Ant - agent/network intelligence
ants optimizing paths on a network
The optimization engine has user controlled and automated modules
ops application
We retrieve information directly from the fleet console microservices
management console
We collect the current status with a single api call
resq = requests.get(url=cred['node_url']+"/users/actions/",headers=headers)
resq = requests.get(url=cred['node_url']+"/geodata/zones/",headers=headers)
resq = requests.get(url=cred['node_url']+"/geodata/zones/"+id_zone+"/areas",headers=headers)
resq = requests.get(url=cred['node_url']+"/geodata/areas/deployment/zone",headers=headers,params={"zoneIdentifier":g['zoneIdentifier']})The fleet engine sits in the middle and steers all operations, tracking and monitoring log the current status
development of objectives
In the fleet engine 4 objects will be defined with an hierarchical structure
metric operations
The purpose of the optimization engine is to compute profitability for each drive and iterate all over the possible solutions
field operation assignement
For each drive is calculated a cost and a rating
For Each task is calculated a revenue and a risk
To enable parallel work we need to move from a graph design
engine design, current scheme
To a linear design where the engine just orchestrate the calls and there is always a cached table to substitute a broken service
engine design, suggested scheme
We draw a cut between field operation and task optimization
infra design
