TY - JOUR A1 - Ghahremani, Sona A1 - Giese, Holger T1 - Evaluation of self-healing systems BT - An analysis of the state-of-the-art and required improvements JF - Computers N2 - Evaluating the performance of self-adaptive systems is challenging due to their interactions with often highly dynamic environments. In the specific case of self-healing systems, the performance evaluations of self-healing approaches and their parameter tuning rely on the considered characteristics of failure occurrences and the resulting interactions with the self-healing actions. In this paper, we first study the state-of-the-art for evaluating the performances of self-healing systems by means of a systematic literature review. We provide a classification of different input types for such systems and analyse the limitations of each input type. A main finding is that the employed inputs are often not sophisticated regarding the considered characteristics for failure occurrences. To further study the impact of the identified limitations, we present experiments demonstrating that wrong assumptions regarding the characteristics of the failure occurrences can result in large performance prediction errors, disadvantageous design-time decisions concerning the selection of alternative self-healing approaches, and disadvantageous deployment-time decisions concerning parameter tuning. Furthermore, the experiments indicate that employing multiple alternative input characteristics can help with reducing the risk of premature disadvantageous design-time decisions. KW - self-healing KW - failure model KW - performance KW - simulation KW - evaluation Y1 - 2020 U6 - https://doi.org/10.3390/computers9010016 SN - 2073-431X VL - 9 IS - 1 PB - MDPI CY - Basel ER - TY - JOUR A1 - Ghahremani, Sona A1 - Giese, Holger A1 - Vogel, Thomas T1 - Improving scalability and reward of utility-driven self-healing for large dynamic architectures JF - ACM transactions on autonomous and adaptive systems N2 - Self-adaptation can be realized in various ways. Rule-based approaches prescribe the adaptation to be executed if the system or environment satisfies certain conditions. They result in scalable solutions but often with merely satisfying adaptation decisions. In contrast, utility-driven approaches determine optimal decisions by using an often costly optimization, which typically does not scale for large problems. We propose a rule-based and utility-driven adaptation scheme that achieves the benefits of both directions such that the adaptation decisions are optimal, whereas the computation scales by avoiding an expensive optimization. We use this adaptation scheme for architecture-based self-healing of large software systems. For this purpose, we define the utility for large dynamic architectures of such systems based on patterns that define issues the self-healing must address. Moreover, we use pattern-based adaptation rules to resolve these issues. Using a pattern-based scheme to define the utility and adaptation rules allows us to compute the impact of each rule application on the overall utility and to realize an incremental and efficient utility-driven self-healing. In addition to formally analyzing the computational effort and optimality of the proposed scheme, we thoroughly demonstrate its scalability and optimality in terms of reward in comparative experiments with a static rule-based approach as a baseline and a utility-driven approach using a constraint solver. These experiments are based on different failure profiles derived from real-world failure logs. We also investigate the impact of different failure profile characteristics on the scalability and reward to evaluate the robustness of the different approaches. KW - self-healing KW - adaptation rules KW - architecture-based adaptation KW - utility KW - reward KW - scalability KW - performance KW - failure profile model Y1 - 2020 U6 - https://doi.org/10.1145/3380965 SN - 1556-4665 SN - 1556-4703 VL - 14 IS - 3 PB - Association for Computing Machinery CY - New York ER - TY - GEN A1 - Ghahremani, Sona A1 - Giese, Holger T1 - Performance evaluation for self-healing systems BT - Current Practice & Open Issues T2 - 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) N2 - Evaluating the performance of self-adaptive systems (SAS) is challenging due to their complexity and interaction with the often highly dynamic environment. In the context of self-healing systems (SHS), employing simulators has been shown to be the most dominant means for performance evaluation. Simulating a SHS also requires realistic fault injection scenarios. We study the state of the practice for evaluating the performance of SHS by means of a systematic literature review. We present the current practice and point out that a more thorough and careful treatment in evaluating the performance of SHS is required. KW - self-healing KW - failure profile KW - evaluation KW - simulator KW - performance Y1 - 2019 SN - 978-1-7281-2406-3 U6 - https://doi.org/10.1109/FAS-W.2019.00039 SP - 116 EP - 119 PB - IEEE CY - New York ER -