The current virtualization solution in the Cloud widely relies on hypervisor-based technologies. Along with the recent popularity of Docker, the container-based virtualization starts receiving more attention for being a promising alternative. Since both of the virtualization solutions are not resource-free, their performance overheads would lead to negative impacts on the quality of Cloud services. To help fundamentally understand the performance difference between these two types of virtualization solutions, we use a physical machine with "just-enough" resource as a baseline to investigate the performance overhead of a standalone Docker container against a standalone virtual machine (VM). With findings contrary to the related work, our evaluation results show that the virtualization's performance overhead could vary not only on a feature-by-feature basis but also on a job-to-job basis. Although the container-based solution is undoubtedly lightweight, the hypervisor-based technology does not come with higher performance overhead in every case. For example, Docker containers particularly exhibit lower QoS in terms of storage transaction speed.
Absrmcl-Performance modeling is an important topic in capacity planning and overload control for web servers. We present an M/GNK*PS queueing model of a web server. The arrival process of HTTP requests is assumed to he Poissonian and the semce discipline is processor sharing. The total number of requests that can he processed at one t i e is l i t e d to K. We obtain closed form expressions for web sewer performance metrics such as average response time, throughput and hlofking probability. The average of the service time requirement and the limit of the number of requests being Served are model parameters. The parameters are estimated by maximizing the log-likelihood function d the measumd average response time. Compared to other models, our model is conceptually simple and it is easy to estimate model parameters. The model has been validated thmngh measurements in our lab. The performance metrics predicted by the model fit well to the experimental outcome.
I. INTRODUCTIONPerformance modeling is an important part of the research area of web servers. Without a correct model of a web server it is difficult to give an accurate prediction of performance metrics. A validated model is the hasis of web server capacity planning, where models are used to predict performance in different settings, see Hu et al. 111 or M e n a d and Almeida 121.
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