![]() The first thing you'll need to do is get a license. There is a very handy python script (CiscoIOUKeygen.py) that I am sure you can find, and run from the VM. Once you have license click on Manage on the menu bar and then on Manage License and paste it into the little box and click Save. Now we need to add an image to use, I will be using an IOS 15 image I found, so again on the Manage tab, click on Manage IOSes. Firstly give it a name and an alias, then browse to the image and select it. So say we wanted to have two core routers (devices 10 and 11) connected to two WAN routers (devices 12 and 13) by a hub then we could do this: So to link device 10 to device 11 on ports 0/1 on each the line would look like: Next we have to design the topology of the lab and this uses a NETMAP file, I am following the example given on the routereflector page.Ī NETMAP file consists of a rows and each row contains an entry for where a link starts and where a link ends and includes the device ID. The 1 at the end signifies that the link type will be an IEEE 802.3 ethernet link. For a list of what you can do refer to this page. Emerging applications-cloud computing, the internet of things, and augmented/virtual reality-demand responsive, secure, and scalable datacenter networks. These networks currently implement simple, per-packet, data-plane heuristics (e.g., ECMP and sketches) under a slow, millisecond-latency control plane that runs data-driven performance and security policies. However, to meet applications' service-level objectives (SLOs) in a modern data center, networks must bridge the gap between line-rate, per-packet execution and complex decision making. In this work, we present the design and implementation of Taurus, a data plane for line-rate inference. ![]() Taurus adds custom hardware based on a flexible, parallel-patterns (MapReduce) abstraction to programmable network devices, such as switches and NICs this new hardware uses pipelined SIMD parallelism to enable per-packet MapReduce operations (e.g., inference). Our evaluation of a Taurus switch ASIC-supporting several real-world models-shows that Taurus operates orders of magnitude faster than a server-based control plane while increasing area by 3.8% and latency for line-rate ML models by up to 221 ns. Furthermore, our Taurus FPGA prototype achieves full model accuracy and detects two orders of magnitude more events than a state-of-the-art control-plane anomaly-detection system. ![]()
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