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Infrastructure Hosts Skill

基础设施主机Skill

Monitor and manage host and process infrastructure including CPU, memory, disk, network, and technology inventory.
监控和管理主机与进程基础设施，包括CPU、内存、磁盘、网络以及技术资产清单。

What This Skill Does

本Skill的功能

  • Discover and inventory hosts across cloud and on-premise environments
  • Monitor host resource utilization (CPU, memory, disk, network)
  • Track process resource consumption and lifecycle
  • Analyze container and Kubernetes infrastructure
  • Discover services via listening ports
  • Manage technology stack versions and compliance
  • Attribute infrastructure costs by cost center and product
  • Validate data quality and metadata completeness
  • Plan capacity and detect resource saturation
  • Correlate infrastructure health across layers
  • 发现并盘点云端和本地环境中的所有主机
  • 监控主机资源利用率（CPU、内存、磁盘、网络）
  • 追踪进程资源消耗情况和生命周期
  • 分析容器和Kubernetes基础设施
  • 通过监听端口发现服务
  • 管理技术栈版本和合规性
  • 按成本中心和产品归属核算基础设施成本
  • 验证数据质量和元数据完整性
  • 规划容量并检测资源饱和情况
  • 跨层级关联基础设施健康状态

When to Use This Skill

适用场景

Use this skill when the user needs to:
  • Inventory: "Show me all Linux hosts in AWS us-east-1"
  • Monitor: "What hosts have high CPU usage?"
  • Troubleshoot: "Which processes are consuming the most memory?"
  • Discover: "What databases are running in production?"
  • Plan: "Track Kubernetes version distribution for upgrade planning"
  • Cost: "Calculate infrastructure costs by cost center"
  • Security: "Find all processes listening on port 22"
  • Compliance: "Identify hosts running EOL Java versions"
  • Quality: "Check data completeness for AWS hosts"
  • Optimize: "Find rightsizing candidates based on utilization"
当用户需要完成以下操作时可使用本Skill：
  • 资产盘点： "展示AWS us-east-1区域的所有Linux主机"
  • 监控： "哪些主机的CPU使用率较高？"
  • 故障排查： "哪些进程消耗的内存最多？"
  • 服务发现： "生产环境中运行了哪些数据库？"
  • 规划： "追踪Kubernetes版本分布以制定升级计划"
  • 成本核算： "按成本中心计算基础设施成本"
  • 安全： "找出所有监听22端口的进程"
  • 合规： "识别运行已停止维护（EOL）Java版本的主机"
  • 质量检查： "检查AWS主机的数据完整性"
  • 优化： "根据资源利用率找出可调整规格的候选资源"

Core Concepts

核心概念

Entities

实体

  • HOST - Physical or virtual machines (cloud or on-premise)
  • PROCESS - Running processes and process groups
  • CONTAINER - Kubernetes containers
  • NETWORK_INTERFACE - Host network interfaces
  • DISK - Host disk volumes
  • HOST - 物理机或虚拟机（云端或本地部署）
  • PROCESS - 运行中的进程和进程组
  • CONTAINER - Kubernetes容器
  • NETWORK_INTERFACE - 主机网络接口
  • DISK - 主机磁盘卷

Metrics Categories

指标分类

  1. Host Metrics - 
    dt.host.cpu.*
    , 
    dt.host.memory.*
    , 
    dt.host.disk.*
    , 
    dt.host.net.*
  2. Process Metrics - 
    dt.process.cpu.*
    , 
    dt.process.memory.*
    , 
    dt.process.io.*
    , 
    dt.process.network.*
  3. Inventory - OS type, cloud provider, technology stack, versions
  4. Cost - 
    dt.cost.costcenter
    , 
    dt.cost.product
  5. Quality - Metadata completeness, version compliance
  1. 主机指标 - 
    dt.host.cpu.*
    、
    dt.host.memory.*
    、
    dt.host.disk.*
    、
    dt.host.net.*
  2. 进程指标 - 
    dt.process.cpu.*
    、
    dt.process.memory.*
    、
    dt.process.io.*
    、
    dt.process.network.*
  3. 资产清单 - 操作系统类型、云厂商、技术栈、版本
  4. 成本 - 
    dt.cost.costcenter
    、
    dt.cost.product
  5. 质量 - 元数据完整性、版本合规性

Alert Thresholds

告警阈值

  • CPU/Memory/Disk: 80% warning, 90% critical
  • Network: >70% high, >85% saturated
  • Disk Latency: >20ms bottleneck
  • Network Errors: Drop rate >1%, error rate >0.1%
  • Swap: >30% warning, >50% critical
  • CPU/内存/磁盘： 使用率达80%触发预警，达90%触发严重告警
  • 网络： 使用率>70%为高负载，>85%为饱和状态
  • 磁盘延迟： >20ms为性能瓶颈
  • 网络错误： 丢包率>1%、错误率>0.1%
  • 交换分区： 使用率>30%触发预警，>50%触发严重告警

Key Workflows

核心工作流

1. Host Discovery and Classification

1. 主机发现与分类

Discover hosts, classify by OS/cloud, inventory resources.
dql
smartscapeNodes "HOST"
| fieldsAdd os.type, cloud.provider, host.logical.cpu.cores, host.physical.memory
| summarize host_count = count(), by: {os.type, cloud.provider}
| sort host_count desc
OS Types: 
LINUX
, 
WINDOWS
, 
AIX
, 
SOLARIS
, 
ZOS
→ For cloud-specific attributes, see references/inventory-discovery.md
发现主机，按操作系统/云厂商分类，盘点资源。
dql
smartscapeNodes "HOST"
| fieldsAdd os.type, cloud.provider, host.logical.cpu.cores, host.physical.memory
| summarize host_count = count(), by: {os.type, cloud.provider}
| sort host_count desc
操作系统类型： 
LINUX
、
WINDOWS
、
AIX
、
SOLARIS
、
ZOS
→ 查看云厂商专属属性，请参考 references/inventory-discovery.md

2. Resource Utilization Monitoring

2. 资源利用率监控

Monitor CPU, memory, disk, network across hosts.
dql
timeseries {
  cpu = avg(dt.host.cpu.usage),
  memory = avg(dt.host.memory.usage),
  disk = avg(dt.host.disk.used.percent)
}, by: {dt.smartscape.host}
| fieldsAdd host_name = getNodeName(dt.smartscape.host)
| filter arrayAvg(cpu) > 80 or arrayAvg(memory) > 80
| sort arrayAvg(cpu) desc
High utilization threshold: 80% warning, 90% critical
→ For detailed CPU analysis, see references/host-metrics.md
→ For memory breakdown, see references/host-metrics.md
监控所有主机的CPU、内存、磁盘、网络情况。
dql
timeseries {
  cpu = avg(dt.host.cpu.usage),
  memory = avg(dt.host.memory.usage),
  disk = avg(dt.host.disk.used.percent)
}, by: {dt.smartscape.host}
| fieldsAdd host_name = getNodeName(dt.smartscape.host)
| filter arrayAvg(cpu) > 80 or arrayAvg(memory) > 80
| sort arrayAvg(cpu) desc
高利用率阈值： 80%触发预警，90%触发严重告警
→ 查看详细CPU分析，请参考 references/host-metrics.md
→ 查看内存细分数据，请参考 references/host-metrics.md

3. Process Resource Analysis

3. 进程资源分析

Identify top resource consumers at process level.
dql
timeseries {
  cpu = avg(dt.process.cpu.usage),
  memory = avg(dt.process.memory.usage)
}, by: {dt.smartscape.process}
| fieldsAdd process_name = getNodeName(dt.smartscape.process)
| filter arrayAvg(cpu) > 50
| sort arrayAvg(cpu) desc
| limit 20
→ For process I/O analysis, see references/process-monitoring.md
→ For process network metrics, see references/process-monitoring.md
在进程层面识别资源消耗最高的对象。
dql
timeseries {
  cpu = avg(dt.process.cpu.usage),
  memory = avg(dt.process.memory.usage)
}, by: {dt.smartscape.process}
| fieldsAdd process_name = getNodeName(dt.smartscape.process)
| filter arrayAvg(cpu) > 50
| sort arrayAvg(cpu) desc
| limit 20
→ 查看进程I/O分析，请参考 references/process-monitoring.md
→ 查看进程网络指标，请参考 references/process-monitoring.md

4. Technology Stack Inventory

4. 技术栈资产盘点

Discover and track software technologies and versions.
dql
smartscapeNodes "PROCESS"
| fieldsAdd process.software_technologies
| expand tech = process.software_technologies
| fieldsAdd tech_type = tech[type], tech_version = tech[version]
| summarize process_count = count(), by: {tech_type, tech_version}
| sort process_count desc
Common Technologies: Java, Node.js, Python, .NET, databases, web servers, messaging systems
→ For version compliance checks, see references/inventory-discovery.md
发现并追踪软件技术和版本。
dql
smartscapeNodes "PROCESS"
| fieldsAdd process.software_technologies
| expand tech = process.software_technologies
| fieldsAdd tech_type = tech[type], tech_version = tech[version]
| summarize process_count = count(), by: {tech_type, tech_version}
| sort process_count desc
常见技术： Java、Node.js、Python、.NET、数据库、Web服务器、消息系统
→ 查看版本合规检查方法，请参考 references/inventory-discovery.md

5. Service Discovery via Ports

5. 基于端口的服务发现

Map listening ports to services for security and inventory.
dql
smartscapeNodes "PROCESS"
| fieldsAdd process.listen_ports, dt.process_group.detected_name
| filter isNotNull(process.listen_ports) and arraySize(process.listen_ports) > 0
| expand port = process.listen_ports
| summarize process_count = count(), by: {port, dt.process_group.detected_name}
| sort toLong(port) asc
| limit 50
Well-known ports: 80 (HTTP), 443 (HTTPS), 22 (SSH), 3306 (MySQL), 5432 (PostgreSQL)
→ For comprehensive port mapping, see references/inventory-discovery.md
将监听端口映射到服务，用于安全检查和资产盘点。
dql
smartscapeNodes "PROCESS"
| fieldsAdd process.listen_ports, dt.process_group.detected_name
| filter isNotNull(process.listen_ports) and arraySize(process.listen_ports) > 0
| expand port = process.listen_ports
| summarize process_count = count(), by: {port, dt.process_group.detected_name}
| sort toLong(port) asc
| limit 50
知名端口： 80（HTTP）、443（HTTPS）、22（SSH）、3306（MySQL）、5432（PostgreSQL）
→ 查看完整端口映射方法，请参考 references/inventory-discovery.md

6. Container and Kubernetes Monitoring

6. 容器和Kubernetes监控

Track container distribution and K8s workload types.
dql
smartscapeNodes "CONTAINER"
| fieldsAdd k8s.cluster.name, k8s.namespace.name, k8s.workload.kind
| summarize container_count = count(), by: {k8s.cluster.name, k8s.workload.kind}
| sort k8s.cluster.name, container_count desc
Workload Types: 
deployment
, 
daemonset
, 
statefulset
, 
job
, 
cronjob
Note: Container image names/versions NOT available in smartscape.
→ For K8s version tracking, see references/container-monitoring.md
→ For container lifecycle, see references/container-monitoring.md
追踪容器分布和K8s工作负载类型。
dql
smartscapeNodes "CONTAINER"
| fieldsAdd k8s.cluster.name, k8s.namespace.name, k8s.workload.kind
| summarize container_count = count(), by: {k8s.cluster.name, k8s.workload.kind}
| sort k8s.cluster.name, container_count desc
工作负载类型： 
deployment
、
daemonset
、
statefulset
、
job
、
cronjob
注意： Smartscape中不提供容器镜像名称/版本数据。
→ 查看K8s版本追踪方法，请参考 references/container-monitoring.md
→ 查看容器生命周期管理方法，请参考 references/container-monitoring.md

7. Cost Attribution and Chargeback

7. 成本归属与分摊

Calculate infrastructure costs by cost center.
dql
smartscapeNodes "HOST"
| fieldsAdd dt.cost.costcenter, host.logical.cpu.cores, host.physical.memory
| filter isNotNull(dt.cost.costcenter)
| fieldsAdd memory_gb = toDouble(host.physical.memory) / 1024 / 1024 / 1024
| summarize 
    host_count = count(),
    total_cores = sum(toLong(host.logical.cpu.cores)),
    total_memory_gb = sum(memory_gb),
    by: {dt.cost.costcenter}
| sort total_cores desc
→ For product-level cost tracking, see references/inventory-discovery.md
按成本中心计算基础设施成本。
dql
smartscapeNodes "HOST"
| fieldsAdd dt.cost.costcenter, host.logical.cpu.cores, host.physical.memory
| filter isNotNull(dt.cost.costcenter)
| fieldsAdd memory_gb = toDouble(host.physical.memory) / 1024 / 1024 / 1024
| summarize 
    host_count = count(),
    total_cores = sum(toLong(host.logical.cpu.cores)),
    total_memory_gb = sum(memory_gb),
    by: {dt.cost.costcenter}
| sort total_cores desc
→ 查看产品级成本追踪方法，请参考 references/inventory-discovery.md

8. Infrastructure Health Correlation

8. 基础设施健康状态关联

Correlate host and process metrics for cross-layer analysis.
dql
timeseries {
  host_cpu = avg(dt.host.cpu.usage),
  host_memory = avg(dt.host.memory.usage),
  process_cpu = avg(dt.process.cpu.usage)
}, by: {dt.smartscape.host, dt.smartscape.process}
| fieldsAdd
    host_name = getNodeName(dt.smartscape.host),
    process_name = getNodeName(dt.smartscape.process)
| filter arrayAvg(host_cpu) > 70
| sort arrayAvg(host_cpu) desc
Health scoring: Critical if any resource >90%, warning if >80%
→ For multi-resource saturation detection, see references/host-metrics.md
关联主机和进程指标，实现跨层级分析。
dql
timeseries {
  host_cpu = avg(dt.host.cpu.usage),
  host_memory = avg(dt.host.memory.usage),
  process_cpu = avg(dt.process.cpu.usage)
}, by: {dt.smartscape.host, dt.smartscape.process}
| fieldsAdd
    host_name = getNodeName(dt.smartscape.host),
    process_name = getNodeName(dt.smartscape.process)
| filter arrayAvg(host_cpu) > 70
| sort arrayAvg(host_cpu) desc
健康评分规则： 任意资源使用率>90%为严重状态，>80%为预警状态
→ 查看多资源饱和检测方法，请参考 references/host-metrics.md

Common Query Patterns

常用查询模式

Pattern 1: Smartscape Discovery

模式1：Smartscape发现

Use 
smartscapeNodes
to discover and classify entities.
dql
smartscapeNodes "HOST"
| fieldsAdd <attributes>
| filter <conditions>
| summarize <aggregations>
使用
smartscapeNodes
发现并分类实体。
dql
smartscapeNodes "HOST"
| fieldsAdd <attributes>
| filter <conditions>
| summarize <aggregations>

Pattern 2: Timeseries Performance

模式2：时序性能分析

Use 
timeseries
to analyze metrics over time.
dql
timeseries metric = avg(dt.host.<metric>), by: {dt.smartscape.host}
| fieldsAdd <calculations>
| filter <thresholds>
使用
timeseries
分析随时间变化的指标。
dql
timeseries metric = avg(dt.host.<metric>), by: {dt.smartscape.host}
| fieldsAdd <calculations>
| filter <thresholds>

Pattern 3: Cross-Layer Correlation

模式3：跨层级关联

Correlate host and process metrics.
dql
timeseries {
  host_cpu = avg(dt.host.cpu.usage),
  process_cpu = avg(dt.process.cpu.usage)
}, by: {dt.smartscape.host, dt.smartscape.process}
关联主机和进程指标。
dql
timeseries {
  host_cpu = avg(dt.host.cpu.usage),
  process_cpu = avg(dt.process.cpu.usage)
}, by: {dt.smartscape.host, dt.smartscape.process}

Pattern 4: Entity Enrichment with Lookup

模式4：通过Lookup实现实体 enrichment

Enrich data with entity attributes. After 
lookup
, reference fields with 
lookup.
prefix.
dql
timeseries cpu = avg(dt.host.cpu.usage), by: {dt.smartscape.host}
| lookup [
    smartscapeNodes HOST
    | fields id, cpuCores, memoryTotal
  ], sourceField:dt.smartscape.host, lookupField:id
| fieldsAdd cores = lookup.cpuCores, mem_gb = lookup.memoryTotal / 1024 / 1024 / 1024
使用实体属性丰富数据。
lookup
操作后，需使用
lookup.
前缀引用字段。
dql
timeseries cpu = avg(dt.host.cpu.usage), by: {dt.smartscape.host}
| lookup [
    smartscapeNodes HOST
    | fields id, cpuCores, memoryTotal
  ], sourceField:dt.smartscape.host, lookupField:id
| fieldsAdd cores = lookup.cpuCores, mem_gb = lookup.memoryTotal / 1024 / 1024 / 1024

Tags and Metadata

标签与元数据

Important Notes

重要说明

  • Generic 
    tags
    field is NOT populated in smartscape queries
  • Use specific tag fields: 
    tags:azure[*]
    , 
    tags:environment
  • Use custom metadata: 
    host.custom.metadata[*]
  • Smartscape查询中不会填充通用
    tags
    字段
  • 使用特定标签字段：
    tags:azure[*]
    、
    tags:environment
  • 使用自定义元数据：
    host.custom.metadata[*]

Available Tags

可用标签

  • Azure Tags: 
    tags:azure[dt_owner_team]
    , 
    tags:azure[dt_cloudcost_capability]
  • Environment: 
    tags:environment
  • Custom Metadata: 
    host.custom.metadata[OperatorVersion]
    , 
    host.custom.metadata[Cluster]
  • Cost: 
    dt.cost.costcenter
    , 
    dt.cost.product
→ For complete tag reference, see references/inventory-discovery.md
  • Azure标签： 
    tags:azure[dt_owner_team]
    、
    tags:azure[dt_cloudcost_capability]
  • 环境标签： 
    tags:environment
  • 自定义元数据： 
    host.custom.metadata[OperatorVersion]
    、
    host.custom.metadata[Cluster]
  • 成本标签： 
    dt.cost.costcenter
    、
    dt.cost.product
→ 查看完整标签参考，请参考 references/inventory-discovery.md

Cloud-Specific Attributes

云厂商专属属性

AWS

AWS

  • cloud.provider == "aws"
  • aws.region
    , 
    aws.availability_zone
    , 
    aws.account.id
  • aws.resource.id
    , 
    aws.resource.name
  • aws.state
    (running, stopped, terminated)
  • cloud.provider == "aws"
  • aws.region
    、
    aws.availability_zone
    、
    aws.account.id
  • aws.resource.id
    、
    aws.resource.name
  • aws.state
    （running、stopped、terminated）

Azure

Azure

  • cloud.provider == "azure"
  • azure.location
    , 
    azure.subscription
    , 
    azure.resource.group
  • azure.status
    , 
    azure.provisioning_state
  • azure.resource.sku.name
    (VM size)
  • cloud.provider == "azure"
  • azure.location
    、
    azure.subscription
    、
    azure.resource.group
  • azure.status
    、
    azure.provisioning_state
  • azure.resource.sku.name
    （虚拟机规格）

Kubernetes

Kubernetes

  • k8s.cluster.name
    , 
    k8s.cluster.uid
  • k8s.namespace.name
    , 
    k8s.node.name
    , 
    k8s.pod.name
  • k8s.workload.name
    , 
    k8s.workload.kind
→ For multi-cloud analysis, see references/inventory-discovery.md
  • k8s.cluster.name
    、
    k8s.cluster.uid
  • k8s.namespace.name
    、
    k8s.node.name
    、
    k8s.pod.name
  • k8s.workload.name
    、
    k8s.workload.kind
→ 查看多云分析方法，请参考 references/inventory-discovery.md

Best Practices

最佳实践

Alerting

告警设置

  1. Use percentiles (p95, p99) for latency metrics
  2. Use 
    max()
    for resource limits
  3. Use 
    avg()
    for utilization trends
  4. Set multi-level thresholds (warning at 80%, critical at 90%)
  1. 延迟指标使用百分位数（p95、p99）
  2. 资源限制使用
    max()
    统计
  3. 利用率趋势使用
    avg()
    统计
  4. 设置多级阈值（80%预警、90%严重告警）

Time Windows

时间窗口选择

  • Real-time: 5-15 minute windows
  • Trends: 24 hours to 7 days
  • Capacity planning: 30-90 days
  • 实时监控： 5-15分钟窗口
  • 趋势分析： 24小时至7天
  • 容量规划： 30-90天

Query Optimization

查询优化

  1. Use filters early in the pipeline
  2. Limit results with 
    | limit N
  3. Use specific entity types in smartscapeNodes
  4. Aggregate before enrichment (lookup)
  1. 在查询管道早期使用过滤条件
  2. 使用
    | limit N
    限制返回结果数量
  3. 在smartscapeNodes中指定具体实体类型
  4. 先聚合再进行enrichment（lookup）操作

Data Quality

数据质量

  1. Validate metadata completeness (target >90%)
  2. Check for duplicate host names
  3. Ensure cost tag coverage
  4. Monitor data freshness (lifetime.end)
  1. 验证元数据完整性（目标>90%）
  2. 检查重复主机名
  3. 确保成本标签覆盖度
  4. 监控数据新鲜度（lifetime.end）

Limitations and Notes

限制与注意事项

Smartscape Limitations

Smartscape限制

  • Container image names/versions NOT available in smartscape
  • Generic 
    tags
    field NOT populated (use specific tag namespaces)
  • Process metadata varies by process type
  • Smartscape中不提供容器镜像名称/版本数据
  • 不会填充通用
    tags
    字段（需使用特定标签命名空间）
  • 进程元数据随进程类型不同存在差异

Platform-Specific

平台专属限制

  • dt.host.cpu.iowait
    available on Linux only
  • AIX has specific CPU metrics (entitlement, physc)
  • Inode metrics available on Linux only
  • dt.host.cpu.iowait
    仅支持Linux系统
  • AIX有专属CPU指标（entitlement、physc）
  • Inode指标仅支持Linux系统

Best Practices

使用建议

  • Use 
    getNodeName()
    to get human-readable names
  • Convert bytes to GB for readability: 
    / 1024 / 1024 / 1024
  • Round aggregated values: 
    round(value, decimals: 1)
  • Use 
    isNotNull()
    checks before array operations
  • 使用
    getNodeName()
    获取人类可读的名称
  • 将字节转换为GB提升可读性：
    / 1024 / 1024 / 1024
  • 对聚合值取整：
    round(value, decimals: 1)
  • 数组操作前先使用
    isNotNull()
    检查

When to Load References

何时加载参考文档

This skill uses progressive disclosure. Start here for 80% of use cases. Load reference files for detailed specifications when needed.
本Skill采用渐进式披露设计，80%的使用场景可通过本文档覆盖，需要详细规范时可加载参考文件。

Load host-metrics.md when:

满足以下需求时加载host-metrics.md：

  • Analyzing CPU component breakdown (user, system, iowait, steal)
  • Investigating memory pressure and swap usage
  • Troubleshooting disk I/O latency
  • Diagnosing network packet drops or errors
  • 分析CPU组件细分占比（用户态、系统态、iowait、steal）
  • 排查内存压力和交换分区使用情况
  • 排查磁盘I/O延迟问题
  • 诊断网络丢包或错误问题

Load process-monitoring.md when:

满足以下需求时加载process-monitoring.md：

  • Analyzing process-level I/O patterns
  • Investigating TCP connection quality
  • Detecting resource exhaustion (file descriptors, threads)
  • Tracking GC suspension time
  • 分析进程级I/O模式
  • 排查TCP连接质量问题
  • 检测资源耗尽问题（文件描述符、线程）
  • 追踪GC暂停时间

Load container-monitoring.md when:

满足以下需求时加载container-monitoring.md：

  • Analyzing container lifecycle and churn
  • Tracking Kubernetes version distribution
  • Managing OneAgent operator versions
  • Planning K8s cluster upgrades
  • 分析容器生命周期和 churn 情况
  • 追踪Kubernetes版本分布
  • 管理OneAgent operator版本
  • 规划K8s集群升级

Load inventory-discovery.md when:

满足以下需求时加载inventory-discovery.md：

  • Performing security audits via port discovery
  • Implementing cost attribution and chargeback
  • Validating data quality and metadata completeness
  • Managing multi-cloud infrastructure
  • 通过端口发现执行安全审计
  • 实现成本归属和分摊
  • 验证数据质量和元数据完整性
  • 管理多云基础设施

References

参考文档

  • host-metrics.md - Detailed host CPU, memory, disk, and network monitoring
  • process-monitoring.md - Process-level CPU, memory, I/O, and network analysis
  • container-monitoring.md - Container inventory, Kubernetes versions, and operator management
  • inventory-discovery.md - Host/process discovery, technology inventory, cost attribution, and data quality
  • host-metrics.md - 详细的主机CPU、内存、磁盘和网络监控说明
  • process-monitoring.md - 进程级CPU、内存、I/O和网络分析说明
  • container-monitoring.md - 容器资产盘点、Kubernetes版本和operator管理说明
  • inventory-discovery.md - 主机/进程发现、技术资产盘点、成本归属和数据质量说明