Tutorial / Cram Notes
1. CPU Utilization:
One of the primary metrics for scaling services is CPU utilization. This measurement indicates the percentage of total computing power that’s being used. If the CPU usage is consistently high, it may be time to scale up the number of compute instances or choose instances with more computational power.
Example: AWS Auto Scaling can be configured to increase the number of EC2 instances when the average CPU utilization exceeds 70%.
2. Memory Utilization:
Similarly, memory utilization is an important metric. If a system is running out of memory, it may start to swap data to disk, which will degrade performance.
3. Latency:
Latency is the time taken to process a single request. High latency can be a sign that your service needs to scale in order to maintain performance as load increases.
Example: An Auto Scaling policy could be set to trigger when the average latency of a load balancer exceeds 200ms over a 5-minute period.
4. Request Count per Second:
The number of requests per second your system is handling can be a clear indicator of whether you need to scale. An increase in traffic might not always lead to high CPU or memory usage, but could still warrant scaling if it results in degraded performance.
5. Error Rate:
The error rate is crucial; a high error rate may suggest that the system is overloaded or experiencing other issues that could be resolved by scaling.
6. Throughput:
Throughput, or the number of transactions per second, is also an important metric. With higher throughput, there might be a need to scale out to handle the load.
7. Disk I/O:
Disk read/write operations can become a bottleneck. If the disk I/O is consistently high, this could indicate that the application needs to be scaled to decrease the load on the current configuration.
8. Network I/O:
Network in/out is the amount of traffic coming into and going out of your system. It’s important to monitor this to ensure that the network isn’t becoming a constraint on performance.
AWS Specific Metrics and Services:
In the context of AWS, there are specific metrics provided by Amazon CloudWatch that can be utilized, and services like AWS Auto Scaling, Elastic Load Balancing (ELB), and Amazon Relational Database Service (RDS) to implement scaling policies.
Elastic Load Balancing (ELB):
- Healthy Host Count: Determines if there is a sufficient number of healthy instances behind a load balancer.
- HTTPCode_Backend_5XX: This metric can indicate issues with application health that may not necessarily be related to the load.
Amazon RDS:
- Database Connections: The number of database connections can indicate the need for more database instances.
- ReadIOPS/WriteIOPS: High IOPS could show that your database is experiencing heavy read/write traffic and may need to scale.
Amazon ECS/EKS:
If running containerized services, CPU and memory reservation levels are key metrics to watch to ensure that containers have enough resources.
Considerations for Choosing Metrics:
When selecting metrics for scaling, it’s important to consider the characteristics of the application, traffic patterns, and the cost implications of scaling. Not every metric needs to be considered for every application; it depends largely on the application workload.
Auto Scaling in Practice:
Assuming we are using AWS Auto Scaling, you can set target values for your chosen metrics and create scaling policies. For example, an Auto Scaling group can be defined with the following simple policy for CPU utilization:
{
“PolicyName”: “ScaleOut”,
“PolicyType”: “TargetTrackingScaling”,
“AdjustmentType”: “ChangeInCapacity”,
“TargetValue”: 70.0,
“EstimatedInstanceWarmup”: 300,
“MetricAggregationType”: “Average”
}
Conclusion:
Effectively scaling services requires a strategic approach to metrics selection. By focusing on key performance indicators such as CPU utilization, memory usage, latency, request rates, error rates, throughput, and disk/network I/O, and leveraging AWS services and features, we can ensure that our systems remain responsive and cost-effective under varying loads. Monitoring the right metrics and automating responses to those metrics is essential for maintaining the performance and reliability of cloud services.
Practice Test with Explanation
True or False: The average CPU utilization is an appropriate metric to use when deciding to scale an EC2 instance.
- A) True
- B) False
Answer: A) True
Explanation: CPU utilization is a common and appropriate metric to monitor for determining when to scale EC2 instances. If the CPU usage is consistently high, it may indicate a need to scale up.
Which metric is not recommended for triggering autoscaling actions?
- A) Disk read/write operations
- B) Network in/out
- C) Memory utilization (for EC2 instances without native support)
- D) Number of active users
Answer: D) Number of active users
Explanation: While the number of active users can be an indirect indicator of load, it’s usually not a direct metric used by AWS services for scaling since it doesn’t reflect the actual resource consumption.
True or False: Latency is a poor metric for scaling web services.
- A) True
- B) False
Answer: B) False
Explanation: Latency is actually a very important indicator for web services performance and can be very appropriate for deciding to scale up or down in order to maintain the desired user experience.
When using auto-scaling, which of the following is an important metric to consider?
- A) CPU utilization
- B) Latency
- C) Request count per target
- D) All of the above
Answer: D) All of the above
Explanation: All mentioned metrics are important to consider when configuring auto-scaling, as they all provide insights into the application’s performance and load.
True or False: Throughput should be monitored when scaling database services.
- A) True
- B) False
Answer: A) True
Explanation: Throughput is a critical metric for database services as it indicates the amount of data processed over time. Monitoring throughput can help determine if scaling is needed to handle increased load or data volume.
Which AWS service provides the underlying technology for Elastic Load Balancing to automatically distribute incoming application traffic?
- A) AWS Lambda
- B) Amazon Route 53
- C) Amazon CloudFront
- D) Amazon EC2
Answer: D) Amazon EC2
Explanation: While Elastic Load Balancing is a service on its own, it utilizes EC2 instances to distribute the incoming application traffic across multiple targets.
True or False: Request count per target is a useful metric for scaling stateless application services.
- A) True
- B) False
Answer: A) True
Explanation: Request count per target gives an indication of the traffic volume that each service instance is handling and is especially useful for scaling stateless applications where each request can be handled independently.
In AWS, which service automatically adjusts capacity to maintain steady, predictable performance at the lowest possible cost?
- A) Amazon EC2 Auto Scaling
- B) AWS Elastic Beanstalk
- C) AWS Lambda
- D) Amazon RDS
Answer: A) Amazon EC2 Auto Scaling
Explanation: Amazon EC2 Auto Scaling helps to ensure that you have the correct number of Amazon EC2 instances available to handle the load for your application.
True or False: It is appropriate to use Amazon CloudWatch alarms based on the DynamoDB read and write capacity units for scaling a DynamoDB table.
- A) True
- B) False
Answer: A) True
Explanation: DynamoDB read and write capacity units are direct measures of demand on the database. CloudWatch alarms can trigger scaling actions based on these capacities.
Which metric indicates the amount of data transferred in and out of Amazon EC2 instances and can be vital for understanding network load?
- A) CPU utilization
- B) Latency
- C) Network throughput
- D) Disk IOPS
Answer: C) Network throughput
Explanation: Network throughput represents the amount of data moving in and out of EC2 instances over time and is a useful metric for understanding and scaling for network load.
True or False: Error rates are appropriate metrics for scaling because they can indicate the quality of service degrades under load.
- A) True
- B) False
Answer: A) True
Explanation: High error rates might indicate that the system is overloaded and not responsive, which can warrant scaling to meet demand and maintain service quality.
Which of the following strategies can be used for scaling an Amazon RDS instance?
- A) Vertical Scaling
- B) Horizontal Scaling
- C) Both A and B
- D) Neither A nor B
Answer: C) Both A and B
Explanation: Amazon RDS supports vertical scaling by changing instance sizes and horizontal scaling by read replicas for certain database engines.
Interview Questions
What are the key performance indicators (KPIs) you would consider when scaling a web application in AWS?
The key KPIs would include CPU utilization, memory usage, network throughput, and latency. These metrics are crucial because they directly impact application performance and user experience. For example, high CPU utilization may indicate the need for scaling up instances or employing auto-scaling policies.
How does Amazon CloudWatch help in scaling services on AWS?
Amazon CloudWatch provides monitoring for AWS cloud resources and the applications running on AWS. It can track various metrics, set alarms, and automatically adjust the resources based on predefined thresholds. This helps in scaling services by adding or removing resources to match the demand without manual intervention.
When scaling a database service in AWS, what metrics would you primarily focus on?
When scaling a database service, important metrics to focus on include database connections, read/write IOPS, CPU utilization, and disk queue depth. Monitoring these metrics can help determine if the database is under-provisioned and needs scaling to maintain optimal performance.
Describe how Elastic Load Balancing (ELB) uses metrics for scaling services in AWS.
ELB automatically distributes incoming application traffic across multiple targets, such as Amazon EC2 instances. It uses metrics such as request count, latency, and error codes to determine the load on resources. Based on these metrics, ELB can trigger auto-scaling actions to add or remove instances to maintain consistent application performance.
Can you explain how custom metrics can be used when auto-scaling in AWS?
Custom metrics can be published to Amazon CloudWatch using the PutMetricData API or CloudWatch agents. These can represent the application-specific performance such as queue length or number of active sessions and can be used in scaling policies to control scaling actions to better reflect the application’s behavior beyond default system metrics.
What is the significance of the ‘Cooldown Period’ in AWS Auto Scaling?
The cooldown period is the duration after a scaling activity during which Auto Scaling does not allow further scaling activities to take place. This period allows the newly launched instances to fully start handling traffic, avoiding rapid, unnecessary scaling actions and ensuring that the scaling activity has achieved the desired effect on application performance.
Discuss how predictive scaling can benefit an AWS environment in contrast to reactive scaling based on traditional metrics.
Predictive scaling uses machine learning algorithms to analyze historical data and predict future traffic patterns. It then proactively scales resources in anticipation of the predicted demand. This approach can help prevent performance degradation even before usage spikes, as opposed to reactive scaling, which only responds once a change in demand is detected.
What is the role of Amazon RDS Performance Insights when scaling a relational database service?
Amazon RDS Performance Insights is an advanced database performance monitoring feature that helps assess database load and determine when to scale. It provides an easy-to-understand dashboard to visualize database performance metrics, such as active sessions and SQL throughput, which aids in making informed scaling decisions.
How can you use AWS Capacity Reservations to manage scaling?
With AWS Capacity Reservations, you can reserve capacity for your EC2 instances in a specific Availability Zone for any duration. This ensures that you have the capacity when needed for predictable performance scaling, and it’s beneficial in environments with predictable load, where you can reserve capacity in advance to meet the demand.
Why is it important to scale both vertically and horizontally in AWS, and how does this relate to metrics?
Scaling vertically (increasing the size of an instance) and horizontally (increasing the number of instances) allows for flexibility in managing various workloads. Metrics are important in this context as they indicate when one method may be more beneficial than the other. Horizontal scaling is typically favored for its higher availability and fault tolerance, while vertical scaling might be appropriate for short-term or immediate performance improvements.
Great blog post! The section on using RPS (Requests per Second) as a scaling metric was very informative.
Great discussion on metrics for scaling services. Very informative!
What are the key metrics to monitor when implementing auto-scaling in AWS?
In my experience, tracking error rates is crucial for identifying potential bottlenecks early.
Is there an ideal threshold for CPU utilization when setting up auto-scaling?
Thanks for the post! Really helpful.
Can anyone share their experience with scaling RDS instances?
Great blog post! Helped me a lot in understanding the metrics for scaling services.