Green data center energy-saving task scheduling strategy based on robust optimization

Abstract

The invention discloses a green data center energy-saving task scheduling strategy based on robust optimization. The green data center energy-saving task scheduling strategy is mainly used for solvingthe problems of high energy consumption, high electricity charge and high pollution of a data center. The green data center energy-saving task scheduling strategy deploys solar cell panels for the data center, and the data center can be powered by solar energy and a traditional power grid in a hybrid manner. In order to solve the characteristics of randomness, discontinuity and instability of solar power generation, the green data center energy-saving task scheduling strategy designs a novel and flexible uncertainty model, defines an uncertain set to limit fluctuations of solar power generation amount by introducing reference distribution, considers the electricity price difference and time-varying characteristic of geographic distributed computing nodes, designs the reasonable task scheduling strategy, and allocates requests submitted to the data center by users to computing nodes and time periods with high solar output and low electricity price for processing, so as to cost the lowest electricity charge and achieve the purposes of saving energy and protecting the environment.

Description

technical field [0001] The invention belongs to the technical field of solar energy, and specifically relates to a robust optimization-based energy-saving task scheduling strategy for a green data center, which is mainly used to solve the problems of high energy consumption, high electricity bills, and high pollution in the data center. Considering the software and service aspects, the present invention distributes the requests submitted by users to the data center to geographically distributed computing nodes for processing. The combination of solar power generation and traditional grid power generation provides power to the data center; the second is to consider the regional differences and time-varying nature of electricity prices; the third is to design an energy-saving task scheduling strategy, that is, on the basis of the first two parts, the user requests that will reach the data center Allocate to computing nodes and time periods with high solar output and low electric...

AI Technical Summary

Problems solved by technology

However, after the use of renewable energy, although the environmental problems have been effectively improved, some problems have followed: First, unlike the controllable and stable power generation mechanism of the traditional power grid, the power generation of renewable energy is highly volatile and unstable. Due to determinism and strong correlation with weather, it is difficult to quantify the amount of solar power generation; secondly, how to properly schedule the user requests of the data center on geographically distributed computing nodes to minimize the data center’s load without violating user service requests. Electricity costs are also a key issue

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