Reliable Composition of MAPE-K Loops in Self-Adaptive Software Systems

Author(s):  
Selma Ouareth ◽  
Soufiane Boulehouache ◽  
Mazouzi Smaine

Self-adaptive systems (SASs) are controlled by autonomic manager (AM). This ensures the QoS of such complex systems within highly dynamic and unpredictable contexts. However, the massive integration of the adaptation abilities increased drastically the complexity of the AMs. To decrease the complexity and ensure correctness adaptation, scholars propose a subdivision into multi-autonomic entities (AEs) as a design approach. In such a design approach, SASs are controlled through multiple interacting AMs implementing each the well-known MAPE-K Loop. In this article, the writers propose a refinement pattern of interacting multiple MAPE-K Loops to achieve global adaptation without conflict. The authors contribute with a notation to describe the interaction of multiple MAPE-K Control Loops. To ensure the coordinated multi-attributes control, the interaction of the AEs is achieved through the knowledge base of the MAPE-K Loops. To validate the proposed pattern, a case study in the field of Electric Vehicle is presented.

Computers ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 27
Author(s):  
Shereen Ismail ◽  
Kruti Shah ◽  
Hassan Reza ◽  
Ronald Marsh ◽  
Emanuel Grant

Adaptivity is the ability of the system to change its behavior whenever it does not achieve the system requirements. Self-adaptive software systems (SASS) are considered a milestone in software development in many modern complex scientific and engineering fields. Employing self-adaptation into a system can accomplish better functionality or performance; however, it may lead to unexpected system behavior and consequently to uncertainty. The uncertainty that results from using SASS needs to be tackled from different perspectives. The Internet of Things (IoT) that utilizes the attributes of SASS presents great development opportunities. Because IoT is a relatively new domain, it carries a high level of uncertainty. The goal of this work is to highlight more details about self-adaptivity in software systems, describe all possible sources of uncertainty, and illustrate its effect on the ability of the system to fulfill its objectives. We provide a survey of state-of-the-art approaches coping with uncertainty in SASS and discuss their performance. We classify the different sources of uncertainty based on their location and nature in SASS. Moreover, we present IoT as a case study to define uncertainty at different layers of the IoT stack. We use this case study to identify the sources of uncertainty, categorize the sources according to IoT stack layers, demonstrate the effect of uncertainty on the ability of the system to fulfill its objectives, and discuss the state-of-the-art approaches to mitigate the sources of uncertainty. We conclude with a set of challenges that provide a guide for future study.


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