scholarly journals A systematic literature review on methods that handle multiple quality attributes in architecture-based self-adaptive systems

2017 ◽  
Vol 90 ◽  
pp. 1-26 ◽  
Author(s):  
Sara Mahdavi-Hezavehi ◽  
Vinicius H.S. Durelli ◽  
Danny Weyns ◽  
Paris Avgeriou
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Adaptive Systems ◽  
Quality Attributes ◽  
Self Adaptive

scholarly journals Concepts and Models of Environment in Self-Adaptive Systems: A Systematic Literature Review

2021 ◽  
Author(s):  
Yong-Jun Shin ◽  
Joon-Young Bae ◽  
Doo-Hwan Bae
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Adaptive Systems ◽  
Research Community ◽  
Current Understanding ◽  
Runtime Environment ◽  
Important Concern ◽  
Runtime Environments ◽  
Self Adaptive

The runtime environment is an important concern for self-adaptive systems (SASs). Although researchers have proposed many approaches for developing SASs that address the issue of uncertain runtime environments, the understanding of these environments varies depending on the objectives, perspectives, and assumptions of the research. Thus, the current understanding of the environment in SAS development is ambiguous and abstract. To make this understanding more concrete, we describe the landscape in this area through a systematic literature review (SLR). We examined 128 primary studies and 14 unique environment models. We investigated concepts of the environment depicted in the primary studies and the proposed environment models based on their ability to aid in understanding. This illustrates the characteristics of the SAS environment, the associated emerging environmental uncertainties, and what is expressed in the existing environment models. This paper makes explicit the implicit understanding about the environment made by the SAS research community and organizes and visualizes them.

scholarly journals Applying Machine Learning in Self-adaptive Systems

2021 ◽  
Vol 15 (3) ◽  
pp. 1-37
Author(s):  
Omid Gheibi ◽  
Danny Weyns ◽  
Federico Quin
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Adaptive Systems ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Large Sets ◽  
Learning Techniques ◽  
Self Adaptation ◽  
Self Adaptive

Recently, we have been witnessing a rapid increase in the use of machine learning techniques in self-adaptive systems. Machine learning has been used for a variety of reasons, ranging from learning a model of the environment of a system during operation to filtering large sets of possible configurations before analyzing them. While a body of work on the use of machine learning in self-adaptive systems exists, there is currently no systematic overview of this area. Such an overview is important for researchers to understand the state of the art and direct future research efforts. This article reports the results of a systematic literature review that aims at providing such an overview. We focus on self-adaptive systems that are based on a traditional Monitor-Analyze-Plan-Execute (MAPE)-based feedback loop. The research questions are centered on the problems that motivate the use of machine learning in self-adaptive systems, the key engineering aspects of learning in self-adaptation, and open challenges in this area. The search resulted in 6,709 papers, of which 109 were retained for data collection. Analysis of the collected data shows that machine learning is mostly used for updating adaptation rules and policies to improve system qualities, and managing resources to better balance qualities and resources. These problems are primarily solved using supervised and interactive learning with classification, regression, and reinforcement learning as the dominant methods. Surprisingly, unsupervised learning that naturally fits automation is only applied in a small number of studies. Key open challenges in this area include the performance of learning, managing the effects of learning, and dealing with more complex types of goals. From the insights derived from this systematic literature review, we outline an initial design process for applying machine learning in self-adaptive systems that are based on MAPE feedback loops.

Understanding and addressing quality attributes of microservices architecture: A Systematic literature review

2021 ◽  
Vol 131 ◽  
pp. 106449
Author(s):  
Shanshan Li ◽  
He Zhang ◽  
Zijia Jia ◽  
Chenxing Zhong ◽  
Cheng Zhang ◽  
...  
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Quality Attributes

scholarly journals Ubiquitous Semantic Applications

2014 ◽  
Vol 10 (1) ◽  
pp. 66-99 ◽  
Author(s):  
Timofey Ermilov ◽  
Ali Khalili ◽  
Sören Auer
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Quality Attributes ◽  
Semantic Data ◽  
Ubiquitous Access

Recently practical approaches for development of ubiquitous semantic applications have made quite some progress. In particular in the area of the ubiquitous access to the semantic data the authors recently observed a large number of approaches, systems and applications being described in the literature. With this survey the authors aim to provide an overview on the rapidly emerging field of Ubiquitous Semantic Applications (UbiSA). The authors conducted a systematic literature review comprising a thorough analysis of 48 primary studies out of 172 initially retrieved papers. The authors obtained a comprehensive set of quality attributes for UbiSA together with corresponding application features suggested for their realization. The quality attributes include aspects such as mobility, usability, heterogeneity, collaboration, customizability and evolvability. The primary studies were surveyed in the light of these quality attributes and the authors performed a thorough analysis of five ubiquitous semantic applications, six frameworks for UbiSA, three UbiSA specific ontologies, five ubiquitous semantic systems and nine general approaches. The proposed quality attributes facilitate the evaluation of existing approaches and the development of novel, more effective and intuitive UbiSA.

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