CPU data, control and analytics plane utilization

Each CPU core is designated as data, control, or analytics, with all usage assigned to the core's designated category. Each even-numbered core is designated as a data plane, each odd-numbered core as a control plane, and the highest odd-numbered core as an analytics plane.

Analytics plane-related processes are typically pinned to the highest odd-numbered core, and no control or analytics processes have access to the even-numbered cores. The processes pinned to the analytics plane usually consume all available CPU cycles when running, which is why they are assigned to a single specific core.

This division occurs when the CPU supports Hyper-Threading Technology (HT Technology). Overview of the HTSplit feature - https://my.f5.com/manage/s/article/K23505424 

The BIG-IP VE system does not support this feature on generic hypervisors such as VMware ESX/i, where each vCPU will be allocated one TMM process.  https://my.f5.com/manage/s/article/K15468#tmsh 

If you are seeing control plane and data plane CPU statistics in the dashboard, this is related to bug I id 969329 - https://my.f5.com/manage/s/article/K000132982  

here is the script:

import requests
from requests.auth import HTTPBasicAuth
from tabulate import tabulate
import json

# BIG-IP
BIGIP_ADDRESS = "192.168.1.10"
USERNAME = "admin"
PASSWORD = "admin"

# disable warnings de SSL
requests.packages.urllib3.disable_warnings()

def get_cpu_stats():
    url = f"https://{BIGIP_ADDRESS}/mgmt/tm/sys/host-info"
    response = requests.get(url, auth=HTTPBasicAuth(USERNAME, PASSWORD), verify=False)
    
    if response.status_code == 200:
        data = response.json()
        
       
        cpu_info_path = data.get("entries", {}).get("https://localhost/mgmt/tm/sys/host-info/0", {}).get("nestedStats", {}).get("entries", {}).get("https://localhost/mgmt/tm/sys/hostInfo/0/cpuInfo", {}).get("nestedStats", {}).get("entries", {})
        
        odd_cpus = []
        even_cpus = []
        
        if cpu_info_path:
            for cpu_key, stats in cpu_info_path.items():
                cpu_id = stats["nestedStats"]["entries"].get("cpuId", {}).get("value")
                one_min_usage = stats["nestedStats"]["entries"].get("oneMinAvgUser", {}).get("value", "N/A")
                five_min_usage = stats["nestedStats"]["entries"].get("fiveMinAvgUser", {}).get("value", "N/A")
                
                if cpu_id % 2 == 0:
                    even_cpus.append([f"cpu{cpu_id}", one_min_usage, five_min_usage])
                else:
                    odd_cpus.append([f"cpu{cpu_id}", one_min_usage, five_min_usage])
        
            
            print("\nTable of Even CPUs:")
            print(tabulate(even_cpus, headers=["CPU", "1 Min Usage (%)", "5 Min Usage (%)"], tablefmt="pretty"))
            
            print("\nTable of Odd CPUs:")
            print(tabulate(odd_cpus, headers=["CPU", "1 Min Usage (%)", "5 Min Usage (%)"], tablefmt="pretty"))
        
        else:
            print("Unable to find CPU information in the returned JSON.")
    else:
        print(f"Failed to retrieve data: {response.status_code} - {response.text}")


get_cpu_stats()