{"id":800,"date":"2019-12-09T04:35:58","date_gmt":"2019-12-09T04:35:58","guid":{"rendered":"http:\/\/se.kaist.ac.kr\/starlab\/?page_id=800"},"modified":"2019-12-09T04:35:58","modified_gmt":"2019-12-09T04:35:58","slug":"dynamic-simva-sos","status":"publish","type":"page","link":"https:\/\/se.kaist.ac.kr\/starlab\/dynamic-simva-sos\/","title":{"rendered":"Dynamic SIMVA-SoS"},"content":{"rendered":"

Background<\/strong><\/p>\n

(1)<\/strong> System of Systems<\/em><\/strong><\/p>\n

The concept of \u201csystem of systems (SoS)\u201d appeared in 1950s, and nowadays it is common to see the SoS concept in different domains. However, there is no standard definition for SoS. There are lots of perspectives from various fields of studies about an SoS. Eisner defined SoS as large, geographically distributed assemblages of systems. The component systems and their integration are deliberate, and centrally planned for a particular purpose. Shenhar stated SoS as a large, widespread collection or network of systems with functioning together to achieve a common purpose. He also described SoS as an array of systems. Maier described SoS as a set of collaboratively integrated systems, and the systems hold two main properties: operational independence and managerial independence of the components. Krygiel suggested that an SoS is a set of different systems so connected or related together to achieve the goal which is not possible by individual system. Jamshidi stated SoS as a large-scaled integrated system which is heterogeneous and independently operable and the systems are connected through the network for the common goal.<\/p>\n

\u00a0<\/em><\/p>\n

(2) Chaos Engineering<\/em><\/strong><\/p>\n

The evolution of large-scale distributed software systems is causing a major change in software engineering. The industry of IT is quickly adopting a way to increase development flexibility and deployment speed. Even if each individual system or service within the distributed system behaves correctly, interactions and collaborations among the services can have unpredictable results. In other words, if an unexpected result occurs due to the very rare but destructive real-world impairment which directly affects the environment of the production, the distributed system becomes \u201cChaos\u201d. Chaos engineering is an approach for learning the systems behavior by applying an empirical experiment in production. In order to apply an empirical experiment, we have to sample stimuli from the space of all possible events that might occur in the realworld. Stimuli are input for the empirical experiment and by injecting the stimuli, we can learn the behaviors of the system. The following are principles of chaos engineering.<\/p>\n