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A hybrid cloud job scheduling method based on q-learning

A job scheduling and hybrid cloud technology, applied in multi-programming devices, program control design, instruments, etc., can solve the problems that the scheduling method cannot meet the requirements of different types of job scheduling, violate users, etc., so as to improve the utilization rate and reduce the default. rate effect

Active Publication Date: 2019-04-26
GUANGDONG UNIV OF PETROCHEMICAL TECH
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Existing cloud job scheduling algorithms are either aimed at computing-intensive cloud jobs, or focus on data-intensive cloud jobs, and there are few scheduling algorithms for hybrid jobs
In a real cloud computing environment, the types of jobs submitted by different users are often different, and the requirements of different types of cloud jobs are often different. The scheduling method designed for a single job type often cannot meet the requirements of different types of job scheduling, resulting in violation of the user level. Agreement

Method used

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  • A hybrid cloud job scheduling method based on q-learning
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  • A hybrid cloud job scheduling method based on q-learning

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Embodiment

[0035] like figure 1 As shown, a hybrid cloud job scheduling method based on Q-learning uses multi-agent parallel learning, that is, each agent independently learns the optimal strategy. When an agent first obtains a strategy that satisfies the condition of error<θ, Knowledge transfer between agents, specifically including:

[0036] Define the state space in Q-learning: the number of active virtual machines in the cloud environment resource pool is the state space;

[0037] Define the action set A in Q-learning: the action set includes two actions, namely accepting the currently scheduled job and rejecting the currently scheduled job;

[0038] Define the immediate return function for the system: Among them, job i .ini indicates the number of instructions executed by the job, job i .fsize indicates the job size, VM j .proc indicates the processing speed of the virtual machine, VM j .bw indicates the bandwidth of the virtual machine;

[0039] Initialize Q(s,a), where Q(s...

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Abstract

The invention discloses a Q-learning-based hybrid cloud job scheduling method. A plurality of agents are used for parallel learning, namely, each agent independently performs optimal policy learning; and when one agent obtains a policy meeting a condition that error is less than theta first, knowledge transfer among the agents is carried out. According to the method, a reinforcement learning-based hybrid cloud jog scheduling method is designed by taking minimum user job completion time and waiting time as optimization goals by analyzing an executive process of a user job in a cloud environment, and the convergence of an optimal policy is accelerated by adopting a parallel multi-agent technology, so that the utilization rate of cloud resources is increased and the default rate of a user level protocol is reduced.

Description

technical field [0001] The invention relates to the field of cloud job scheduling, in particular to a hybrid cloud job scheduling method based on reinforcement learning. Background technique [0002] Job scheduling is one of the key technologies of cloud computing, which is of great significance to meet user needs and improve the service quality and economic benefits of cloud service providers. Job scheduling is one of the key technologies of cloud computing, which is of great significance to meet user needs and improve the service quality and economic benefits of cloud service providers. Existing cloud job scheduling algorithms are either aimed at computing-intensive cloud jobs, or focus on data-intensive cloud jobs, and there are few scheduling algorithms for hybrid jobs. In a real cloud computing environment, the types of jobs submitted by different users are often different, and the requirements of different types of cloud jobs are often different. The scheduling method...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/48G06F9/455
CPCG06F9/45558G06F9/4881G06F9/4887G06F2009/4557
Inventor 彭志平崔得龙李启锐许波柯文德
Owner GUANGDONG UNIV OF PETROCHEMICAL TECH
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