hurdles in being a viable technology, due to both the capital outlay required for specialized hardware as well as the logic required to support the offloaded kernels on the FPGA. This thesis seeks to change that by making it easy to communicate clusters of FPGAs over IP networks and providing infrastructure for common application use cases, allowing authors to focus on their application and not the procurement and details of interacting with a specific FPGA. Our approach is twofold. First, we develop an FPGA IP network stack and bitfile management system allowing users to upload their logic to a server and have it run on FPGAs accessible through the Internet. Second, we engineer a programmable logic interface which authors can use to move data to their application kernels. This interface provides communication over the Internet as well as the scaffolding typically re-invented for each application by providing I/O between application logic, even if spread across different FPGAs. We utilize Partial Reconfiguration to divide the FPGAs into regions, each of which can host different applications from different users. We then provide a web service through which users can upload their FPGA logic. The service finds a spot for the logic on the FPGAs, reconfigures them to contain the logic, then sends back the user their IP addresses. To ease development of the application pieces themselves, our framework abstracts away the complexity of communicating over IP networks as well as between different FPGAs. Instead we provide an interface to applications consisting simply of a RAM port. Applications write packets of data into the port, and they appear at the other end, whether that other end is across an IP network or another FPGA. Finally, we then prove the feasibility and utility of our approach by implementing it on an array of Xilinx Virtex 5 FPGAs, linked together with GTP serial links and connected via Gigabit Ethernet. We port a compute-bound application based on regular expression string matching to the framework, demonstrating that our approach is feasible for implementing a realistic application.Item Large-scale network analytics(2011-08) Song, Han Hee, 1978-; Zhang, Yin, doctor of computer scienceScalable and accurate analysis of networks is essential to a wide variety of existing and emerging network systems. Specifically, network measurement and analysis helps to understand networks, improve existing services, and enable new data-mining applications. To support various services and applications in large-scale networks, network analytics must address the following challenges: (i) how to conduct scalable analysis in networks with a large number of nodes and links, (ii) how to flexibly accommodate various objectives from different administrative tasks, (iii) and how to cope with the dynamic changes in the networks. This dissertation presents novel path analysis schemes that effectively address the above challenges in analyzing pair-wise relationships among networked entities. In doing so, we make the following three major contributions to large-scale IP networks, social networks, and application service networks. For IP networks, we propose an accurate and flexible framework for path property monitoring. Analyzing the performance side of paths between pairs of nodes, our framework incorporates approaches that perform exact reconstruction of path properties as well as approximate reconstruction. Our framework is highly scalable to design measurement experiments that span thousands of routers and end hosts. It is also flexible to accommodate a variety of design requirements. For social networks, we present scalable and accurate graph embedding schemes. Aimed at analyzing the pair-wise relationships of social network users, we present three dimensionality reduction schemes leveraging matrix factorization, count-min sketch, and graph clustering paired with spectral graph embedding. As concrete applications showing the practical value of our schemes, we apply them to the important social analysis tasks of proximity estimation, missing link inference, and link prediction. The results clearly demonstrate the accuracy, scalability, and flexibility of our schemes for analyzing social networks with millions of nodes and tens of millions of links. For application service networks, we provide a proactive service quality assessment scheme. Analyzing the relationship between the satisfaction level of subscribers of an IPTV service and network performance indicators, our proposed scheme proactively (i.e., detect issues before IPTV subscribers complain) assesses user-perceived service quality using performance metrics collected from the network. From our evaluation using network data collected from a commercial IPTV service provider, we show that our scheme is able to predict 60% of the service problems that are complained by customers with only 0.1% of false positives.