Efficient Analysis at Edge

Abstract

Digitalization changes traditional business models by using digital technologies to improve existing offerings and to create new offerings. Current technological trends such as artificial intelligence, autonomous systems, and predictive maintenance are ideal candidate technologies to enable digitalization use cases. Often, these technologies rely on the availability of large amounts of data and the capability to process these data efficiently. In contrast to consumer markets, industrial products must fulfill higher non-functional requirements such as fast response times, 24/7 availability and stability, real-time processing, safety, or security requirements. As a consequence, processing capabilities -- ranging from multicore and manycores to even high end parallel clusters -- have to be exploited to achieve necessary performance and stability needs. In this paper, we introduce a Distributed Multicore Monitoring Framework (MoMo) which is a reference monitoring solution developed at Siemens Corporate Technology. It can be used to easily build efficient and stable diagnostic solutions which can help to understand the correctness, availability, reliability, and performance of large-scale distributed systems based on live data. Due to its small footprint MoMo can be used to analyze data directly at the data source which, for instance, can significantly reduce the network load. While MoMo's efficiency comes from the usage of multicore processors (CPUs) for running analysis in parallel, its usability is guaranteed by its capability to easily integrate with other monitoring frameworks and its usage of SPL - a domain-specific language which allows user to easily define diagnostic algorithms.