Motivated by the high-interest in increasing the utilization of non-general purpose devices in reaching computational objectives with a reduced cost, we propose a new model for scheduling microservices over heterogeneous Cloud-Edge environments. Our model uses a particular mathematical formulation for describing an architecture that includes heterogeneous machines that can handle different microservices. Since any new model asks for an early risk-analysis of the solution, we improved the CloudSim simulation framework to be suitable for an experiment that includes that kind of systems. In this paper, we discuss two examples of real-life utilizations of our proposed scheduling architecture. For an objective appreciation of the first example, we also include some experimental results based on the developed simulation tool. As a result of our interpretation of the experimental results we find out that some very simple scheduling algorithms may outperform some others in given situations that are frequently present in Cloud-Edge environments when we are using a microservice-oriented approach.

Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance.