Understanding the Benefits of Graphiant’s Network-as-a-Service
Businesses cannot run a digital business on a legacy network. Graphiant’s NaaS provides an interesting option certainly over traditional WANs but also over many SD-WANs.Direct Connect — Part 2 — Public VIF
< MEDIUM: https://towardsaws.com/direct-connect-part-2-public-vif-5bc0a2d2c478 >
First Post ( Direct Connect – Part 1 )- https://raaki-88.medium.com/direct-connect-part-1-dc3e9369933
Direct Connect offering though it connects to AWS has a difference in operation depending on the VIF we connect.
Public VIF
→ So when we have this setup, this is in no way related to VPC at all, all this does is advertise Amazon-owned Public Prefixes for services like S3/EC2(Elastic-IP only, not your Private IP), and that’s all to it.
→ There is flexibility at the customer end to scope the advertisement propagation t LOCAL, CONTINENT, and GLOBAL levels within AWS in an outbound direction and has the flexibility to filter inbound updates which are advertised toward him.
Here is by default, how the Community scope looks like, you also have the flexibility to filter routes inbound to customers.
Note: Outbound communities restrict the advertisement of prefixes to region/continent/global scope for any sort of Any-cast implementations.
if the Customer sends a route with a community
7224:9100 → This will be local to the region
7224:9200 → This will be local to the continent, the scope is till the EU
7224:9300 → Global, by default its global even if you don’t export Continue reading
On Applicability of MPLS Segment Routing (SR-MPLS)
Whenever I compare MPLS-based Segment Routing (SR-MPLS) with it’s distant IPv6-based cousin (SRv6), someone invariably mentions the specter of large label stacks that some hardware cannot handle, for example:
Do you think vendors current supported label max stack might be an issue when trying to route a packet from source using Adj-SIDs on relatively big sized (and meshed) cores? Many seem to be proposing to use SRv6 to overcome this.
I’d dare to guess that more hardware supports MPLS with decent label stacks than SRv6, and if I’ve learned anything from my chats with Laurent Vanbever, it’s that it sometimes takes surprisingly little to push the traffic into the right direction. You do need a controller that can figure out what that little push is and where to apply it though.
On Applicability of MPLS Segment Routing (SR-MPLS)
Whenever I compare MPLS-based Segment Routing (SR-MPLS) with it’s distant IPv6-based cousin (SRv6), someone invariably mentions the specter of large label stacks that some hardware cannot handle, for example:
Do you think vendors current supported label max stack might be an issue when trying to route a packet from source using Adj-SIDs on relatively big sized (and meshed) cores? Many seem to be proposing to use SRv6 to overcome this.
I’d dare to guess that more hardware supports MPLS with decent label stacks than SRv6, and if I’ve learned anything from my chats with Laurent Vanbever, it’s that it sometimes takes surprisingly little to push the traffic into the right direction. You do need a controller that can figure out what that little push is and where to apply it though.
Tech Bytes: LiveAction Integrates NDR And Network Visibility (Sponsored)
The Tech Bytes podcast welcomes sponsor LiveAction, which provides network visibility and NDR products for network engineers. We’ll get an overview of LiveAction’s portfolio and take a closer look at new security capabilities in its ThreatEye Network Detection and Response product.
The post Tech Bytes: LiveAction Integrates NDR And Network Visibility (Sponsored) appeared first on Packet Pushers.
Tech Bytes: LiveAction Integrates NDR And Network Visibility (Sponsored)
The Tech Bytes podcast welcomes sponsor LiveAction, which provides network visibility and NDR products for network engineers. We’ll get an overview of LiveAction’s portfolio and take a closer look at new security capabilities in its ThreatEye Network Detection and Response product.Network Break 403: Startup Hedgehog Fuses SONiC And Kubernetes; Google, Intel Launch Mount Evans SmartNIC
Take a Network Break! This week we cover a lot of news including a new SONiC startup, Cisco and Microsoft teaming up on collaboration, new hardware from Google and Intel, a new SOC from Palo Alto Networks, space networking, and more.
The post Network Break 403: Startup Hedgehog Fuses SONiC And Kubernetes; Google, Intel Launch Mount Evans SmartNIC appeared first on Packet Pushers.
Network Break 403: Startup Hedgehog Fuses SONiC And Kubernetes; Google, Intel Launch Mount Evans SmartNIC
Take a Network Break! This week we cover a lot of news including a new SONiC startup, Cisco and Microsoft teaming up on collaboration, new hardware from Google and Intel, a new SOC from Palo Alto Networks, space networking, and more.Dispelling Three Myths of Network Automation
Once misconceptions about network automation are dispelled, network teams are in a good position to begin implementing automation.netlab Router-on-a-Stick Example
In early June 2022 I described a netlab topology using VLAN trunks in netlab. That topology provided pure bridging service for two IP subnets. Now let’s go a step further and add a router-on-a-stick:
- S1 and S2 are layer-2 switches (no IP addresses on red or blue VLANs).
- ROS is a router-on-a-stick routing between red and blue VLANs.
- Hosts on red and blue VLANs should be able to ping each other.
Lab topology
netlab Router-on-a-Stick Example
In early June 2022 I described a netlab topology using VLAN trunks in netlab. That topology provided pure bridging service for two IP subnets. Now let’s go a step further and add a router-on-a-stick:
- S1 and S2 are layer-2 switches (no IP addresses on red or blue VLANs).
- ROS is a router-on-a-stick routing between red and blue VLANs.
- Hosts on red and blue VLANs should be able to ping each other.
Lab topology
netlab Release 1.3.3: Bug Fixes
Just FYI: I pushed out netlab release 1.3.3 yesterday. It’s a purely bug fix release, new functionality and a few breaking changes are coming in release 1.4 in a few weeks.
Some of the bugs we fixed weren’t exactly pleasant; if you’re using release 1.3.2 you might want to upgrade with pip3 install --upgrade networklab.
netlab Release 1.3.3: Bug Fixes
Just FYI: I pushed out netlab release 1.3.3 yesterday. It’s a purely bug fix release, new functionality and a few breaking changes are coming in release 1.4 in a few weeks.
Some of the bugs we fixed weren’t exactly pleasant; if you’re using release 1.3.2 you might want to upgrade with pip3 install --upgrade networklab.
Linear Regression Notes
Introduction
When it comes to stats, one of the first topics we learn is linear regression. But most people don’t realize how deep the linear regression topic is, and observing blind applications in day-to-day life makes me cringe. This post is not about virtue-signaling(as I know some areas I haven’t explored myself), but to share my notes which may be helpful to others.
Linear Model
A basic stastical model with single explanatory variable has equation describing the relation between x and the mean
$\mu$ of the conditional distribution of Y at each value of x.
$ E(Y_{i}) = \beta_{0} + \beta_{1}x_{i} $
Alternative formulation for the model expresses $Y_{i}$
$ Y_{i} = \beta_{0} + \beta_{1}x_{i} + \epsilon_{i} $
where $\epsilon_{i}$ is the deviation of $Y_{i}$ from $E(Y_{i}) = \beta_{0} + \beta_{1}x_{i} + \epsilon_{i}$ is called
the error term, since it represents the error that results from using the conditional expectation of Y at $x_{i}$ to
predict the individual observation.
Least Squares Method
For the linear model $E(Y_{i}) = \beta_{0} + \beta_{1}x_{i}$, with a sample of n observations the least squares method determines the value of $\hat{\beta_{0}}$ and $\hat{\beta_{1}}$ that minimize the sum of squared residuals.
$ \sum_{i=1}^{n}(y_{i}-\hat{\mu_{i}})^2 = \sum_{i=1}^{n}[y_{i}-(\hat{\beta_{0}} + Continue reading
Linear Regression Rough Notes
Introduction
When it comes to stats, one of the first topics we learn is linear regression. But many people don’t realize how deep the linear regression topic is. Below are my partial notes on Linear Regression for anyone who may find this helpful.
Linear Model
A basic statistical model with single explanatory variable has equation describing the relation between x and the mean
$\mu$ of the conditional distribution of Y at each value of x.
$ E(Y_{i}) = \beta_{0} + \beta_{1}x_{i} $
Alternative formulation for the model expresses $Y_{i}$
$ Y_{i} = \beta_{0} + \beta_{1}x_{i} + \epsilon_{i} $
where $\epsilon_{i}$ is the deviation of $Y_{i}$ from $E(Y_{i}) = \beta_{0} + \beta_{1}x_{i} + \epsilon_{i}$ is called
the error term, since it represents the error that results from using the conditional expectation of Y at $x_{i}$ to
predict the individual observation.
Least Squares Method
For the linear model $E(Y_{i}) = \beta_{0} + \beta_{1}x_{i}$, with a sample of n observations the least squares method determines the value of $\hat{\beta_{0}}$ and $\hat{\beta_{1}}$ that minimize the sum of squared residuals.
$ \sum_{i=1}^{n}(y_{i}-\hat{\mu_{i}})^2 = \sum_{i=1}^{n}[y_{i}-(\hat{\beta_{0}} + \hat{\beta_{1}}x_{i})]^2 = \sum_{i=1}^{n}e^{2}_{i} $
As a function of model parameters $(\beta_{0} , \beta_{1})$, the expression is quadratic in $\beta_{0},\beta_{1}$