Edge Computing and Its Integration with Cloud Questions and Answers for Viva

Answer-1: Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed to improve response times and save bandwidth.

Answer-2: Edge computing processes data near the data source, while cloud computing processes data in centralized data centers.

Answer-3: Reduced latency, improved bandwidth efficiency, enhanced data security, and real-time data processing.

Answer-4: Edge devices handle local data processing and send summarized or necessary data to the cloud for further analysis and storage.

Answer-5: IoT, autonomous vehicles, AR/VR, smart cities, healthcare monitoring, and industrial automation.

Answer-6: By processing data locally near the data source, it avoids delays caused by transmitting data to distant cloud servers.

Answer-7: IoT devices generate massive amounts of data that can be processed at the edge to reduce cloud dependency.

Answer-8: Data synchronization, security management, network reliability, and infrastructure complexity.

Answer-9: Fog computing extends cloud capabilities to the edge of the network, acting as an intermediary between edge devices and the cloud.

Answer-10: Edge handles real-time processing, while cloud manages large-scale analytics, storage, and centralized management.

Answer-11: Edge devices can be physically exposed and less secure, requiring robust endpoint security and data encryption.

Answer-12: Yes, edge can operate autonomously for local processing but often integrates with the cloud for broader insights.

Answer-13: Edge computing reduces bandwidth use by processing data locally and only sending necessary data to the cloud.

Answer-14: Cloud orchestration tools automate deployment, scaling, and monitoring of edge workloads.

Answer-15: Gateways, routers, micro data centers, and specialized edge servers.

Answer-16: An edge gateway connects local devices to the cloud and often handles initial data processing and protocol translation.

Answer-17: By processing data near its source, edge computing enables faster insights and immediate actions.

Answer-18: They enable lightweight, portable applications that can run efficiently on edge devices.

Answer-19: 5G offers higher bandwidth and lower latency, enabling more efficient data transfer and edge device communication.

Answer-20: Endpoint computing refers to devices at the network edge, while edge computing includes the processing infrastructure near these endpoints.

Answer-21: Through synchronization protocols, data replication, and conflict resolution strategies.

Answer-22: Edge devices often have limited power, so energy-efficient hardware and software are critical.

Answer-23: AI models can run locally for fast inference, reducing the need to send data to the cloud.

Answer-24: Remote patient monitoring where data is processed locally to provide instant alerts.

Answer-25: Local data processing can help meet data sovereignty and privacy regulations by limiting data transfer.

Answer-26: MEC brings cloud-computing capabilities to the edge of the mobile network to reduce latency for mobile users.

Answer-27: By offering edge services, infrastructure, and integration tools such as AWS Greengrass, Azure IoT Edge, and Google Distributed Cloud Edge.

Answer-28: APIs enable communication and data exchange between edge devices and cloud services.

Answer-29: Via over-the-air (OTA) updates managed centrally through cloud orchestration.

Answer-30: Managing numerous distributed devices and ensuring consistent performance and updates.

Answer-31: By implementing local redundancies, failover mechanisms, and cloud backup.

Answer-32: Cloud-native apps can be designed to run flexibly both on the cloud and at the edge.

Answer-33: By providing faster response times and reducing dependence on internet connectivity.

Answer-34: Collecting and summarizing data locally before sending it to the cloud to reduce volume.

Answer-35: Using encryption protocols such as TLS and VPNs.

Answer-36: Virtualization enables running multiple isolated workloads on a single edge device.

Answer-37: A software and hardware environment that supports deploying, managing, and running edge applications.

Answer-38: Edge devices have limited compute power compared to centralized cloud data centers.

Answer-39: Latency impacts real-time responsiveness; edge computing minimizes it by local processing.

Answer-40: Using resilient protocols and local buffering to manage temporary connectivity loss.

Answer-41: Processing data close to its source reduces latency and compliance risks.

Answer-42: By enabling fast decision-making without relying on cloud connectivity.

Answer-43: Tools include remote management platforms, telemetry services, and cloud dashboards.

Answer-44: Through abstraction layers, containerization, and standard APIs.

Answer-45: Reduced data transfer costs, improved operational efficiency, and better resource utilization.

Answer-46: By processing data from sensors locally to enable real-time traffic, lighting, and safety management.

Answer-47: Cloud bursting temporarily offloads workload to cloud; edge computing processes data locally continuously.

Answer-48: Edge allows local continuity; cloud provides centralized backups and recovery.

Answer-49: Applications needing minimal delay drive edge computing deployment.

Answer-50: Increased AI at the edge, 5G expansion, improved orchestration, and hybrid cloud-edge platforms.