综合 APM 和 NPM 工具有什么作用？
不要让瓶颈、延迟、Wi-Fi 问题和其他难以确定的问题影响网络服务（如统一通信、VoIP、SaaS和企业应用）和性能。 Hawkeye 通过仿真一系列真实的流量负载、交互和应用组合，可以定期运行验证测试，以便监测网络性能、隔离故障以并主动检测问题。 人工智能支持的异常检测和合格/不合格指标可以帮助您确定故障诊断的优先级，然后逐跳进行分析，以便您轻松分析根本原因，进而修复问题区域。
保持各种移动应用和网站的服务质量不能只依靠传统的应用性能监测。 它意味着要管理各种各样的变量——包括器件、操作系统等。 Eggplant 监测工具能够在苹果和安卓环境中仿真屏幕分辨率、上传、下载和网络延迟，使您能够在所有可访问的系统、网站和应用中确保获得始终如一的移动体验和功能。
不要让复杂的拓扑结构和盲点影响您在分散的混合网络中保持服务质量。 随着关键的计算资源和工作负载从云端转移到网络边缘，网络运营团队需要使用综合 NPM 工具来检测影响性能的因素，并找出根本原因。 通过一系列基于硬件和软件的网络端点，Hawkeye 可以执行从核心到边缘的端到端服务检查。 同时，节点到节点的流量可视化功能可以加快故障诊断，最大限度减少上门服务。
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.