问题描述
Here 是我参考的文档
配置/etc/ceilometer/pipeline.yaml，添加以下内容
sources:
- name: memory_util_source
meters:
- "memory"
- "memory.usage"
sinks:
- memory_util_sink
sinks:
- name: memory_util_sink
transformers:
- name: "arithmetic"
parameters:
target:
name: "memory.usage"
unit: "%"
type: "gauge"
expr: "100 * $(memory.usage) / $(memory)"
publishers:
- gnocchi://?filter_project=service&archive_policy=ceilometer-low
gnocchi archive-policy show ceilometer-low
+---------------------+------------------------------------------------------------------+
| Field | Value |
+---------------------+------------------------------------------------------------------+
| aggregation_methods | max,min,mean |
| back_window | 0 |
| deFinition | - points: 8640,granularity: 0:05:00,timespan: 30 days,0:00:00 |
| name | ceilometer-low |
+---------------------+------------------------------------------------------------------+
Gnocchi 资源内存使用指标衡量间隔粒度是每小时。每五分钟只有一个数据粒度，为什么会出现这么奇怪的现象。
解决方法
我尝试了一种解决方法来获取实例的内存利用率，步骤如下。
（1）在/ceilometer/compute/pollsters/instance_stats.py文件中添加如下代码。
class MemoryUtilPollster(InstanceStatsPollster):
sample_name = 'memory_util'
sample_unit = '%'
sample_stats_key = 'memory_util'
（2）修改/ceilometer/compute/virt/libvirt/inspector.py文件中计算实例内存使用的逻辑代码
class LibvirtInspector(virt_inspector.Inspector):
def inspect_instance(self,instance,duration=None):
domain = self._get_domain_not_shut_off_or_raise(instance)
memory_used = memory_resident = None
memory_swap_in = memory_swap_out = None
memory_stats = domain.memoryStats()
# Stat provided from libvirt is in KB,converting it to MB.
if 'usable' in memory_stats and 'available' in memory_stats:
memory_used = (memory_stats['available'] -
memory_stats['usable']) / units.Ki
elif 'available' in memory_stats and 'unused' in memory_stats:
memory_used = (memory_stats['available'] -
memory_stats['unused']) / units.Ki
if 'rss' in memory_stats:
memory_resident = memory_stats['rss'] / units.Ki
if 'swap_in' in memory_stats and 'swap_out' in memory_stats:
memory_swap_in = memory_stats['swap_in'] / units.Ki
memory_swap_out = memory_stats['swap_out'] / units.Ki
# Tristack: add memory_util
memory_total = memory_stats['available'] / units.Ki
memory_util = int(100 * memory_used / memory_total)
# TODO(sileht): stats also have the disk/vnic info
# we could use that instead of the old method for Queen
stats = self.connection.domainListGetStats([domain],0)[0][1]
cpu_time = 0
current_cpus = stats.get('vcpu.current')
# Iterate over the maximum number of CPUs here,and count the
# actual number encountered,since the vcpu.x structure can
# have holes according to
# https://libvirt.org/git/?p=libvirt.git;a=blob;f=src/libvirt-domain.c
# virConnectGetAllDomainStats()
for vcpu in six.moves.range(stats.get('vcpu.maximum',0)):
try:
cpu_time += (stats.get('vcpu.%s.time' % vcpu) +
stats.get('vcpu.%s.wait' % vcpu))
current_cpus -= 1
except TypeError:
# pass here,if there are too many holes,the cpu count will
# not match,so don't need special error handling.
pass
if current_cpus:
# There wasn't enough data,so fall back
cpu_time = stats.get('cpu.time')
return virt_inspector.InstanceStats(
cpu_number=stats.get('vcpu.current'),cpu_time=cpu_time,# Tristack: add memory_util
memory_util=memory_util,memory_usage=memory_used,memory_resident=memory_resident,memory_swap_in=memory_swap_in,memory_swap_out=memory_swap_out,cpu_cycles=stats.get("perf.cpu_cycles"),instructions=stats.get("perf.instructions"),cache_references=stats.get("perf.cache_references"),cache_misses=stats.get("perf.cache_misses"),memory_bandwidth_total=stats.get("perf.mbmt"),memory_bandwidth_local=stats.get("perf.mbml"),cpu_l3_cache_usage=stats.get("perf.cmt"),)
（3）在/ceilometer/compute/virt/inspector.py文件中添加InstanceStats对象的memory_util属性
class InstanceStats(object):
fields = [
'cpu_number',# number: number of CPUs
'cpu_time',# time: cumulative CPU time
'cpu_util',# util: CPU utilization in percentage
'cpu_l3_cache_usage',# cachesize: Amount of CPU L3 cache used
'memory_util',# Tristack: add memory_util
'memory_usage',# usage: Amount of memory used
'memory_resident',#
'memory_swap_in',# memory swap in
'memory_swap_out',# memory swap out
'memory_bandwidth_total',# total: total system bandwidth from one
# level of cache
'memory_bandwidth_local',# local: bandwidth of memory traffic for a
# memory controller
'cpu_cycles',# cpu_cycles: the number of cpu cycles one
# instruction needs
'instructions',# instructions: the count of instructions
'cache_references',# cache_references: the count of cache hits
'cache_misses',# cache_misses: the count of caches misses
]
def __init__(self,**kwargs):
for k in self.fields:
setattr(self,k,kwargs.pop(k,None))
if kwargs:
raise AttributeError(
"'InstanceStats' object has no attributes '%s'" % kwargs)
（4）在setup.cfg文件中ceilometer.poll.compute下添加memory_util插件
ceilometer.poll.compute =
memory_util = ceilometer.compute.pollsters.instance_stats:MemoryUtilPollster
（5）打包编译安装ceilometer，打包编译过程参考链接如下。可以找到 openstack-ceilometer-11.0.1-1.el7.src.rpm here
# groupadd mockbuild
# useradd mockbuild -g mockbuild
# rpm -ivh openstack-ceilometer-11.0.1-1.el7.src.rpm
After the installation is complete,the rpm build project is automatically deployed in
/root/rpmbuild/SPECS
/root/rpmbuild/SOURCES
cd /root/rpmbuild/SPECS
rpmbuild -bb openstack-ceilometer.spec
The ceilometer rpm package is in the /root/rpmbuild/RPMS directory,install these packages.
（6）在/etc/ceilometer/gnocchi_resources.yaml文件中添加如下配置。
resources:
- resource_type: instance
# Tristack: add memory_util
memory_util:
（7）在/etc/ceilometer/polling.yaml文件中添加如下配置。
sources:
- name: some_pollsters
interval: 300
meters:
# Tristack: add memory_util
- memory_util
（8）在/etc/ceilometer/pipeline.yaml文件中添加如下配置。
sources:
# Tristack: add memory_util
- name: memory_util_source
meters:
- "memory_util"
sinks:
- memory_util_sink
sinks:
# Tristack: add memory_util
- name: memory_util_sink
publishers:
- gnocchi://?filter_project=service&archive_policy=ceilometer-low
（9）最后重启openstack-ceilometer-compute服务