Why E2E Cloud Platform Emerged as the Choice for Clovia?

February 22, 2018

About Company: CLOVIA is a full stack lingerie brand backed by Ivy Cap Ventures, addressing India’s underserved women’s innerwear and sleepwear market. Clovia product offerings comprise a wide variety of bras, panties, nightwear, camisoles & shapewear. The company sells its products through its own e-store clovia.com and other partner websites. The brand delivers products in more than 970 cities and serves over 10000 pin codes locations. The website currently serves monthly traffic of over 8.7 lakh users with over 40.9 Million-page views. This article illustrates the benefits that are associated with Cloud Infrastructure migration from any other public cloud provider to E2E Networks. The results directly encompass the benefits of significant cost reductions and better performance associated with E2E Networks Infrastructure.

E2E NETWORKS successfully migrated clovia.com from AWS to E2E Networks with a Cloud Agnostic approach.

Client Requirements:

  • Increase website performance with reduced cost
  • Infrastructure management and efficient utilization of resources
  • Performance of stacks & database queries equivalent or better than existing services
  • A holistic solution that would accelerate performance, condense resources and implement best-practices

The Challenge:

The client’s portfolio involved many products and the functions of the IT Admins & DevOps team were overloaded with work comprising repetitive problems. Just like many other companies, the client’s focus also to cut down their existing cloud Infrastructure and operational costs while delivering best-in-class service to its’ customers.

E2E’s Prescriptive Approach

  • Migration of CDN from AWS CloudFront to E2E Networks based CDN setup.
  • Setup MySQL Database from AWS-RDS to E2E Networks based HA-DRBD MySQL
  • Migration of Mongo node from SPOF to HA-Mongo replica set cluster
  • Setup Distributed Memcached system with 4 services for users and sessions management
  • Move single Redis and RabbitMQ to cluster setup
  • Transform web stack from Apache + mod_wsgi to Nginx with Gunicorn + supervisord.

The Solution

Strategy Overview-

Phase 1 – Preparing right-sized infrastructure resources to migrate Clovia to E2E Networks. We had to ensure that every component used here should be Highly Available. Phase 2 – Testing components functionality and working on performance optimization strategies to reduce lag by tweaking server specification and tuning software stack configuration.

The Execution –

Before we started working on it, Clovia’s AWS architecture involved a Single Point of Failure with recurrence load issues on the server during peak hours. We monitored the website and server performance while on AWS for a few day before performing any activities. During this phase, we audited and analyzed the right-sized setup requirements.

Phase 1: High Availability setup

We initiated High Availability setup with E2E NETWORKS infrastructure CDN by using PCS cluster technology with Nginx instances acting as a CDN machine serving from origin like S3 buckets. With respect to this, we worked on setup involving a HAProxy Load balancer with a set of instances using PCS web servers configured with Nginx as frontend with proxy pass Gunicorn + supervisor as the application was based on Django stack.

  • Additionally, for MySQL Database we selected PCS with DRBD technology possessing read replica as an alternative to RDS
  • For NoSQL Mongo Database we used a replica set cluster with a set of nodes with Arbiter to avoid split brain
  • To replace elastic cache, we deployed distributed Memcached system with 4 different processes split into two processes each handled by a server to manage page cache
  • To maintain user session, we used Redis Master/Slave setup with sentinel mechanism.
  • For message queuing we used RabbitMQ cluster with mirror queuing setup, an engine we use for searches in sites driven by solr engine
  • Not forgetting the security aspects, we implemented a setup on separate VLAN by deploying zentyal firewall and NAT- cluster. This enabled secure server access and avoided Network obstruction.

Finally, we shared this new setup and initially, sync’d code & database to perform load testing and benchmarking.

Phase 2 – Benchmarking

All validation, user testing, performance and security tests were performed as per the stages set in the pre-production environment so that everything should be ready for the final sync of the code, database & DNS switchover on the final day. During our benchmarking sessions, we were challenged by the RDS query execution time, which took around 0.6 – 0.7 sec. as compared to our tuned HA-DRBD MySQL setup with high specification server which took around 1.5 sec. The problem identification consumed a lot of time and energy. We deep dived into a lot of parameters such as MySQL changes, hardware configuration etc. to identify the root cause. Finally, we narrowed down the problem to the disk IOPS availability. To rectify the problem, we re-flashed the servers with RAID 10 disk configuration as compared to the previous RAID 1 configuration which brought down query execution time from previously 1.5 sec. to now 0.4- 0.5 sec, even better than RDS.

The Results

Clovia was impressed with service and support they received from E2E Networks

“We chose E2E for their competitive prices when compared to AWS. This was technically a winning choice for Clovia as E2E provides better machines, configuration and support as compared to AWS. E2E uses APM tool – Instana which in turn increased Clovia’s performance significantly. Their 24X7 support commitment and prompt ticket resolutions is commendable. E2E team’s skill set has enabled Clovia to grow with agility. We highly recommend E2E for it’s cost effectiveness and infrastructure security specially for fast moving start-ups.

-Clovia”

Following a fully operational production environment on E2E Networks, we helped Clovia to enhance and optimize their capabilities. The website is presently serving from E2E Networks infrastructure and our client is more than happy with the with the value that we provided. Finally, clovia.com was live on E2E Networks infrastructure. Following a fully operational production environment on E2E Networks, we helped Clovia to enhance and optimize their capabilities. The website is currently serving from E2E Networks infrastructure with excellent results. Our infrastructure offered significant performance & operational enhancements

– Cheaper – Clovia witnessed more than 48% savings after migrating to E2E Networks Infrastructure.

Faster – The website currently serves 2X traffic smoothly with better CDN performance and less query execution time as compared to the initial stages which have brought additional savings to the client.

Better – E2E Networks redundant & Highly Available infrastructure easily handles everything with better performance.

More Resources – They didn’t have a staging environment on AWS but we provided them with a 4 node staging setup which made their testing and deployments slicker. Fostering growth & innovation nowadays, the site experiences an increase in the traffic: there is no fluctuation in server resources, E2E Networks servers easily handle traffic with an average monthly visit of 8.7 lakh users & over 40.9 Million-page views. E2E Networks believes that the information in this document is accurate as of its publication date; such information is subject to change without notice. E2E Networks acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this blog.

Latest Blogs
This is a decorative image for: A Complete Guide To Customer Acquisition For Startups
October 18, 2022

A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

  • Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
  • Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

This is a decorative image for: Constructing 3D objects through Deep Learning
October 18, 2022

Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

  • Highly calibrated cameras that take a photograph of the image from various angles.
  • Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

  • Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts
October 18, 2022

A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

Build on the most powerful infrastructure cloud

A vector illustration of a tech city using latest cloud technologies & infrastructure