Kubernetes (K8s) is promising software for application systems since it makes application systems more flexible and robust by auto-scaling, which automatically scales up the application system resources when the application system is overloaded, and self-healing, which automatically recovers the application system from a failure. However, auto-scaling and self-healing make system operators' tasks complex. First, there is a delay, which is the time difference between executing auto-scaling or self-healing and recovering degraded application performance metrics such as response time. Second, the delay depends on types of abnormalities (i.e., overloads and failures). Moreover, the auto-scaling and self-healing cannot always recover the abnormality. Therefore, system operators need to understand the degree of abnormality (i.e., how much the application performance is degraded and how long the delay is). Although many anomaly detection methods have been developed, they have not considered auto-scaling or self-healing when the abnormality occurs. In this paper, we analyze the performance of anomaly detection methods with auto-scaling and self-healing in K8s by implementing anomaly detection methods, and deploying a web application system on K8s. Specifically, first, we verified that there is a delay that depends on types of abnormality by injecting anomalies into the web application system. Then, we evaluated the anomaly detection accuracy of each method by using the data collected from the web application. Finally, a clustering approach is used for anomaly scores, which are the outputs of these methods, to investigate whether anomaly detection methods can provide the degree of abnormality. The evaluations show that our analysis provides useful information for operators to manage the K8s with auto-scaling and self-healing.

As data collection by mobile devices becomes feasible, network operators expect that the end-to-end measured data widely collected by users' devices will result in a deeper understanding of the current network conditions, identification of the performance-degraded components in a network, and estimation of the degree of their performance degradation. One method of achieving the above with such end-to-end measured data is network tomography. Meanwhile, network virtualization by software-defined networking has advanced, and each end-to-end measurement flow collected by users may pass through different paths even between the same origin-destination node pair. It is difficult and costly to identify through which paths the individual flows have passed, so it is difficult to naively apply conventional network tomography methods to virtualized networks. We propose a novel network tomography for virtualized networks to which the routing paths cannot be uniquely identified. The basic idea of our method is to introduce routing probability in accordance with the aggregated information on measurement flows. Our method involves two proposed algorithms, and we evaluated the performance of these algorithms by comparing them with algorithms of conventional tomography using determined route information and by varying the network conditions through simulation.

The demand to access multimedia data such as live video is increasing due to the spread of broadband access network. However, the Internet only provides the so called best-effort service. The network condition of the Internet shows fluctuation. Delivering video without any control causes degradation of the final video quality. To avoid such degradation, we must control video traffic to adapt to the fluctuating available bandwidth. In this paper, we propose a technique to find the available bandwidth for end-to-end stream connections over the Internet. We claim that the correlation coefficient of the network delay of an end-to-end stream is closely related to the bottleneck-link load for the end-to-end stream connection. We show and prove the relationship through analysis and simulations.

The demand to access multimedia data such as live video is increasing due to the spread of broadband access network. However, the Internet only provides so called best-effort service. The network condition of the Internet shows fluctuation. Delivering video without any control causes degradation of the final video quality. In this paper we propose a stream-rate control algorithm RC/sup 3/ that uses correlation of network delay to evaluate available bandwidth. We show performance of RC/sup 3/ derived from simulations.