Device, system and method for dynamically controlling number of newly-increased tasks at cloud data center

Abstract

The invention discloses a device, system and method for dynamically controlling the number of newly-increased tasks at a cloud data center, and belongs to the field of cloud computing. The variation trend of loads and physical machine resources in the actual running process of the cloud data center is tracked, according to predicted values of the newly-increased task load capable of being borne by a system in the future, the window sizes of the new tasks are dynamically determined, task congestion and computing resource idling are avoided, optimizing balance is achieved, and the performance and economical efficiency of the cloud system are both taken into consideration.

Description

technical field [0001] The invention belongs to the field of cloud computing, and in particular relates to a dynamic control device, system and method for the number of newly added tasks in a cloud data center. Background technique [0002] Cloud computing refers to a computing model that distributes tasks on a large number of distributed computers, uses a cloud computing platform, and provides users with information services through the network. Compared with traditional software forms, cloud computing has significant advantages such as loose coupling, on-demand, cost control, resource virtualization, and heterogeneous collaboration, making it more suitable for today's e-commerce, flexible manufacturing, and mobile Internet applications. Cloud computing includes two meanings: one is the underlying cloud computing platform infrastructure, which is the basis for building upper-layer applications; the other is the cloud computing applications built on this basic platform. [...

AI Technical Summary

Problems solved by technology

[0006] The traditional cloud data center task management technology always sets a constant size window of acceptable new tasks. This technology has the following disadvantages: it does not consider the dynamic changes in the load of the physical machine, and it is easy to process the remaining tasks on the physical machine. A large number of new tasks are still arranged under the condition of rapid load growth, which leads to task congestion after a period of time after task distribution, and the system performance drops sharply; In the case of a decline, fewer new tasks are still scheduled, resulting in a situation where the physical machine is idling after a period of time after the task is distributed, causing a waste of computing resources

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