Edge Computing vs Cloud Computing Questions and Answers for Viva

Answer-1: Edge computing processes data near the data source, while cloud computing relies on centralized data centers.

Answer-2: Edge computing reduces latency, supports real-time processing, and minimizes bandwidth usage.

Answer-3: Edge computing is preferred in applications requiring low latency, such as IoT, autonomous vehicles, and real-time analytics.

Answer-4: and how does it differ in cloud vs edge computing?

Answer-5: No, edge computing complements cloud computing by handling time-sensitive tasks locally.

Answer-6: Edge computing distributes data, potentially increasing security risks; cloud computing centralizes data, enabling better control.

Answer-7: Smart traffic lights that process video and sensor data locally for real-time traffic management.

Answer-8: Google Drive storing and managing files on centralized cloud servers.

Answer-9: Edge devices process data locally and send necessary data to the cloud for long-term storage or further analysis.

Answer-10: Edge computing, as it enables real-time responses and reduces the need for constant cloud connectivity.

Answer-11: Lower latency, reduced bandwidth, offline capability, and faster response times.

Answer-12: Scalability, cost-efficiency, centralized data storage, and easier maintenance.

Answer-13: Security risks, limited processing power, and difficulty in managing distributed infrastructure.

Answer-14: Latency, dependency on internet connectivity, and potential data privacy issues.

Answer-15: An edge device is hardware that processes data at the edge of the network, such as sensors, routers, or gateways.

Answer-16: Cloud computing can be more cost-effective for large-scale data storage and batch processing; edge reduces costs in real-time scenarios.

Answer-17: Edge processes data locally and transmits selectively; cloud requires sending all data to a centralized location.

Answer-18: Fog computing extends cloud capabilities to the edge by placing computation closer to the data source, often between edge and cloud.

Answer-19: 5G enables faster, more reliable connections, making real-time edge processing more efficient.

Answer-20: Cloud offloading is when devices push intensive tasks to the cloud for processing to save local resources.

Answer-21: Edge offloading is shifting certain cloud-based processes to the edge to reduce latency or improve performance.

Answer-22: Yes, because only essential data is sent to the cloud, reducing the overall network load.

Answer-23: Manufacturing, healthcare, autonomous vehicles, retail, and smart cities.

Answer-24: Finance, e-commerce, education, media, and enterprise IT.

Answer-25: It enables real-time decision-making by processing sensor data locally without relying on cloud latency.

Answer-26: Yes, hybrid approaches use edge for real-time data and cloud for storage and analytics.

Answer-27: Edge AI refers to running artificial intelligence models locally on edge devices for immediate insights and actions.

Answer-28: Cloud allows centralized control and auditing; edge may require distributed integrity checks and synchronization mechanisms.

Answer-29: Micro data centers provide local computing and storage resources near the data source, reducing dependency on cloud.

Answer-30: Cloud computing offers greater scalability due to elastic resource availability; edge is limited by local hardware.

Answer-31: Physical tampering, decentralized control, and inconsistent security policies across devices.

Answer-32: Centralized control, consistent policies, regular updates, and managed security services.

Answer-33: Processing data as it is generated to allow immediate decision-making, best achieved with edge computing.

Answer-34: Edge computing, since it does not rely on continuous internet access.

Answer-35: Cloud is centralized (data processed in a few locations), edge is decentralized (data processed at or near the source).

Answer-36: Not entirely, but it can offload traffic and reduce dependency on centralized processing.

Answer-37: Edge computing may use less energy per transaction but more overall due to many distributed devices; cloud data centers are optimized for efficiency.

Answer-38: Models like event-driven architecture, real-time OS, and containerized microservices are common.

Answer-39: Through smart devices, edge servers, or localized gateways in the field or near the data source.

Answer-40: Via large-scale data centers offering services over the internet through providers like AWS, Azure, and Google Cloud.

Answer-41: Cloud computing, due to its centralized resources and scalability.

Answer-42: Edge computing, due to its low latency and proximity to the data source.

Answer-43: Cloud updates are centrally managed; edge updates must be pushed to individual devices, increasing complexity.

Answer-44: An edge gateway bridges communication between edge devices and the cloud, often performing preprocessing.

Answer-45: It allows data to be processed locally, aiding compliance with regional data protection laws.

Answer-46: Yes, with edge ML models, devices can perform inference locally, reducing reliance on cloud.

Answer-47: Smart cameras processing video locally (edge) but uploading key clips to cloud for archiving and analysis.

Answer-48: Latency needs, data sensitivity, cost, bandwidth availability, and computational demands.

Answer-49: An architecture where data is collected at the device level and transmitted directly to the cloud for processing.

Answer-50: Data is first processed locally at the edge and then sent to the cloud for storage, further analysis, or training.