Analysis of resource sharing in transparent networks

Transparent optical networking promises a cost-efficient solution for future core and metro networks because of the efficacy of switching high-granularity trunk traffic without opto-electronic conversion. Network availability is an important performance parameter for network operators, who are incorporating protection and restoration mechanisms in the network to achieve competitive advantages. This paper focuses on the reduction in Capital Expenditures (CapEx) expected from implementing sharing of backup resources in path-protected transparent networks. We dimension a nationwide network topology for different protection mechanisms using transparent and opaque architectures. We investigate the CapEx reductions obtained through protection sharing on a population of 1000 randomly generated biconnected planar topologies with 14 nodes. We show that the gain for transparent networks is heavily dependent on the offered load, with almost no relative gain for low load (no required parallel line systems). We also show that for opaque networks the CapEx reduction through protection sharing is independent of the traffic load and shows only a small dependency on the number of links in the network. The node CapEx reduction for high load (relative to the number of channels in a line system) is comparable to the CapEx reduction in opaque OTN systems. This is rather surprising as in OTN systems the number of transceivers and linecards and the size of the OTN switching matrix all decrease, while in transparent networks only the degree of the ROADM (number and size of WSSs in the node) decreases while the number of transponders remains the same

Monitoring Challenges and Approaches for P2P File-Sharing Systems

Since the release of Napster in 1999, P2P file-sharing has enjoyed a dramatic rise in popularity. A 2000 study by Plonka on the University of Wisconsin campus network found that file-sharing accounted for a comparable volume of traffic to HTTP, while a 2002 study by Saroiu et al. on the University of Washington campus network found that file-sharing accounted for more than treble the volume of Web traffic observed, thus affirming the significance of P2P in the context of Internet traffic. Empirical studies of P2P traffic are essential for supporting the design of next-generation P2P systems, informing the provisioning of network infrastructure and underpinning the policing of P2P systems. The latter is of particular significance as P2P file-sharing systems have been implicated in supporting criminal behaviour including copyright infringement and the distribution of illegal pornograph

TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environments

Deep neural networks (DNNs) have become core computation components within low latency Function as a Service (FaaS) prediction pipelines: including image recognition, object detection, natural language processing, speech synthesis, and personalized recommendation pipelines. Cloud computing, as the de-facto backbone of modern computing infrastructure for both enterprise and consumer applications, has to be able to handle user-defined pipelines of diverse DNN inference workloads while maintaining isolation and latency guarantees, and minimizing resource waste. The current solution for guaranteeing isolation within FaaS is suboptimal -- suffering from "cold start" latency. A major cause of such inefficiency is the need to move large amount of model data within and across servers. We propose TrIMS as a novel solution to address these issues. Our proposed solution consists of a persistent model store across the GPU, CPU, local storage, and cloud storage hierarchy, an efficient resource management layer that provides isolation, and a succinct set of application APIs and container technologies for easy and transparent integration with FaaS, Deep Learning (DL) frameworks, and user code. We demonstrate our solution by interfacing TrIMS with the Apache MXNet framework and demonstrate up to 24x speedup in latency for image classification models and up to 210x speedup for large models. We achieve up to 8x system throughput improvement.Comment: In Proceedings CLOUD 201

Novel approaches, including systems biology, to HIV vaccine research and development: Report from a Global HIV Vaccine Enterprise Working Group

The Global HIV Vaccine Enterprise convened a two-day workshop on August 10-11 2009, at the Fred Hutchison Cancer Research Center offices in Seattle, WA, to discuss the application of novel approaches,including systems biology, to HIV vaccine research and development. The goals of this Working Group were to identify key scientific issues and opportunities that have emerged since the Enterprise Scientific Strategic Plan1 was published in 2005, and to make recommendations to Enterprise stakeholders

Novel approaches, including systems biology, to HIV vaccine research and development: Report from a Global HIV Vaccine Enterprise Working Group

The Global HIV Vaccine Enterprise convened a two-day workshop on August 10-11 2009, at the Fred Hutchison Cancer Research Center offices in Seattle, WA, to discuss the application of novel approaches,including systems biology, to HIV vaccine research and development. The goals of this Working Group were to identify key scientific issues and opportunities that have emerged since the Enterprise Scientific Strategic Plan1 was published in 2005, and to make recommendations to Enterprise stakeholders

Investing in Curation: A Shared Path to Sustainability

No abstract available