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docs add in load balance env Dec 12, 2018
park spark system code under maintenance, need to work with latest updates Oct 18, 2019
.gitignore option for shuffling Apr 9, 2019
.gitmodules Update May 25, 2019
Vagrantfile message passing support for AQM env using ZMQ Mar 1, 2019 Add pandas May 3, 2019


Real system interface

import park
import agent_impl  # implemented by user

env = park.make('congestion_control')

# the run script will start the real system
# and periodically invoke agent.get_action
agent = Agent(env.observation_space, env.action_space)

The should implement

class Agent(object):
    def __init__(self, state_space, action_space, *args, **kwargs):
        self.state_space = state_space
        self.action_space = action_space

    def get_action(self, obs, prev_reward, prev_done, prev_info):
        act = self.action_space.sample()
        # implement real action logic here
        return act

Simulation interface

Similar to OpenAI Gym interface.

import park

env = park.make('load_balance')

obs = env.reset()
done = False

while not done:
    # act = agent.get_action(obs)
    act = env.action_space.sample()
    obs, reward, done, info = env.step(act)


Environment env_id Committers
Adaptive video streaming abr, abr_sim Hongzi Mao, Akshay Narayan
Spark cluster job scheduling spark, spark_sim Hongzi Mao, Malte Schwarzkopf
SQL database query optimization query_optimizer Parimarjan Negi
Network congestion control congestion_control Akshay Narayan, Frank Cangialosi
Network active queue management aqm Mehrdad Khani, Songtao He
Tensorflow device placement tf_placement, tf_placement_sim Ravichandra Addanki
Circuit design circuit_design Hanrui Wang, Jiacheng Yang
CDN memory caching cache Haonan Wang, Wei-Hung Weng
Multi-dim database indexing multi_dim_index Vikram Nathan
Account region assignment region_assignment Ryan Marcus
Server load balancing load_balance Hongzi Mao
Switch scheduling switch_scheduling Ravichandra Addanki, Hongzi Mao


Note: to use argparse that is compatiable with park parameters, add parameters using

from park.param import parser
config = parser.parse_args()
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