Questo documento è destinato ad architetti, sviluppatori e amministratori che pianificano, progettano, eseguono il deployment e gestiscono i carichi di lavoro in Google Cloud.
I consigli di questo pilastro possono aiutare la tua organizzazione a operare
in modo efficiente, migliorare la soddisfazione dei clienti, aumentare le entrate e ridurre i costi.
Ad esempio, quando il tempo di elaborazione backend di un'applicazione diminuisce, gli utenti
sperimentano tempi di risposta più rapidi, il che può portare a una maggiore fidelizzazione degli utenti e
a maggiori entrate.
Il processo di ottimizzazione delle prestazioni può comportare un compromesso tra prestazioni e costi. Tuttavia, l'ottimizzazione delle prestazioni a volte può aiutarti a
ridurre i costi. Ad esempio, quando il carico aumenta, la scalabilità automatica può contribuire a
fornire prestazioni prevedibili assicurando che le risorse di sistema non siano
sovraccariche. La scalabilità automatica ti aiuta anche a ridurre i costi rimuovendo le risorse inutilizzate
durante i periodi di carico ridotto.
L'ottimizzazione del rendimento è un processo continuo, non un'attività una tantum. Il
seguente diagramma mostra le fasi del processo di ottimizzazione del rendimento:
Il processo di ottimizzazione del rendimento è un ciclo continuo che include le seguenti fasi:
Definisci i requisiti: definisci requisiti di rendimento granulari per ogni livello dello stack di applicazioni prima di progettare e sviluppare le tue applicazioni. Per pianificare
l'allocazione delle risorse, considera le caratteristiche principali del carico di lavoro e le aspettative
di rendimento.
Progettazione e deployment: utilizza pattern di progettazione elastici e scalabili che possono
aiutarti a soddisfare i requisiti di rendimento.
Monitora e analizza: monitora continuamente il rendimento utilizzando log,
tracciamento, metriche e avvisi.
Ottimizza: valuta potenziali riprogettazioni man mano che le tue applicazioni si evolvono.
Ridimensiona correttamente le risorse cloud e utilizza nuove funzionalità per soddisfare i requisiti di prestazioni in evoluzione.
Come mostrato nel diagramma precedente, continua il ciclo di monitoraggio,
rivalutazione dei requisiti e aggiustamento delle risorse cloud.
[[["Facile da capire","easyToUnderstand","thumb-up"],["Il problema è stato risolto","solvedMyProblem","thumb-up"],["Altra","otherUp","thumb-up"]],[["Difficile da capire","hardToUnderstand","thumb-down"],["Informazioni o codice di esempio errati","incorrectInformationOrSampleCode","thumb-down"],["Mancano le informazioni o gli esempi di cui ho bisogno","missingTheInformationSamplesINeed","thumb-down"],["Problema di traduzione","translationIssue","thumb-down"],["Altra","otherDown","thumb-down"]],["Ultimo aggiornamento 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"]]