Efficient Utility-Driven Self-Healing Employing Adaptation Rules for Large Dynamic Architectures

Self-adaptation can be realized in various ways. Rule-based approaches prescribe the adaptation to be executed if the system or environment satisfy certain conditions and result in scalable solutions, however, with often only satisfying adaptation decisions. In contrast, utility-driven approaches determine optimal adaptation decisions by using an often costly optimization step, which typically does not scale well for larger problems. We propose a rule-based and utility-driven approach that achieves the beneficial properties of each of these directions such that the adaptation decisions are optimal while the computation remains scalable since an expensive optimization step can be avoided. The approach can be used for the architecture-based self-healing of large software systems. We define the utility for large dynamic architectures of such systems based on patterns capturing issues the self-healing must address and we use patternbased adaptation rules to resolve the issues. Defining the utility as well as the adaptation rules pattern-based 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. We demonstrate the efficiency and optimality of our scheme in comparative experiments with a static rule-based scheme as a baseline and a utility-driven approach using a constraint solver

Towards Highly Scalable Runtime Models with History

Advanced systems such as IoT comprise many heterogeneous, interconnected, and autonomous entities operating in often highly dynamic environments. Due to their large scale and complexity, large volumes of monitoring data are generated and need to be stored, retrieved, and mined in a time- and resource-efficient manner. Architectural self-adaptation automates the control, orchestration, and operation of such systems. This can only be achieved via sophisticated decision-making schemes supported by monitoring data that fully captures the system behavior and its history. Employing model-driven engineering techniques we propose a highly scalable, history-aware approach to store and retrieve monitoring data in form of enriched runtime models. We take advantage of rule-based adaptation where change events in the system trigger adaptation rules. We first present a scheme to incrementally check model queries in the form of temporal logic formulas which represent the conditions of adaptation rules against a runtime model with history. Then we enhance the model to retain only information that is temporally relevant to the queries, therefore reducing the accumulation of information to a required minimum. Finally, we demonstrate the feasibility and scalability of our approach via experiments on a simulated smart healthcare system employing a real-world medical guideline.Comment: 8 pages, 4 figures, 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS2020

mRUBiS: An Exemplar for Model-Based Architectural Self-Healing and Self-Optimization

Self-adaptive software systems are often structured into an adaptation engine that manages an adaptable software by operating on a runtime model that represents the architecture of the software (model-based architectural self-adaptation). Despite the popularity of such approaches, existing exemplars provide application programming interfaces but no runtime model to develop adaptation engines. Consequently, there does not exist any exemplar that supports developing, evaluating, and comparing model-based self-adaptation off the shelf. Therefore, we present mRUBiS, an extensible exemplar for model-based architectural self-healing and self-optimization. mRUBiS simulates the adaptable software and therefore provides and maintains an architectural runtime model of the software, which can be directly used by adaptation engines to realize and perform self-adaptation. Particularly, mRUBiS supports injecting issues into the model, which should be handled by self-adaptation, and validating the model to assess the self-adaptation. Finally, mRUBiS allows developers to explore variants of adaptation engines (e.g., event-driven self-adaptation) and to evaluate the effectiveness, efficiency, and scalability of the engines

On Learning in Collective Self-adaptive Systems: State of Practice and a 3D Framework

Collective self-adaptive systems (CSAS) are distributed and interconnected systems composed of multiple agents that can perform complex tasks such as environmental data collection, search and rescue operations, and discovery of natural resources. By providing individual agents with learning capabilities, CSAS can cope with challenges related to distributed sensing and decision-making and operate in uncertain environments. This unique characteristic of CSAS enables the collective to exhibit robust behaviour while achieving system-wide and agent-specific goals. Although learning has been explored in many CSAS applications, selecting suitable learning models and techniques remains a significant challenge that is heavily influenced by expert knowledge. We address this gap by performing a multifaceted analysis of existing CSAS with learning capabilities reported in the literature. Based on this analysis, we introduce a 3D framework that illustrates the learning aspects of CSAS considering the dimensions of autonomy, knowledge access, and behaviour, and facilitates the selection of learning techniques and models. Finally, using example applications from this analysis, we derive open challenges and highlight the need for research on collaborative, resilient and privacy-aware mechanisms for CSAS

Evaluation of Self-Healing Systems: An Analysis of the State-of-the-Art and Required Improvements

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

Loss of autocrine endothelial-derived VEGF significantly reduces hemangiosarcoma development in conditional p53-deficient mice

Malignant transformation of the endothelium is rare, and hemangiosarcomas comprise only 1% of all sarcomas. For this reason and due to the lack of appropriate mouse models, the genetic mechanisms of malignant endothelial transformation are poorly understood. Here, we describe a hemangiosarcoma mouse model generated by deleting p53 specifically in the endothelial and hematopoietic lineages. This strategy led to a high incidence of hemangiosarcoma, with an average latency of 25 weeks. To study the in vivo roles of autocrine or endothelial cell autonomous VEGF signaling in the initiation and/or progression of hemangiosarcomas, we genetically deleted autocrine endothelial sources of VEGF in this mouse model. We found that loss of even a single conditional VEGF allele results in substantial rescue from endothelial cell transformation. These findings highlight the important role of threshold levels of autocrine VEGF signaling in endothelial malignancies and suggest a new approach for hemangiosarcoma treatment using targeted autocrine VEGF inhibition

2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)

On behalf of the entire ACSOS 2021 organizing committee, we would like to welcome you to the second edition of the IEEE International Conference on Autonomic Computing and Self-Organizing Systems. We are looking forward to an interactive, varied, exciting, and educational conference experience with you

Efficient ROSA26-based conditional and/or inducible transgenesis using RMCE-compatible F1 hybrid mouse embryonic stem cells

The conditional Cre/loxP system and/or the doxycycline (Dox) inducible Tet-on/off system are widely used in mouse transgenesis but often require time consuming, inefficient cloning/screening steps and extensive mouse breeding strategies. We have therefore developed a highly efficient Gateway- and recombinase-mediated cassette exchange (RMCE)-compatible system to target conditional and/or inducible constructs to the ROSA26 locus of F1 hybrid Bl6/129 ESCs, called G4 ROSALUC ESCs. By combining the Cre/loxP system with or without the inducible Tet-on system using Gateway cloning, we can rapidly generate spatial and/or temporal controllable gain-of-function constructs that can be targeted to the RMCE-compatible ROSA26 locus of the G4 ROSALUC ESCs with efficiencies close to 100 %. These novel ESC-based technologies allow for the creation of multiple gain-of-function conditional and/or inducible transgenic ESC clones and mouse lines in a highly efficient and locus specific manner. Importantly, incorporating insulator sequences into the Dox-inducible vector system resulted in robust, stable transgene expression in undifferentiated ESCs but could not fully overcome transgene mosaicism in the differentiated state

Empirical Characterization of User Reports about Cloud Failures

Cloud services are important for healthcare, banking, communication, and other purposes. Inevitably, such services fail, harming the processes and disturbing the people that depend on them. Understanding failure in cloud services is challenging, but important to help preventing them. Much work has studied failure logs and reports provided by infrastructure operators. However, there is a paucity of information about how users perceive the failures of cloud services. In this work, we collect user-reported failures and characterize them empirically. We collect failures reported by users to the trusted aggregator Outage Report for 12 cloud services over 16 months spread across 2019 and 2020. We show evidence that user-reported failures not only capture major failures also self-reported by cloud operators, but also provide information about additional failures. We count and analyze time patterns in these reports. We make 6 main observations about how users perceive failure in cloud services. We find over 10x differences in request failure rates across microservice structures when using user reported traces compared to using a constant failure distribution. Overall, our study provides the first long-term characterization of user-reported cloud failures