Chapter 8 - Serverless Processing Systems

The process of adjusting the settings of a serverless application to achieve optimal performance and cost-efficiency.

An application's behavior can be significantly influenced by the values assigned to runtime parameters.

The process of finding the right balance between performance and cost-efficiency for a serverless application.

A serverless platform's ability to automatically scale resources up or down based on demand.

Google App Engine (GAE) uses parameters like the minimum number of instances, the maximum number of instances, and the idle timeout to manage its autoscaling behavior.

A characteristic of applications where workload demands fluctuate significantly over time, often with periods of high usage and periods of low or no usage.

A type of cloud computing where resources are provisioned and billed only when they are actively in use. This allows for automatic scaling to meet peak demands without incurring costs for idle resources.

The ability of a system to adjust its resources (such as servers, storage, or processing power) automatically to handle changes in workload, ensuring optimal performance and cost efficiency.

The practice of using cloud resources based on an as-needed basis, paying only for consumed resources, which helps optimize costs and minimize waste.

The significant financial burden associated with utilizing cloud resources excessively or inefficiently, leading to high cloud service bills.

A critical aspect of cloud computing where resources are dynamically adjusted to meet changing workload demands, ensuring optimal performance while controlling costs.

The ability to rapidly and dynamically adapt system resources (like servers, storage, or processing power) to handle fluctuations in workload, enabling systems to deliver consistent performance under various demands.

The challenge of planning the appropriate capacity for a system to handle anticipated workload demands, ensuring sufficient resources without overprovisioning and incurring unnecessary costs.

Google App Engine (GAE) is a platform provided by Google Cloud Platform (GCP) that enables developers to run HTTP-based applications on Google's managed infrastructure.

GAE supports various programming languages like Go, Java, Python, Node.js, PHP, .NET, and Ruby.

GAE manages application execution dynamically, launching compute resources based on request demand.

GAE offers a choice between two environments: the Standard and the Flexible environment. The Standard environment is tightly managed by GAE, providing rapid scaling but with language version restrictions. The Flexible environment is a tailored version of Google Compute Engine (GCE) providing more development flexibility and running applications in Docker containers.

In the Standard environment, developers upload their application code to a GAE project associated with a URL, and GAE routes requests to processing instances called resident instances to execute the application code.

Resident instances are the key component of the cost incurred for using GAE.

GAE allows developers to utilize popular HTTP-based application frameworks built with GAE runtime libraries. For example, in Python, applications can leverage Flask, Django, and web2py.

GAE provides access to managed persistent storage options like Firestore and Google Cloud SQL, and messaging services like Cloud Pub/Sub.

A technique to explore different configurations of a system to find the optimal setting for a desired outcome.

The percentage of available resources that are actively being used by a system at a given time.

A performance metric that measures the rate at which requests are processed by a system.

A performance metric that measures the number of requests a system can simultaneously handle at a given time.

The cost associated with running a system, taking into account factors like infrastructure, power consumption, and cloud services.

A configuration parameter that defines the target utilization level for a system's resources, such as CPU or memory.

The ability of a system to handle increased workload and adapt to changing demands.

The process of finding the optimal configuration for a system's resources, balancing performance with cost.

A setting that determines the minimum number of instances GAE will keep running at any given time. This can be 0, which is ideal for applications with infrequent traffic, as it prevents unnecessary costs.

A setting that determines the maximum number of instances GAE will keep running at any given time. This helps control costs by preventing GAE from running too many instances unnecessarily.

The time it takes for GAE to load and start a new application instance. This time varies based on the runtime environment, libraries used, and other factors.

A feature of GAE that enables it to route requests to instances dynamically based on the current load. This ensures that requests are handled efficiently and instances are not overloaded.

An application model that assumes that instances have no memory of past requests and handle each request independently. This is essential for efficient load balancing and scalability.

The process of releasing instances when the load drops, thus reducing unnecessary costs.

The standard environment is a platform that is designed specifically for scalable applications and supports a variety of programming languages. GAE manages the runtime environment, ensuring that applications can be loaded and executed efficiently.

GAE dynamically adjusts the number of processing instances for an application based on incoming traffic load. It ensures optimal performance by scaling up when needed and scaling down when idle.

GAE will not deploy any instances if there are no incoming requests, saving resources.

When a request arrives, GAE deploys an instance to process it. This deployment can take some time (hundreds of milliseconds to a few seconds).

To reduce latency for initial requests, you can specify a minimum number of instances to keep available for processing. This ensures quicker response times but comes at a cost.

GAE scales up the number of instances as the request load grows to handle requests efficiently.

The 'max-pending-latency' parameter controls the maximum time a request can wait in the queue before GAE starts additional instances to reduce latency.

The 'max-pending-latency' parameter impacts the speed of application scaling. Lower values lead to faster scaling but potentially higher costs.

GAE autoscaling parameters enable fine-tuning of a service's performance and cost. By adjusting settings, developers can balance responsiveness and resource consumption.