DeSARM: A Decentralized Mechanism for Discovering Software Architecture Models at Runtime in Distributed Systems

Runtime models play a critical role in modern self-adaptive systems. Hence, runtime architectural models are needed when making adaptation decisions in architecture-based self-adaptive systems. However, when these systems are distributed and highly dynamic, there is an added need to discover the systems software architecture model at runtime. Current methods of runtime architecture discovery take a centralized approach, in which the process is carried out from a single location. These methods are inadequate for large distributed systems because they do not scale up well and have a single point of failure. Also, systems of such size consist of nodes that are typically highly dynamic in nature. Existing approaches to architecture discovery are not capable of addressing these concerns. This paper describes DeSARM (Decentralized Software Architecture discoveRy Mechanism), a completely decentralized and automated approach for runtime discovery of software architecture models of distributed systems based on gossiping and message tracing. DeSARM is able to identify at runtime important architectural characteristics such as components and connectors, in addition to synchronous and asynchronous communication patterns. Furthermore, through its use of gossiping, it exhibits the properties of scalability, global consistency among participating nodes, and resiliency to failures. The paper discusses DeSARM’s architecture and detailed design, and demonstrates its properties through experimentation.