Summary of the 9th Workshop on [email protected]

The [email protected] workshop (MRT) series offers a discussion forum for the rising need to leverage modeling techniques at runtime for the software of the future. MRT has become a mature research topic, which is, e.g., reflected in separate sessions at conferences covering MRT approaches only. The target venues of the workshops audience changed from workshops to conferences. Hence, new topics in the area of MRT need to be identified, which are not yet mature enough for conferences. In consequence, the main goal of this edition was to reflect on the past decade of the workshop's history and to identify new future directions for the workshop

A systematic literature review on Energy Efficiency in Cloud Software Architectures

Cloud-based software architectures introduce more complexity and require new competences for migration, maintenance, and evolution. Although cloud computing is often considered as an energy-efficient technology, the implications of cloud-based software on energy efficiency lack scientific evidence. At the same time, energy efficiency is becoming a crucial requirement for cloud service provisioning, as energy costs significantly contribute to the Total Cost of Ownership (TCO) of a data center. In this paper, we present the results of a systematic literature review that investigates cloud software architectures addressing energy efficiency as a primary concern. The aim is to provide an analysis of the state-of-the-art in the field of energy-efficient software architectures

Challenges and Directions in Formalizing the Semantics of Modeling Languages

Developing software from models is a growing practice and there exist many model-based tools (e.g., editors, interpreters, debuggers, and simulators) for supporting model-driven engineering. Even though these tools facilitate the automation of software engineering tasks and activities, such tools are typically engineered manually. However, many of these tools have a common semantic foundation centered around an underlying modeling language, which would make it possible to automate their development if the modeling language specification were formalized. Even though there has been much work in formalizing programming languages, with many successful tools constructed using such formalisms, there has been little work in formalizing modeling languages for the purpose of automation. This paper discusses possible semantics-based approaches for the formalization of modeling languages and describes how this formalism may be used to automate the construction of modeling tools

CRIKEY! ― It's co-ordination in temporal planning

Temporal planning contains aspects of both planning and scheduling. Many temporal planners assume a loose coupling between these two sub-problems in the form of "blackbox" durative actions, where the state of the world is not known during the action's execution. This reduces the size of the search space and so simplifies the temporal planning problem, restricting what can be modelled. In particular, the simplification makes it impossible to model co-ordination, where actions must be executed concurrently to achieve a desired effect. Coordination results from logical and temporal constraints that must both be met, and for this reason, the planner and scheduler must communicate in order to find a valid temporal plan. This communication effectively increases the size of the search space, so must be done intelligently and as little as possible to limit this increase. This thesis contributes a comprehensive analysis of where temporal constraints appear in temporal planning problems. It introduces the notions of minimum and maximum temporal constraints, and with these isolates where the planning and scheduling are coupled together tightly, in the form of co-ordination, it characterises this with the new concepts of envelopes and contents. A new temporal planner written, called СRIKЕҮ, uses this theory to solve temporal problems involving co-ordination that other planners are unable to solve. However, it does this intelligently, using this theory to minimise the communication between the sub-solvers, and so does not expand the search space unnecessarily. The novel search space that CRIKEY uses docs not specify the timings of future events and this allows for the handling of duration inequalities, which again, few other temporal planners are able to solve. Results presented show СRIKЕҮ to be a competitive planner, whilst not making the same simplifying assumptions that other temporal planners make as to the nature of temporal planning problems

SACRE: Supporting contextual requirements' adaptation in modern self-adaptive systems in the presence of uncertainty at runtime

Runtime uncertainty such as unpredictable resource unavailability, changing environmental conditions and user needs, as well as system intrusions or faults represents one of the main current challenges of self-adaptive systems. Moreover, today's systems are increasingly more complex, distributed, decentralized, etc. and therefore have to reason about and cope with more and more unpredictable events. Approaches to deal with such changing requirements in complex today's systems are still missing. This work presents SACRE (Smart Adaptation through Contextual REquirements), our approach leveraging an adaptation feedback loop to detect self-adaptive systems' contextual requirements affected by uncertainty and to integrate machine learning techniques to determine the best operationalization of context based on sensed data at runtime. SACRE is a step forward of our former approach ACon which focus had been on adapting the context in contextual requirements, as well as their basic implementation. SACRE primarily focuses on architectural decisions, addressing self-adaptive systems' engineering challenges. Furthering the work on ACon, in this paper, we perform an evaluation of the entire approach in different uncertainty scenarios in real-time in the extremely demanding domain of smart vehicles. The real-time evaluation is conducted in a simulated environment in which the smart vehicle is implemented through software components. The evaluation results provide empirical evidence about the applicability of SACRE in real and complex software system domains.Comment: 45 pages, journal article, 14 figures, 9 tables, CC-BY-NC-ND 4.0 licens

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

SACRE: Supporting contextual requirements’ adaptation in modern self-adaptive systems in the presence of uncertainty at runtime

Runtime uncertainty such as unpredictable resource unavailability, changing environmental conditions and user needs, as well as system intrusions or faults represents one of the main current challenges of self-adaptive systems. Moreover, today’s systems are increasingly more complex, distributed, decentralized, etc. and therefore have to reason about and cope with more and more unpredictable events. Approaches to deal with such changing requirements in complex today’s systems are still missing. This work presents SACRE (Smart Adaptation through Contextual REquirements), our approach leveraging an adaptation feedback loop to detect self-adaptive systems’ contextual requirements affected by uncertainty and to integrate machine learning techniques to determine the best operationalization of context based on sensed data at runtime. SACRE is a step forward of our former approach ACon which focus had been on adapting the context in contextual requirements, as well as their basic implementation. SACRE primarily focuses on architectural decisions, addressing selfadaptive systems’ engineering challenges. Furthering the work on ACon, in this paper, we perform an evaluation of the entire approach in different uncertainty scenarios in real-time in the extremely demanding domain of smart vehicles. The real-time evaluation is conducted in a simulated environment in which the smart vehicle is implemented through software components. The evaluation results provide empirical evidence about the applicability of SACRE in real and complex software system domains.Peer ReviewedPostprint (author's final draft