Fog computing
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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.
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Fog computing
- 2. Plan • Introduction • What is Fog Computing? • Characteristics • Fog vs Cloud • Architecture • Applications • Research directions • Products 2/20
- 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 3/20
- 4. CLOUD What is Fog Computing? • Distributed computing paradigm • Extends the services provided by the cloud to the edge of the network 4/20
- 5. Fog Computing Characteristics • Reduction of network traffic • Suitable for IoT tasks and queries • Low-latency requirement • Scalability 5/20
- 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 6/20
- 7. Fog Computing architecture IOT applications and solutions Software-Defined resource management Cloud Services and Resources Network Sensors, Edge devices, gateways and apps 7/20
- 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 8/20
- 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 9/20
- 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 11/20
- 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 14/20
- 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 15/20
- 16. Software-defined resource management • Authentication • Authorization • Cryptography Flow and task placement Knowledge base Performance prediction Raw data management Monitoring Profiling Resource Provisioning Security 16/20
- 17. Applications • Connected vehicles • Smart cities • Mobile users • Healthcare applications • Augmented Reality 17/20
- 18. Research directions • Programming models • Security and reliability • Resource management • Energy minimization 18/20
- 19. Products 19/20
- 20. Conclusion • Solution for data processing problem in IOT • Edge of the network • Reduce latency • Higher privacy • Many challenges: security, energy usage minimization 20/20
- 21. Thank You