이 문서는 Google Cloud에서 워크로드를 계획, 설계, 배포, 관리하는 설계자, 개발자, 관리자를 대상으로 합니다.
이 필라의 권장사항은 조직이 효율적으로 운영하고, 고객 만족도를 높이고, 수익을 늘리고, 비용을 절감하는 데 도움이 됩니다.
예를 들어 애플리케이션의 백엔드 처리 시간이 단축되면 사용자의 응답 시간이 빨라져 사용자 유지율과 수익이 증가할 수 있습니다.
성능 최적화 프로세스에는 성능과 비용 간의 절충이 포함될 수 있습니다. 하지만 성능을 최적화하면 비용을 절감할 수 있습니다. 예를 들어 부하가 증가할 때 자동 확장은 시스템 리소스가 과부하되지 않도록 하여 예측 가능한 성능을 제공하는 데 도움이 됩니다. 또한 자동 확장을 사용하면 부하가 낮은 기간에 사용되지 않는 리소스를 삭제하여 비용을 절감할 수 있습니다.
성능 최적화는 일회성 활동이 아닌 연속적인 프로세스입니다. 다음 다이어그램은 성능 최적화 프로세스의 단계를 보여줍니다.
성능 최적화 프로세스는 다음 단계를 포함하는 지속적인 사이클입니다.
요구사항 정의: 애플리케이션을 설계하고 개발하기 전에 애플리케이션 스택의 각 레이어에 대한 세부 성능 요구사항을 정의합니다. 리소스 할당을 계획하려면 주요 워크로드 특성과 성능 기대치를 고려하세요.
설계 및 배포: 성능 요구사항을 충족하는 데 도움이 될 수 있는 탄력적이고 확장 가능한 설계 패턴을 사용합니다.
모니터링 및 분석: 로그, 추적, 측정항목, 알림을 사용하여 성능을 지속적으로 모니터링합니다.
최적화: 애플리케이션이 발전함에 따라 잠재적인 재설계를 고려합니다.
클라우드 리소스의 크기를 적절하게 조정하고 새로운 기능을 사용하여 변화하는 성능 요구사항을 충족합니다.
위의 다이어그램에 표시된 것처럼 모니터링, 요구사항 재평가, 클라우드 리소스 조정 주기를 계속합니다.
AI 및 ML 워크로드와 관련된 성능 최적화 원칙 및 권장사항은 Well-Architected Framework의 AI 및 ML 관점: 성능 최적화를 참조하세요.
핵심 원칙
Well-Architected Framework의 성능 최적화 원칙의 권장사항은 다음 핵심 원칙에 매핑됩니다.
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 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"]]