A Roadmap for Energy Saving Using Dynamic Load Balancing in Cloud and Fog Architecture
- Resource Type
- Conference
- Authors
- Pani, Luina; Singh, Kamakhya; Dutta, Arijit; Misra, Chinmaya; Roy, Ruben
- Source
- 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2022 10th International Conference. :1-5 Oct, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Robotics and Control Systems
Computer architecture
Quality of service
Medical services
Data models
Real-time systems
Energy efficiency
Internet of Things
Fog Computing
SDN
Adaptive Load balancing
Energy saving
- Language
In recent years there is an exponential surge in healthcare IOT devices that subsequently led to generation of massive amount of data. IOT devices send these complex and huge medical data to cloud for analysis and storage. Most of the organization do not prefer this due to latency, privacy and security issues. To overcome the limitations of cloud-based systems, a novel paradigm called as fog computing has been created. Even though fog nodes have several advantages, they require high amount of energy to function. Software Defined Networking or SDN is a cutting-edge technology which enables intelligent and centralized network management also ‘programming’ using software applications. In this paper we present a energy efficient SDN enabled fog computing architecture for healthcare data by controlling the service rate. In this model based on buffer load it will be decided whether they will upload it in batch mode with higher processing speed or they will process the data in listening interval with low processing speed. The proposed SDN based architecture perform effectively and save energy compared to existing model. This model balances the load dynamically and handle real time data traffic concurrently.