자가적응 시스템을 위한 타임드 오토마타에 관한 연구
A Study of Timed Automata for Self-Adaptive System
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초록
실세계의 다양한 현상과 시스템들은 주어진 사용 상황에 따라 반응하며 센서에 의한 다양한 정보를 제공하며, 그들의 행동을 조절할 수 있다. 우리는 수많은 의사소통과 상호작용하는 것들에 의해 제공 받는다. 또한, 많은 휴대 전화 응용 프로그램은 보다 유연한 서비스를 수행하고자 기존 소프트웨어 앱에서의 상황 인식 소프트웨어에 의존하고 있다. 하지만, 동적환경에 따라 변화는 조건들은 시스템 상에서 원하지 않은 방식으로 행동할 수 있다. 게다가, 바람직하지 않은 행동들에 의해, 전력전지 및 임베디드 시스템들과 같은 웹 서비스 자원에 의존할 수 있다. 또한, 서비스 품질 및 정밀도로 인해 시스템 내부의 특정 자원 또는 노드의 장애로부터 기인할 수 있다. 결국, 적응형 시스템은 일반적으로 시스템 환경에 대한 정보를 활용하여 특정 사용 상황에 적응하고자 한다. 이에, 본 논문에서는 다양한 시스템에 적용 가능한 자가 적응형 설계 방법을 제안하고자 한다. 형식 설명 탐색 모델 기반 아키텍처를 설계하고, 자가 적응형 시스템을 위한 정형 명세 방법론을 제안한다. 나아가, 방법론적 접근 방법을 통해 빅데이터의 분류체계를 제시하고, 이에 따른 형성 및 진화 방안에 대해 유용한 시사점을 제공하고자 한다.
Abstract
Many phenomenons and systems in real world can be adapted their behavior in reaction to a given usage situation, and using the possibilities of sensors. We are surrounded with a hugh number of communicating and interacting things. Such a system is characterized by being distributed with a smart device or component from a large class. In addition, many cellular phone applications are able to provide a context-aware behavior, in which expose more flexible service and conventional software APP. However, by changing conditions on the dynamic environment act in an undesire behavior on their system. And also, by the undesirable behavior, web services such as power cells and embedded systems can be rely on their resources. Moreover, It can also be caused by the failure of certain resources or nodes in their system that provide to the quality of service and accuracy. After all, Adaptive systems typically use information about their system environment to adapt themselves to certain usage situations. Accordingly, this paper is required newly design methodology for Self-Adaptive with enable to a various systems. For this reason, we design formal description exploration model-based architecture. Especially, we present to optimization with synthesizing and optimizing system level for self-adaptive system, as well as propose Formal specification method for designing self-adaptive system. Futhermore, we show a classification of big data with methodological approach, and provide useful implications with their formation and evolution method.
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