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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Elastic Load Balancer in AWS and its Benefits

Chetan
Chetan Last Updated : 03 Sep, 2022
7 min read

This article was published as a part of the Data Science Blogathon.

Introduction

The cloud trend has gained tremendous importance in the technology industry and the field of science in recent years. The most important aspect of cloud computing is the on-demand application delivery paradigm from the cloud customer’s perspective. As a result, cloud services can be updated to accommodate more new users and expand when the network has insufficient capacity. 

The result is a more efficient use of the physical system and a cost-effective cloud system. It is the most up-to-date and common IT and analytics technology with high features such as virtualization and on-demand (dynamic) resource allocation. For stable QOS customer service, a load-balancing mechanism is required in cloud environments. In conjunction with the application and distribution of incoming users, an auto-scaling function must also be implemented to prevent network congestion and collapse.

Creating an external load balancer is quite brain-intensive, while creating an elastic load balancer is simple and easy. In Amazon Web Services (AWS), Elastic Load Balancing works as a Load balancing service. It automatically distributes scale resources to meet traffic and incoming application traffic demands. ELB helps the IT team adjust capacity based on incoming applications and network traffic. Let’s see how this works.

amazon AWS
https://aws.amazon.com/

Elastic load balancer

Because elastic load balancing uses request routing algorithms to distribute incoming application traffic among multiple instances or scale it as needed, it increases the fault tolerance of your applications. Elastic Load Balancer (ELB) can automatically scale load balancers and applications based on real-time traffic.  It uses system health checks to find application pool members (application servers), properly route traffic to available servers, manage failover for high-availability targets, or add additional capacity. Spin-up.
AWS
https://aws.amazon.com

 

How does an elastic load balancer work?

Elastic Load Balancing scales your load balancer as the traffic on your server changes. It routes incoming application traffic to the instance automatically. The Elastic Load Balancer acts as a point of contact for incoming traffic. By monitoring the health of instances, the Elastic Load Balancing Service can forward traffic requests to healthy instances.

Does Avi offer an elastic load balancer?

Yes. The AV Vantage Platform includes a 100% software-based elastic load balancer that can be scaled automatically through the application performance monitoring capabilities built into the AV Vantage Platform. With the on-demand scaling of load balancers and the ability to trigger scaling of backend application servers through ecosystem integration with the orchestration platform, AV Vantage provides a superior end-user experience. By automatically scaling up or down in response to traffic patterns, the platform eliminates common practices of overprovisioning application load balancing capabilities, a problem with hardware load balancers.
amazon
https://aws.amazon.com/

 

Types of Elastic Load Balancers

There are three types of load balancers.
1. Classic Load Balancer (CLB)
These were used for EC2-Classic instances, which are the previous generation. Works at both connection level request levels. However, it does not support features such as host-based routing or path-based routing. Once configured, it distributes the load across all registered instances regardless of what is on the server. Therefore, it can be used to distribute traffic to only one URL.
2. Application Load Balancer (ALB)
A network model called the OSI Model (Open System Interconnection) is used to explain how a computer network works. This load balancer works on this application layer, hence the name. It also provides advanced routing features such as host-based and path-based routing and works with the host-based routing of containers and microservices.  Each site is hosted on two EC2 instances for high availability, and you want to split incoming web traffic between them.

 

If you were to use CLB, you would have to create two load balancers for each site. But you can do the same with a single ALB. Therefore you will save money as you will only pay for one ALB instead of two CLBs. Let’s say your company website is payzello.com, and the company blog is hosted at payzello.com/blog. The operations team decided to host the main website and blog in different cases. With ALB, you can route traffic based on the path to the desired URL. So again, a single ALB is enough to get you through.

3. Network Load Balancer (NLB)
This load balancer works on the network layer of the OSI model, hence the name. Let’s say your company’s website runs on four m4-large instances, and you use ALB to distribute traffic between them. Today your company launched a new product that went viral, and your website is starting to receive millions of requests per second. This is where NLB shines. It can handle sudden spikes in traffic because it operates at the connection level. It also provides support for static IP addresses. I hope by this time you have a rough idea of ​​load balancers.

Why Elastic Load Balancer?

ELB helps the IT team adjust capacity based on incoming applications and network traffic. Users enable ELB to maintain consistent application performance within a single Availability Zone or across multiple Availability Zones. Historically, load balancing has caused a computer to distribute the amount of work among multiple computers so that users can generally receive services faster.
ELASTIC LOAD BALANCER

https://aws.amazon.com/

 

High availability
The most well-known service that relies on ELB is Amazon EC2 because ELB performs a health check to make sure the instance is still running before it sends traffic. ELB routes traffic to the remaining healthy EC2 instances when an instance fails or becomes unhealthy. If all EC2 instances in a particular Availability Zone are unhealthy, ELB can route traffic to other Availability Zones until the original instance is restored to a healthy state.
Automatic scaling
A developer can use the AWS auto-scaling feature to guarantee that they have enough EC2 instances running behind ELB. The developer sets autoscaling conditions, and when the condition is met, the new EC2 instance can spin up to meet the required minimum. A developer can also set a condition to launch new EC2 instances to reduce latency.
Protection
The IT team can determine whether it wants to be Internet oriented or as an internal load balancer. The second option allows the developer to route traffic through the ELB using a private IP address. A developer can route traffic between different application tiers using multiple Internet-connected and internal load balancers; This approach allows the IT team to use a security group with a private IP address while exposing only the web tier and its public IP address.

Using Elastic Load Balancer

Cloud computing refers to storing and accessing data and programs on a computer’s hard drive or remote servers hosted on the Internet instead of on a local server. It is also called Internet-based computing.
Amazon Web Services
Elastic Load Balancer is a service provided by Amazon in which incoming traffic is efficiently distributed across many backend servers, increasing speed and performance as this site may be down due to high traffic during the competition. A solution is needed to handle both situations effectively. An elastic Load Balancer is a solution to this problem!! It automatically scales up your resources and balances the load enough to handle heavy incoming traffic.

AWS Elastic Load Balancing Components

Load Balancer

A load balancer distributes incoming application traffic among multiple targets, such as EC2 instances, across multiple Availability Zones (AZs), increasing the availability of your application. You add one or more listeners to the load balancer.

Listener instance or vSRX

The Listener is the process of validating connection requests using the protocol and port that you have configured. Listeners, like vSRX instances, examine connection requests from clients using protocols and ports that you configure and forward the requests to one or more target groups based on rules that you define. The traffic is directed to the target group when the condition is met. You must define a default rule for each vSRX instance, and you can add rules that determine different target groups based on the content of the request (also known as content-based routing).

Target Groups or VSRX Application Workload

When you create each vSRX instance as a listener rule, you specify the vSRX application and conditions. When a rule condition is met, traffic is routed to the appropriate vSRX application. You can create different vSRX applications for different types of requirements. For example, create one vSRX application for common requests and another vSRX application for microservices requests for your application.

 load balancers in AWS ELB

Classic, Network, and Application Load Balancer. You can choose a load balancer based on your application requirements. For more information about AWS ELB load balancer types, see AWS Elastic Load Balancing.

Advantages of Elastic Load Balancer

• Provides elastic load balancing for Intra reach zones by automatically distributing incoming traffic.
• Provides the flexibility to host multiple applications on a single instance and virtualize application targets by allowing you to centrally manage Transport Layer Security (TLS) settings and offload CPU-intensive workloads from your applications.
• Supports automatic scaling to a sufficient number of applications to meet different application load levels without the need for manual intervention.
• Enables you to monitor your applications and their performance in real-time with Amazon CloudWatch metrics, logging, and request tracking.

Conclusion

So we can see several benefits based on how AWS Elastic Load Balancer works. Security is paramount with the convenience of many security features in load balancers on AWS. Additionally, elastic load balancing capabilities ensure adaptability to rapid fluctuations in network traffic patterns. Deep integration with Auto Scaling also helps verify sufficient application capacity to handle different application load levels without any human intervention. High availability and flexibility are also some notable benefits of elastic load balancing. Thus, increased scalability, redundancy, and other features of AWS ELB websites and web application traffic can be easily handled. This makes the AWS ELB service provided by SNDK Corp unique.
  • Suppose your organization runs a high-traffic website or database that receives many queries, SNDK Corp.’s Elastic Load Balancing service. It offers several benefits by optimizing resource utilization, response time, and data delivery.
  • In a high-traffic environment, elastic load balancing enables smooth and accurate fulfillment of user requests. This saves users the frustration of struggling with unresponsive apps and resources. It helps IT departments ensure the scalability and availability of services. Its advanced and unique traffic management feature can help an enterprise process requests for the right resources more efficiently and for each end user.
  • With the on-demand scaling of load balancers and the ability to trigger scaling of backend application servers through ecosystem integration with the orchestration platform, AV Vantage provides a superior end-user experience.

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amazon awsAWSblogathonelastic load balancer
Chetan
Chetan

Data Analyst who love to drive insights by visualizing the data and extracting the knowledge from it. Automating various tasks using python & builds Real time Dashboard's using tech like React and node.js. Capable of Creaking complex SQL queries to fetch the accurate data.

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Yashna Behera
Yashna Behera

very informative article

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