Resource allocation and overhead optimization method for cloud video

Abstract

The invention discloses a resource allocation and overhead optimization method for a cloud video. The method comprises the steps that a mathematical model used for describing the relations between channel distribution, user bandwidth distribution, total operation cost and quality of service (QoS); the model is proved and solved to be NP-hard; by the introduction of a penalty function, the problem of minimizing overhead through channel copying and bandwidth allocation is equivalently transformed into the problem of maximizing benefits through the channel copying and the bandwidth allocation; a resource allocation and overhead optimization method, namely DREAM, in a cloud data center is put forward to solve the problems of reservation and allocation of the bandwidth of a cloud platform and to determine the copy layout of channels of the cloud data center; the principle of locality is integrated in a resource allocation and overhead optimization algorithm, and a DREAM-L algorithm is put forward. Compared with the prior art, the method allows a cloud system to provide satisfactory watching quality, locality of reference and data availability for video-on-demand service at a low price.

Description

Technical field [0001] The present invention relates to cloud video data communication technology, and more specifically, to a cloud video resource allocation and cost optimization method. Background technique [0002] With the popularity and promotion of cloud computing and cloud storage, many video service providers have begun to migrate video data and application services to cloud platforms. To enable the cloud system to provide video-on-demand services with satisfactory viewing quality, access locality, and data availability at a low cost. [0003] There are usually two resource optimization scheduling methods on cloud platforms: one method uses the cloud server as the scheduling granularity, and the other method uses the video channel as the scheduling granularity. The former focuses on adjusting the location of cloud servers or virtual machines (VM) and the number of resources it owns, while the latter focuses on dynamically managing video channels and video streams from the...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the above defects of the prior art, the present invention proposes a cloud video resource allocation and overhead optimization method with video channels as the scheduling granularity, the purpose of which is to improve data availability and access locality and optimize prices, thus solving the problems in cloud platforms. Issues such as video-on-demand service bandwidth allocation and video copy placement can meet the Service Level Agreement (Service Level Agreement) in video-on-demand services at a low cost

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