Data migration method of cross-data center cloud computing system

Abstract

The invention discloses a data migration method of a cross-data center cloud computing system. The data migration method comprises the steps of obtaining system parameters from the cloud computing system, obtaining an objective function and constraint according to the system parameters, obtaining a flicker-penalty factor of the objective function and an upper bound of the flicker-penalty factor through a Lyapunov optimization framework; extracting a data distribution item from the upper bound of the flicker-penalty factor and generating the data migration method. Due to the fact that a technical method of using the Lyapunov optimization framework to obtain the flicker-penalty factor of the objective function and the upper bond of the flicker-penalty factor and extract the data distribution item is utilized, the cost of cross-data centers is balanced, and the scheduling problem of the cross-data center cloud computing system is solved in high performance, high availability and least cost.

Description

technical field [0001] The invention relates to the field of virtualized cloud, in particular, to a data migration method across data center cloud computing systems. Background technique [0002] The rate of data generation has never been faster: YouTube generates nearly 4 billion video views and nearly 432,000 hours of new video every day. With the advent of the era of big data, the amount of data in all walks of life continues to grow, and its potential huge value is worth exploring. For example, social networking sites such as FaceBook can reveal user usage patterns and Potential relationship, and use it to detect social hotspot events or serve its market decision-making. However, the rapid processing of geographically dispersed large-capacity data is extremely complex, so that traditional PCs can no longer meet its processing requirements. Therefore, many companies have deployed multi-data center clouds and hybrid clouds to deal with this problem. These cloud technolog...

AI Technical Summary

Problems solved by technology

The most noteworthy issues include: ① How to migrate large-scale data from different locations to geographically distributed data centers? ② How much computing resources need to be provided in these data centers to ensure performance while minimizing cost? The heterogeneity and diversity of big data and the dynamic utility-driven resource price model make these two problems very challenging

In addition, the internal dependencies between multiple stages of distributed computing, such as the interaction between the mapping stage and the specification stage in MapReduce computing, further intensify the data migration, resource supply, and reducer selection among geographically distributed multi-data centers. complexity of the problem

[0004] Aiming at the lack of a mapping-reduction model-based cross-data center cloud computing system scheduling scheme in the prior art, there is currently no effective solution

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