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Maintaining container sustainability through machine learning
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DOI：10.1007/s10586-021-03359-4.png)
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摘要
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Container-based virtualization is a new technology used by cloud providers to provide cloud services to end-user. This technology has various advantages (e.g. lightweight, quickly deployable, and efficient for resource utilization) for executing an application. It reduces the operating cost, carbon emission, and allocates the resources dynamically. Different cloud applications have different requirements. Deploying resources according to peak requirements always can be costly. On the other hand, always having minimum computing resources may not meet workload's peak requirements, and may cause degraded system performance, less throughput, more response time and service level agreement violations. Hence, it becomes a challenge to maintain optimal level of resources to fulfill the SLA requirements for the applications. To address the above issues, we propose an auto-scaler which uses proactive approach (Support Vector Regression) to perform horizontal elasticity for Docker containers in response to fluctuating workload for real-time applications. As the workload increases, additional resources will be allocated dynamically supporting elasticity. The increase in capacity of a machine dynamically is termed as elasticity. The effective mechanism of elasticity avoids the violation of SLA and penalties in terms of user's loss. The proposed auto-scaler uses the IBM computing model, MAPE-K principle to perform elasticity using the workload predictions made by the SVR model. The predicted workload helps auto-scaler to find out the minimum numbers of replicas needed for a container of a cluster so that it handles the future workload. The experimental results show that the results of SVM prediction keep the performance of the system sustainable with fluctuating workload.
Keyword:
Cloud computing
Elasticity
Container
Auto-scaling
MAPE model
Prediction models
Support vector machine
Machine learning
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