Fog computing is a distributed computing paradigm that extends cloud computing and services to the edge of the network. It aims to address issues with cloud computing like high latency and privacy concerns by processing data closer to where it is generated, such as at network edges and end devices. Fog computing characteristics include low latency, location awareness, scalability, and reduced network traffic. Its architecture involves sensors, edge devices, and fog nodes that process data and connect to cloud services and resources. Research is ongoing in areas like programming models, security, resource management, and energy efficiency to address open challenges in fog computing.

1. Fog
Computing

2. Plan
• Introduction
• What is Fog Computing?
• Characteristics
• Fog vs Cloud
• Architecture
• Applications
• Research directions
• Products
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3. Introduction
• Data collected and sent to Cloud
• Cloud Computing -> Big data
processing
• Low-Latency, high velocity,
multiple location distribution ->
Cloud fails
• Privacy concerns
CLOUD
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4. CLOUD
What is Fog Computing?
• Distributed computing
paradigm
• Extends the services provided
by the cloud to the edge of the
network
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5. Fog Computing Characteristics
• Reduction of network traffic
• Suitable for IoT tasks and queries
• Low-latency requirement
• Scalability
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6. Fog vs Cloud
Requirement Cloud Fog
Latency High Low
Delay Jitter High Very Low
Location of server nodes Within internet Edge of local network
Distance between client
and server
Multiple hops Single hop
Security Undefined Can be defined
Attack on data router High probability Vary less probability
Location awareness No Yes
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7. Fog Computing architecture
IOT applications and
solutions
Software-Defined resource
management
Cloud Services and
Resources
Network
Sensors, Edge devices,
gateways and apps
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8. Software-defined resource management
• Reduce the cost of using Cloud
• Acceptable level of latency
• Pushing task execution to Fog nodes
Flow and task
placement
Knowledge
base
Performance
prediction
Raw data
management
Monitoring Profiling
Resource
Provisioning
Security
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9. Software-defined resource management
• State of available Cloud, Fog and network resources
• Identify incoming task best candidate
Flow and task
placement
Knowledge
base
Performance
prediction
Raw data
management
Monitoring Profiling
Resource
Provisioning
Security
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10. Software-defined resource management
• Store historical information (application and resource demands)
• Support decision making process
Flow and task
placement
Knowledge
base
Performance
prediction
Raw data
management
Monitoring Profiling
Resource
Provisioning
Security
10/20

11. Software-defined resource management
• Utilizes information of Knowledge base
• Estimate the performance of available resources
Flow and task
placement
Knowledge
base
Performance
prediction
Raw data
management
Monitoring Profiling
Resource
Provisioning
Security
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12. Software-defined resource management
• Direct access to data sources
• Provides views from data for other services
• SQL, NOSQL APIs …
Flow and task
placement
Knowledge
base
Performance
prediction
Raw data
management
Monitoring Profiling
Resource
Provisioning
Security
12/20

13. Software-defined resource management
• Keeps track of performance and status of applications
• Supplies this information to others
Flow and task
placement
Knowledge
base
Performance
prediction
Raw data
management
Monitoring Profiling
Resource
Provisioning
Security
13/20

14. Software-defined resource management
• Utilizes information of Knowledge base
• Builds resource and application profiles
Flow and task
placement
Knowledge
base
Performance
prediction
Raw data
management
Monitoring Profiling
Resource
Provisioning
Security
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15. Software-defined resource management
• Acquiring Cloud, Fog and network resources for hosting applications
• Dynamic allocation
Flow and task
placement
Knowledge
base
Performance
prediction
Raw data
management
Monitoring Profiling
Resource
Provisioning
Security
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16. Software-defined resource management
• Authentication
• Authorization
• Cryptography
Flow and task
placement
Knowledge
base
Performance
prediction
Raw data
management
Monitoring Profiling
Resource
Provisioning
Security
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17. Applications
• Connected vehicles
• Smart cities
• Mobile users
• Healthcare applications
• Augmented Reality
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18. Research directions
• Programming models
• Security and reliability
• Resource management
• Energy minimization
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19. Products
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20. Conclusion
• Solution for data processing problem in IOT
• Edge of the network
• Reduce latency
• Higher privacy
• Many challenges: security, energy usage minimization
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21. Thank You