A resource allocation prediction method and system in mobile cloud computing

Abstract

The present invention relates to a method and system for predicting resource allocation in mobile cloud computing. The method includes: counting resource index values ​​in mobile cloud computing within a preset time period before the target time node to form an index value set A, and calculating the resources in the set A The index values ​​are graded, and each grade corresponds to a state space; the weighted Markov chain algorithm is used to calculate the state probability row vector of the target time node; the resource index value mean column vector is established according to the classification standard, combined with the state probability row vector, the target is calculated The predicted value of the time node. This method can directly obtain the predicted value, simplifies the model to a certain extent, reduces the amount of calculation, and improves the efficiency. The present invention uses the mean column vector to reduce the impact of the superposition probability of each interval on the entire prediction result, improves the prediction accuracy, and solves the problem that the weighted Markov chain prediction model ignores the different prediction probabilities of each order during the superposition process and causes the final prediction result This problem deviates from the actual results.

Description

technical field [0001] The present invention relates to the technical field of cloud computing, in particular to a resource allocation prediction method and system in mobile cloud computing. Background technique [0002] Cloud computing is the product of distributed computing, parallel computing, and grid computing. It is a computing model that uses the Internet to access shared resource pools anytime, anywhere, on demand, and conveniently. With the continuous emergence and development of mobile devices, mobile cloud computing based on Android, IOS, etc. has also emerged as the times require. Currently, cloud computing allows tenants to lease resources on a pay-as-you-go basis, which has the potential to provide a more cost-effective solution than on-premises computing by eliminating the need for tenants to maintain complex infrastructure. To realize this benefit, applications running in the cloud need to be provided with the right amount of resources, which are less static...

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Problems solved by technology

In addition, although the absolute distribution superposition of each order (various step sizes) Markov chain is applied to predict the state, it does not consider the role of the absolute probability of each order Markov chain in the superposition, that is, each order The absolute probability of the Markov chain plays the same role, which is obviously unscientific

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