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Infrastructure, Cloud Computing

Amino

Amino - decentralized infrastructure for cloud computing

Rating: 3.8

Official contacts
Location: Australia & New Zealand
Web-resources:   Whitepaper Website
Social networks:
Product source tracking: https://github.com/Aminonet
Details Description Risks Full analysis Team

Detailed review

Stage of product development
MVP and developed OS deployed in the commercial environment in 2016-2017. Oct 2018 -OS Alpha on slave nodes on the distributed network with Proof of Contribution model (single token system). Q1 2019 - master and slave nodes and double token system. Q1 2020 - full three-layer Testnet. Q2 2020 - Network Beta (we guess, the team will introduce mainnet here).
Roadmap
October, 2018
OS Alpha on slave nodes on the distributed network with Proof of Contribution model (single token system)
March, 2019
Master and slave nodes and double token system
March, 2020
Test with three-layer architecture
June, 2020
Network Beta (we guess, the team will introduce mainnet here).
Token description
ICO date
TBA
Token price
N/A
Platform
N/A
Blockchain
Amino
Token distribution date
N/A
Consensus method
Proof of Contribution
Escrow
N/A
ICO currencies
USD , ETH
Bounty camping
N/A
Markets
N/A
Token functions
GAS - reward for fair work. AMO - voting and locking for setting a node
Risks
MARKET

• Strong competition from other cloud computing blockchain projects

• Not significant edge on competitors

• Amino will release the product after competitors

PRODUCT

• Testnet is about to be launched only in Q1 2020

• No prototype available yet

BUSINESS MODEL

• Three-layer node architecture may turn too complicated and ineffective

FINANCES

• Financial information is not available

TEAM

• The team appears to be too academic and suffers from the lack of experienced business leaders

Full analysis
Market

Cloud platforms are enabling complex business models and orchestration of larger globally integrated networks surpassing all prior predictions by analysts. Leading market research organizations are revising their estimates for cloud usage/growth as they see more utility for new applications, along with higher than expected adoption by mid-tier and small and medium enterprises (SME). Cloud computing expenditure is growing at 4.5 times the rate of IT spending since 2009 and is expected to grow at more than 6 times the rate of IT spending from 2015 through 2020. Gartner's prediction for Cloud Computing Market expects an increase from USD 67 billion in 2015 to USD 162 billion in 2020 attaining a CAGR of 19%. The Amino team claims, that some modules have been tested with commercial cloud platforms. By the end of 2017, 3000 Amino nodes have been deployed on the high-performance computing terminals in over 20 locations on the Asia Pacific region. If this statement is true, Amino is obviously to have a significant edge over competitors. But if we look at the technology, Amino is not far different from competitors like Ankr, Cartesi, Solana, DeepCloud AI, HyperNet, Uranus, Covalent, Hadron, Perlin, Oasis Labs, Dfinity. Moreover, it does not demonstrate anything new to the market, so it is hard to say now, how Amino is going to turn a leader. The only thing is different is tree-tier node system that is about to resolve distribution and scheduling problem.

Product

Amino is a next-generation decentralised infrastructure for sharing computing resources, based on blockchain technology. Amino offers high performance and cost-effective distributed computing services, on demand and at a commercial grade. This is enabled by encouraging all kind of computer equipment owners to contribute their idle computing resources to consumers on the Amino network. The team claims, that some modules have been tested with commercial cloud platforms. By the end of 2017, 3000 Amino nodes have been deployed on the high-performance computing terminals in over 20 locations on the Asia Pacific region and that they have an experience in providing high-performance computing power for over 2 years. The node architecture will apply three-tier model - super nodes (manage tasks, automatically analyse statistics from other nodes, analyse current resources and find an optimum to way in scheduling task for master nodes), master nodes (provide reliable computing resources, manage tasks for slave nodes and gather statistics of other nodes performance) and slave nodes (provide idle computing resources) that are different in requirements and expected revenue. Amino introduces 3 whitepapers: General, OS and Network covering relative issues. The roadmap is clear and is following. Oct 2018 -OS Alpha on slave nodes on the distributed network with Proof of Contribution model (single token system). Q1 2019 - master and slave nodes and double token system. Q1 2020 - full three-layer Testnet. Q2 2020 - Network Beta (we guess, the team will introduce mainnet here). It must be noticed, that testnet is about to be launched only in Q1 2020, that too long time especially in comparison to competitors.

Business model

Amino will apply two-token model. GAS aims to measure the computational effort it will take to execute certain operations - determines available computing resources and the use of those resources to complete computing transactions. AMO token provides decentralised governance of Amino - it gives a voting right (e.g. in determining master and super nodes. Slave nodes set customized Amino OS,  lend their idle resources and receive GAS. Master and Super nodes receive GAS for their work as well, but also have to lock AMO tokens to ensure reliable work. Super nodes are chosen via voting among AMO holder that have unlocked AMO.

Finances

No financial information is available, and Amino is still on Seed stage.

Team

The team includes 15 members - 12 core team member and 3 advisors. The team is generally from the University of Aukland and mostly have rather business experience than a business one.CEO Mike O'Sullivan achieved PhD from Stanford University in 2001 (Management Science and Engineering). He has strong both academic experience (16 years as a Lecturer in the University of Auckland) and business-research experience (2 full-time years and 16 part-time advisory and research). Chief Scientist is PhD in Mathematics, The University of Auckland and was promoted to Associate Professor in 2015. His area of research is computational analytics. Software engineer has PhD in Engineering Science for the University of Auckland and is both a tutor and software developer in the university. Also, he have had some research for 3 years in Light Metals Research Centre. The team has two team managers in New Zealand and Australia; the first one has 17 years of experience in software development, and the second one has 25 years of experience in software development and management in electronic payments, security and e-commerce platforms. The team has some good marketing, analytics, finance specialists.

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