Demystifying latency in webpage requests

July 21, 2009

Since the advent of the Internet and the World Wide Web (www) in the last century, till today there have been trillions and trillions of Internet page requests made across the world. However, webmasters feel all the more curious to know how actually a webpage request is being served on their user’s computer and to use that understanding to try to speed it up.

Data on the Internet is packaged and transported across in small data packets. The regular or irregular flow of these data packets affect the user’s Internet experience. Whenever, one sees a continuous flow of data on his screen, this in turn means that the data packets are moving across smoothly and in a timely fashion. However, if the same data packets move across with large and visible delays, that means that the user’s experience is degraded and he may feel frustrated at the poor network connection and speed.

In this article, I seek to demystify all such beliefs and bring parity to our understanding of webpage request. I shall make an attempt creating a better understanding of latency effects of network and low bandwidth on a webpage request.

Lets first develop an understanding of some of the networking concepts to build our follow up understanding of the webpage request and latency. With the advent of networking, it was thought that millions and millions of users would be connected through a common network, hence the idea of TCP / IP model.

The key features of the TCP/IP model is encapsualtion, which is the concept of collecting the data and covering it with a common container for transmission. The common container is called the “IP Datagram”, also known as “IP Packet” or just the “Packet”. This IP Packet is a simple thing, with a header which contains the information used for routing the the packet to the destination and followed by data which is any information sent across.

Lets now concentrate all our energies on understanding another important concept in networking, the OSI model. It was created to lay out the process of turning the application data into something that can be transported through the Internet. The upper layers of the OSI model describe as to what happens within the applications running on the computer. These include the human-machine interface, conversion of the high level language into machine language, encryption, authentication and permissions. The lower layers are the ones where to and from applications are turned into data to move across the network. This is where data encapsulation occurs and the IP Datagram or “packet” is built.

The transport of data across the network is a 3 step process:

1. Data from the source is passed through the TCP/IP stack and wrapped into IP Datagrams, commonly known as “Packets”. These packets are then transmitted by the source computer in the network

2. Packets are passed along the network until they reach the destination computer

3. Packets are received by the destination computer and are passed through the stack

According to Wikipedia, Latency is the time delay between the moment something is initiated, and the moment one of its effects begins or becomes detectable. The most common understanding of Latency is it takes time for web pages to load and for emails to reach the destination inbox. Though, this is a form of latency, however, lets take down latency as the time delay imparted by each element involved in the transmission of data. Lets develop our understanding of what causes latency. The are many logical and physical elements involved in networking.

Application Latency

The need to read and write to disk causes some time delays. The processor could be very strong and highly rated, however, it still has limitations to as to what it can read and write in stipulated time. It takes a finite amount of time to manufacture data and present it. There are a lot of hardware limitations as well, such as the amount of memory which affects application performance.

Serialization Latency

The encapsulation of data (as discussed above) is called serialization and takes a finite amount of time. It is calculated as follows: Serialization Delay = Packet Size in bits / Transmission Rate in bits per second Serialization can lead to significant delays and latency on link that operate on low transmission rates.

Routing & Switching Latency

A network request causes data to flow from point A to point B. This would be simple, if the network was just 2 computers, however, this is not to be. In networks like the Internet, data and hence the packets are transmitted from source to destination through a series of routers and switches connected through circuits, which are hardware devices needed for transmission through the network.These hardware machines have to manage the Internet traffic causing delays caused by the routing and switching process. This refers to the amount of processing time for a router or switch to receive a packet, process it and transmit it.These days, with the advancements in the computer hardware technology, these delays have reduced to only a few nanoseconds. High performance routers and switches each usually add upto 200µs of latency to the link.

Queuing Latency

Queuing latency refers to the amount of time a packet spends sitting in a queue waiting for transmission due to over utilization of the link. Though over-utilization of high speed Internet backbone is very rare, but it can be easily seen on lower speed networks. Congestion can cause these delays to become infinite since packets may be dropped when router becomes full. Routers use various queueing management algorights to ensure latency is minimized. The most commonly used WRED algorithms bound queueing latency at 20 ms.

Propagation Latency

Propagation latency is the delay caused by the transmission medium. The amount of slowing down is known as the Velocity Factor (VF). Typically, there are 3 medium of transmission of data across the networks, copper cables having a VF in the range of 40% – 80% of the speed of light, fibre-optic cables leading to a VF of around 70% of the speed of light and the electro-magnetic radio waves having the least possible VF. This delay happens even without considering the amount of data being transferred, the transmission rate, the protocol being used or the link problems.

Transmission Rate and Bandwidth Latency

Transmission Rate is the term used to define the number of bits that can be extracted from the medium. It is commonly measured in the terms of number of bits per second. The maximum transmission rate defines the fundamental limitation of the transmission medium. Generally, Copper links have a maximum transmission rate of 10, 100 or 1000 Mbps. For Fibre-optic links, transmission rates vary from around 50 Mbps to 10 Gbps.Wireless LANs and satellite links use a modem to convert the bits into a modulated wave and then on transmission convert them back into bits using the demodulator. The limiting factor in these type of links is the limited bandwidth available to these signals. The amount of radio spectrum occupied by any given signal is called its bandwidth. Since radio spectrum is a limited resource, the occupied radio bandwidth is an important limiting factor in wireless and satellite links.

Protocol Latency

Lets now take a look at the network data exchanges. Connectionless data exchange is the one where data is pushed through with any consideration. Here the packet traverses the Internet to search for its destination, however, if something happens to it midway, nothing can be done. This is usually used for streaming music, videos, VOIP. The protocol used is User Datagram Protocol (UDP). It doesn’t have any overhead or connection management. There is no retransmission of data as well.

On the other hand are the connection based data exchanges. They rely on the establishment of the connection which manages every packet that is transmitted. The transport protocol used is the Transmission Control Protocol (TCP). It provides for the error free delivery of packets and hence the data. 

TCP connections have 3 phases:

1. Establish the connection

2. Send the data

3. Close the connection

All this adds to the time being taken while the data is transmitted and hence the delay and Latency. This puts the webpage request on the table and opens it thread bare to clear the air on what goes behind each of our clicks while we are on the Internet connected to the millions or billions of users and trillions of data. We make an understanding of the time delays or Latency and now agree that it is imperative and necessary.

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References

1. What is network latency? and Why does it matter? –http://www.o3bnetworks.com/docs/O3b_latency_white_paper2.pdf

2. Satellite Internet Access – http://www.sisp.net/broadband/satellite.htm

3. Network bandwidth and Latency – http://compnetworking.about.com/od/speedtests/a/network_latency.htm

4. Anatomy of a HTTP Request –http://www.websiteoptimization.com/secrets/metrics/10-21-http-request.html

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Project Management for AI-ML-DL Projects

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An efficient project manager will ensure that there is ample time from the concept to the final product so that a client’s requirements are met without any delays and issues.

How is Project Management Done For AI, ML or DL Projects?

As already established, efficient project management is of great importance in AI/ML/DL projects. So, if you are planning to move into this field as a professional, here are some tips –

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The first step toward managing an AI project is the identification of the problem. What are we trying to solve or what outcome do we desire? AI is a means to receive the outcome that we desire. Multiple solutions are chosen on which AI solutions are built.

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After the problem has been identified, then testing the solution is done. We try to find out whether we have chosen the right solution for the problem. At this stage, we can ideally understand how to begin with an artificial intelligence or machine learning or deep learning project. We also need to understand whether customers will pay for this solution to the problem.

AI and ML engineers test this problem-solution fit through various techniques such as the traditional lean approach or the product design sprint. These techniques help us by analysing the solution within the deadline easily.

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If you have a stable customer base for your AI, ML or DL solutions, then begin the project by collecting data and managing it. We begin by segregating the available data into unstructured and structured forms. It is easy to do the division of data in small and medium companies. It is because the amount of data is less. However, other players who own big businesses have large amounts of data to work on. Data engineers use all the tools and techniques to organise and clean up the data.

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To keep the blog simple, we will try not to mention the technical side of AI algorithms in the content here. There are different types of algorithms which depend on the type of machine learning technique we employ. If it is the supervised learning model, then the classification helps us in labelling the project and the regression helps us predict the quantity. A data engineer can choose from any of the popular algorithms like the Naïve Bayes classification or the random forest algorithm. If the unsupervised learning model is used, then clustering algorithms are used.

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For training algorithms, one needs to use various AI techniques, which are done through software developed by programmers. While most of the job is done in Python, nowadays, JavaScript, Java, C++ and Julia are also used. So, a developmental team is set up at this stage. These developers make a minimum threshold that is able to generate the necessary statistics to train the algorithm.  

  • Deployment of the project-

After the project is completed, then we come to its deployment. It can either be deployed on a local server or the Cloud. So, data engineers see if the local GPU or the Cloud GPU are in order. And, then they deploy the code along with the required dashboard to view the analytics.

Final Words-

To sum it up, this is a generic overview of how a project management system should work for AI/ML/DL projects. However, a point to keep in mind here is that this is not a universal process. The particulars will alter according to a specific project. 

Reference Links:

https://www.datacamp.com/blog/how-to-manage-ai-projects-effectively

https://appinventiv.com/blog/ai-project-management/#:~:text=There%20are%20six%20steps%20that,product%20on%20the%20right%20platform.

https://www.datascience-pm.com/manage-ai-projects/

https://community.pmi.org/blog-post/70065/how-can-i-manage-complex-ai-projects-#_=_

This is a decorative image for Top 7 AI & ML start-ups in Telecom Industry in India
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Top 7 AI & ML start-ups in Telecom Industry in India

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The availability of artificial intelligence and machine learning in almost all industries today, including the telecom industry in India, has helped change the way of operational management for many existing businesses and startups that are the exclusive service providers in India.

 

In addition to that, the awareness and popularity of cloud GPU servers or other GPU cloud computing mediums have encouraged AI and ML startups in the telecom industry in India to take up their efficiency a notch higher by combining these technologies with cloud computing GPU. Let us look into the 7 AI and ML startups in the telecom industry in India 2022 below.

 

Top AI and ML Startups in Telecom Industry 

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Wiom

Founded in 2021, Wiom is a telecom startup using various technologies like deep learning and artificial intelligence to create a blockchain-based working model for internet delivery. It is an affordable scalable model that might incorporate GPU cloud servers in the future when data flow increases. 

TechVantage

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Manthan

As one of the strongest performers is the customer analytics solutions, Manthan is a supporting startup in India in the telecom industry. It is an almost business assistant that can help with leveraging deep analytics for improved efficiency. For denser database management, NVIDIA A100 80 GB is one of their top choices. 

NetraDyne

Just as NVIDIA is known as a top GPU cloud provider, NetraDyne can be named as a telecom startup, even if not directly. It aims to use artificial intelligence and machine learning to increase road safety which is also a key concern for the telecom providers, for their field team. It assists with fleet management. 

KeyPoint Tech

This AI- and ML-driven startup is all set to combine various technologies to provide improved technology solutions for all devices and platforms. At present, they do not use any available cloud GPU servers but expect to experiment with GPU cloud computing in the future when data inflow increases.

 

Helpshift

Actively known to resolve customer communication, it is also considered to be a startup in the telecom industry as it facilitates better communication among customers for increased engagement and satisfaction. 

Facilio

An AI startup in Chennai, Facilio is a facility operation and maintenance solution that aims to improve the machine efficiency needed for network tower management, buildings, machines, etc.

 

In conclusion, the telecom industry in India is actively looking to improve the services provided to customers to ensure maximum customer satisfaction. From top-class networking solutions to better management of increasing databases using GPU cloud or other GPU online services to manage data hungry workloads efficiently, AI and MI-enabled solutions have taken the telecom industry by storm. Moreover, with the introduction of artificial intelligence and machine learning in this industry, the scope of innovation and improvement is higher than ever before.

 

 

References

https://www.inventiva.co.in/trends/telecom-startup-funding-inr-30-crore/

https://www.mygreatlearning.com/blog/top-ai-startups-in-india/

This is a decorative image for Top 7 AI Startups in Education Industry
June 29, 2022

Top 7 AI Startups in Education Industry

The evolution of the global education system is an interesting thing to watch. The way this whole sector has transformed in the past decade can make a great case study on how modern technology like artificial intelligence (AI) makes a tangible difference in human life. 

In this evolution, edtech startups have played a pivotal role. And, in this write-up, you will get a chance to learn about some of them. So, read on to explore more.

Top AI Startups in the Education Industry-

Following is a list of education startups that are making a difference in the way this sector is transforming –

  1. Miko

Miko started its operations in 2015 in Mumbai, Maharashtra. Miko has made a companion for children. This companion is a bot which is powered by AI technology. The bot is able to perform an array of functions like talking, responding, educating, providing entertainment, and also understanding a child’s requirements. Additionally, the bot can answer what the child asks. It can also carry out a guided discussion for clarifying any topic to the child. Miko bots are integrated with a companion app which allows parents to control them through their Android and iOS devices. 

  1. iNurture

iNurture was founded in 2005 in Bengaluru, Karnataka. It provides universities assistance with job-oriented UG and PG courses. It offers courses in IT, innovation, marketing leadership, business analytics, financial services, design and new media, and design. One of its popular products is KRACKiN. It is an AI-powered platform which engages students and provides employment with career guidance. 

  1. Verzeo

Verzeo started its operations in 2018 in Bengaluru, Karnataka. It is a platform based on AI and ML. It provides academic programmes involving multi-disciplinary learning that can later culminate in getting an internship. These programmes are in subjects like artificial intelligence, machine learning, digital marketing and robotics.

  1. EnglishEdge 

EnglishEdge was founded in Noida in 2012. EnglishEdge provides courses driven by AI for getting skilled in English. There are several programmes to polish your English skills through courses provided online like professional edge, conversation edge, grammar edge and professional edge. There is also a portable lab for schools using smart classes for teaching the language. 

  1. CollPoll

CollPoll was founded in 2013 in Bengaluru, Karnataka. The platform is mobile- and web-based. CollPoll helps in managing educational institutions. It helps in the management of admission, curriculum, timetable, placement, fees and other features. College or university administrators, faculty and students can share opinions, ideas and information on a central server from their Android and iOS phones.

  1. Thinkster

Thinkster was founded in 2010 in Bengaluru, Karnataka. Thinkster is a program for learning mathematics and it is based on AI. The program is specifically focused on teaching mathematics to K-12 students. Students get a personalised experience as classes are conducted in a one-on-one session with the tutors of mathematics. Teachers can give scores for daily worksheets along with personalised comments for the improvement of students. The platform uses AI to analyse students’ performance. You can access the app through Android and iOS devices.

  1. ByteLearn 

ByteLearn was founded in Noida in 2020. ByteLean is an assistant driven by artificial intelligence which helps mathematics teachers and other coaches to tutor students on its platform. It provides students attention in one-on-one sessions. ByteLearn also helps students with personalised practice sessions.

Key Highlights

  • High demand for AI-powered personalised education, adaptive learning and task automation is steering the market.
  • Several AI segments such as speech and image recognition, machine learning algorithms and natural language processing can radically enhance the learning system with automatic performance assessment, 24x7 tutoring and support and personalised lessons.
  • As per the market reports of P&S Intelligence, the worldwide AI in the education industry has a valuation of $1.1 billion as of 2019.
  • In 2030, it is projected to attain $25.7 billion, indicating a 32.9% CAGR from 2020 to 2030.

Bottom Line

Rising reliability on smart devices, huge spending on AI technologies and edtech and highly developed learning infrastructure are the primary contributors to the growth education sector has witnessed recently. Notably, artificial intelligence in the education sector will expand drastically. However, certain unmapped areas require innovations.

With experienced well-coordinated teams and engaging ideas, AI education startups can achieve great success.

Reference Links:

https://belitsoft.com/custom-elearning-development/ai-in-education/ai-in-edtech

https://www.emergenresearch.com/blog/top-10-leading-companies-in-the-artificial-intelligence-in-education-sector-market

https://xenoss.io/blog/ai-edtech-startups

https://riiid.com/en/about

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