Performance Tuning

adobe/skills/plugins/aem/cloud-service/skills/dispatcher/performance-tuning

作者 adobe940b8795c0df3e25de68ce3881dc90855129b215Apache-2.0197 個星標收錄於 2026年10月9日更新於 2026年10月9日儲存庫今天更新

Optimize performance of the Adobe Dispatcher Apache HTTP Server module and related HTTPD configuration for AEMaaCS cloud workflows only, with cloud-specific baseline and runtime verification.

僅含說明DevOps & Cloud
AI 產生的概覽

使用 Dispatcher MCP 工具為 AEMaaCS 雲端部署調校 Adobe Dispatcher 與 HTTPD 設定效能。

功能
指導針對 AEM as a Cloud Service 部署的 Adobe Dispatcher Apache HTTP Server 模組及相關 HTTPD 設定進行效能調校。它會擷取基準指標與快取證據、套用雲端護欄、依優先順序選擇低風險變更、進行最小幅度編輯,並透過 validate、lint 與 sdk 檢查驗證。輸出包含基準快照、附影響與風險的優化清單、變更檔案、前後對照證據、所選測試 ID 以及回復方案。
適用情境
適用於提升雲端服務 Dispatcher 與 HTTPD 設定的快取效率、延遲或輸送量。僅針對雲端部署,部署變體由技能目錄固定。邊緣、WAF 或純 CDN 相關問題會轉由其他指引處理,不在此處理。
執行需求
需要設定為雲端變體的 Dispatcher MCP(AEM_DEPLOYMENT_MODE=cloud),以及 validate、lint、sdk、trace_request、inspect_cache、monitor_metrics 和 tail_logs 工具。不附帶指令碼,僅為指示與參考文件。

Dispatcher Performance Tuning (Cloud)

Improve cache efficiency, latency, and throughput for cloud deployments that use the Adobe Dispatcher Apache HTTP Server module and related HTTPD configuration.

Variant Scope

  • This skill is cloud-service-only.
  • Scope is fixed by this skill directory; do not ask the user to choose deployment variant.

MCP Tool Contract

Use only these Dispatcher MCP tools:

  • validate
  • lint
  • sdk
  • trace_request
  • inspect_cache
  • monitor_metrics
  • tail_logs

Workflow

  1. Capture baseline metrics and cache evidence.
  2. Apply cloud guardrails (immutable/default includes, reserved probe paths, and CDN-vs-Dispatcher ownership) before proposing changes.
  3. Prioritize low-risk/high-impact changes.
  4. Apply minimal edits.
  5. Verify with validate, lint, and sdk.
  6. Compare before/after runtime evidence.

Verification Scope Selection

Use shared references to select optimization evidence depth:

  • mode-specific-verification-matrix.md
  • test-case-catalog.md

Output Contract

Always return:

  • baseline metrics snapshot
  • prioritized optimization list with impact/risk
  • changed files and intent
  • executed checks + before/after evidence
  • selected test IDs and outcomes
  • rollback plan and open risks

Guardrails

  • Do not claim improvement without measurable comparison.
  • Keep high-risk tuning opt-in unless user explicitly requests it.
  • Keep cloud assumptions explicit for each recommendation batch.
  • Route edge/WAF/CDN-only concerns to CDN layer guidance instead of Dispatcher config changes.

References

  • optimization-patterns.md
  • performance-scenario-playbooks.md – scenario-driven tuning flows adapted from broader MCP prompt surfaces
  • load-testing-guidance.md
  • performance-monitoring-setup.md
  • quick-start-execution-path.md – fast entry path for optimization requests
  • repo-layout-workflows.md – map performance findings to cache, farm, vhost, and rewrite file families
  • playbook-command-linkage.md – exact MCP command chains for tuning playbooks
  • mode-specific-verification-matrix.md
  • cloud-service-aemaacs-guardrails.md – cloud-service-only immutable/include/runtime boundary checks from AEMaaCS patterns
  • test-case-catalog.md
  • change-risk-and-rollback-template.md
  • public-docs-index.md
  • public-doc-citation-rules.md
  • core-7-tools-reference.md

來源與署名

來源:adobe/skills位於plugins/aem/cloud-service/skills/dispatcher/performance-tuning提交940b879

授權條款: Apache-2.0

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