Data center energy efficiency evaluation method based on service quality

Abstract

The invention provides a data center energy efficiency evaluation method based on service quality. The method comprises the following steps: constructing a data center service quality evaluation index system comprising a target layer, a decision layer and an influence factor layer; constructing a judgment matrix, and calculating a weight vector of each decision-making layer and a weight vector of an influence factor layer in each decision-making layer index; dividing the service quality of the data center into G grades, and obtaining the service quality evaluation grade of each influence factor in combination with the related information of the data center service quality influence factor layer; calculating a service quality evaluation matrix of each decision-making layer index; and obtaining a service quality value of the data center through a fuzzy comprehensive evaluation method. According to the invention, a more systematic and more comprehensive evaluation index system is constructed, the problem of objective weight distribution during multi-influence factor decision making is solved through an analytic hierarchy process, and meanwhile, the service quality is quantified by using a fuzzy comprehensive evaluation method; and improvement measures are provided through the quadrants where the influence factors are located, so that single and one-sided evaluation on the service quality is avoided.

Description

technical field [0001] The invention belongs to the field of cloud computing, and in particular relates to a data center energy efficiency evaluation method based on service quality. Background technique [0002] Cloud computing is the growth, usage and delivery model of Internet-based related services, usually involving the provision of dynamically scalable and often virtualized resources over the Internet. The virtualization of software and hardware resources by cloud services increases the power of a single server, and the energy consumption of large-scale cloud computing data centers is huge, and its power consumption is several times or even dozens of times that of traditional data centers. With the development of cloud computing technology, data center technology has become one of the most concerned issues in the field of cloud computing. [0003] The advent of the cloud era has made the problem of high energy consumption in data centers more and more serious. High pe...

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Problems solved by technology

[0004] The existing data center energy efficiency evaluation methods mostly subjectively select the evaluation indicators of different aspects of the existing data center, and assign corresponding weights through expert knowledge, so as to obtain an evaluation level of data center energy efficiency, which is applied to various data center energy efficiency The comparison among them gives a rough sorting result; most of the studies on data center service quality use the user evaluation matrix to obtain the overall evaluation value based on the user’s rating of the recent service, but the specific user evaluation is not given. For which aspects, the correspondence between these aspects and data center data

The existing evaluation model can only obtain the energy efficiency level of the data center, not the specific value, and cannot reflect the improvement of the energy efficiency of a single data center before and after; at the same time, the existing evaluation model simply uses the evaluation value of service quality and the energy consumption of the data center The ratio of the data center represents the energy efficiency of the data center, and there is a certain degree of subjectivity in the selection of service quality parameters. The user evaluation matrix introduced can only reflect the user's feelings and cannot be combined with the real situation of the data center; for the service quality parameter weight The assigned value lacks objectivity; at the same time, these evaluation models can only obtain an overall evaluation value, but cannot obtain specific deficiencies in the service quality of the data center, resulting in the inability to obtain targeted improvement plans

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