Elasticity and Scalability in Cloud Computing Questions and Answers for Viva

Answer-1: Elasticity is the ability of a cloud system to automatically increase or decrease computing resources dynamically to meet workload demands.

Answer-2: Scalability refers to the capacity of a system to handle increasing workload by adding resources either vertically or horizontally.

Answer-3: Elasticity involves automatic, dynamic resource adjustment, while scalability is the system's ability to handle growth, which can be manual or automatic.

Answer-4: Vertical scaling (scaling up) and horizontal scaling (scaling out).

Answer-5: Vertical scaling means increasing the capacity of a single resource like adding CPU, RAM, or storage to a server.

Answer-6: Horizontal scaling means adding more instances of resources like adding more servers or virtual machines.

Answer-7: It ensures cost efficiency and optimal performance by matching resource usage to actual demand in real-time.

Answer-8: Auto-scaling automatically adjusts the number of active resources based on pre-defined policies and real-time metrics.

Answer-9: Upgrading a virtual machine from 4GB RAM to 8GB RAM is vertical scaling.

Answer-10: Adding two more virtual machines to a web server cluster is horizontal scaling.

Answer-11: A scale-out policy defines conditions under which new resource instances are added to a cloud environment.

Answer-12: A scale-in policy defines conditions under which excess resource instances are removed.

Answer-13: By dynamically reallocating resources, elasticity can compensate for failing or overloaded components.

Answer-14: CPU usage, memory consumption, network traffic, and request latency.

Answer-15: Cloud bursting is the ability to offload workloads to a public cloud during peak demand to maintain performance.

Answer-16: Most IaaS and PaaS providers like AWS Auto Scaling, Azure Scale Sets, and Google Cloud Autoscaler support elasticity.

Answer-17: A load balancer distributes traffic evenly across multiple resources to improve availability and scalability.

Answer-18: Reactive scaling adjusts resources based on current demand, while proactive scaling predicts demand and adjusts resources in advance.

Answer-19: By scaling down resources when demand decreases, elasticity reduces unnecessary cloud costs.

Answer-20: Delayed scaling response, prediction inaccuracies, and complexity in managing policies.

Answer-21: A scaling bottleneck is a limiting factor that prevents a system from effectively scaling, such as a database or network.

Answer-22: Microservices allow independent scaling of different components rather than scaling an entire monolithic application.

Answer-23: Manual scaling requires user intervention to add or remove resources, while automatic scaling uses policies to adjust resources dynamically.

Answer-24: Orchestration platforms like Kubernetes automatically manage container scaling based on workload.

Answer-25: Capacity planning forecasts resource needs to ensure a system can handle future workload increases.

Answer-26: Caching reduces load on backend systems by storing frequently accessed data closer to users.

Answer-27: Elasticity helps maintain availability by adjusting resources to meet changing demand and avoiding overload.

Answer-28: AWS Elastic Load Balancing and Auto Scaling groups provide automated elasticity and scalability.

Answer-29: Elasticity complements redundancy by dynamically allocating additional resources as backups or replacements.

Answer-30: A CDN caches and distributes content globally, reducing load on origin servers and improving scalability.

Answer-31: Sharding divides a database into smaller parts, allowing distributed queries and improved scalability.

Answer-32: SLAs define performance and uptime guarantees, influencing how elasticity and scalability are implemented.

Answer-33: Yes, cloud storage services can scale automatically based on data volume, exemplifying storage elasticity.

Answer-34: Virtualization allows dynamic allocation of hardware resources to multiple virtual machines, enabling elasticity.

Answer-35: By quickly reallocating resources after failures, elasticity aids in faster recovery and business continuity.

Answer-36: Horizontal scaling offers better fault tolerance and can handle larger workload growth.

Answer-37: It has physical limits and may require downtime to upgrade resources.

Answer-38: Elastic systems automatically provision additional resources in response to sudden traffic spikes.

Answer-39: A predefined limit on a metric (like CPU usage) that triggers scaling actions when exceeded.

Answer-40: By forecasting demand trends, predictive analytics enable proactive scaling decisions.

Answer-41: Over-provisioning is allocating more resources than needed; elasticity minimizes it by adjusting resources dynamically.

Answer-42: Features like microservices, serverless computing, and containerization support scalability.

Answer-43: Serverless automatically provisions and scales compute resources based on invocation without manual intervention.

Answer-44: Scale-up increases the power of existing machines, scale-out adds more machines.

Answer-45: Network scalability ensures sufficient bandwidth and low latency as resource and user counts increase.

Answer-46: Slow response times, increased errors, and system crashes under load.

Answer-47: Yes, some databases support automatic scaling of resources based on workload.

Answer-48: Monitoring provides data needed to make informed scaling decisions and maintain performance.

Answer-49: By maintaining consistent performance during load changes, elasticity improves user experience.

Answer-50: Use automated scaling policies, monitor metrics closely, design for failure, and use distributed architectures.