Event-driven architecture is a pattern where components and services within an application communicate through the generation and processing of events. In AWS Lambda, a Lambda function is triggered in response to an event, concurrently processing it and invoking other services as needed. This enables users to build loosely-coupled, scalable, and efficient systems by decoupling event-producing services from event-consuming services.

The function execution model involves Lambda provisioning and managing the resources required to run your functions. Whenever an event occurs, AWS Lambda automatically scales your function instances based on the number of concurrent requests. Each instance of your function, known as a container, is instantiated with specific settings such as memory, compute power, runtime, and trigger configuration. Lambda is designed to horizontally scale without direct user interaction or intervention, allowing you to focus on the code and logic.

Lambda@Edge is an extension of the AWS Lambda service that allows you to execute serverless functions at edge locations closer to your users. This reduces latency and enables you to perform real-time processing of HTTP requests and responses, tailoring the content delivered to users based on factors like location, device type, or authentication status. Lambda@Edge functions are triggered by Amazon CloudFront events and can be used for tasks such as dynamic origin routing, URL rewriting, or custom authentication and authorization.

AWS Lambda comes with certain limitations for function execution, such as memory, execution duration (by default up to 15 minutes), function environment variables (limited storage), disk space (including libraries and dependencies), and concurrent invocation limits (by default 1000 per region). These restrictions must be considered while designing and developing your application for effective and efficient use of AWS Lambda. Function limitations can be configured to some extent, but understanding these constraints is essential to ensure the smooth functioning of your application on this platform.

Cold start and warm start refer to the two different states of a Lambda function container. A cold start occurs when your function is invoked for the first time or after a period of inactivity. During the cold start, AWS Lambda provisions a new container, loads runtime environments, and initializes the function code, increasing the overall execution time. A warm start occurs when a container is reused for subsequent invocations, reducing latency and improving execution performance. Maintaining an optimal number of warm containers is critical in minimizing response times and providing a better user experience.

Lambda Triggers are the AWS services or resources that can invoke or start an AWS Lambda function on specific events. These services include Amazon S3, Amazon DynamoDB, Amazon Kinesis, and various other AWS resources. The Lambda function accepts an event in JSON format that contains information related to the triggering event, which helps the function to process the data as per the requirement. Using Lambda triggers allows you to create event-driven processing pipelines, serverless data processing, and decouple your application components to improve the scalability and manageability of your applications.

Lambda Layers is a concept in AWS Lambda that allows you to manage and distribute shared code, libraries, and other function dependencies that are required across multiple Lambda functions. A layer is an archive containing libraries, a custom runtime, or other kind of reusable code. You can include the layer in a Lambda function by specifying the layer's Amazon Resource Name (ARN). This eliminates the need to include the same code or libraries in every Lambda function that requires it, reducing duplication, simplifying management, and reducing package size. Layers are versioned, so you can easily manage updates and rollbacks.

Provisioned Concurrency is a feature in AWS Lambda that helps in reducing function invocation latency and ensures consistent performance of your Lambda function when it is required to handle frequent, spiky workloads. It pre-initializes a specific number of execution environments or instances, which are ready to respond immediately to your function requests. You can also configure auto-scaling for your provisioned concurrency to adjust the number of instances based on the demand. This helps in reducing the cold start latency, as a pre-warmed execution environment is readily available to handle invocations.

Function Versioning and Aliases are concepts in AWS Lambda that provide additional control over your application's deployment and rollback process. A version is an immutable snapshot of the Lambda function code and its configuration, including the runtime, memory size, and environment variables. Each version has a unique Amazon Resource Name (ARN) which remains unchanged throughout its lifecycle. An alias is a pointer to a specific version, which allows you to reference a Lambda function in your applications without updating them whenever the underlying function version changes. You can have multiple aliases pointing to different versions, which allows you to implement blue-green deployments, canary releases, and other advanced deployment strategies.

AWS Lambda Permission Model is a set of Identity and Access Management (IAM) policies that define and control the permissions required for a Lambda function to access AWS resources as well as to be accessed by other resources. This includes execution role policies that grant the necessary permissions to a Lambda function to perform its operation and resource-based policies that provide the other AWS resources access to invoke the Lambda function. This permission model ensures that your applications are secure at the individual function level and follow the principle of least privilege by granting only the necessary permissions required for a function to perform its intended task.