What is Taxa Network (TXT) | What is TXT token

In this article, we'll discuss information about the Taxa Network project and TXT token. 

Taxa Network provides trustless applications and services with guaranteed integrity and confidentiality. This enables developers to create high performance, privacy-preserving and highly developable Trustless Services (tServices) for their web3 applications. Powered by trusted computing technologies, Taxa Network can reduce the cost of trust-related business logic in fintech, big data, ADs and more.

Taxa Network is a TEE-enabled blockchain agnostic infrastructure which enables high performance, privacy preserving, interactive smart contracts, namely Taxa Trusted Services. Taxa serves as a middleware component in the layered blockchain design where applications’ business logic and private data are processed with performance, integrity, and confidentiality, and empowers various use cases in performance/privacy-centric scenarios, such as DeFi, NFT, GameFi, data analytics, fintech, advertising, AI, identity etc.

Build interactive Web 3.0 applications easily on a TEE-enabled, blockchain agnostic platform.

Computationally powerful. Resizable offchain CPU resource upon request.

Data confidentiality. Data processed in trusted execution environment.

Highly developable. Rich library support.

Blockchain agnostic. Easy to integrate with your choice of smart contract platform.

Turn invention ideas into products.

Collaborative Computation: Empower analytics/AI based on various sources of data in a sealed environment with no data leakage.

Supply Chain: Allow a tracable and attestable log of your supply chain activities for your internal and external clients.

Identity Authentication: Authorize senstitive identity data and credentials to only the trusted parties’s attestable uses.

Asymetric Info Game: Now you can build the dapps of the most popular asymetric info games like Pokers, e-sports.

DeFi: Manage and exchange your digital assets, without having to trust or rely on a third party.

Digital Rights Management: Separate the ownership and right of uses of your digital properities such as sensitive data.

Hyper Smart Contracts

Leveraging the state-of-the-art hardware-based trusted computing technology and a PBFT-derived consensus mechanism, Taxa aims at becoming the universal logic layer that enables high-performance, privacy-preserving, and highly-developable Hyper Smart Contracts for all.

Layered Design

At high level, Taxa is introducing a layered design to move on-chain logic processing to off-chain.

 

In this layered design, Taxa executes stateless, high-performance, privacy-preserving smart contracts, while interacting with the consensus layer of public blockchains to store the immutable, persistent states.

Taxa brings the core functionalities to every blockchain and DApp:

• Performance: Computing capabilities ft. low overhead at an accelerated speed allow applications to perform complex business logics.
• Privacy: Hardware level isolation with secure I/O channels guarantees the sensitive data’s security in storage, transmission and execution, concealing DApps’ private info from the public and the nodes.
• Reliability: A fault-tolerant consensus model is married with trusted hardware to guarantee the integrity, confidentiality and availability.
• Developability: Rich SDK support, and a general-purpose, developer-friendly interpreter environment for flexible industrial use.

With Taxa, developers are able to unlock whole new use cases in analytics, fintech, advertising, AI, identity, healthcare, legal, and more!

Token Economy

Taxa Tokens (TXT) shall be launched in 2021. As the value unit of Taxa Network, TXT are ERC-20 tokens at the time of launch (as we initially support Ethereum based smart contracts). It can be used in the following scenarios:

• The miner fee of using tService / mining incentives for node operators;
• Value transfer medium of tServices, serving as a universal token on dApps on different blockchains.

How and Where to Buy TXT token?

TXT has been listed on a number of crypto exchanges, unlike other main cryptocurrencies, it cannot be directly purchased with fiats money. However, You can still easily buy this coin by first buying Bitcoin, ETH, USDT, BNB from any large exchanges and then transfer to the exchange that offers to trade this coin, in this guide article we will walk you through in detail the steps to buy TXT token.

You will have to first buy one of the major cryptocurrencies, usually either Bitcoin (BTC), Ethereum (ETH), Tether (USDT), Binance (BNB)…

We will use Binance Exchange here as it is one of the largest crypto exchanges that accept fiat deposits.

Binance is a popular cryptocurrency exchange which was started in China but then moved their headquarters to the crypto-friendly Island of Malta in the EU. Binance is popular for its crypto to crypto exchange services. Binance exploded onto the scene in the mania of 2017 and has since gone on to become the top crypto exchange in the world.

Once you finished the KYC process. You will be asked to add a payment method. Here you can either choose to provide a credit/debit card or use a bank transfer, and buy one of the major cryptocurrencies, usually either Bitcoin (BTC), Ethereum (ETH), Tether (USDT), Binance (BNB)…

☞ SIGN UP ON BINANCE

Step by Step Guide : What is Binance | How to Create an account on Binance (Updated 2021)

Next step - Transfer your cryptos to an Altcoin Exchange

Since TXT is an altcoin we need to transfer our coins to an exchange that TXT can be traded. Below is a list of exchanges that offers to trade TXT in various market pairs, head to their websites and register for an account.

Once finished you will then need to make a BTC/ETH/USDT/BNB deposit to the exchange from Binance depending on the available market pairs. After the deposit is confirmed you may then purchase TXT from the exchange.

The top exchange for trading in TXT token is currently:  Gate.io ☞ SIGN UP Gate.io

ETH Contract: 0x547b2f82cecfab9c2b1d36fdda96ef9f58c63b8c

Find more information TXT token

☞ Website ☞ Explorer ☞ Social Channel ☞ Social Channel 2 ☞ Social Channel 3 ☞ Coinmarketcap

Top exchanges for token-coin trading. Follow instructions and make unlimited money

☞ Binance ☞ Bittrex ☞ Poloniex ☞ Bitfinex ☞ Huobi ☞ MXC ☞ ProBIT ☞ Gate.io ☞ Coinbase

🔺DISCLAIMER: The Information in the post isn’t financial advice, is intended FOR GENERAL INFORMATION PURPOSES ONLY. Trading Cryptocurrency is VERY risky. Make sure you understand these risks and that you are responsible for what you do with your money.

🔥 If you’re a beginner. I believe the article below will be useful to you ☞ What You Should Know Before Investing in Cryptocurrency - For Beginner

⭐ ⭐ ⭐The project is of interest to the community. Join to Get free ‘GEEK coin’ (GEEKCASH coin)!

☞ **-----https://geekcash.org-----**⭐ ⭐ ⭐

I hope this post will help you. Don't forget to leave a like, comment and sharing it with others. Thank you!

#bitcoin #cryptocurrency

What is GEEK

Buddha Community

Words Counted: A Ruby Natural Language Processor.

WordsCounted

We are all in the gutter, but some of us are looking at the stars.

-- Oscar Wilde

WordsCounted is a Ruby NLP (natural language processor). WordsCounted lets you implement powerful tokensation strategies with a very flexible tokeniser class.

Are you using WordsCounted to do something interesting? Please tell me about it.

 

Demo

Visit this website for one example of what you can do with WordsCounted.

Features

• Out of the box, get the following data from any string or readable file, or URL:
  • Token count and unique token count
  • Token densities, frequencies, and lengths
  • Char count and average chars per token
  • The longest tokens and their lengths
  • The most frequent tokens and their frequencies.
• A flexible way to exclude tokens from the tokeniser. You can pass a string, regexp, symbol, lambda, or an array of any combination of those types for powerful tokenisation strategies.
• Pass your own regexp rules to the tokeniser if you prefer. The default regexp filters special characters but keeps hyphens and apostrophes. It also plays nicely with diacritics (UTF and unicode characters): Bayrūt is treated as ["Bayrūt"] and not ["Bayr", "ū", "t"], for example.
• Opens and reads files. Pass in a file path or a url instead of a string.

Installation

Add this line to your application's Gemfile:

gem 'words_counted'

And then execute:

$ bundle

Or install it yourself as:

$ gem install words_counted

Usage

Pass in a string or a file path, and an optional filter and/or regexp.

counter = WordsCounted.count(
  "We are all in the gutter, but some of us are looking at the stars."
)

# Using a file
counter = WordsCounted.from_file("path/or/url/to/my/file.txt")

.count and .from_file are convenience methods that take an input, tokenise it, and return an instance of WordsCounted::Counter initialized with the tokens. The WordsCounted::Tokeniser and WordsCounted::Counter classes can be used alone, however.

API

WordsCounted

WordsCounted.count(input, options = {})

Tokenises input and initializes a WordsCounted::Counter object with the resulting tokens.

counter = WordsCounted.count("Hello Beirut!")

Accepts two options: exclude and regexp. See Excluding tokens from the analyser and Passing in a custom regexp respectively.

WordsCounted.from_file(path, options = {})

Reads and tokenises a file, and initializes a WordsCounted::Counter object with the resulting tokens.

counter = WordsCounted.from_file("hello_beirut.txt")

Accepts the same options as .count.

Tokeniser

The tokeniser allows you to tokenise text in a variety of ways. You can pass in your own rules for tokenisation, and apply a powerful filter with any combination of rules as long as they can boil down into a lambda.

Out of the box the tokeniser includes only alpha chars. Hyphenated tokens and tokens with apostrophes are considered a single token.

#tokenise([pattern: TOKEN_REGEXP, exclude: nil])

tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise

# With `exclude`
tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise(exclude: "hello")

# With `pattern`
tokeniser = WordsCounted::Tokeniser.new("I <3 Beirut!").tokenise(pattern: /[a-z]/i)

See Excluding tokens from the analyser and Passing in a custom regexp for more information.

Counter

The WordsCounted::Counter class allows you to collect various statistics from an array of tokens.

#token_count

Returns the token count of a given string.

counter.token_count #=> 15

#token_frequency

Returns a sorted (unstable) two-dimensional array where each element is a token and its frequency. The array is sorted by frequency in descending order.

counter.token_frequency

[
  ["the", 2],
  ["are", 2],
  ["we",  1],
  # ...
  ["all", 1]
]

#most_frequent_tokens

Returns a hash where each key-value pair is a token and its frequency.

counter.most_frequent_tokens

{ "are" => 2, "the" => 2 }

#token_lengths

Returns a sorted (unstable) two-dimentional array where each element contains a token and its length. The array is sorted by length in descending order.

counter.token_lengths

[
  ["looking", 7],
  ["gutter",  6],
  ["stars",   5],
  # ...
  ["in",      2]
]

#longest_tokens

Returns a hash where each key-value pair is a token and its length.

counter.longest_tokens

{ "looking" => 7 }

#token_density([ precision: 2 ])

Returns a sorted (unstable) two-dimentional array where each element contains a token and its density as a float, rounded to a precision of two. The array is sorted by density in descending order. It accepts a precision argument, which must be a float.

counter.token_density

[
  ["are",     0.13],
  ["the",     0.13],
  ["but",     0.07 ],
  # ...
  ["we",      0.07 ]
]

#char_count

Returns the char count of tokens.

counter.char_count #=> 76

#average_chars_per_token([ precision: 2 ])

Returns the average char count per token rounded to two decimal places. Accepts a precision argument which defaults to two. Precision must be a float.

counter.average_chars_per_token #=> 4

#uniq_token_count

Returns the number of unique tokens.

counter.uniq_token_count #=> 13

Excluding tokens from the tokeniser

You can exclude anything you want from the input by passing the exclude option. The exclude option accepts a variety of filters and is extremely flexible.

1. A space-delimited string. The filter will normalise the string.
2. A regular expression.
3. A lambda.
4. A symbol that names a predicate method. For example :odd?.
5. An array of any combination of the above.

tokeniser =
  WordsCounted::Tokeniser.new(
    "Magnificent! That was magnificent, Trevor."
  )

# Using a string
tokeniser.tokenise(exclude: "was magnificent")
# => ["that", "trevor"]

# Using a regular expression
tokeniser.tokenise(exclude: /trevor/)
# => ["magnificent", "that", "was", "magnificent"]

# Using a lambda
tokeniser.tokenise(exclude: ->(t) { t.length < 4 })
# => ["magnificent", "that", "magnificent", "trevor"]

# Using symbol
tokeniser = WordsCounted::Tokeniser.new("Hello! محمد")
tokeniser.tokenise(exclude: :ascii_only?)
# => ["محمد"]

# Using an array
tokeniser = WordsCounted::Tokeniser.new(
  "Hello! اسماءنا هي محمد، كارولينا، سامي، وداني"
)
tokeniser.tokenise(
  exclude: [:ascii_only?, /محمد/, ->(t) { t.length > 6}, "و"]
)
# => ["هي", "سامي", "وداني"]

Passing in a custom regexp

The default regexp accounts for letters, hyphenated tokens, and apostrophes. This means twenty-one is treated as one token. So is Mohamad's.

/[\p{Alpha}\-']+/

You can pass your own criteria as a Ruby regular expression to split your string as desired.

For example, if you wanted to include numbers, you can override the regular expression:

counter = WordsCounted.count("Numbers 1, 2, and 3", pattern: /[\p{Alnum}\-']+/)
counter.tokens
#=> ["numbers", "1", "2", "and", "3"]

Opening and reading files

Use the from_file method to open files. from_file accepts the same options as .count. The file path can be a URL.

counter = WordsCounted.from_file("url/or/path/to/file.text")

Gotchas

A hyphen used in leu of an em or en dash will form part of the token. This affects the tokeniser algorithm.

counter = WordsCounted.count("How do you do?-you are well, I see.")
counter.token_frequency

[
  ["do",   2],
  ["how",  1],
  ["you",  1],
  ["-you", 1], # WTF, mate!
  ["are",  1],
  # ...
]

In this example -you and you are separate tokens. Also, the tokeniser does not include numbers by default. Remember that you can pass your own regular expression if the default behaviour does not fit your needs.

A note on case sensitivity

The program will normalise (downcase) all incoming strings for consistency and filters.

Roadmap

Ability to open URLs

def self.from_url
  # open url and send string here after removing html
end

Contributors

See contributors.

Contributing

1. Fork it
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create new Pull Request

---

Author: abitdodgy
Source code: https://github.com/abitdodgy/words_counted
License: MIT license

#ruby  #ruby-on-rails 

WordsCounted: A Ruby Natural Language Processor

WordsCounted

We are all in the gutter, but some of us are looking at the stars.

-- Oscar Wilde

WordsCounted is a Ruby NLP (natural language processor). WordsCounted lets you implement powerful tokensation strategies with a very flexible tokeniser class.

Features

• Out of the box, get the following data from any string or readable file, or URL:
  • Token count and unique token count
  • Token densities, frequencies, and lengths
  • Char count and average chars per token
  • The longest tokens and their lengths
  • The most frequent tokens and their frequencies.
• A flexible way to exclude tokens from the tokeniser. You can pass a string, regexp, symbol, lambda, or an array of any combination of those types for powerful tokenisation strategies.
• Pass your own regexp rules to the tokeniser if you prefer. The default regexp filters special characters but keeps hyphens and apostrophes. It also plays nicely with diacritics (UTF and unicode characters): Bayrūt is treated as ["Bayrūt"] and not ["Bayr", "ū", "t"], for example.
• Opens and reads files. Pass in a file path or a url instead of a string.

Installation

Add this line to your application's Gemfile:

gem 'words_counted'

And then execute:

$ bundle

Or install it yourself as:

$ gem install words_counted

Usage

Pass in a string or a file path, and an optional filter and/or regexp.

counter = WordsCounted.count(
  "We are all in the gutter, but some of us are looking at the stars."
)

# Using a file
counter = WordsCounted.from_file("path/or/url/to/my/file.txt")

.count and .from_file are convenience methods that take an input, tokenise it, and return an instance of WordsCounted::Counter initialized with the tokens. The WordsCounted::Tokeniser and WordsCounted::Counter classes can be used alone, however.

API

WordsCounted

WordsCounted.count(input, options = {})

Tokenises input and initializes a WordsCounted::Counter object with the resulting tokens.

counter = WordsCounted.count("Hello Beirut!")

Accepts two options: exclude and regexp. See Excluding tokens from the analyser and Passing in a custom regexp respectively.

WordsCounted.from_file(path, options = {})

Reads and tokenises a file, and initializes a WordsCounted::Counter object with the resulting tokens.

counter = WordsCounted.from_file("hello_beirut.txt")

Accepts the same options as .count.

Tokeniser

The tokeniser allows you to tokenise text in a variety of ways. You can pass in your own rules for tokenisation, and apply a powerful filter with any combination of rules as long as they can boil down into a lambda.

Out of the box the tokeniser includes only alpha chars. Hyphenated tokens and tokens with apostrophes are considered a single token.

#tokenise([pattern: TOKEN_REGEXP, exclude: nil])

tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise

# With `exclude`
tokeniser = WordsCounted::Tokeniser.new("Hello Beirut!").tokenise(exclude: "hello")

# With `pattern`
tokeniser = WordsCounted::Tokeniser.new("I <3 Beirut!").tokenise(pattern: /[a-z]/i)

See Excluding tokens from the analyser and Passing in a custom regexp for more information.

Counter

The WordsCounted::Counter class allows you to collect various statistics from an array of tokens.

#token_count

Returns the token count of a given string.

counter.token_count #=> 15

#token_frequency

Returns a sorted (unstable) two-dimensional array where each element is a token and its frequency. The array is sorted by frequency in descending order.

counter.token_frequency

[
  ["the", 2],
  ["are", 2],
  ["we",  1],
  # ...
  ["all", 1]
]

#most_frequent_tokens

Returns a hash where each key-value pair is a token and its frequency.

counter.most_frequent_tokens

{ "are" => 2, "the" => 2 }

#token_lengths

Returns a sorted (unstable) two-dimentional array where each element contains a token and its length. The array is sorted by length in descending order.

counter.token_lengths

[
  ["looking", 7],
  ["gutter",  6],
  ["stars",   5],
  # ...
  ["in",      2]
]

#longest_tokens

Returns a hash where each key-value pair is a token and its length.

counter.longest_tokens

{ "looking" => 7 }

#token_density([ precision: 2 ])

Returns a sorted (unstable) two-dimentional array where each element contains a token and its density as a float, rounded to a precision of two. The array is sorted by density in descending order. It accepts a precision argument, which must be a float.

counter.token_density

[
  ["are",     0.13],
  ["the",     0.13],
  ["but",     0.07 ],
  # ...
  ["we",      0.07 ]
]

#char_count

Returns the char count of tokens.

counter.char_count #=> 76

#average_chars_per_token([ precision: 2 ])

Returns the average char count per token rounded to two decimal places. Accepts a precision argument which defaults to two. Precision must be a float.

counter.average_chars_per_token #=> 4

#uniq_token_count

Returns the number of unique tokens.

counter.uniq_token_count #=> 13

Excluding tokens from the tokeniser

You can exclude anything you want from the input by passing the exclude option. The exclude option accepts a variety of filters and is extremely flexible.

1. A space-delimited string. The filter will normalise the string.
2. A regular expression.
3. A lambda.
4. A symbol that names a predicate method. For example :odd?.
5. An array of any combination of the above.

tokeniser =
  WordsCounted::Tokeniser.new(
    "Magnificent! That was magnificent, Trevor."
  )

# Using a string
tokeniser.tokenise(exclude: "was magnificent")
# => ["that", "trevor"]

# Using a regular expression
tokeniser.tokenise(exclude: /trevor/)
# => ["magnificent", "that", "was", "magnificent"]

# Using a lambda
tokeniser.tokenise(exclude: ->(t) { t.length < 4 })
# => ["magnificent", "that", "magnificent", "trevor"]

# Using symbol
tokeniser = WordsCounted::Tokeniser.new("Hello! محمد")
tokeniser.tokenise(exclude: :ascii_only?)
# => ["محمد"]

# Using an array
tokeniser = WordsCounted::Tokeniser.new(
  "Hello! اسماءنا هي محمد، كارولينا، سامي، وداني"
)
tokeniser.tokenise(
  exclude: [:ascii_only?, /محمد/, ->(t) { t.length > 6}, "و"]
)
# => ["هي", "سامي", "وداني"]

Passing in a custom regexp

The default regexp accounts for letters, hyphenated tokens, and apostrophes. This means twenty-one is treated as one token. So is Mohamad's.

/[\p{Alpha}\-']+/

You can pass your own criteria as a Ruby regular expression to split your string as desired.

For example, if you wanted to include numbers, you can override the regular expression:

counter = WordsCounted.count("Numbers 1, 2, and 3", pattern: /[\p{Alnum}\-']+/)
counter.tokens
#=> ["numbers", "1", "2", "and", "3"]

Opening and reading files

Use the from_file method to open files. from_file accepts the same options as .count. The file path can be a URL.

counter = WordsCounted.from_file("url/or/path/to/file.text")

Gotchas

A hyphen used in leu of an em or en dash will form part of the token. This affects the tokeniser algorithm.

counter = WordsCounted.count("How do you do?-you are well, I see.")
counter.token_frequency

[
  ["do",   2],
  ["how",  1],
  ["you",  1],
  ["-you", 1], # WTF, mate!
  ["are",  1],
  # ...
]

In this example -you and you are separate tokens. Also, the tokeniser does not include numbers by default. Remember that you can pass your own regular expression if the default behaviour does not fit your needs.

A note on case sensitivity

The program will normalise (downcase) all incoming strings for consistency and filters.

Roadmap

Ability to open URLs

def self.from_url
  # open url and send string here after removing html
end

---

Are you using WordsCounted to do something interesting? Please tell me about it.

 

RubyDoc documentation.

Demo

Visit this website for one example of what you can do with WordsCounted.

---

Contributors

See contributors.

Contributing

1. Fork it
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create new Pull Request

Author: Abitdodgy
Source Code: https://github.com/abitdodgy/words_counted 
License: MIT license

#ruby #nlp 

PAID NETWORK Review, Is it worth Investing in? Token Sale Coming Soon !!

Hey guys, in this video I review PAID NETWORK. This is a DeFi project that aims to solve complex legal process using decentralised protocols and DeFi products for 2021.

PAID Network is an ecosystem DAPP that leverages blockchain technology to deliver DeFi powered SMART Agreements to make business exponentially more efficient. We allow users to create their own policy, to ensure they Get PAID.

📺 The video in this post was made by Crypto expat
The origin of the article: https://www.youtube.com/watch?v=ZIU5javfL90
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
Cryptocurrency trading is VERY risky. Make sure you understand these risks and that you are responsible for what you do with your money
🔥 If you’re a beginner. I believe the article below will be useful to you ☞ What You Should Know Before Investing in Cryptocurrency - For Beginner
⭐ ⭐ ⭐The project is of interest to the community. Join to Get free ‘GEEK coin’ (GEEKCASH coin)!
☞ **-----CLICK HERE-----**⭐ ⭐ ⭐
Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#bitcoin #blockchain #paid network #paid network review #token sale #paid network review, is it worth investing in? token sale coming soon !!

SafeMoon Clone | Create A DeFi Token Like SafeMoon | DeFi token like SafeMoon

SafeMoon is a decentralized finance (DeFi) token. This token consists of RFI tokenomics and auto-liquidity generating protocol. A DeFi token like SafeMoon has reached the mainstream standards under the Binance Smart Chain. Its success and popularity have been immense, thus, making the majority of the business firms adopt this style of cryptocurrency as an alternative.

A DeFi token like SafeMoon is almost similar to the other crypto-token, but the only difference being that it charges a 10% transaction fee from the users who sell their tokens, in which 5% of the fee is distributed to the remaining SafeMoon owners. This feature rewards the owners for holding onto their tokens.

Read More @ https://bit.ly/3oFbJoJ

#create a defi token like safemoon #defi token like safemoon #safemoon token #safemoon token clone #defi token

Autonomous Driving Network (ADN) On Its Way

Talking about inspiration in the networking industry, nothing more than Autonomous Driving Network (ADN). You may hear about this and wondering what this is about, and does it have anything to do with autonomous driving vehicles? Your guess is right; the ADN concept is derived from or inspired by the rapid development of the autonomous driving car in recent years.

Driverless Car of the Future, the advertisement for “America’s Electric Light and Power Companies,” Saturday Evening Post, the 1950s.

The vision of autonomous driving has been around for more than 70 years. But engineers continuously make attempts to achieve the idea without too much success. The concept stayed as a fiction for a long time. In 2004, the US Defense Advanced Research Projects Administration (DARPA) organized the Grand Challenge for autonomous vehicles for teams to compete for the grand prize of $1 million. I remembered watching TV and saw those competing vehicles, behaved like driven by drunk man, had a really tough time to drive by itself. I thought that autonomous driving vision would still have a long way to go. To my surprise, the next year, 2005, Stanford University’s vehicles autonomously drove 131 miles in California’s Mojave desert without a scratch and took the $1 million Grand Challenge prize. How was that possible? Later I learned that the secret ingredient to make this possible was using the latest ML (Machine Learning) enabled AI (Artificial Intelligent ) technology.

Since then, AI technologies advanced rapidly and been implemented in all verticals. Around the 2016 time frame, the concept of Autonomous Driving Network started to emerge by combining AI and network to achieve network operational autonomy. The automation concept is nothing new in the networking industry; network operations are continually being automated here and there. But this time, ADN is beyond automating mundane tasks; it reaches a whole new level. With the help of AI technologies and other critical ingredients advancement like SDN (Software Defined Network), autonomous networking has a great chance from a vision to future reality.

In this article, we will examine some critical components of the ADN, current landscape, and factors that are important for ADN to be a success.

The Vision

At the current stage, there are different terminologies to describe ADN vision by various organizations.

Even though slightly different terminologies, the industry is moving towards some common terms and consensus called autonomous networks, e.g. TMF, ETSI, ITU-T, GSMA. The core vision includes business and network aspects. The autonomous network delivers the “hyper-loop” from business requirements all the way to network and device layers.

On the network layer, it contains the below critical aspects:

• Intent-Driven: Understand the operator’s business intent and automatically translate it into necessary network operations. The operation can be a one-time operation like disconnect a connection service or continuous operations like maintaining a specified SLA (Service Level Agreement) at the all-time.
• **Self-Discover: **Automatically discover hardware/software changes in the network and populate the changes to the necessary subsystems to maintain always-sync state.
• **Self-Config/Self-Organize: **Whenever network changes happen, automatically configure corresponding hardware/software parameters such that the network is at the pre-defined target states.
• **Self-Monitor: **Constantly monitor networks/services operation states and health conditions automatically.
• Auto-Detect: Detect network faults, abnormalities, and intrusions automatically.
• **Self-Diagnose: **Automatically conduct an inference process to figure out the root causes of issues.
• **Self-Healing: **Automatically take necessary actions to address issues and bring the networks/services back to the desired state.
• **Self-Report: **Automatically communicate with its environment and exchange necessary information.
• Automated common operational scenarios: Automatically perform operations like network planning, customer and service onboarding, network change management.

On top of those, these capabilities need to be across multiple services, multiple domains, and the entire lifecycle(TMF, 2019).

No doubt, this is the most ambitious goal that the networking industry has ever aimed at. It has been described as the “end-state” and“ultimate goal” of networking evolution. This is not just a vision on PPT, the networking industry already on the move toward the goal.

David Wang, Huawei’s Executive Director of the Board and President of Products & Solutions, said in his 2018 Ultra-Broadband Forum(UBBF) keynote speech. (David W. 2018):

“In a fully connected and intelligent era, autonomous driving is becoming a reality. Industries like automotive, aerospace, and manufacturing are modernizing and renewing themselves by introducing autonomous technologies. However, the telecom sector is facing a major structural problem: Networks are growing year by year, but OPEX is growing faster than revenue. What’s more, it takes 100 times more effort for telecom operators to maintain their networks than OTT players. Therefore, it’s imperative that telecom operators build autonomous driving networks.”

Juniper CEO Rami Rahim said in his keynote at the company’s virtual AI event: (CRN, 2020)

“The goal now is a self-driving network. The call to action is to embrace the change. We can all benefit from putting more time into higher-layer activities, like keeping distributors out of the business. The future, I truly believe, is about getting the network out of the way. It is time for the infrastructure to take a back seat to the self-driving network.”

Is This Vision Achievable?

If you asked me this question 15 years ago, my answer would be “no chance” as I could not imagine an autonomous driving vehicle was possible then. But now, the vision is not far-fetch anymore not only because of ML/AI technology rapid advancement but other key building blocks are made significant progress, just name a few key building blocks:

• software-defined networking (SDN) control
• industry-standard models and open APIs
• Real-time analytics/telemetry
• big data processing
• cross-domain orchestration
• programmable infrastructure
• cloud-native virtualized network functions (VNF)
• DevOps agile development process
• everything-as-service design paradigm
• intelligent process automation
• edge computing
• cloud infrastructure
• programing paradigm suitable for building an autonomous system . i.e., teleo-reactive programs, which is a set of reactive rules that continuously sense the environment and trigger actions whose continuous execution eventually leads the system to satisfy a goal. (Nils Nilsson, 1996)
• open-source solutions

#network-automation #autonomous-network #ai-in-network #self-driving-network #neural-networks