Resource allocation prediction method and system for mobile cloud computing

Abstract

The invention relates to a resource allocation prediction method and system for mobile cloud computing. The method comprises the steps of performing statistics on resource index values in mobile cloudcomputing in a preset time period before a target time node to form an index value set A, and performing grading on the resource index values in the set A, wherein each grade corresponds to a state space; by utilizing a weighted Markov chain algorithm, calculating a state probability row vector of the target time node; according to grading standards, establishing mean column vectors of the resource index values, and in combination with the state probability row vector, calculating a predicted value of the target time node. According to the method, the predicted value can be directly obtained,so that a model is simplified to a certain extent, the calculation amount is reduced, and the efficiency is improved. By utilizing the mean column vector, the influence of an interval superposition probability on a whole prediction result is reduced, the prediction accuracy is improved, and the problem that the final prediction result deviates from an actual result due to the fact that the weighted Markov chain algorithm ignores difference of prediction probabilities in stages in the superposition process is solved.

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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