big data and internet thinkingwuct/bdit/slides/lec4.pdfamazon aws iot architecture aws iot...
TRANSCRIPT
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Big Data and Internet Thinking
Chentao WuAssociate Professor
Dept. of Computer Science and [email protected]
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Download lectures
• ftp://public.sjtu.edu.cn
•User: wuct
•Password: wuct123456
•http://www.cs.sjtu.edu.cn/~wuct/bdit/
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Schedule
• lec1: Introduction on big data, cloud computing & IoT
• Iec2: Parallel processing framework (e.g., MapReduce)
• lec3: Advanced parallel processing techniques (e.g., YARN, Spark)
• lec4: Cloud & Fog/Edge Computing
• lec5: Data reliability & data consistency
• lec6: Distributed file system & objected-based storage
• lec7: Metadata management & NoSQL Database
• lec8: Big Data Analytics
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Collaborators
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Contents
Intro. to Cloud/Fog Computing1
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Fog Computing
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Challenges
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Cloud-Fog-Edge
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Cloud-Fog-Edge Devices
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Cloud-Fog-Edge Architecture (1)
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Cloud-Fog-Edge Architecture (2)
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Functions of Cloud-Fog-Edge
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Fog/Edge Computing is the Primary Choice to Handle Real Time Data
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IoT End-to-End Value Chain
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IoT in the cloud and on the edge
IoT in the Cloud
▪ Remote monitoring and control
▪ Merging remote data from
across multiple IoT devices
▪ Near infinite compute and
storage to train machine
learning and other advanced AI
tools
IoT on the Edge➔ Low latency tight control
loops require near real-time response
➔ Public internet inherently unpredictable
➔ Privacy of data and protection of IP
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Data Processing in Cloud-Fog-Edge
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Heterogeneous/Homogeneous Cloud/Fog computing
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Heterogeneous/Homogeneous Computing Framework• Cloud: Parallel and Distributed Computing
Map-Reduce, Graph Computing, Stream Computing
• Edge/Fog: Approximate Computing
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Why Approximate Computing?
▪ Inexact/imprecise input data
▪ Approximate/iterative algorithms
▪ Loose constraints on output
image, sound and video processing
image rendering
sensor data analysis,computer vision
✓
✓
simulations, games, search, machine learning
✓
✓
Where a lot of (most?) resources go!
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Point of View on Approximate Computing
Perf
orm
ance
Resource usage (e.g., energy)
2
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Approximate Computing Example - Images(1)
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Approximate Computing Example - Images(2)
EnergyErrors EnergyErrors
EnergyErrors EnergyErrors
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Approximate Computing in Different Areas
Architecture
OS/DB
Runtime
Compiler
PL
Hardware
EnerJ (UW), Passert (MSR/UW), Rely (MIT), Relax (Wisconsin)Uncertain<T> (MSR), Eon (UMass)
Probabilistic CMOS (Rice), approximate components (Purdue)
ANNs (UW, MSR, INRIA, Wisconsin, Qualcomm)
Stochastic Processors (UIUC), ERSA (Stanford), Flikker(MSR), QUORA (Purdue), Approximate Storage (MSR, UW)
Using Neural Nets for code approximation (GAtech/UW/MSR)Stream Processing (Princeton)
Green (MSR), PowerDial (MIT), soft error control (UCLA), SAGE & Paraprox (Michigan), Swat (UIUC)
BlinkDB (Berkeley/MIT)
Probabilistic transformations (MIT)
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Approximate Computing Using Neutral Networks
NeuralRepresentation
Code1 Code2 Code3 Code4 Code5 Code6
…
CPU NPU
SourceCode
CommonIntermediate
Representation
Acceleration
+×
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Approximate Computing – Program (1)
Program
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Approximate Computing – Program (2)
Program
Find an approximate
program component
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Approximate Computing – Program (3)
Program
Compile the program
and train a neural network
Find an approximate
program component
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Approximate Computing – Program (4)
Program
Compile the program
and train a neural network
Execute on a fast Neural
Processing Unit (NPU)
Find an approximate
program component
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Approximate Computing – Neutral Network Acceleration
CPU NPU
CPU GPU FPGADigitalASIC
FPAAAnalog
ASIC
(Speed: ~4×↑,Energy: ~10×↓,Quality: 5%↓)
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Approximate CodesDisciplined Approximate Programming
(EnerJ, EnerC,...)
int p = 5;
@Approx int a = 7;
for (int x = 0..) {
a += func(2);
@Approx int z;
z = p * 2;
p += 4;
}
a /= 9;
p += 10;
socket.send(z);
write(file, z);
Relaxed AlgorithmsλAggressive Compilationɸ
Approximate Data Storage
Variable-Accuracy ISAALU
Approximate Logic/Circuits
Variable-quality wireless communication
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Contents
Intro. to Fog/Edge Networking2
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Edge Architecture
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Edge-Fog-Cloud Network
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Layered Network (1)
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Layered Network (2)
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Different Requirements on Latency
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Different Network Topology
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Different Network Protocols
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Different Network Accesses
•RAN - Radio Area
Network for LTE/5G
•RNC- Radio Network Controller for WiFi
•CMTS- Cable Modem Termination System
•PON OLT for fiber
•EPC – evolved PacketCore
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Different Network Connections
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Applications – Device Location
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Applications – Video Analytics
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Applications – Content Optimization
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Applications – DNS Caching
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Applications – Application Optimization
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Edge-Fog-Cloud Network Example: TelcoFog (1)
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Edge-Fog-Cloud Network Example: TelcoFog (2)
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Edge-Fog-Cloud Network Example: TelcoFog (3)
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Edge-Fog-Cloud Network Example: TelcoFog (4)
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Contents
Industrial Solutions3
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Google IoT Solution
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Google Edge Computing (1)
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Google Edge Computing (2)
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Cloud & Edge Fusion – System Architecture
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Cloud & Edge Fusion – Model Training
▪ Fog: Collecting Data
▪ Cloud: Rendering & Training Data
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Cloud & Edge Fusion – Virtualization (1)
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Cloud & Edge Fusion – Virtualization (2)
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Cloud & Edge Fusion – Virtualization (3)
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Cloud & Edge Fusion – Virtualization (4)
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Amazon AWS IoT Solution
▪ FreeRTOS: IoT operating system
▪ Greengrass: Seamless expansion to edge devices
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Amazon AWS IoT Architecture
AWS IoT Architecture
Endpoints
Fleet onboarding, management and
SW updates
Fleet audit and protection
IoT data analytics and intelligence
Gateway
ThingsSense & Act
CloudStorage & Compute
Secure localtriggers, actions,
and data sync
IntelligenceInsights & Logic → Action
Secure device connectivity
and messaging
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Free RTOS - an open source IoT OS
▪ FreeRTOS: https://www.freertos.org/
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Greengrass – AWS Edge Computing Platform (1)
▪ Greengrass is an edge/ fog node with certain computing and processing capability in AWS
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Greengrass – AWS Edge Computing Platform (2)
▪ Greengrass provides connector, connecting edge-fog-cloud nodes, and realizing adaptive configuration
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Greengrass – AWS Edge Computing Platform (3)
▪ Greengrass provides good authorization and privacy protection mechanisms
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Greengrass – AWS Edge Computing Platform (4)
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AWS IoT Core – Edge node (1)
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AWS IoT Core – Edge node (2)
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Greengrass → IoT Analytics
![Page 70: Big Data and Internet Thinkingwuct/bdit/slides/lec4.pdfAmazon AWS IoT Architecture AWS IoT Architecture Endpoints Fleet onboarding, management and SW updates Fleet audit and protection](https://reader035.vdocuments.us/reader035/viewer/2022081607/5ec7ff1d5ed7c55e9a73e7db/html5/thumbnails/70.jpg)
AWS IoT Analytics (1)
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AWS IoT Analytics (2)
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AWS Lambda (1)
▪ AWS lambda is a fine-grained method for deploying code, managing services, and monitoring the health of lightweight services. similar to Alibaba microservice.
![Page 73: Big Data and Internet Thinkingwuct/bdit/slides/lec4.pdfAmazon AWS IoT Architecture AWS IoT Architecture Endpoints Fleet onboarding, management and SW updates Fleet audit and protection](https://reader035.vdocuments.us/reader035/viewer/2022081607/5ec7ff1d5ed7c55e9a73e7db/html5/thumbnails/73.jpg)
AWS Lambda (2)
▪ AWS lambda is a new pricing and service model
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AWS Lambda (3)
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Microsoft IoT Solution
Simple processingfiltering, batching, compression
Complex processingAzure Stream Analytics,
Cognitive Services
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Microsoft IoT Core Innovations (1)
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Microsoft IoT Core Innovations (2)
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Microsoft IoT Processing Procedure
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Microsoft IoT Intelligent ProcessingLambda Architecture
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Microsoft IoT Connection Procedure
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Microsoft IoT Edge-Fog-Cloud Fusion (1)
![Page 82: Big Data and Internet Thinkingwuct/bdit/slides/lec4.pdfAmazon AWS IoT Architecture AWS IoT Architecture Endpoints Fleet onboarding, management and SW updates Fleet audit and protection](https://reader035.vdocuments.us/reader035/viewer/2022081607/5ec7ff1d5ed7c55e9a73e7db/html5/thumbnails/82.jpg)
Microsoft IoT Edge-Fog-Cloud Fusion (2)
![Page 83: Big Data and Internet Thinkingwuct/bdit/slides/lec4.pdfAmazon AWS IoT Architecture AWS IoT Architecture Endpoints Fleet onboarding, management and SW updates Fleet audit and protection](https://reader035.vdocuments.us/reader035/viewer/2022081607/5ec7ff1d5ed7c55e9a73e7db/html5/thumbnails/83.jpg)
Thank you!