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From: "Tibrewala, Sujata" <sujata.tibrewala@intel.com>
To: "announce@dpdk.org" <announce@dpdk.org>
Cc: "O'Driscoll, Tim" <tim.odriscoll@intel.com>,
	"thomas@monjalon.net" <thomas@monjalon.net>,
	Trishan de Lanerolle <tdelanerolle@linuxfoundation.org>
Subject: [dpdk-announce] DPDK hands on lab Bangalore March 10th
Date: Thu, 15 Feb 2018 00:12:00 +0000	[thread overview]
Message-ID: <301551346413B542BD414A314176248607576709@ORSMSX109.amr.corp.intel.com> (raw)


Agenda for the DPDK hands on lab  Bangalore has been published. Please apply to attend at [1].
If you want to stay in touch with our local Bangalore DPDK events do not forget to join our meet up group at [2].

DPDK hands on Lab
What are latest new features DPDK brings into 2018? 
We will provide an overview of the new features of the latest DPDK release including source code browsing and API listing of top two new features of latest DPDK release. And on top of that, there will be a hands-on lab, on the Intel(r) microarchitecture (code name Skylake) servers, to learn how getting started with DPDK will become much simpler and powerful.

NFVI Enabling Kit Demo/Lab
An easy-to-use, automatic, self-contained toolkit to accelerate ODM* benchmarking NFVi-ready server designs on Whitley platform based on golden benchmark to characterize baseline performance test on DPDK, QAT and OVS, running on a single Xeon SP server.

Centralized Emergency Traffic Optimizer NEV SDK

We will be showcasing our CETO (Centralized Emergency Traffic Optimizer), a V2X and connected cars use case utilizing mobile edge computing framework using edge and centralized computing and analytics engine. This use case will showcase how edge traffic control engine is used to find the shortest path and create fastest traffic route for emergency vehicles by clearing the traffic of each traffic junction before the emergency vehicle arrives at the junction. To calculate the path, it considers the current density of each traffic junction and predicted density on each junction on the emergency vehicle suggested using the analytics engine running on the edge node. Assuming all cars are connected cars, It also connects to each car to suggest an alternative route to their destination if the car is on the same path as ambulance to reduce traffic congestion and faster route for all the vehicles at the same time. 

There are three ways to show case it, 
1) Using our cloud ran, MME, UE and Intel's MEC which will be deployed on their network. The challenge in this approach is we are still not very clear on the connectivity part during the hands-on session - i..e, connectivity of the laptop at the premise to the server that will run remotely in your New Mexico lab. Once we test this, we will be sure. 
2) Complete our own setup including MEC on our own laptop - this will be the backup with very limited features. 

[1] http://bit.ly/2mU41YZ
[2] https://www.meetup.com/Out-of-the-Box-Network-Developers-Bangalore/events/246703001/

Sujata Tibrewala @sujatatibre
Community Development Manager 
Intel Developer Zone
NPG Marketing Training PM (DOT)

                 reply	other threads:[~2018-02-15  0:12 UTC|newest]

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