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Which of these statements best describes autoscaling?
Autoscaling is a scale up/scale down solution. It migrates an application to more powerful hardware during periods of high demand, and moves it back to less powerful hardware when demand drops.
Autoscaling requires an administrator to actively monitor the workload on a system. If the workload increases and response times start to drop, the administrator can trigger autoscaling to help increase the throughput of the system.
Autoscaling is a scale out/scale in solution. The system can scale out when specified resource metrics indicate increasing usage, and scale in when these metrics drop.
Scaling in and out provides better availability than autoscaling.
Which of these scenarios is a suitable candidate for autoscaling?
The number of users requiring access to an application varies according to a regular schedule. For example, more users use the system on a Friday than other days of the week.
The system is subject to a sudden influx of requests that grinds your system to a halt. The workload has increased exponentially and there appears to be no reason for this surge of activity.
Your organization is running a promotion and expects to see increased traffic to their web site for the next couple of weeks. You base your autoscaling strategy on counters that measure business processes.
The DevOps team for a large food delivery company is configuring an Azure Spring Apps implementation. Friday night is typically the busiest time. Conversely, 7 AM on Wednesday is generally the quietest time. Which of the following autoscale rule types should be configured?
A metric-based rule.
An app-insight rule.
A schedule-based rule.
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