Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations

The large number of possible configurations of modern software-based systems, combined with the large number of possible environmental situations of such systems, prohibits enumerating all adaptation options at design time and necessitates planning at run time to dynamically identify an appropriate configuration for a situation. While numerous planning techniques exist, they typically assume a detailed state-based model of the system and that the situations that warrant adaptations are known. Both of these assumptions can be violated in complex, real-world systems. As a result, adaptation planning must rely on simple models that capture what can be changed (input parameters) and observed in the system and environment (output and context parameters). We therefore propose planning as optimization: the use of optimization strategies to discover optimal system configurations at runtime for each distinct situation that is also dynamically identified at runtime. We apply our approach to CrowdNav, an open-source traffic routing system with the characteristics of a real-world system. We identify situations via clustering and conduct an empirical study that compares Bayesian optimization and two types of evolutionary optimization (NSGA-II and novelty search) in CrowdNav

IAS: an IoT Architectural Self-adaptation Framework

International audienceThis paper develops a generic approach to model control loops and their interac- tion within the Internet of Things (IoT) environments. We take advantage of MAPE-K loops to enable architectural self-adaptation. The system’s architectural setting is aligned with the adaptation goals and the components run-time situation and constraints. We introduce an integrated framework for IoT Architectural Self-adaptation (IAS) where functional control elements are in charge of environmental adaptation and autonomic control elements handle the functional system’s architectural adaptation. A Queuing Networks (QN) approach was used for modeling the IAS. The IAS-QN can model control levels and their interaction to perform both architectural and environmental adaptations. The IAS-QN was modeled on a smart grid system for the Melle-Longchamp area (France). Our architectural adaptation approach successfully set the propositions to enhance the performance of the electricity trans- mission system. This industrial use-case is a part of CPS4EU European industrial innovation pro ject

Influence of ESG on Sustainability Reporting: Mediation Rule of Green Innovation and Investor Sentiment

This study aims to investigate if the quality of sustainability reporting is particularly influenced by a company’s commitment and approach to relevant environmental, social, and governmental (ESG) factors. Companies that seriously implement good sustainability reporting are likely to produce more detailed, relevant, and measurable ESG practices. The sample criteria were non-financial sector companies that had completed sustainability reports. This study collected 430 pieces of data from 215 companies. Data were collected from the 2021-2022 sustainability reports. The results show that ESG has a positive and significant effect on green innovation, investor sentiment, and sustainability reporting, and green innovation has a significant effect on sustainability reporting. However, investor sentiment does not significantly mediate the relationship between ESG and sustainability reporting. This study can help companies understand the factors that contribute to sustainability reporting. The limitation of this study is the development of theoretical models to anticipate the controversial debate behind the effect of ESG. Future research can combine and develop theoretical models on ESG and environmental uncertainty, which refers to the different, unpredictable, and constantly changing nature of the environment in which organizations operate. Environmental uncertainty can pose risks to organizations, including risks associated with supply chain disruptions and regulatory changes

A Goal-Based Modeling Approach to Develop Requirements of an Adaptive System with Environmental Uncertainty

Dynamically adaptive systems (DASs) are intended to monitor the execution environment and then dynamically adapt their behavior in response to changing environmental conditions. The uncertainty of the execution environment is a major motivation for dynamic adaptation; it is impossible to know at development time all of the possible combinations of environmental conditions that will be encountered. To date, the work performed in requirements engineering for a DAS includes requirements monitoring and reasoning about the correctness of adaptations, where the DAS requirements are assumed to exist. This paper introduces a goal-based modeling approach to develop the requirements for a DAS, while explicitly factoring uncertainty into the process and resulting requirements. We introduce a variation of threat modeling to identify sources of uncertainty and demonstrate how the RELAX speci- cation language can be used to specify more exible requirements within a goal model to handle the uncertainty

Synergizing domain expertise with self-awareness in software systems:a patternized architecture guideline

To promote engineering self-aware and self-adaptive software systems in a reusable manner, architectural patterns and the related methodology provide an unified solution to handle the recurring problems in the engineering process. However, in existing patterns and methods, domain knowledge and engineers' expertise that is built over time are not explicitly linked to the self-aware processes. This linkage is important, as the knowledge is a valuable asset for the related problems and its absence would cause unnecessary overhead, possibly misleading results and unwise waste of the tremendous benefit that could have been brought by the domain expertise. This paper highlights the importance of synergizing domain expertise and the self-awareness to enable better self-adaptation in software systems, relying on well-defined expertise representation, algorithms and techniques. In particular, we present a holistic framework of notions, enriched patterns and methodology, dubbed DBASES, that offers a principled guideline for the engineers to perform difficulty and benefit analysis on possible synergies, in an attempt to keep "engineers-in-the-loop". Through three tutorial case studies, we demonstrate how DBASES can be applied in different domains, within which a carefully selected set of candidates with different synergies can be used for quantitative investigation, providing more informed decisions of the design choices.Comment: Accepted manuscript to the Proceedings of the IEEE. Please use the following citation: Tao Chen, Rami Bahsoon, and Xin Yao. 2020. Synergizing Domain Expertise with Self-Awareness in Software Systems: A Patternized Architecture Guideline. Proc. IEEE, in pres

Improving efficiency, scalability and efficacy of adaptive computation offloading in pervasive computing environments

As computing becomes more mobile and pervasive, there is a growing demand for increasingly rich, and therefore more computationally heavy, applications to run in mobile spaces. However, there exists a disparity between mobile platforms and the desktop environments upon which computationally heavy applications have traditionally run, which is likely to persist as both domains evolve at a competing pace. Consequently, an active research area is Adaptive Computation Offloading or cyber foraging that dynamically distributes application functionality to available peer devices according to resource availability and application behaviour. Integral to any offloading strategy is an adaptive decision making algorithm that computes the optimal placement of application components to remote devices based on changing environmental context. As this decision is typically computed by constrained devices and may occur frequently in dynamic environments, such algorithms should be both resource efficient and yield efficacious adaptation results. However, existing adaptive offloading approaches incur a number of overheads, which limit their applicability in mobile and pervasive spaces. This thesis is concerned with improving upon these limitations by specifically focusing on the efficiency, scalability and efficacy aspects of two major sub processes of adaptation: 1) Adaptive Candidate Device Selection and 2) Adaptive Object Topology Computation. To this end, three novel approaches are proposed. Firstly, a distributed approach to candidate device selection, which reduces the need to communicate collaboration metrics, and allows for the partial distribution of adaptation decision-making, is proposed. The approach is shown to reduce network consumption by over 90% and power consumption by as much as 96%, while maintaining linear memory complexity in contrast to the quadratic complexity of an existing approach. Hence, the approach presents a more efficient and scalable alternative for candidate device selection in mobile and pervasive environments. Secondly, with regards to the efficacy of adaptive object topology computation, a new type of adaptation granularity that combines the efficacy of fine-grained adaptation with the efficiency of coarse level approaches is proposed. The approach is shown to improve the efficacy of adaptation decisions by reducing network overheads by a minimum of 17% to as much 99%, while maintaining comparable decision making efficiency to coarse level adaptation. Thirdly, with regards to efficiency and scalability of object topology computation, a novel distributed approach to computing adaptation decisions is proposed, in which each device maintains a distributed local application sub-graph, consisting only of components in its own memory space. The approach is shown to reduce network cost by 100%, collaboration-wide memory cost by between 37% and 50%, battery usage by between 63% and 93%, and adaptation time by between 19% and 98%. Lastly, since improving the utility of adaptation in mobile and pervasive environments requires the simultaneous improvement of its sub processes, an adaptation engine, which consolidates the individual approaches presented above, is proposed. The consolidated adaptation engine is shown to improve the overall efficiency, scalability and efficacy of adaptation under a varying range of environmental conditions, which simulate dynamic and heterogeneous mobile environments