Understanding the JunOS routing table

I was just about to finish another blog post on MPLS when I got a question from a colleague about Junos routing tables. He was confused as to how to interpret the output of a basic Juniper routing table. I spent some time trying to find some resource to point him at – and was amazed at how hard it was to find anything that answered his questions specifically. Sure, there are lots of blogs and articles that explain RIB/FIB separation, but I couldn’t find any that backed it up with examples and the level of detail he was looking for. So while this is not meant to be exhaustive – I hope it might provide you some details about how to interpret the output of some of the more popular show commands. This might be especially relevant for those of you who might be coming from more of a Cisco background (like myself a number of years ago) as there are significant differences between the two vendors in this area.

Let’s start with a basic lab that looks a lot like the one I’ve been using in the previous MPLS posts…

For the sake of focusing on the real Continue reading

The SamKnows Cloudflare Platform

The SamKnows Cloudflare Platform

This is a guest post by Jamie Mason, who is the Head of Test Servers at SamKnows. This post originally appears on the SamKnows Megablog.

The SamKnows Cloudflare Platform

We leveraged Cloudflare Workers to expand the SamKnows measurement infrastructure.

At SamKnows, we run lots of tests to measure internet performance. Actually, that’s an understatement. Our software is embedded on tens of millions of devices, and that number grows daily.

The SamKnows Cloudflare Platform

We measure performance between the user’s home and the internet, across dozens of metrics. Some of these metrics measure the performance of major video-streaming services, popular games, or large websites. Others focus on the more traditional ‘quality of service’ metrics: speed, latency, and packet loss.

In order to measure speed, latency, and packet loss, SamKnows needs test servers to carry out the measurements against. These servers should be relatively near to the user’s home - this ensures that we’re measuring solely the user’s internet connection (i.e. what their Internet Service Provider sells them) and not some external factor.

As a result, we manage high-capacity test servers all over the world. Some are donated by research groups, some we host ourselves in major data centers, and still others are run inside ISPs’ own networks.

Customers Continue reading

Consumer Electronics Show: Everything’s Connected, But What About Security and Privacy?

We spent last week at the Consumer Electronics Show (aka CES) in Las Vegas, with over 180,000 of our closest friends. And with 4,500 exhibitors present, you’d have less than 30 seconds at each booth if you wanted to talk to all of them. Many articles have covered the cool new things, so in this blogpost we are going to discuss our overall impressions as they relate to our work on consumer IoT security and privacy.

Not surprisingly, there were many interesting conference sessions and a wide variety of innovative products on display, including some that seemed to push the bounds of credibility in their claims. Integration of devices with voice-driven and other platforms was everywhere – Amazon Alexa, Google Assistant, Apple HomeKit, and Samsung SmartThings being the most widely adopted to date. 5G was a hot topic, especially for its improved speeds and flexibility, though specifics about its availability are still hard to pin down.

Everything these days is getting connected to the Internet – from cat toys to sports simulators to home automation. One area that seems to be gaining more traction because it has gone beyond the “gadget” stage and is solving real problems is health and Continue reading

Stuff The Internet Says On Scalability For January 18th, 2019

Sorry, Stuff The Internet Says On Scalability has been called on the account of wind, rain, power outages and general mayhem. We're all safe, but it's hard to write a post using stone knives and bear skins. See you next week.

 

 

Get 3 Years of NordVPN Service for Just $2.99 Per Month – Deal Alert

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Get 3 Years of NordVPN Service for Just $2.99 Per Month – Deal Alert

NordVPN promises a private and fast path through the public internet, with no logs, unmetered access for 6 simultaneous devices and access to 5,232 servers worldwide. They are currently running a promotion, but you'll have to use this link to find it. Its typical price has been discounted for 3 years of service -- a good deal at just $2.99 per month.  See the $2.99/month NordVPN deal here. To read this article in full, please click here

Get 3 Years of NordVPN Service for Just $2.99 Per Month – Deal Alert

NordVPN promises a private and fast path through the public internet, with no logs, unmetered access for 6 simultaneous devices and access to 5,232 servers worldwide. They are currently running a promotion, but you'll have to use this link to find it. Its typical price has been discounted for 3 years of service -- a good deal at just $2.99 per month.  See the $2.99/month NordVPN deal here. To read this article in full, please click here

Towards a hands-free query optimizer through deep learning

Towards a hands-free query optimizer through deep learning Marcus & Papaemmanouil, CIDR’19

Where the SageDB paper stopped— at the exploration of learned models to assist in query optimisation— today’s paper choice picks up, looking exclusively at the potential to apply learning (in this case deep reinforcement learning) to build a better optimiser.

Why reinforcement learning?

Query optimisers are traditionally composed of carefully tuned and complex heuristics based on years of experience. Feedback from the actual execution of query plans can be used to update cardinality estimates. Database cracking, adaptive indexing, and adaptive query processing all incorporate elements of feedback as well.

In this vision paper, we argue that recent advances in deep reinforcement learning (DRL) can be applied to query optimization, resulting in a “hands-free” optimizer that (1) can tune itself for a particular database automatically without requiring intervention from expert DBAs, and (2) tightly incorporates feedback from past query optimizations and executions in order to improve the performance of query execution plans generated in the future.

If we view query optimisation as a DRL problem, then in reinforcement learning terminology the optimiser is the agent, the current query plan is the state, and each available action Continue reading

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