Resource scheduling implementation method based on energy consumption and QoS collaborative optimization

Abstract

The invention discloses a resource scheduling implementation method based on energy consumption and QoS collaborative optimization. The method comprises the following steps: constructing a cloud task arrival queuing model of multiple virtual machines in a cloud computing data center environment; extracting QoS (Quality of Service) features of a data center by utilizing a stacked noise reduction automatic encoder technology to obtain a matrix for describing QoS feature information after dimension reduction, and solving the maximum response time of a current virtual machine to perfect constraint conditions of a collaborative optimization objective function; and obtaining a resource scheduling algorithm based on a Lyapunov optimization theory by combining a cloud task arrival queuing model, a collaborative optimization objective function and a Lyapunov optimization method, and realizing resource scheduling based on energy consumption and QoS collaborative optimization by adopting the resource scheduling algorithm. According to the method, energy consumption of the data center is effectively reduced while QoS is guaranteed, and interference of fluctuation of cloud task arrival in a real scene of the cloud computing data center on optimization problem solving is overcome.

Description

technical field [0001] The invention belongs to the field of cloud computing energy-saving scheduling, and in particular relates to a resource scheduling implementation method based on energy consumption and QoS collaborative optimization. Background technique [0002] Cloud computing has long been a popular research project in the global IT field by virtue of its ultra-large-scale service capabilities. With the continuous development of cloud computing technology, more and more data centers have emerged around the world, and the energy consumption generated by infrastructure has also shown an exponential growth trend. Currently, the carbon emissions of the global IT industry account for 3-5% of the total global carbon emissions. According to recent reports, Google data centers consume nearly 300 million watts, while Facebook's data centers consume 60 million watts. Data centers consume more electricity than energy-intensive manufacturing. McKinsey, an international resea...

AI Technical Summary

Problems solved by technology

In actual data center scenarios, due to load fluctuations and uncertain factors, the dual goals of energy saving and QoS guarantee become more complicated

Previous optimization methods require all entities (users, cloud tasks, service providers, etc.) to satisfy a single QoS constraint throughout the entire cloud computing scheduling process, which is unrealistic in real cloud computing environments

Moreover, the solution process of these optimization methods is complex, the convergence speed is slow, and it is difficult to meet the real-time scheduling requirements of large-scale cloud computing data centers

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