Active updating method, device and server for physical machines in IaaS cloud system

Abstract

The invention discloses an active updating method, device and server for physical machines in an IaaS cloud system. The active updating method includes the following steps: obtaining historical fault data of each physical machine in the IaaS cloud system; performing analysis processing on the obtained historical fault data of the physical machines one by one and obtaining the distribution regularity of the historical fault data of each physical machine and predicting next-time fault data of a corresponding physical machine based on the obtained distribution regularity; and executing updating operations on a corresponding physical machine according to the prediction result. The active updating method for the physical machines in the IaaS cloud system performs analysis and modeling on the historical fault data of each physical machine so as to obtain the generation regularity of faults of the physical machines and predict a next-time fault generation points according to the obtained regularity and thus the updating time of the physical machines is adjusted dynamically. Compared with a method in which a fixed updating time is set or which only bases on the average value of historical fault data in the prior art, the method can better taking system overhead and cloud system reliability into consideration.

Description

technical field [0001] The invention belongs to the technical field of cloud computing, and relates to resource implementation scheduling and optimization control technology in cloud computing operation, in particular to an active updating method, device and server of a physical machine (Physical Machine, PM) in an IaaS cloud system. Background technique [0002] Cloud computing refers to a computing model that distributes computing 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. [0003] Cloud computing includes two meanings: one is the underlying cloud computing platform infrastructure, ...

AI Technical Summary

Problems solved by technology

[0006] The above post-event repair measures have certain shortcomings: there is still a period of delay from the failure of the PM to the detection of the failure by the system, so there may be an error of "the system continues to dispatch tasks to the PM that has failed"; The state is different from the state represented by the saved VM image at the latest archive point, so after the PM is repaired, the involved VM is actually restored to a certain moment before the failure rather than the state at the time of the failure, which actually leads to useless work; The system overhead of repairing after a failure is large, and has a large negative impact on the performance of the IaaS cloud system

However, due to the dynamic variability of system load and reliability, updating at fixed intervals is often difficult to meet the requirements of low update operation overhead and high system reliability;

[0011] (2) Lack of mechanism for quantitative trend prediction

The existing IaaS cloud maintenance technology does not fully analyze and model the historical fault data of the system, but mostly uses historical average or recent historical fault data as the basis for control decision-making;

[0012] (3) The difference between different PMs is not considered

The existing technology treats all PMs in the IaaS cloud system equally, adopts the same control strategy and control time interval, and does not consider the differences in load, execution efficiency, and reliability of different PMs

Images