Abstract-Wireless home networks are increasingly deployed in people's homes worldwide. Unfortunately, home networks have evolved using protocols designed for backbone and enterprise networks, which are quite different in scale and character to home networks. We believe this evolution is at the heart of widely observed problems experienced by users managing and using their home networks. In this paper we investigate redesign of the home router to exploit the distinct social and physical characteristics of the home.We extract two key requirements from a range of ethnographic studies: users desire greater understanding of and control over their networks' behaviour. We present our design for a home router that focuses on monitoring and controlling network traffic flows, and so provides a platform for building user interfaces that satisfy these two user requirements. We describe and evaluate our prototype which uses NOX and OpenFlow to provide per-flow control, and a custom DHCP implementation to enable traffic isolation and accurate measurement from the IP layer. It also provides finer-grained per-flow control through interception of wireless association and DNS resolution. We evaluate the impact of these modifications, and thus the applicability of flow-based network management in the home.
Abstract-Home networks have evolved to become small-scale versions of enterprise networks. The tools for visualizing and managing such networks are primitive and continue to require networked systems expertise on the part of the home user. As a result, non-expert home users must manually manage nonobvious aspects of the network -e.g., MAC address filtering, network masks, and firewall rules, using these primitive tools.The Homework information plane architecture uses stream database concepts to generate derived events from streams of raw events. This supports a variety of visualization and monitoring techniques, and also enables construction of a closed-loop, policybased management system. This paper describes the information plane architecture and its associated policy-based management infrastructure. Exemplar visualization and closed-loop management applications enabled by the resulting system (tuned to the skills of non-expert home users) are discussed.
Modern servers have become heterogeneous, often combining multicore CPUs with many-core GPGPUs. Such heterogeneous architectures have the potential to improve the performance of data-intensive stream processing applications, but they are not supported by current relational stream processing engines. For an engine to exploit a heterogeneous architecture, it must execute streaming SQL queries with sufficient data-parallelism to fully utilise all available heterogeneous processors, and decide how to use each in the most effective way. It must do this while respecting the semantics of streaming SQL queries, in particular with regard to window handling.We describe SABER, a hybrid high-performance relational stream processing engine for CPUs and GPGPUs. SABER executes windowbased streaming SQL queries in a data-parallel fashion using all available CPU and GPGPU cores. Instead of statically assigning query operators to heterogeneous processors, SABER employs a new adaptive heterogeneous lookahead scheduling strategy, which increases the share of queries executing on the processor that yields the highest performance. To hide data movement costs, SABER pipelines the transfer of stream data between CPU and GPGPU memory. Our experimental comparison against state-of-the-art engines shows that SABER increases processing throughput while maintaining low latency for a wide range of streaming SQL queries with both small and large window sizes.
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