Building an Edge Computing Strategy - Distributed infrastructure.pptx
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The document outlines an edge computing strategy that emphasizes the benefits of distributed infrastructure closer to data sources, which can improve data security, reduce latency, and enhance business efficiency. It highlights various use cases in healthcare, autonomous vehicles, drones, and gaming, demonstrating how edge computing can enable real-time processing and decision-making. The future of edge computing is projected to expand significantly, with a substantial portion of enterprise-generated data expected to be created outside traditional data centers by 2023.
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Building an Edge Computing Strategy - Distributed infrastructure.pptx
- 1. Edge Computing Strategy - Distributed infrastructure Mandakini Kumari – CTO @ Vivriti Asset Management
- 2. Problem faced by Origination
- 4. Basic Architecture of Edge Computing & Distributed infrastructure
- 5. What is Edge Computing • The ‘Edge’ refers to having computing infrastructure closer to the source of data. • Networking, compute & storage closer to the consumer.
- 6. What is Edge Computing
- 7. Edge Computing - Potential Impact on Business CONFIDENTIAL VAM 7 • Increasing data security & privacy • Reduce 65% latency & network cost. • 25% reduction in the cyberattacks • Caching, buffering, and optimizing data • Improving business efficiency and reliability • Real time data processing
- 8. Edge Computing use case ? Latency: Can be reason of failure in Healthcare Real-time responsiveness: Sometimes the reaction time can be a critical factor. Privacy: Avoid sending all raw data to be stored and processed on cloud servers.
- 9. Edge Computing for Healthcare ?
- 10. Edge Computing for Healthcare ? It store data closer to the sources of data, to improve response times and save bandwidth
- 11. Why Edge Computing ? Reliability: The system is capable to work even when disconnected to cloud servers. Removes a single point of failure. It enabling processing at greater speeds and volumes. Decrease network traffic Leading to greater real time analytics. Reduce bandwidth cost
- 12. Edge Computing for Autonomous vehicles Autonomous cars will generate ~ 40 terabytes of data for every eight hours of driving.
- 13. Why Edge Computing for Autonomous vehicles ? In cases of self-driving cars, real-time or quick decisions are an essential need. Here edge computing infrastructure will come to rescue. Self-driving cars need to take decisions is split of a second whether to stop or not else consequences can be disastrous.
- 14. Why Edge Computing for Drones ? Let’s say we are using drones to identify people or deliver relief packages then the machines should be intelligent enough to take basic decisions like changing the path to avoid obstacles locally.
- 15. Edge Computing for gaming - pokemon Augmented Reality based pokemon game, wouldn’t have been possible if “real-timeliness” It was made possible because the smartphone itself was doing AR not the central servers.
- 16. Future of Edge Computing 60% of servers will be located in an edge data center by 2025. Enterprises are using edge topological ideas to cut WAN costs by half, while improving resiliency and UX by 200% By 2023, more than half of enterprise-generated data will be created and processed outside of data centers, and outside of cloud A 2021 Cowen report shows edge growing to more than $50 billion in the next five years with a 45% CAGR
- 17. Future of Edge Computing - 4G, 5G and Edge Computing
- 18. What are use case ? Autonomous vehicles Smart cities Smart homes Stronger security Healthcare Manufacturing and industrial processes Virtual and augmented reality Streaming services and content delivery Enhanced customer services
- 19. Potential Impact on Business CONFIDENTIAL VAM 19 Azure Virtual Desktop Desktop-as-a-Service (DaaS)
- 20. Edge computing in Communication-as-a-Service
- 21. Key Components of an Edge Strategy Develop service offerings that support edge deployments Utilize more near-edge data center deployments to manage data and traffic in-region Improve the connectivity capacity from the edge to the core Deploy more services at the edge Define what needs to be managed at the edge Drop and dump non-critical data at the edge Take action on critical data
- 22. Challenges
- 23. Challenges
- 24. Challenges
- 25. Cloud and Edge Computing • Cloud Computing • Centralized • High latency • Artificial Intelligence processing power • Cyber Security • Huge Storage Capacity • High-processing and compute power • Edge Computing • Decentralized • Lowest latency • Saves Bandwidth • Processing and networking limitations • Potential data privacy and cybersecurity concerns • Edge Cloud • Decentralized • Low latency • Devoted Bandwidth where most needed • Artificial Intelligence processing power • Networking effect with other edge site nodes • Cybersecure
- 26. Thank you Mandakini Kumari CTO @ Vivriti Asset Management • https://www.linkedin.com/in/mandakini-kumari/ • https://www.slideshare.net/mandakinikumari • https://www.youtube.com/watch?v=tM51hj3xP8c • https://m.facebook.com/1789784148/