Intel® Platform Resource Manager

Note: This is Alpha code for evaluation purposes.

Intel® Platform Resource Manager (Intel® PRM) is a suite of software packages to help you to co-locate best-efforts jobs with latency-critical jobs on a node and in a cluster. The suite contains the following:

• Agent (eris agent) to monitor and control platform resources (CPU Cycle, Last Level Cache, Memory Bandwidth, etc.) on each node.
• Analysis tool (analyze tool) to build a model for platform resource contention detection.

Table of Contents

• Prerequisites
• Environment setup
• Command line arguments
  • eris agent
  • analyze tool
• Typical usage
• Contribution

Prerequisites

• Python 3.6.x
• Python lib: numpy, pandas, scipy, scikit-learn, docker, prometheus-client
• Golang compiler
• gcc
• git
• Docker

Environment setup

Assuming all requirements are installed and configured properly, follow the steps below to set up a working environment.

1. Install the intel-cmt-cat tool with the commands:

   git clone https://github.com/intel/intel-cmt-cat
   cd intel-cmt-cat
   make
   sudo make install PREFIX=/usr
   

2. Build the Intel® Platform Resource Manager with the commands:

   git clone https://github.com/intel/platform-resource-manager
   cd platform-resource-manager
   ./setup.sh
   cd eris
   

3. Prepare the workload configuration file.

   To use the Intel® PRM tool, you must provide a workload configuration json file in advance. Each row in the file describes name, id, type (Best-Effort, Latency-Critical), request CPU count of one task (Container).

The following is an example file demonstrating the file format.

{
    "cassandra_workload": {
        "cpus": 10,
        "type": "latency_critical"
    },
    "django_workload": {
        "cpus": 8,
        "type": "latency_critical"
    },
    "memcache_workload_1": {
        "cpus": 2,
        "type": "latency_critical"
    },
    "memcache_workload_2": {
        "cpus": 2,
        "type": "latency_critical"
    },
    "memcache_workload_3": {
        "cpus": 2,
        "type": "latency_critical"
    },
    "stress-ng": {
        "cpus": 2,
        "type": "best_efforts"
    },
    "tensorflow_training": {
        "cpus": 1,
        "type": "best_efforts"
    }
}

Command line arguments

This section lists command line arguments for the eris agent and the analyze tool.

eris agent

usage: eris.py [-h] [-v] [-g] [-d] [-c] [-r] [-i] [-e] [-n] [-p]
               [-u UTIL_INTERVAL] [-m METRIC_INTERVAL] [-l LLC_CYCLES]
               [-q QUOTA_CYCLES] [-k MARGIN_RATIO] [-t THRESH_FILE]
               workload_conf_file

eris agent monitor container CPU utilization and platform metrics, detect
potential resource contention and regulate task resource usages

positional arguments:
  workload_conf_file    workload configuration file describes each task name,
                        type, id, request cpu count

optional arguments:
  -h, --help            show this help message and exit
  -v, --verbose         increase output verbosity
  -g, --collect-metrics
                        collect platform performance metrics (CPI, MPKI,
                        etc..)
  -d, --detect          detect resource contention between containers
  -c, --control         regulate best-efforts task resource usages
  -r, --record          record container CPU utilizaton and platform metrics
                        in csv file
  -i, --key-cid         use container id in workload configuration file as key
                        id
  -e, --enable-hold     keep container resource usage in current level while
                        the usage is close but not exceed throttle threshold
  -n, --disable-cat     disable CAT control while in resource regulation
  -x, --exclusive-cat   use exclusive CAT control while in resource regulation
  -p, --enable_prometheus
                        allow eris send metrics to prometheus
  -u UTIL_INTERVAL, --util-interval UTIL_INTERVAL
                        CPU utilization monitor interval (1, 10)
  -m METRIC_INTERVAL, --metric-interval METRIC_INTERVAL (2, 60)
                        platform metrics monitor interval
  -l LLC_CYCLES, --llc-cycles LLC_CYCLES
                        cycle number in LLC controller
  -q QUOTA_CYCLES, --quota-cycles QUOTA_CYCLES
                        cycle number in CPU CFS quota controller
  -k MARGIN_RATIO, --margin-ratio MARGIN_RATIO
                        margin ratio related to one logical processor used in
                        CPU cycle regulation
  -t THRESH_FILE, --thresh-file THRESH_FILE
                        threshold model file build from analyze.py tool

analyze tool

usage: analyze.py [-h] [-v] [-t THRESH]
                  [-f {quartile,normal,gmm-strict,gmm-normal}]
                  [-m METRIC_FILE]
                  workload_conf_file

This tool analyzes CPU utilization and platform metrics collected from eris
agent and build data model for contention detect and resource regulation.

positional arguments:
  workload_conf_file    workload configuration file describes each task name,
                        type, id, request cpu count

optional arguments:
  -h, --help            show this help message and exit
  -v, --verbose         increase output verbosity
  -t THRESH, --thresh THRESH
                        threshold used in outlier detection
  -a {gmm-standard, gmm-origin}, --fense-method {gmm-standard, gmm-origin}
                        fense method in outiler detection
  -f {gmm-strict,gmm-normal}, --fense-type {gmm-strict,gmm-normal}
                        fense type used in outlier detection
  -m METRIC_FILE, --metric-file METRIC_FILE
                        metrics file collected from eris agent
  -u UTIL_FILE, --util-file UTIL_FILE
                        Utilization file collected from eris agent
  -o, --offline         do offline analysis based on given metrics file
  -i, --key-cid         use container id in workload configuration file as key
                        id

Typical usage

1. Run latency critical tasks and stress workloads on one node. The CPU utilization will be recorded in util.csv and platform metrics will be recorded in metrics.csv.

   sudo python eris.py --collect-metrics --record workload.json
   

2. Analyze data collected from the eris agent and build the data model for resource contention detection and regulation. This step generates a model file threshold.json.

   sudo python analyze.py workload.json
   

3. Add best-efforts task to node, restart monitor, and detect potential resource contention.

   sudo python eris.py --collect-metrics --record --detect workload.json
   

Optionally, you can enable resource regulation on best-efforts tasks with the following command:

sudo python eris.py --collect-metrics --record --detect --control workload.json

Contribution

Intel® PRM is an open source project licensed under the Apache v2 License.

Coding style

Intel® PRM follows the standard formatting recommendations and language idioms set out in C, Go, and Python.

Pull requests

We accept github pull requests.

Issue tracking

If you have a problem, please let us know. If you find a bug not already documented, please file a new issue in github so we can work toward resolution.