Efficient Resource Utilization of serverless and dedicated instances
Make sure to use autoscaling to ensure proper resource utilization of serverless and dedicated instances
Utilize a cost-effective and scalable virtualization layer (e.g. AWS EC2, KVM, Docker, etc.) to maximize resource utilization
Implement proper monitoring and logging tools to optimize resource utilization
Efficient Logic, queries of serverless and dedicated instances
Use an efficient query language (e.g. SQL, NoSQL, etc.) to efficiently query data from serverless and dedicated instances
Utilize indexing techniques to optimize queries and reduce the amount of resources needed for query execution
Leverage caching techniques (e.g. in-memory/disk caching) to reduce the amount of queries needed to be executed
Efficient Storage, read/write of serverless and dedicated instances
Utilize a distributed storage system (e.g. AWS S3, HDFS, etc.) for efficient storage of data across serverless and dedicated instances
Implement proper data partitioning techniques (e.g. sharding, replication, etc.) to optimize read/write performance
Utilize an efficient data compression algorithm (e.g. GZIP, ZLIB, etc.) to reduce the size of stored data and improve read/write performance
Concurrency (Hardware/Software) of serverless and dedicated instances
Utilize an efficient distributed computing framework (e.g. Apache Spark, Hadoop, etc.) to efficiently parallelize tasks across serverless and dedicated instances
Implement proper synchronization techniques (e.g. locks, semaphores, etc.) to ensure concurrent access to shared resources is properly handled
Utilize an efficient capacity planning system (e.g. AWS EC2, Heroku, etc.) to ensure capacity is properly planned for serverless and dedicated instances
Implement proper resource monitoring and scaling techniques (e.g. autoscaling, predictive scaling, etc.) to ensure proper capacity is available when needed
Leverage efficient resource scheduling techniques (e.g. batch scheduling, dynamic resource scheduling, etc.) to optimize resource utilization and ensure proper capacity is available
Optimize your code: Make sure your code is optimized for performance, using techniques such as reducing the number of function calls, minimizing memory allocation, and using caching.
Use AWS X-Ray: Use AWS X-Ray to identify and troubleshoot performance bottlenecks in your Lambda function.
Use smaller memory sizes: Allocate the minimum amount of memory required for your function. The more memory you allocate, the more CPU and network bandwidth are available to your function. Allocating more memory will also consume more CPU and network resources, which could negatively impact the performance of the function.
Use environment variables: Use environment variables to configure your Lambda function, rather than hard-coding values in your code.
Use the appropriate runtime: Use the appropriate runtime for your function, as different runtimes can have different performance characteristics.
Use provisioned concurrency: Use provisioned concurrency to ensure that your function is fully warmed up when it's first invoked.
Use smaller packages: Package your function code and dependencies in a smaller package to reduce the deployment package size.
Use Lambda layers: Use Lambda layers to share common libraries and dependencies across multiple functions, which can help to reduce the size of your deployment package.
Use CloudWatch metrics: Use CloudWatch metrics to monitor the performance of your Lambda function and identify any issues.
Use Amazon CloudFront: Use Amazon CloudFront to cache your function's response and reduce the load on your function.
Use environment variables instead of hard-coded values: This will allow you to easily change configuration settings without modifying the code.
Minimize the number of dependencies: The more dependencies your function has, the longer it will take to start up. Use only the libraries that are essential for your function to run.
Use connection pooling: If your function is connecting to a database, use a connection pool to reuse existing connections, instead of creating a new connection for each request.
Enable automatic scaling: Set up automatic scaling so that your function can handle increased traffic without manual intervention.
Use a caching mechanism: Use a caching mechanism like Redis or Memcached to store frequently accessed data, so that your function does not need to retrieve it from a slower storage system each time.
Use the appropriate instance type: Select the instance type that best matches the workload of your application.
Use an SSD for storage: SSDs provide faster read and write speeds than traditional HDDs, which can greatly improve the performance of your instances.
Use an Amazon Machine Image (AMI) with optimized configurations: Some AMIs are preconfigured with optimized settings that can improve the performance of your instances.
Use a placement group: Placement groups allow you to group instances together in a single availability zone to reduce network latency.
Use a load balancer: Load balancers can distribute incoming traffic across multiple instances, reducing the load on any one instance and improving overall performance.
Use auto-scaling: Auto-scaling allows you to automatically add or remove instances based on the current traffic, ensuring that you have enough resources to handle the load.
Use a Content Delivery Network (CDN): CDNs can cache content closer to users, reducing the time it takes for content to be delivered to them.
Use an Elastic IP: An Elastic IP allows you to remap your IP address to any instance in your account, avoiding downtime when instances are stopped or terminated.
Use Performance Monitoring: Use Amazon CloudWatch to monitor the performance of your instances and detect any potential issues.
Use the right Operating system: Optimize the operating system of your instances for the workload. Use Linux for web servers, Windows for desktop applications, and so on.
Use EBS Optimized instances: EBS Optimized instances are designed to provide optimal performance for EBS volumes, allowing for faster I/O operations and reducing latency.
Use enhanced networking: Enhanced networking provides higher network throughput and lower latency by using the advanced network interface of the host.
Use provisioned IOPS: Provisioned IOPS (Input/Output Operations Per Second) allows you to set the number of IOPS that your EBS volume can support, ensuring that your instances have the I/O performance they need.
Use Amazon Elastic Block Store (EBS) snapshots: EBS snapshots allow you to create point-in-time backups of your EBS volumes, which you can use to quickly restore your instances in case of failure.
Use Amazon Elastic File System (EFS): EFS allows you to create a file system that can be accessed by multiple instances, providing a shared storage solution that can help improve performance.
Use custom AMIs: Custom AMIs allow you to create a custom image of your instances that includes your applications, configurations, and data. This can help speed up the process of launching new instances.
Use a virtual private cloud (VPC): VPC allows you to isolate your instances from the public internet and create a secure, private network for your instances to communicate on. This can help improve security and performance.
Underutilized instances: Trusted Advisor can identify instances that are running below their CPU utilization threshold, and recommend that you resize or terminate them to save on costs.
Overutilized instances: Trusted Advisor can identify instances that are running at or above their CPU utilization threshold, and recommend that you resize them to ensure optimal performance.
Unused instances: Trusted Advisor can identify instances that have been running for an extended period of time without any CPU or network activity, and recommend that you terminate them to save on costs.
Unassociated Elastic IP addresses: Trusted Advisor can identify Elastic IP addresses that are not associated with any instances, and recommend that you release them to save on costs.
Idle load balancers: Trusted Advisor can identify load balancers that have not received any traffic in the past 24 hours, and recommend that you terminate them to save on costs.
EBS optimized instances: Trusted Advisor can identify instances that are not EBS-optimized, which can cause poor EBS I/O performance.
IOPS Optimization: Trusted Advisor can identify if instances are not properly optimized for IOPS and recommend to adjust the IOPS according to the workload.
Instance age: Trusted Advisor can identify instances that have been running for a long time and recommend to perform regular maintenance and updates to keep them in good working condition.