Container Load Forecast And Consolidation Strategy In Cloud Environment

The scale of cloud computing has been continuously expanding in recent years,with the rapid development of container technology,the market share of cloud data centers that use Container as a Service(CaaS)has been expanding rapidly as well,which leads to a prominent problem of high energy consumption.In the inner part of the cloud data center,container loads and host loads are the key factors that affect its change of energy consumption.Having an accurate control of the load change's trend contributes to the reasonable scheduling of resources,making it possible to reduce energy consumption of the cloud data center efficiently.However,there are many types of loads in cloud data centers,resulting in a need for exploring more general and extensive models to predict multiple loads.Moreover,in the cloud data center based on CaaS,both the container and the virtual machine need to be taken into consideration in the scheduling process so that scheduling strategy of the cloud data center can take energy saving and service quality into account.Based on this background,the main contribution of this article are made as follows:(1)Having investigated the commonly used analysis methods and prediction models of time series,and taken the characteristics of load data in the cloud environment into consideration,this paper proposes a prediction method based on CNN-LSTM model and EEMD decomposition(EEMD-CNN-LSTM).The method first decomposes the load data in the cloud environment into several Intrinsic Modal Functions(IMFs)through Ensemble Empirical Mode Decomposition,selects the first three IMFs and merges them into new components and then uses CNN-LSTM with attention mechanism to predict.For the other low-frequency components,LSTM is used to predict.With the attention mechanism added,CNN-LSTM has better improved the prediction effects of time series data with long sequences and large fluctuations.The experimental results show that the EEMD-CNN-LSTM method can predict various types of load data in the cloud environment with high prediction accuracy.(2)In order to effectively reduce the energy consumption of cloud data centers based on CaaS architecture,this paper proposes a Container consolidation Strategy based on Load similarity and Improved Genetic Algorithm(CSLIGA).The strategy first determines whether the host is overloaded according to the prediction result of the EEMD-CNN-LSTM method;then on the overloaded host,the container is selected for migration according to the load similarity between the container and the host.Meanwhile,for the container duplication,it judges whether to expand or shrink the capacity after load forecast so that the container duplication can increase or decrease to the point.Next,for the migrated containers,they are placed on the appropriate destination host by Container Placement of Improved Genetic Algorithm;finally,the strategy will make the host sleep which is determined to be underloaded by Low-Loading Host Processing Algorithm.The experiment was performed on the Cloud Sim platform with the experimental results showing that the CSLIGA strategy can effectively decrease the energy consumption of cloud data centers(consume 4% less energy than other),and ensure the quality of service(SLA violation rate is less than 5%).