Este documento está dirigido a arquitectos, desarrolladores y administradores que planifican, diseñan, implementan y administran cargas de trabajo en Google Cloud.
Las recomendaciones de este pilar pueden ayudar a tu organización a operar de manera eficiente, mejorar la satisfacción del cliente, aumentar los ingresos y reducir los costos.
Por ejemplo, cuando el tiempo de procesamiento de backend de una aplicación disminuye, los usuarios experimentan tiempos de respuesta más rápidos, lo que puede generar una mayor retención de usuarios y más ingresos.
El proceso de optimización del rendimiento puede implicar una compensación entre el rendimiento y el costo. Sin embargo, optimizar el rendimiento a veces puede ayudarte a reducir costos. Por ejemplo, cuando aumenta la carga, el ajuste de escala automático puede ayudar a proporcionar un rendimiento predecible, ya que garantiza que los recursos del sistema no se sobrecarguen. El ajuste de escala automático también te ayuda a reducir costos, ya que quita los recursos que no se usan durante los períodos de carga baja.
La optimización del rendimiento es un proceso continuo, no una actividad única. En el siguiente diagrama, se muestran las etapas del proceso de optimización del rendimiento:
El proceso de optimización del rendimiento es un ciclo continuo que incluye las siguientes etapas:
Define los requisitos: Define requisitos de rendimiento detallados para cada capa de la pila de aplicaciones antes de diseñar y desarrollar tus aplicaciones. Para planificar la asignación de recursos, considera las características clave de la carga de trabajo y las expectativas de rendimiento.
Diseña e implementa: Usa patrones de diseño elásticos y escalables que puedan ayudarte a cumplir con tus requisitos de rendimiento.
Supervisa y analiza: Supervisa el rendimiento de forma continua con registros, seguimiento, métricas y alertas.
Optimiza: Considera posibles rediseños a medida que evolucionan tus aplicaciones.
Redimensiona los recursos de la nube y usa funciones nuevas para satisfacer los requisitos de rendimiento cambiantes.
Como se muestra en el diagrama anterior, continúa el ciclo de supervisión, reevaluación de los requisitos y ajuste de los recursos de la nube.
Si deseas conocer los principios y las recomendaciones de optimización del rendimiento específicos para las cargas de trabajo de IA y AA, consulta Perspectiva de IA y AA: Optimización del rendimiento en Well-Architected Framework.
Principios básicos
Las recomendaciones del pilar de optimización del rendimiento del Framework de Well-Architected se asignan a los siguientes principios básicos:
[[["Fácil de comprender","easyToUnderstand","thumb-up"],["Resolvió mi problema","solvedMyProblem","thumb-up"],["Otro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Información o código de muestra incorrectos","incorrectInformationOrSampleCode","thumb-down"],["Faltan la información o los ejemplos que necesito","missingTheInformationSamplesINeed","thumb-down"],["Problema de traducción","translationIssue","thumb-down"],["Otro","otherDown","thumb-down"]],["Última actualización: 2024-12-06 (UTC)"],[[["\u003cp\u003eThis document, part of the Google Cloud Well-Architected Framework, offers guidance on optimizing the performance of workloads in Google Cloud for architects, developers, and administrators.\u003c/p\u003e\n"],["\u003cp\u003ePerformance optimization is an ongoing process that includes defining requirements, designing and deploying, monitoring and analyzing, and optimizing resources in a continuous cycle.\u003c/p\u003e\n"],["\u003cp\u003eThe core principles of performance optimization in this framework include planning resource allocation, taking advantage of elasticity, promoting modular design, and continuously monitoring and improving performance.\u003c/p\u003e\n"],["\u003cp\u003eOptimizing performance can lead to improved operational efficiency, enhanced customer satisfaction, increased revenue, and reduced costs, with potential trade-offs between performance and cost.\u003c/p\u003e\n"],["\u003cp\u003eThere is a guide available for AI and ML specific performance optimization, in the AI and ML perspective of the Well-Architected Framework.\u003c/p\u003e\n"]]],[],null,["# Well-Architected Framework: Performance optimization pillar\n\n| To view the content in the performance optimization pillar on a single page or to to get a PDF output of the content, see [View on one page](/architecture/framework/performance-optimization/printable).\n\nThis pillar in the\n[Google Cloud Well-Architected Framework](/architecture/framework)\nprovides recommendations to optimize the performance of workloads in\nGoogle Cloud.\n\nThis document is intended for architects, developers, and administrators who\nplan, design, deploy, and manage workloads in Google Cloud.\n\nThe recommendations in this pillar can help your organization to operate\nefficiently, improve customer satisfaction, increase revenue, and reduce cost.\nFor example, when the backend processing time of an application decreases, users\nexperience faster response times, which can lead to higher user retention and\nmore revenue.\n\nThe performance optimization process can involve a trade-off between\nperformance and cost. However, optimizing performance can sometimes help you\nreduce costs. For example, when the load increases, autoscaling can help to\nprovide predictable performance by ensuring that the system resources aren't\noverloaded. Autoscaling also helps you to reduce costs by removing unused\nresources during periods of low load.\n\nPerformance optimization is a continuous process, not a one-time activity. The\nfollowing diagram shows the stages in the performance optimization process:\n\nThe performance optimization process is an ongoing cycle that includes the\nfollowing stages:\n\n1. **Define requirements**: Define granular performance requirements for each layer of the application stack before you design and develop your applications. To plan resource allocation, consider the key workload characteristics and performance expectations.\n2. **Design and deploy**: Use elastic and scalable design patterns that can help you meet your performance requirements.\n3. **Monitor and analyze**: Monitor performance continually by using logs, tracing, metrics, and alerts.\n4. **Optimize**: Consider potential redesigns as your applications evolve.\n Rightsize cloud resources and use new features to meet changing performance\n requirements.\n\n As shown in the preceding diagram, continue the cycle of monitoring,\n re-assessing requirements, and adjusting the cloud resources.\n\n\nFor performance optimization principles and recommendations that are specific to AI and ML workloads, see\n[AI and ML perspective: Performance optimization](/architecture/framework/perspectives/ai-ml/performance-optimization)\nin the Well-Architected Framework.\n\nCore principles\n---------------\n\nThe recommendations in the performance optimization pillar of the Well-Architected Framework\nare mapped to the following core principles:\n\n- [Plan resource allocation](/architecture/framework/performance-optimization/plan-resource-allocation)\n- [Take advantage of elasticity](/architecture/framework/performance-optimization/elasticity)\n- [Promote modular design](/architecture/framework/performance-optimization/promote-modular-design)\n- [Continuously monitor and improve performance](/architecture/framework/performance-optimization/continuously-monitor-and-improve-performance)\n\nContributors\n------------\n\nAuthors:\n\n- [Daniel Lees](https://www.linkedin.com/in/daniellees) \\| Cloud Security Architect\n- [Gary Harmson](https://www.linkedin.com/in/garyharmson) \\| Principal Architect\n- [Luis Urena](https://www.linkedin.com/in/urena-luis) \\| Developer Relations Engineer\n- [Zach Seils](https://www.linkedin.com/in/zachseils) \\| Networking Specialist\n\n\u003cbr /\u003e\n\nOther contributors:\n\n- [Filipe Gracio, PhD](https://www.linkedin.com/in/filipegracio) \\| Customer Engineer, AI/ML Specialist\n- [Jose Andrade](https://www.linkedin.com/in/jmandrade) \\| Customer Engineer, SRE Specialist\n- [Kumar Dhanagopal](https://www.linkedin.com/in/kumardhanagopal) \\| Cross-Product Solution Developer\n- [Marwan Al Shawi](https://www.linkedin.com/in/marwanalshawi) \\| Partner Customer Engineer\n- [Nicolas Pintaux](https://www.linkedin.com/in/nicolaspintaux) \\| Customer Engineer, Application Modernization Specialist\n- [Ryan Cox](https://www.linkedin.com/in/ryanlcox) \\| Principal Architect\n- [Radhika Kanakam](https://www.linkedin.com/in/radhika-kanakam-18ab876) \\| Program Lead, Google Cloud Well-Architected Framework\n- [Samantha He](https://www.linkedin.com/in/samantha-he-05a98173) \\| Technical Writer\n- [Wade Holmes](https://www.linkedin.com/in/wholmes) \\| Global Solutions Director\n\n\u003cbr /\u003e"]]