How can you monitor HTTP response latencies effectively in an application without a load balancer, using Cloud Monitoring?

To monitor HTTP response latencies effectively in an application without a load balancer using Cloud Monitoring, it is essential to utilize the appropriate metric kind and visualization method. Choosing to create a metric with a metricKind set to GAUGE and a valueType set to DISTRIBUTION is optimal because a GAUGE allows for capturing point-in-time measurements, which is suitable for tracking latencies that can vary significantly over time.

Using a valueType of DISTRIBUTION enables the aggregation of latency measurements, allowing you to analyze the latency across different observations. This can help in understanding the range, frequency, and overall performance characteristics of your application’s response times. Visualizing this data with a Heatmap graph provides a clear view of how the response latencies are distributed, highlighting areas where latency may be particularly high or low, which is essential for pinpointing performance issues.

The other options do not align as effectively with the goal of monitoring HTTP response latencies. For example, DELTA and CUMULATIVE metrics are more suitable for counting events over time rather than measuring fluctuating metrics like latencies. Additionally, using a Pie chart does not effectively convey the variations in latency data as it does not represent distribution or trends comprehensively. Therefore, option C is the most