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應用和網路效能監控
使用整合式 APM 和 NPM 工具,主動監控網路和應用的效能
利用整合監控將干擾降到最低
整合的網路效能監控(NPM)和應用效能監控(APM)工具,可模擬真實流量、追蹤網路、網站和應用程式的回應時間,讓您能比使用者更早發現問題。
探索網路和應用效能監控軟體
什麼是整合監控?
不同於真實使用者監控(RUM)工具依賴即時網路和應用流量,整合監控工具會產生模擬的應用流量,將其傳送到您的網路和應用中,然後量測到達預定目的地所需的時間。 不間斷地進行測試,讓網路運作和應用交付團隊能夠在各種條件下監控 QoE 和服務品質(QoS),以找出服務品質下滑、網路/應用延遲,或傳輸瓶頸等問題。
整合 APM 和 NPM 工具有哪些用途?
防止網路延遲並確保一致的 QoS
不要讓網路瓶頸、延遲、Wi-Fi 問題,以及其他難以發現的問題,讓網路服務品質(例如整合通訊、VoIP、SaaS 和商業應用)和網路效能下滑。 Hawkeye 可模擬各種真實流量負載、互動和應用的不同組合,來執行預定的驗證測試,以監控網路效能、隔離問題,然後主動偵測問題。 利用 AI 支援的異常偵測和通過/不通過指標,您可排定進行故障排除的優先順序,而逐點分析功能則可讓您輕鬆執行問題根源分析並立即排除問題。
讓所有行動裝置使用者都能獲得一致的體驗
要維持行動應用程式和網站的服務品質(QoS),僅僅進行傳統的應用效能監控是不夠的, 您還需管理各種變數,包括裝置、作業系統等等。 Eggplant Monitoring 可模擬 Apple 和 Android 環境中的螢幕解析度、上傳和下載速度,以及網路延遲,讓您能確保所有可存取的系統、網站和應用程式,都能為使用者提供一致的行動體驗和功能。
從核心到邊緣,更快進行故障排除並最佳化服務品質
不要讓複雜的拓撲和盲點,影響您在分散式混合網路中維持 QoS 的能力。 隨著關鍵運算資源和工作負載從雲端轉移到網路邊緣,網路運作團隊需透過整合的 NPM 工具,來偵測效能變慢的情形,才能找出導致問題的根源。 Hawkeye 提供一系列基於硬體和軟體的網路端點,讓您能執行從核心到邊緣的端對端服務檢測。 同時,節點到節點的流量可視化可讓您加速進行故障排除,進而減少到府服務的需求。
從使用者的角度監控網站和應用程式
減輕因為處理大型 JavaScript 檔、未壓縮的圖像和影片,導致網站效能下滑的問題。 Eggplant Monitoring 可模擬使用者行為,然後持續測試您的網站和應用程式的載入時間、下載速度,以及其他與 UX 相關的效能指標。 模擬用戶端因素(重試邏輯和可變參數化)和後端互動,例如 API、行動、FTP 和雙重認證,以查看客戶體驗、找出傳輸瓶頸,試圖找出最佳化機會。
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應用和網路效能監控軟體常見問答集
What is network performance monitoring (NPM)?
Network performance monitoring (NPM) tools enable you to visualize, monitor, troubleshoot, and maximize a given network's performance, availability, and quality of service.
These tools operate one of two ways: reporting live network traffic or generating synthetic traffic and sending it across the network to various hardware- or software-based endpoints. Frequently, these tools display critical metrics and KPIs such as packet loss, jitter, delay, response time, and mean opinion score. A highly-visual, real-time dashboard utilizing AI or machine learning makes it easy for network operations teams to identify outliers and potential issues to follow up on.
What is application performance monitoring (APM)?
Application performance monitoring (APM) tools enable IT personnel and DevOps teams to ensure enterprise and customer-facing applications meet users' expected performance, reliability, and user experience (UX) goals.
APM tools generally fall into one of two categories: real user monitoring (RUM) or synthetic monitoring. RUM platforms capture and report on traffic metrics and performance checks derived from real application users — providing real-time insights into UX and performance. Conversely, synthetic monitoring tools emulate user interactions to benchmark application performance under various conditions and scenarios — enabling operations teams to identify and remediate potential bottlenecks faster.
What is synthetic monitoring?
Traditional monitoring tools rely on actual traffic data, sometimes called passive data. Synthetic monitoring (active monitoring) tools generate simulated application traffic, inject it into your network, and capture key performance indicators. Running simulations lets you observe your network's performance under various conditions and note where performance does not meet expectations.
The process is active because you control the type and mix of applications and the traffic volume in each simulation. Since your monitoring tool is not dependent on live traffic, you can anticipate performance problems and test the impact of potential fixes. You move from being passive and reactive to being proactive.
How can you use synthetic monitoring tools?
Synthetic monitoring is excellent for assessing network readiness before deploying SD-WAN, distributed unified communications, cloud applications, or voice and video services like Microsoft Teams or Zoom. Since these tools rely on simulated traffic to measure response time, quality, or latency, you can predict performance and pinpoint bottlenecks before going live. Moreover, most industry-leading tools offer a library of application signatures — enabling you to build highly accurate tests with the exact type of traffic you expect while varying the volume to model changes in demand.
A flexible monitoring platform lets you simulate traffic from various endpoints across your distributed network, so that you can measure performance in a wide range of operating scenarios. You can test node-to-node connections in a distributed network, validate end-user experience using cloud-based applications, or ensure large-scale network deployments are ready for release.
In addition to pre-deployment and live network assessments, you can also use continuous active monitoring to proactively maintain QoS. Tracking daily simulation results makes it easy to identify deviations from the norm — giving you an early indication of when performance falls below minimum service levels.
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