Which practice increases resource availability during high demand periods?

Which practice increases resource availability during high demand periods?

• A. Capacity planning
• B. Vertical Scaling
• C. Horizontal Scaling
• D. Network partitioning

Answer: (C)

The practice that effectively increases resource availability during high demand periods is horizontal scaling. This approach involves adding more instances of resources, such as additional servers or virtual machines, to handle the increased load. By distributing the workload across multiple units, horizontal scaling enhances system performance and ensures that application or service availability remains high even during peak usage times. This method is particularly beneficial in cloud environments, where resources can be dynamically provisioned and adjusted based on real-time demand. It allows organizations to manage spikes in traffic efficiently, improving responsiveness and minimizing the risk of server overload, which can lead to downtimes. While vertical scaling, which involves enhancing the capacity of a single resource (like upgrading a server’s CPU or RAM), can increase performance, it does not provide the same level of redundancy and fault tolerance as horizontal scaling. Network partitioning, on the other hand, is more about dividing a network into segments for performance or security reasons rather than directly addressing resource availability during demand surges. Capacity planning focuses on forecasting future resource needs but does not inherently provide a mechanism for scaling resources during high demand.

The practice that effectively increases resource availability during high demand periods is horizontal scaling. This approach involves adding more instances of resources, such as additional servers or virtual machines, to handle the increased load. By distributing the workload across multiple units, horizontal scaling enhances system performance and ensures that application or service availability remains high even during peak usage times.

This method is particularly beneficial in cloud environments, where resources can be dynamically provisioned and adjusted based on real-time demand. It allows organizations to manage spikes in traffic efficiently, improving responsiveness and minimizing the risk of server overload, which can lead to downtimes.

While vertical scaling, which involves enhancing the capacity of a single resource (like upgrading a server’s CPU or RAM), can increase performance, it does not provide the same level of redundancy and fault tolerance as horizontal scaling. Network partitioning, on the other hand, is more about dividing a network into segments for performance or security reasons rather than directly addressing resource availability during demand surges. Capacity planning focuses on forecasting future resource needs but does not inherently provide a mechanism for scaling resources during high demand.