#如何定义自定义成本模型
本指南介绍如何通过定义自定义成本模型来添加新的计费项。工作流程分为四个部分：
- 准备使用量指标
- 准备标签指标
- 为 Cost Management Agent 添加采集配置
- 为 Cost Management Server 添加展示配置
作为一个具体示例，我们将添加两个 GPU 计费项：GPU Compute(cores) 和 GPU Memory，并启用按标签定价。
#准备使用量指标
使用量指标表示需要计费的消耗量。请提供一个按步长聚合使用量的 PromQL 查询（例如 5m）。每个步长会生成一个数据点，表示前一个窗口内的使用量；最终使用量是所选时间范围内的总和。
Prometheus 指标类型包括 Counter、Gauge、Summary 和 Histogram。对于使用量，请使用 Counter 或 Gauge。示例：
# Counter example (e.g., GPU core usage time):
vgpu_core_usage_seconds_total{"containeridx":"0","deviceuuid":"GPU-2eec4202-80dc-870f-3ca5-25879d96eca7","endpoint":"monitor","instance":"10.3.0.130:9395","ip":"192.168.131.32","job":"hami-scheduler","namespace":"kube-system","node_name":"192.168.131.32","nodename":"192.168.133.48","pod":"hami-scheduler-86b7ff47c6-sp7kb","podname":"pytorch-cuda12-1-57d9ff4544-bqkvg","podnamespace":"yulin-2","service":"hami-scheduler","zone":"vGPU"}
# Corresponding usage query (step = 5m):
sum by (deviceuuid, podnamespace) (rate(vgpu_core_usage_seconds_total{}[5m]))
# Gauge example (e.g., GPU memory usage in bytes):
vgpu_memory_usage_bytes{"containeridx":"0","deviceuuid":"GPU-2eec4202-80dc-870f-3ca5-25879d96eca7","endpoint":"monitor","instance":"10.3.0.130:9395","ip":"192.168.131.32","job":"hami-scheduler","namespace":"kube-system","node_name":"192.168.131.32","nodename":"192.168.133.48","pod":"hami-scheduler-86b7ff47c6-sp7kb","podname":"pytorch-cuda12-1-57d9ff4544-bqkvg","podnamespace":"yulin-2","service":"hami-scheduler","zone":"vGPU"}
# Corresponding usage query (step = 5m):
sum by (deviceuuid, podnamespace) (avg_over_time(vgpu_memory_usage_bytes{}[5m]))#准备标签指标
标签指标为使用量附加属性，以便按标签定价。它们必须能够与使用量指标进行关联——至少共享一个标签（例如 deviceuuid）——这样系统才能使用这些属性来丰富使用量。
对于标签指标，请使用 Gauge。示例：
# Label metrics (label fields start with label_ in this example):
vgpu_device_labels{"deviceuuid":"GPU-2eec4202-80dc-870f-3ca5-25879d96eca7","label_device":"nvidia","label_modelName":"Tesla T4","podnamespace":"yulin-1"}
# Query for label metrics:
vgpu_device_labels{}
# How the usage and label queries relate (GPU core & memory example):
# 1) The usage query groups by (deviceuuid, podnamespace) and each series includes deviceuuid
# 2) The label query groups by its own labels and each series includes deviceuuid
# 3) Because both include the unique deviceuuid, labels from the label query can be attached to usage#添加采集配置（Cost Management Agent）
在运行 Cost Management Agent 的每个集群中创建一个 ConfigMap，用于声明要采集的内容。
一个典型的采集记录（GPU core/memory）与配置字段的映射关系如下：
{
"id": "cab9881e380fcf72726ccf45565ffc2d", # Auto-generated from agreed fields
"kind": "Vgpu", # Matches kind in config
"name": "GPU-2eec4202-80dc-870f-3ca5-25879d96eca7", # From usage.mappers.name
"namespace": "cpaas-system", # From usage.mappers.namespace
"cluster": "default-cluster", # Cluster where the agent runs
"project": "cpaas-system", # Derived from namespace
"labels": { # From labels.query if configured
"key1": "val1",
"key2": "val2"
},
"date": "2024-04-29T00:00:00Z", # Start of that day
"period": "hourly", # Matches period in config
"start": "2024-04-29T00:05:00Z", # Period start
"end": "2024-04-29T00:59:59Z", # Period end
"category": "VgpuCore", # Matches category in config
"item": "VgpuCoreUsed", # Matches item in config
"usage": 200 # From usage query result
}现在为 agent（GPU core 和 memory）创建 ConfigMap：
apiVersion: v1
data:
config: |
- kind: Vgpu # Required; must be consistent with server config
category: VgpuCore # Required; must be consistent with server config
item: VgpuCoreUsed # Required and unique; must match server config
period: Hourly # Required; Hourly or Daily; prefer Hourly
labels: # Optional; enrich usage with labels from this query
query: "vgpu_core_labels{}" # E.g., add GPU model, vendor to labels
mappers:
name: deviceuuid # Map deviceuuid as name
namespace: podnamespace # Map podnamespace as namespace
cluster: "" # Leave empty to auto-fill current cluster
project: "" # Leave empty to auto-fill project from namespace
usage: # Required; usage query (grouped by deviceuuid,podnamespace)
query: "sum by (deviceuuid, podnamespace) (rate(vgpu_core_usage_seconds_total{}[5m]))"
step: 5m # Required; step for sampling points
mappers:
name: deviceuuid # Map deviceuuid to name
namespace: podnamespace # Map podnamespace to namespace
cluster: "" # Auto-fill if empty
project: "" # Auto-fill if empty
- kind: Vgpu # Second billing item
category: VgpuMemory # Must be consistent with server config
item: VgpuMemoryUsed # Unique item name
period: Hourly
labels:
query: "vgpu_core_labels{}"
mappers:
name: deviceuuid
namespace: podnamespace
cluster: ""
project: ""
usage:
query: "sum by (deviceuuid, podnamespace) (avg_over_time(vgpu_memory_usage_bytes{}[5m]))"
step: 5m
mappers:
name: deviceuuid
namespace: podnamespace
cluster: ""
project: ""
kind: ConfigMap
metadata:
labels:
cpaas.io/slark.collection.config: "true" # Required; enables collection config
name: slark-agent-project-config-vgpu
namespace: cpaas-system # Required;添加 yaml 后，需要重启 Agent Pod 以重新加载配置。
kubectl delete pods -n cpaas-system -l service_name=slark-agent#添加展示/存储配置（Cost Management Server）
在运行 Cost Management Server 的集群中创建一个 ConfigMap，用于声明计费项、计费方式、单位和显示名称。这会告诉服务器要计费什么，以及如何计费。
apiVersion: v1
data:
config: |
- name: VgpuCore # Billing item name; must match category above
displayname:
zh: "Vgpu"
en: "Vgpu"
methods: # List of billing methods (unique names)
- name: Usage # Method name
displayname:
zh: "使用量"
en: "Used Usage"
item: VgpuCoreUsed # Must match the agent config item
divisor: 1000 # Unit conversion (e.g., mCPU to cores)
unit:
zh: "core-hours"
en: "core-hours"
- name: VgpuMemory # Second billing item
displayname:
zh: "Vgpu 显存"
en: "VgpuMemory"
methods:
- name: Used
displayname:
zh: "使用量"
en: "Used Usage"
item: VgpuMemoryUsed
divisor: 1073741824 # bytes -> Gi
unit:
zh: "Gi-hours"
en: "Gi-hours"
kind: ConfigMap
metadata:
labels:
cpaas.io/slark.display.config: "true" # Required; enables display/storage config
name: slark-display-config-for-vgpu
namespace: kube-public # Required;添加 yaml 后，需要重启 Server Pod 以重新加载配置。
kubectl delete pods -n cpaas-system -l service_name=slark-server#注意事项和最佳实践
- 保持 agent 和 server 配置中的命名一致：
kind、category和item必须匹配。 - 为了获得更细的粒度和更快的反馈，建议优先使用 Hourly。
- 确保标签指标可以通过共享标签与使用量关联（例如
deviceuuid）。 - 在发布前先在本地验证 PromQL；在初次运行时监控数据是否正常。
- 先从小规模开始（少量集群/计费项），验证后再扩展。