Este documento é destinado a arquitetos, desenvolvedores e administradores que
planejam, projetam, implantam e gerenciam cargas de trabalho no Google Cloud.
As recomendações neste pilar podem ajudar sua organização a operar de maneira eficiente, melhorar a satisfação do cliente, aumentar a receita e reduzir custos.
Por exemplo, quando o tempo de processamento de back-end de um aplicativo diminui, os usuários
têm tempos de resposta mais rápidos, o que pode aumentar a retenção de usuários e
a receita.
O processo de otimização de desempenho pode envolver uma compensação entre desempenho e custo. No entanto, otimizar o desempenho às vezes pode ajudar você a reduzir custos. Por exemplo, quando a carga aumenta, o escalonamento automático ajuda a
fornecer um desempenho previsível, garantindo que os recursos do sistema não sejam
sobrecarregados. O escalonamento automático também ajuda a reduzir custos removendo recursos não utilizados durante períodos de baixa carga.
A otimização do desempenho é um processo contínuo, e não uma atividade única. O
diagrama a seguir mostra os estágios no processo de otimização de desempenho:
O processo de otimização de desempenho é um ciclo contínuo que inclui as
seguintes etapas:
Defina requisitos: defina requisitos de desempenho granulares para
cada camada da pilha de aplicativos antes de projetar e desenvolver seus
aplicativos. Para planejar a alocação de recursos, considere as principais características da carga de trabalho e as expectativas de desempenho.
Projetar e implantar: use padrões de design elásticos e escalonáveis que podem
ajudar você a atender aos requisitos de performance.
Monitorar e analisar: monitore o desempenho continuamente usando registros, rastreamento, métricas e alertas.
Otimize: considere possíveis reformulações à medida que seus aplicativos evoluem.
Dimensionar corretamente os recursos da nuvem e usar novos recursos para atender aos requisitos de desempenho
em constante mudança.
Conforme mostrado no diagrama anterior, continue o ciclo de monitoramento,
reavaliação de requisitos e ajuste dos recursos da nuvem.
Para princípios e recomendações de otimização de performance específicos para cargas de trabalho de IA e ML, consulte
Perspectiva de IA e ML: otimização de performance
no Well-Architected Framework.
Princípios básicos
As recomendações no pilar de otimização de performance do framework bem arquitetado são mapeadas para os seguintes princípios básicos:
[[["Fácil de entender","easyToUnderstand","thumb-up"],["Meu problema foi resolvido","solvedMyProblem","thumb-up"],["Outro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Informações incorretas ou exemplo de código","incorrectInformationOrSampleCode","thumb-down"],["Não contém as informações/amostras de que eu preciso","missingTheInformationSamplesINeed","thumb-down"],["Problema na tradução","translationIssue","thumb-down"],["Outro","otherDown","thumb-down"]],["Última atualização 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"]]