Rylan  Becker

Rylan Becker

1625173260

Using Microsoft Azure Load Balancers and NGINX Plus

Customers using Microsoft Azure have three options for load balancing: NGINX Plus, the Azure load balancing services, or NGINX Plus in conjunction with the Azure load balancing services. This post aims to give you enough information to make a decision and also shows you how using NGINX Plus with Azure Load Balancer can give you a highly available HTTP load balancer with rich Layer 7 functionality.

Overview

Microsoft Azure gives its users two choices of a load balancer: Azure Load Balancer for basic TCP/UDP load balancing (at Layer 4, the network layer) and Azure Application Gateway for HTTP/HTTPS load balancing (at Layer 7, the application layer). While these solutions work for simple use cases, they do not provide many features that come standard with NGINX Plus.

Here is a general comparison between NGINX Plus and the Azure load‑balancing offerings:

Comparing NGINX Plus and Azure Load Balancing Services

Load Balancing Methods

NGINX Plus offers a choice of several load‑balancing methods in addition to the default Round Robin method:

  • Least Connections – Each request is sent to the server with the lowest number of active connections.
  • Least Time – Each request is sent to the server with the lowest score, which is calculated from a weighted combination of average latency and lowest number of active connections.
  • IP Hash – Each request is sent to the server determined by the source IP address of the request.
  • Generic Hash – Each request is sent to the server determined from a user‑defined key, which can contain any combination of text and NGINX variables, for example the variables corresponding to the Source IP Address and Source Port header fields, or the URI.
  • Random – Each request is sent to a server selected at random. When the two parameter is included, NGINX Plus selects two servers at random and then chooses between them using either the Least Connections algorithm (the default) or Least Time, as configured.

#blog #tech #session persistence #http/2 #ssl/tls termination #microsoft azure

What is GEEK

Buddha Community

Using Microsoft Azure Load Balancers and NGINX Plus
Hal  Sauer

Hal Sauer

1593444960

Sample Load balancing solution with Docker and Nginx

Most of today’s business applications use load balancing to distribute traffic among different resources and avoid overload of a single resource.

One of the obvious advantages of load balancing architecture is to increase the availability and reliability of applications, so if a certain number of clients request some number of resources to backends, Load balancer stays between them and route the traffic to the backend that fills most the routing criteria (less busy, most healthy, located in a given region … etc).

There are a lot of routing criteria, but we will focus on this article on fixed round-robin criteria — meaning each backend receives a fixed amount of traffic — which I think rarely documented :).

To simplify we will create two backends “applications” based on flask Python files. We will use NGINX as a load balancer to distribute 60% of traffic to application1 and 40% of traffic to application2.

Let’s start the coding, hereafter the complete architecture of our project:

app1/app1.py

from flask import request, Flask
import json

app1 = Flask(__name__)
@app1.route('/')
def hello_world():
return 'Salam alikom, this is App1 :) '
if __name__ == '__main__':
app1.run(debug=True, host='0.0.0.0')

app2/app2.py

from flask import request, Flask
import json

app1 = Flask(__name__)
@app1.route('/')
def hello_world():
return 'Salam alikom, this is App2 :) '
if __name__ == '__main__':
app1.run(debug=True, host='0.0.0.0')

Then we have to dockerize both applications by adding the requirements.txt file. It will contain only the flask library since we are using the python3 image.

#load-balancing #python-flask #docker-load-balancing #nginx #flask-load-balancing

Aisu  Joesph

Aisu Joesph

1624327316

Securing Microsoft Active Directory

Clustering

K-means is one of the simplest unsupervised machine learning algorithms that solve the well-known data clustering problem. Clustering is one of the most common data analysis tasks used to get an intuition about data structure. It is defined as finding the subgroups in the data such that each data points in different clusters are very different. We are trying to find the homogeneous subgroups within the data. Each group’s data points are similarly based on similarity metrics like a Euclidean-based distance or correlation-based distance.

The algorithm can do clustering analysis based on features or samples. We try to find the subcategory of sampling based on attributes or try to find the subcategory of parts based on samples. The practical applications of such a procedure are many: the best use of clustering in amazon and Netflix recommended system, given a medical image of a group of cells, a clustering algorithm could aid in identifying the centers of the cells; looking at the GPS data of a user’s mobile device, their more frequently visited locations within a certain radius can be revealed; for any set of unlabeled observations, clustering helps establish the existence of some structure of data that might indicate that the data is separable.

What is K-Means Clustering?

K-means the clustering algorithm whose primary goal is to group similar elements or data points into a cluster.

K in k-means represents the number of clusters.

A cluster refers to a collection of data points aggregated together because of certain similarities.

K-means clustering is an iterative algorithm that starts with k random numbers used as mean values to define clusters. Data points belong to the group represented by the mean value to which they are closest. This mean value co-ordinates called the centroid.

Iteratively, the mean value of each cluster’s data points is computed, and the new mean values are used to restart the process till the mean stops changing. The disadvantage of k-means is that it a local search procedure and could miss global patterns.

The k initial centroids can be randomly selected. Another approach of determining k is to compute the entire dataset’s mean and add _k _random co-ordinates to it to make k initial points. Another method is to determine the principal component of the data and divide it into _k _equal partitions. The mean of each section can be used as initial centroids.

#ad #microsoft #microsoft-azure #azure #azure-functions #azure-security

What is Microsoft Azure?

It’s one of the leaders in the cloud computing space, but what is Azure cloud and what is it used for? This ACG Fundamentals episode will give you a high-level overview of Microsoft Azure cloud, so you can understand this cloud computing platform’s strengths and weaknesses, use cases, market share and competition, and how the Azure services all work together.

Introduction (0:00)
Azure Infrastructure (1:07)
Azure Competitors (3:43)
Azure Strengths and Weaknesses (4:18)
Azure Use Cases (6:12)
What’s Next? (7:39)

#microsoft azure #azure #what is microsoft azure?

Rylan  Becker

Rylan Becker

1625173260

Using Microsoft Azure Load Balancers and NGINX Plus

Customers using Microsoft Azure have three options for load balancing: NGINX Plus, the Azure load balancing services, or NGINX Plus in conjunction with the Azure load balancing services. This post aims to give you enough information to make a decision and also shows you how using NGINX Plus with Azure Load Balancer can give you a highly available HTTP load balancer with rich Layer 7 functionality.

Overview

Microsoft Azure gives its users two choices of a load balancer: Azure Load Balancer for basic TCP/UDP load balancing (at Layer 4, the network layer) and Azure Application Gateway for HTTP/HTTPS load balancing (at Layer 7, the application layer). While these solutions work for simple use cases, they do not provide many features that come standard with NGINX Plus.

Here is a general comparison between NGINX Plus and the Azure load‑balancing offerings:

Comparing NGINX Plus and Azure Load Balancing Services

Load Balancing Methods

NGINX Plus offers a choice of several load‑balancing methods in addition to the default Round Robin method:

  • Least Connections – Each request is sent to the server with the lowest number of active connections.
  • Least Time – Each request is sent to the server with the lowest score, which is calculated from a weighted combination of average latency and lowest number of active connections.
  • IP Hash – Each request is sent to the server determined by the source IP address of the request.
  • Generic Hash – Each request is sent to the server determined from a user‑defined key, which can contain any combination of text and NGINX variables, for example the variables corresponding to the Source IP Address and Source Port header fields, or the URI.
  • Random – Each request is sent to a server selected at random. When the two parameter is included, NGINX Plus selects two servers at random and then chooses between them using either the Least Connections algorithm (the default) or Least Time, as configured.

#blog #tech #session persistence #http/2 #ssl/tls termination #microsoft azure

Aisu  Joesph

Aisu Joesph

1623096960

Azure Load Balancer insights using Azure Monitor for Networks

Erich Robinson-Tillenburg joins Scott Hanselman to demo and explain health monitoring and configuration analysis for Azure Load Balancer using Azure Monitor for Networks, a central hub that provides access to health and connectivity monitoring for all your network resources.

0:00 – Overview

1:16 – Load Balancer insights

4:00 – Visualize functional dependencies

6:20 – Exploring the Metrics dashboard

10:58 – Flow Distribution Help

11:57 – Network Connectivity Monitoring

13:18 – Azure Monitor for Networks hub

14:12 – Wrap-up

#azure load balancer #azure monitor