AnkrDistributed Computing with Idle Resources

Ankr strives to leverage idle

processing power in data centers and

connected devices for a distributed,

secure and intuitive cloud.

“ “

I. Cloud evolution and the problem today

II. Distributed cloud enablers - why now

III. Ankr technology and milestones

IV. “Sky is the limit” - rapid go-to-market strategy

V. Team behind the distributed Cloud

I. Cloud evolution and the problem today

On-going trends:

• IoT and fog computing (geographically distributed data sources)

Pharma/bio/chemistry Industrial research

Engineering & manufacturing simulation

Film/AR/VR rendering

• Growth of serverless computing (focus on computational task, not computational machine)

Serverless architecture

Distributed data (Bringing compute and data closer to the users)

Rising market needs for large processing power:

History of computing

Mainframes

On-premises data center

Private cloud (e.g., Rackspace)/colocation data centers

Public cloud(e.g., AWS, Azure, Alibaba)

Whats next?

Cloud evolution and customer trends

Low utilization rate of data centersAccording to research from McKinsey and Stanford,

• Roughly 30% of servers (including standard server, hypervisors and VMs) in centers around the world have showed no signs of network, user, connection, memory or CPU activity in six months or more

• Roughly 35% of servers have showed occasional signs of activity (compute and network) <5% of the time

• In total, near 70% “non-performing” server asset!

Problems: Data centers aren’t performing Opportunities: Win-Win

Data centers aren’t able to compete

with the public cloud

• Users trust large CSPs (AWS, Azure, Digital Ocean, AlibabaCloud) due to their wide brand

awareness & recognition, while they trust small data centers without brand names much less

• AWS & other large CSPs exploit economies of scale and network effects

• AWS & other public cloud provide similar, universal interfaces that engineers find easy to use

• Pay-as-you-go model (eg. Lambda) charges higher unit-time price, but lower total cost

Idle data centers are long-term headaches

• Companies can’t entirely sell their data centers, using them internally for specific needs

• Data centers require large CapEx for companies, & underperforming servers are

deadweight with annual depreciation lost, adding pressure on companies’ bottom line

• Public cloud provider only offers to buy up the entire premise for low price

• Rental agreements for partial access to data centers are big-ticket slow sales, and

integration for renter causes tremendous overhead concerns

Elastically utilize local data centers for cloud computation is a win-win

• Prevailing low utilization rate of data centers means large computation potential

• Data center owners can keep certain application/data on-premise

• Elastic computation resource utilization will outperform traditional data center broker model, solving resource planning and integration problems

• Ankr’s containerized solution can be deployed to data centers in just hours

• Additional flexible revenue stream for data center owners

• Local distributed cloud can deliver favorable performance given the growth of IoT and local data sources

For consumers:Ankr uses local data centers to offer fast & cheap computation

For small data centers:Ankr offers new revenue stream for under-utilized data centers

Problems and opportunities

II. Distributed Cloud – Why Now?

Enabler: containerization & cluster orchestration

Docker was released and quickly became

the most popular container

Originated from Google Borg, K8’s container

orchestration has been widely adopted

Apache Mesos takes isolation to the next

step, supporting both VMs and containers

Kubernetes-as-a-Service has seen rapid growth on mainstream

cloud providers

Linux Container (LXC) was introduced for

running multiple Linux systems

VMs are known for better

security control.

2009 2013 2014 2016 2018

Large enterprises are abandoning legacy code in their monolithic applications as cost to manage and upgrade piles up

Microservices leveraging IaaS and PaaS can be deployed in different languages and scaled independently

Faas significantly reduces cost in auto-scaling compared to microservices

Virtual Machines vs. Containers Trend: Monolithic application →Microservices → FaaS

Virtual Machines

Containers Containers are lightweight, virtualizing only the

Operating System and take only seconds to start

Security Speed Management

VMs incur slower and more complex DevOps resource

management

VMs take MUCH more system resources (CPU and RAM), &

take minutes to start

However, containers enable MUCH less code migration and robust security updates

The underlying orchestration technology enable easier and

faster DevOps resource management

Enabler: TEE (trusted execution environments)

Intel SGX was built into 6th generation

processors

NVIDIA introduced open source stack for

TEE

AMD SEV was started for EYPC processor line

Intel SGX 2 will be released

ARM TrustZone’s hardware-based

isolation launched

2014 2015 2016 2016 2018

Not achieved yet after decades of research work & very slow, even when it’ll be achieved Will approach untrusted computing performance

Fully homomorphic encryption (FHE) TEEs are engineering solution equivalent to FHE

Encrypted data in, encrypted data out

Protects confidentiality and integrity of data during computation

Enclave application guarantees confidentiality and integrity

Encrypted data in, decrypted data out

III. Ankr technology and milestones

Ankr distributed cloud for idle data centers

Orchestration and containerization make Ankr distributed cloud computing network easy to

integrate with resources from diverse, independent data centers into one homogeneous cloud

Enabling technologies

Docker is the de-facto standard technology for containerization of applications into micro-

services to be deployed on Kubernetes clusters

Kubernetes is a general-purpose vendor-agnostic orchestration service that makes it easy to deploy

containerized applications in any data center

Cost reduction enabled by distributed cloud

Cost reduction for Distributed Cloud

Single Server*

42 Servers

*Dell R320 as sample

$55 Server

$29 Power

$80 Space

$1

64

/mo

nth

$2

00

/mo

nth

$8

,40

0/m

on

th

$55*42 Servers

$29*42 Power

$80 Space + $27 Rack

$3

,63

5/m

on

th

($55*42)+$80 = ($2390) lost/month

when servers arenot utilized

Assumptions(per month):

• Nominal cost = $55• Power usage costs = $29

520.8kWh energy consumptionPower supplies pull 30% and are 80% efficient1.7 PUE average265.2 kWh actual power consumption$0.11 average commercial price per kWh in US

• Total location cost = $80$10 per square footServer size of 8 square feet

Distributed Cloud Cash Flow Advantages:

Lose money (up to $2,390) each month on

idle servers

AWS, using same set of servers, create a $8,400

revenue potential

Flexible structure enables flexible pricing

between $30-$200

• Challenges can be issued such that responding to to them correctly is guaranteed to necessitate actual work

• Solution to the delegated task can be quickly and verifiably reconstructed from the workers’ response

• Able to utilize otherwise wasteful work

Ankr distributed cloud for idle connected devices

PouW vs. PoW

• Work and energy expended to prove that work had in fact been done

• Mining requires highly specialized computer software to run complicated algorithms

• Computations guarantee the security of the network but cannot be applied to any other fields

• Providers and consumers run a daemon to interact with the network• Consumer uploads computational task (e.g., SGX enclave application)

to the Ankr network• Providers compete to be assigned the computational task• Select provider executes the task using its computational resources• Consumer verifies the results (e.g., SGX remote attestation) and pays

the provider

Peer-to-peer network of computational resource providers and consumers

Technical milestones & next steps

Milestones

Next Steps

Q4 2017 - Q1 2018

Research and theoretical framework validation

May 2018

Distributed cloud for connected devices with TEE proof of concept

July 2018

Distributed cloud for connected devices with TEE prototype

Q1 2019

Distributed cloud for idle data center resources proof of concept

September 2018

Distributed cloud for connected devices with TEE test network

October 2018

Distributed cloud for idle data center resources system design

Seek strategic corporate partner with data centers and pilot customers to achieve data center sharing end-to-end proof-of-concept and customer feedback

• Deploy applications using CLI to one of available data centers

• Ankr Hub manages the contributing data centers

Launch corporate-facing Initial service

• Elastic computation resource service (simulation, rendering and modeling)

• Accounting and payments for server providers and customers

Launch additional services

• Storage

• Data analytics / machine learning

• Content Delivery Network

IV. “Sky is the limit” - rapid go-to-market strategy

Analytics 4 Life’s product is an AI-powered medical imaging and

mathematical modelling platform.

• Tremendous spending on cloud

• Not enough cloud DevOps

• Hard-to-use existing platform: “I wish there’s a cloud button.”

HTC Vive produces VR Headsets. Dr. Liu is a data scientist and a Kaggle master.

• Existing cloud infrastructure hard to configure and expensive

• Often ended up just using university resources which due to its nature are bad consumer facing

Use Cases:

Monte Carlo simulations (e.g., Pharma/bio/chemistry Industrial

research)

Time-sensitive signal processing (offloading) (e.g., rendering for AR/VR)

Offline data analytics without deadlines

Content delivery from large data center to end-user (mobile) device

Internet-of-Things data collection from remote sensors to large data center

Use cases and future customer interviews

Analytics 4 Life HTC Vive Dr. Liu

● Significant overhead affecting user experiences

● Mostly compute-heavy applications

Asset-light sharing economy has proven its expanding speed

Airbnb Uber

Airbnb’s4 million listings

worldwide

More listings than the top 5 major hotel brand combined

Airbnb’s valuation compared to leading hotel brands (2017)

$9.3 B

$21 B

$31 B

$39 B

Number of Airbnb Guest Arrivals per Year

6 M16 M

40 M

80 M100 M

Projected Growth:

2016Revenue: $1.7 BProfit: $0.1 B

2017Revenue: $2.8 BProfit: $0.45 B

2020Revenue: $8.5 BProfit: $3.5 B

“60% of consumers who

have used Airbnb prefer it over

traditional hotels”

Uber is the first company to reach a $41 billion market capitalization in less than 6 years

Uber is valued higher than these 3 famous companies:

21st Century Fox$52.4 B

GM$49.9 B

Paypal$51.7 B

Uber’s Global Quarterly Gross Bookings:

$5.4 B$6.9 B $7.5 B $8.7 B $9.7 B $11.1 B $11.3 B

$12.0 B

75 M Riders

Uber’s by the numbers (2018):

3 MDrivers

10 B Total Rides

15 MDaily Rides

Uber’s 2018 Valuation: $72 Billion

Active Community Testing

Reputable Venture Funding

After the launching of Ankr’s MVP there are already

400 developer downloads

across the globe and we have cultivated an active developer

community for testing feedback

Shanghai Jiao Tong University (SJTU): Leading research institute in

Computer Science in the world

Work with SJTU’s departments to research and develop Ankr’s core components:

• Institute of Parallel & Distributed Computing

• Institute of Cryptography & Information SecurityDistinguished Alumni: Stanley Wu (CTO of Ankr)

(graduated in top 1% of class from SJTU)

Current focus:

• Test our functionalities using existing technology like SAP Hana

• Deploy functionalities on the SAP App Center

• Reach wide global enterprise audience

Ongoing projects:

• Using BOINC to reach a wider audience with scientific & academic backgrounds

• BOINC’s strong awareness is shown by its 200k+ contributors across the globe

• Caters to users with high likelihood of adopting Ankr

Partnership advantage:

Strong global awareness & active community testing

Berkeley Open Infrastructure for Network Computing

Advisor: Professor David Anderson

(creator of BOINC project and SETI@Home)

Leading Enterprise Software and Cloud Service Provider

V. Team behind the distributed Cloud

Prof. David AndersonTechnical Advisor

• Creator of BOINC and SETI@home• Researcher at Berkeley Space Science Lab

Research Engineering Product & Growth

Dr. Giacomo GhidiniChief Scientist

• NSF Grant Recipient • Assistant Professor at University of Texas

Arlington• Researcher at Oracle• Expert in blockchain and IoT

Dr. Wenli ZhengResearcher

• Assistant Professor at Shanghai Jiao Tong University

• Research Assistant at Ohio State University• Expert in computer architecture and data center

infrastructure

Dr. Quan ChenResearcher

• Assistant Professor at Shanghai Jiao Tong University

• Post-doc Researcher at Columbia University and University of Michigan Ann Arbor

• Expert in distributed systems

Stanley WuCTO

• 11 years at Amazon as Tech Lead and L6 Engineer

• Among the first few engineers to join AWS EC2• Expert in large-scale cloud service

Song LiuChief Security Engineer

• Principal Engineer at Palo Alto Networks and Gigamon

• Ethical Hacker and active open source contributor

• Presented multiple security vulnerabilities to Microsoft

Ambarish KrishnamurthyChief Architect

• VP Engineering at Morgan Stanley and Bank of America

• Led multiple ultra-low-latency trading systems • 25 years of experience in C/C++

Chandler SongCEO

• Serial Entrepreneur: Led the mobile product of CitySpade, an on-demand real estate broker service

• Software Engineer at Amazon Lab126 and SAP

Ryan FangCOO

• Serial Entrepreneur: Sold Peer Potential, an education competition start-up

• Investment Banking Analyst at Morgan Stanley and Credit Suisse

Appendix

Appendix: Current public cloud offerings

Centralized public cloud service providers (CSPs)

Cloud computing basic services

Compute

Storage: block and object

Networking: firewalls, caching, etc.

Access control, management, billing

Other services built on top of basic services

Big Data (e.g., AWS Elastic MapReduce)

Machine learning (e.g., MXNet on AWS)…

New computing paradigm

Serverless computing (e.g. AWS Lambda)

Current scenario Elastic compute pricing (AWS, Alibaba, Azure)

Serverless computing pricing

AWST3.medium (2 vCPUs, 4 GiB)

Alibabaecs.n4.large (2 CPUs, 4 GB)

AzureB2S (2 cores, 4 GiB)

All three providers feature similar pricing for elastic compute products

$0.0416/hr

$0.047/hr

$0.047/hr

Azure Functions

AWS Lambda

Alibaba Function Compute

All three providers feature same pricing for serverless products

$0.20/M executions$0.000016/GB-s duration

$0.20/M requests (i.e., executions)$0.00001667/GB-s duration

$0.20/1 million calls (i.e., executions)$0.00001668/GB-second duration fee

DreamLab’s Historic Success

Lacked Incentives

Vodafone lacked demand to draw new customers through its services

No tangible rewards to entice users besides number of hours contributed

Limited Experience

Only offered on mobile and only when phone is fully charged

Requires opening app every night, relying on users to actively remember

Difficult to scale due to lack of active user efforts and high friction experience

App launched by Vodafone Foundation and The Garvan Institute of Medical Research in 2015 that allows users to find a cure to cancer by pooling their smartphones’ computing power

“DreamLab aims to create the nation's first "smartphone

supercomputer", and if 100,000 users pooled their phones'

processing capabilities researchers would be able to crunch data

approximately 3,000 times faster than the current rate,” the Institute

said.

DreamLab’s Shortcomings

Appendix: DreamLab

95% of BOINC users are in

North America and Western

Europe

90% of BOINC users are male 35-50 years old with a strong IT background

Median income of BOINC users is highly above average

BOINC Demographics

LEADERBOARD

1 1.5 B (1999)

2 364 M (2017)

3 444 M (2017)

4 80 M (2018)

5 293 M (2001)

6 196 M (2017)

7 478 M (1999)

8 214 M (1999)

9 157 M (2017)

RankTotal Credit

YearJoined

$4.4 M worth of Compute instances

BOINC’s Challenges:

Complex

Privacy

Redundant

Incentive

Lack enterprise functionality due to lack of privacy

Work checked by sending job to two parties, doubling efforts

Volunteer-based fails to gain traction among consumers

Academia focused platform ignores other segments’ UX

Appendix: BOINC

Thanks!