Multi-target task scheduling method and system under cloud computing system
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A technology of task scheduling and cloud computing, applied in the field of multi-objective optimization
Pending Publication Date: 2020-04-07
SHANDONG NORMAL UNIV
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[0009] In order to solve the above problems, the present disclosure proposes a multi-objective task scheduling method and system under the cloud computing system, which uses a hybrid discrete artificial bee colony algorithm to optimize the flexible task scheduling problem under the cloud computing system, and models it as an HFS model; Embedding 8 perturbation
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Embodiment 1
[0065] The present disclosure provides a multi-objective task scheduling method under a cloud computing system, including:
[0066] S1: The flexible task scheduling problem under the cloud computing system is modeled as a hybrid shop scheduling (HFS) problem, and two different types of HFS are considered at the same time;
[0067] S2: Determine the optimization goals and constraints;
[0068] S3: Solve using the hybrid discrete artificial bee colony (ABC) algorithm;
[0069] S4: Propose several different types of perturbation structures to enhance the search capability;
[0070] S5: An improved adaptive perturbation structure is embedded, and a deep development operator is designed;
[0071] S6: Verify the effectiveness of the above method for solving the HFS problem in which different devices have different processing capabilities in the cloud computing system.
[0072] Many studies in the literature generally agree that task scheduling has a significant impact on performa...
Embodiment 2
[0178] The present disclosure provides a multi-objective task scheduling system under a cloud computing system, including:
[0179]The scheduling optimization model building block is used to construct the task scheduling under the cloud computing system as a hybrid workshop scheduling model with the goal of minimizing the maximum completion time, minimizing the maximum equipment workload and minimizing the total workload of all equipment;
[0180] A scheduling optimization scheme solving module, which is used to solve the hybrid workshop scheduling model by using a hybrid discrete artificial bee colony algorithm embedded in a disturbance structure to obtain a scheduling optimization scheme;
[0181] A scheduling module, which is used to schedule tasks under the cloud computing system by using the obtained scheduling optimization scheme.
Embodiment 3
[0183] The present disclosure provides a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions are suitable for being loaded by a processor of a terminal device and executing the steps of a method for scheduling multi-objective tasks in a cloud computing system.
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Abstract
The invention discloses a multi-target task scheduling method and system under a cloud computing system. The multi-target task scheduling method comprises the steps: taking the minimization of the maximum completion time, the minimization of the maximum equipment workload and the minimization of the total workload of all equipment as targets, and constructing task scheduling under the cloud computing system into a mixed workshop scheduling model; solving the hybrid workshop scheduling model by adopting a hybrid discrete artificial bee colony algorithm embedded with a disturbance structure to obtain a scheduling optimization scheme; and scheduling the tasks under the cloud computing system by utilizing the obtained scheduling optimization scheme. A hybrid discrete artificial bee colony algorithm is adopted, and the flexible task scheduling problem under a cloud computing system is optimized, and an HFS model is modeled; eight disturbance structures are embedded, so that the developmentcapability of the algorithm is enhanced; the adaptive disturbance structure balances the development and exploration capabilities, and the improved following bee mechanism has a deep mining function,so that the local search capability can be further enhanced; and the convergence capability of the algorithm can be improved by designing a good reconnaissance bee algorithm.
Description
technical field [0001] The present disclosure relates to the technical field of multi-objective optimization, in particular to a multi-objective task scheduling method and system under a cloud computing system. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] In a cloud computing system, jobs proposed by users should be assigned to capable devices, and usually each job consists of several consecutive tasks, which should be processed in a certain order on different or the same devices. The whole process can be modeled as a Hybrid Flow Shop Scheduling (HFS) problem. Task scheduling in cloud systems has been studied in recent years, such as Wang et al. developed a multidisciplinary approach to artificial swarm intelligence for heterogeneous computing and cloud scheduling. However, published literature mainly discusses task allocation in clo...
Claims
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