README.en.md

CloudSimPy data center job scheduling simulation framework

CloudSimPy is based on the discrete event simulation framework SimPy, implemented using Python language; The scientific computing, deep learning, and machine learning ecology of the Python language is more complete than other programming languages. CloudSimPy works well with deep learning frameworks with Python support (such as TensorFlow, PyTorch) The combination helps to study resource management methods based on machine learning or deep learning.

The data center job scheduling algorithm based on deep reinforcement learning in CloudSimPy/playground/Non_DAG/algorithm/DeepJS/DRL.py is implemented by TensorFlow, and it is inferred and trained in its eager mode.

CloudSimPy

As a data center job scheduling simulation framework CloudSimPy contains two Python packages core and playground.

Core

core abstracts and models each entity (entity) in the data center job scheduling problem. Thecore package contains the following modules:

• TaskInstanceConfig, TaskConfig and JobConfig in config give the configuration of task instances, tasks and jobs (resource requirements, duration, etc.)
• TaskInstance, Task and Job in job are the modeling of task instances, tasks and jobs respectively
• machine is to model the machine
• cluster is a model for computing clusters, the classCluster maintains a list of machines in the cluster
• The interface of the scheduling algorithm is defined in alogrithm. The user-defined scheduling algorithm must implement this interface, which is the key to the realization of strategic mode
• scheduler is the modeling of the scheduler. Through the design pattern of strategy mode, different Scheduler instances can be scheduled using different scheduling algorithms
• broker implements the class Broker, Broker replaces users to submit jobs to the computing cluster
• Monitor implements the class Monitor, which is used to monitor and record the state of the simulation during the simulation process
• simulation is the modeling of a simulation. A simulation must construct a cluster Cluster instance; construct a series of job configuration JobConfig instances, and use these job configuration instances to construct a Broker instance; Construct a scheduler Scheduler instance. In a simulation, you can choose whether to use a Monitor instance to monitor the simulation process

Playground

The playground package is designed to be convenient for software package users to conduct experiments. It mainly includes the DAG package and the Non_DAG package (supports simulation experiments when considering dependencies between tasks and without considering dependencies between tasks), and auxiliary package. Both DAG and Non_DAG pre-implement some heuristic job scheduling algorithms and job scheduling algorithms based on deep reinforcement learning. For example, the data center job scheduling algorithm based on deep reinforcement learning implemented in Non_DAG/algorithm/DeepJS:

• agent agent, which realizes strategy gradient in reinforcement learning
• brain TensorFlow implemented neural network structure
• DRL data center job scheduling algorithm based on deep reinforcement learning
• reward_giver reinforcement learning reward function

The auxiliary package provides some auxiliary classes and functions:

• The Episode class in episode is used for episodic simulation experiments
• multiprocessing_run in tools is used for training in multi-process mode; average_slowdown and average_completion are used to extract calculation statistics from an object of class Episode

High-performance simulation

In the data center, the task instance TaskInstance is the actual resource consumer and the executor of the actual business logic, so conceptually the TaskInstance in the core package job module is designed as a * SimPy * Process (Process), The class Task is designed as a collection of TaskInstance, and the class Job is designed as a collection of Task. The running status of Job and Taskis implemented using thepropertyfeature under *Python*, And use the information transmission mechanism shown in the following figure to realize the synthesis ofTask and Job` states.

When we ask about the status of a Job, the Job instance will ask about the status of its Task instances, and the Task instance will ask about the status of their respective TaskInstance instances, The Task instance synthesizes its own state according to the state of the respective TaskInstance instances, and then the Job instance synthesizes its own state according to the state of its Task instances, that is, the state information backpropagates and finally returns to the Job Examples. This design can not only ensure the accuracy and consistency of the status information of Job and Task, but more importantly, it does not actively maintain the status information of Job and Taskat every simulation time step. Instead, the acquisition of the status ofJob and Taskis postponed to the passive inquiry of the status ofJob and Task, which allows us to turn off the monitoring function (that is, not to query the status of Job and Task`) Let the simulation run quickly and efficiently. Passive query replaces active maintenance, and hotpath is optimized during the simulation process, so that the operations performed on hotpath are as few and fast as possible.

In addition to TaskInstance being conceptually designed as a SimPy process, Broker, Scheduler, and Monitor are also designed as SimPy processes. The Broker process continuously submits the jobs described in the job configuration list to the cluster Cluster instance according to the job submission time. Until all jobs are submitted, Broker stops submitting and destroying them. The Scheduler is continuously scheduled according to the scheduling time step until the simulation Simulation is marked as ended (when Broker is destroyed (ie no new jobs will arrive) and all submitted jobs are executed, Simulation is marked as end). Monitor continuously monitors and records the simulation status according to the monitoring time step until the simulation Simulation is marked as end.

In addition, the entities Simulation,Cluster, Machine, Task, and Job in the job scheduling problem of the data center are common class concepts, and serve only as managers of related information.

Strategy Mode

The strategy pattern is a behavioral design pattern. A series of algorithms are defined in the strategy pattern, each algorithm is placed in a separate class, and objects of these classes are interchangeable. In the strategy mode, we have a class that can perform specific operations in different ways, such as the scheduler Scheduler class here. It can perform scheduling with different scheduling algorithms (scheduling strategies), we can extract all these Into individual classes called strategies. The original class (called the context) holds a reference to the strategy and delegates the work to the strategy instead of directly performing the work on its own. The original class is not responsible for selecting the appropriate algorithm, instead, the user passes the required strategy to it. In fact, the original class knows very little about strategy, it calls all strategies through the same common interface. In this way, the context becomes independent of the specific strategy, and we can add new algorithms or modify existing algorithms without changing the code of the original class or other strategies.

By using the strategy design pattern, the implementation of Scheduler and the implementation of the scheduling algorithm used by Scheduler are separated in CloudSimPy, And put them in the core package and the playground/DAG/algorithm and playground/Non_DAG/algorithm packages respectively.

The strategy mode is also used in layground/DAG/algorithm/DeepJS/reward_giver.py to provide different reward calculation methods for job scheduling models based on deep reinforcement learning with different optimization goals:

• MakespanRewardGiver gives rewards for optimizing completion time (Makespan)
• AverageSlowDownRewardGiver gives the reward for optimizing the average SlowDown
• AverageCompletionRewardGiver gives rewards for optimizing average completion time

Papers using CloudSimPy

1. DeepJS: Job Scheduling Based on Deep Reinforcement Learning in Cloud Data Center

Run examples

Requirements

1. Python 3.6
2. SimPy 3.0.11
3. TensorFlow 1.12.0
4. Numpy 1.15.3
5. Pandas 0.23.4

Install and run

1. git clone git@github.com:RobertLexis/CloudSimPy.git
2. Add the path to directory cloudsimpy to system environment PYTHONPATH
3. cd cloudsimpy/playground/Non_DAG/launch_scripts
4. python main-makespan.py