Auto-scaling and CloudWatch Related Questions and Answers for AWS Interview

Introduction

In AWS, Auto-scaling coupled with CloudWatch is pivotal for seamless and efficient cloud infrastructure management. Auto-scaling ensures your application can dynamically adapt to varying workloads, automatically adjusting the number of servers to maintain optimal performance. CloudWatch, on the other hand, provides real-time insights into system health and performance metrics. By setting thresholds and alarms, CloudWatch allows proactive responses to fluctuations, preventing potential issues.

Read on to learn more about how you can ace your AWS interview with these must-know Auto-scaling and CloudWatch interview questions and answers.

Q1: How Do AWS Auto-Scaling Groups And Scaling Policies Assist In Managing Virtual Servers On EC2?

A: Auto-scaling groups, integrated into the EC2 service, simplify the adjustment of virtual server instances to match system requirements. Discover how to create a dynamic server pool by:

• Launching multiple virtual servers of the same type using auto-scaling groups.

• Adapting virtual server numbers based on CPU load through CloudWatch.

• Adjusting the virtual server count per schedule to accommodate recurring traffic patterns.

• Employing a load balancer as an entry point to the dynamic server pool.

• Using a queue to segregate job execution from the dynamic server pool.

Q2: What Are The Prerequisites For Horizontally Scaling An Application, Involving Adjusting The Number Of Virtual Servers Based On The Current Workload?

A: To enable horizontal scaling, where the number of virtual servers can flexibly adapt to the workload, two essential prerequisites must be met:

• Servers intended for scaling must be stateless. Achieving statelessness involves utilizing services like RDS (SQL database), DynamoDB (NoSQL database), or S3 (object store) for data storage instead of relying on local or network-attached disks exclusive to a single server.

• A designated entry point to the dynamic server pool is essential for effectively distributing the workload among multiple servers, allowing for the decoupling of servers.

Q3: What Are The Critical Parameters In Launch Configuration For Starting Virtual Servers On AWS?

A: The launch configuration parameters include:

• ImageId: Identifies the Amazon Machine Image (AMI) from which to initiate a virtual server.

• InstanceType: Specifies the size of new virtual servers, denoted by instance types like t2.micro.

• User[removed] representing a list of security group names.

• IamInstanceProfile: Links an IAM instance profile to an IAM role, identified by name or Amazon Resource Name (ARN).

• SpotPrice: Specifies using a spot instance instead of an on-demand instance, with the maximum hourly price.

• EbsOptimized: Activates EBS optimization for the EC2 instance, providing dedicated throughput to EBS root volumes with defined IOPS in the AMI.

Q4: How Can Scheduled Scaling Actions Adapt To Recurring Load Patterns In A Blogging Platform?

A: Operating a blogging platform often involves recognizing predictable load patterns, such as increased article readership during lunch breaks (11:00 AM to 1:00 PM) and a surge in registration page requests post TV advertisements. To address these, scheduled scaling actions come into play, offering:

• One-time-only actions: Defined by the start time parameter.

• Recurring actions: Established using the recurrence parameter.

Both can be easily implemented through the CLI. For example, a command can create a scheduled scaling action setting the desired capacity of the auto-scaling group "web app" to 4 on January 1, 2016, at noon (UTC). Note: Running this command without the auto-scaling group "web app" is not recommended.

Q5: What Default Metrics Does An EC2 Instance Publish To CloudWatch, And How Can These Metrics Be Utilized For Scaling Virtual Servers?

A: By default, an EC2 instance sends crucial metrics to CloudWatch, including CPU, network, and disk utilization. Notably, memory usage lacks a dedicated metric currently. However, these metrics serve as critical indicators for scaling virtual servers in response to bottlenecks. For instance, scaling can be triggered when the CPU operates at total capacity.

CloudWatch metrics are characterized by:

• Namespace: Identifying the source of the metric (e.g., AWS/EC2).

• Dimensions: Defining the metric scope (e.g., all virtual servers in an auto-scaling group).

• MetricName: Providing a unique name for the metric (e.g., CPU utilization).

CloudWatch alarms, essential for triggering actions, are built upon these CloudWatch metrics.

Q6: How Does Scaling Based On CPU Load Work With Virtual Servers Offering Burstable Performance, And What Considerations Should Be Considered?

A: Virtual servers like the t2 instance family provide burstable performance, featuring a baseline CPU performance and the ability to burst based on credits temporarily. Once credits are depleted, the instance reverts to baseline, with t2.micro having a baseline at 10% of the underlying physical CPU's performance.

Scaling based on CPU load with burstable performance servers involves a challenge. The scaling strategy must factor in credit availability for performance bursts. To navigate this, alternative metrics like the number of sessions could be explored, or opting for an instance type without burstable performance may be considered. Efficient use involves saving credits during low-load periods and utilizing them to address high-load spikes.

Q7: How Can Asynchronous Decoupling Of A Dynamic Server Pool Through A Queue Enhance Scaling Based On Workload, And What Advantage Does It Provide In Scenarios Like A Social Bookmark Service?

A: Decoupling a dynamic server pool asynchronously presents a strategic advantage in workload-based scaling. By diverting requests into a queue, the number of servers can be scaled proportionally to the queue length. This approach yields a precise metric for scaling, ensuring no request loss during load peaks, as requests are safely stored in the queue.

Consider a scenario like a social bookmark service, where users frequently add new bookmarks. Here, the conversion from URL to PNG for preview is sluggish during peak evening hours. Employing asynchronous decoupling allows efficient scaling based on the queue length, ensuring timely responses without losing requests during high-demand periods and enhancing user satisfaction.

Q8: Which Services Constitute The Highly Available Scaling Architecture For This Setup, Involving EC2 Instances Running Apache For WordPress, RDS With Multi-AZ Deployment, S3 For Media Files, ELB For Synchronous Decoupling, And Auto-Scaling With CloudWatc

A: The highly available scaling architecture comprises the following services:

• EC2 instances: Hosting Apache to serve WordPress, a PHP application.

• RDS: Providing a MySQL database with Multi-AZ deployment for enhanced availability.

• S3: Functions as a storage solution for media files (images and videos) and is integrated with a WordPress plug-in.

• ELB (Elastic Load Balancer): Facilitating synchronous decoupling of web servers from visitors.

• Auto-scaling and CloudWatch: Dynamically scaling the number of web servers based on the current CPU load of all virtual servers.

Q9: How Is The Concept Of Stateless Servers Implemented In Two Examples, Namely A WordPress Blog And URL2PNG, For Taking Screenshots Of URLs, Featuring Decoupling, Scaling With Auto-Scaling And CloudWatch, And Data Outsourcing?

A: Stateless server implementation is exemplified in two scenarios:

WordPress Blog:

• Decoupling: Achieved with ELB (Elastic Load Balancer).

• Scaling: Utilizes auto-scaling and CloudWatch, responding to CPU utilization.

• Data Outsourcing: RDS and S3 are employed to externalize data, ensuring statelessness.

URL2PNG - Screenshots of URLs:

• Decoupling: Implemented through SQS (queue).

• Scaling: Relies on auto-scaling and CloudWatch, scaling based on queue length.

• Data Outsourcing: DynamoDB and S3 store data, aligning with the stateless server paradigm.

Q10: What Are The Critical Parameters For Configuring A CloudWatch Alarm To Trigger Scaling Based On The CPU Utilization Of All Virtual Servers Within An Auto-Scaling Group?

A:  Condition Statistic:

• Description: Statistical function applied to a metric.

• Possible values: Average, Sum, Minimum, Maximum, SampleCount.

Condition Period:

• Description: Defines a time-based slice of values from a metric.

• Possible values: Seconds (multiple of 60).

Condition EvaluationPeriods:

• Description: Number of periods to evaluate when checking for an alarm.

• Possible values: Integer.

Condition Threshold:

• Description: Threshold for triggering an alarm.

• Possible values: Number.

Condition ComparisonOperator:

• Description: Operator to compare the threshold against the result from a statistical function.

• Possible values: GreaterThanOrEqualToThreshold, GreaterThanThreshold, LessThanThreshold, LessThanOrEqualToThreshold.

Metric Namespace:

• Description: Source of the metric.

• Possible values: AWS/EC2 for metrics from the EC2 service.

Metric Dimensions:

• Description: The scope of the metric depends on the metric and references the auto-scaling group for an aggregated metric over all associated servers.

Metric MetricName:

• Description: Name of the metric (e.g., CPU utilization).

Action AlarmActions:

• Description: Actions to trigger if the threshold is reached.

• Reference to the scaling policy.

These parameters collectively define the configuration of a CloudWatch alarm tailored for scaling based on CPU utilization within an auto-scaling group.

Q11: How Can Unnecessary Scaling Be Avoided In An Auto-Scaling Group, And Why Is It Crucial To Define Reasonable Cooldown And HealthCheckGracePeriod Values?

A: To prevent unnecessary scaling, setting sensible values for Cooldown and HealthCheckGracePeriod is imperative. While there's a typical inclination to opt for short periods, caution is advised. A too-short Cooldown period may lead to premature scaling adjustments, triggering unnecessary scaling up and down. 

Similarly, an excessively short HealthCheckGracePeriod might prompt the launch of new instances because the prior instance needs to complete bootstrapping swiftly enough. Such premature actions result in the launch of unnecessary instances, contributing to avoidable expenses. Hence, judiciously defining Cooldown and HealthCheckGracePeriod values is crucial for efficient and cost-effective auto-scaling.

Q12: What Is The Purpose Of AWS Auto-Scaling Groups, And How Is The Process Structured Into Three Key Components?

A: AWS auto-scaling groups are designed to assist in managing a dynamic server pool, offering the ability to:

• Run a desired number of virtual servers: Adjust dynamically based on the workload.

• Launch, configure, and deploy uniform virtual servers.

The auto-scaling process comprises three essential parts:

• Launch Configuration: Defines virtual server size, image, and configuration.

• Auto-scaling Group: Specifies the required number of running virtual servers based on the launch configuration.

• Scaling Policies: Adjust the desired number of servers within the auto-scaling group.

Since the auto-scaling group relies on a launch configuration, creating the launch configuration is necessary first. This dependency is automatically resolved when utilizing a template, such as CloudFormation

Conclusion

Auto-scaling and CloudWatch are essential in AWS, ensuring a responsive cloud environment. JanBask Training's AWS courses provide in-depth knowledge on leveraging these services effectively. Learn to optimize resource usage, monitor system metrics proactively, and scale applications dynamically. With hands-on training and practical insights, JanBask equips you to master AWS, enabling you to implement robust auto-scaling and CloudWatch strategies.