Channel frequency response for a low voltage indoor cable up to 1GHz

Abstract: The Channel Frequency Response (CFR) is essential in the understanding and specification of Power Line Communications (PLC) equipment. In this paper a 10m indoor low voltage Flat Twin and Earth power cable is characterized up to 1GHz. This is higher in frequency than existing PLC specifications. This is done because the effect of frequencies in the upper VHF and lower UHF bands on PLC is largely unknown. Measurements and simulation are used to show that at higher frequencies, the traditional transmission line models of wave propagation on a power line are no longer valid. Classic attenuation models do not predict the correct CFR above typically 250MHz for a 10m cable. This effect is explained in terms of electromagnetic radiation from the cable. After 500MHz it is speculated that the line becomes a waveguide. These transmission modes have important implications for UHF PLC, either directly (connected cable) or when used in contactless mode where the power line is used as antenna

Challenges and Experiences in Designing Interpretable KPI-diagnostics for Cloud Applications

Automated root cause analysis of performance problems in modern cloud computing infrastructures is of a high technology value in the self-driving context. Those systems are evolved into large scale and complex solutions which are core for running most of today’s business applications. Hence, cloud management providers realize their mission through a “total” monitoring of data center flows thus enabling a full visibility into the cloud. Appropriate machine learning methods and software products rely on such observation data for real-time identification and remediation of potential sources of performance degradations in cloud operations to minimize their impacts. We describe the existing technology challenges and our experiences while working on designing problem root cause analysis mechanisms which are automatic, application agnostic, and, at the same time, interpretable for human operators to gain their trust. The paper focuses on diagnosis of cloud ecosystems through their Key Performance Indicators (KPI). Those indicators are utilized to build automatically labeled data sets and train explainable AI models for identifying conditions and processes “responsible” for misbehaviors. Our experiments on a large time series data set from a cloud application demonstrate that those approaches are effective in obtaining models that explain unacceptable KPI behaviors and localize sources of issues