Abstract: Two established techniques to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation. Recently, we also observe a rapid growing interest in applying machine learning (ML) to support different adaptation mechanisms. While MAPE and CT have particular characteristics and strengths to be applied independently, in this paper we are concerned with the question of how these techniques are related with one another and whether combining them and supporting them with ML can produce better adaptive systems. We motivate the combined use of different adaptation techniques using scenarios from two different domains and illustrate the analysis involved in combining different adaptation techniques. The paper concludes with suggestions for further research in this interesting area.

Abstract: Two of the main paradigms used to build adaptive software employ different types of properties to capture relevant aspects of the system’s run-time behavior. On the one hand, control systems consider properties that concern static aspects like stability, as well as dynamic properties that capture the transient evolution of variables such as settling time. On the other hand, self-adaptive systems consider mostly non-functional properties that capture concerns such as performance, reliability, and cost. In general, it is not easy to reconcile these two types of properties or identify under which conditions they constitute a good fit to provide run-time guarantees. There is a need of identifying the key properties in the areas of control and self-adaptation, as well as of characterizing and mapping them to better understand how they relate and possibly complement each other. In this paper, we take a first step to tackle this problem by: (1) identifying a set of key properties in control theory, (2) illustrating the formalization of some of these properties employing temporal logic languages commonly used to engineer self-adaptive software systems, and (3) illustrating how to map key properties that characterize self-adaptive software systems into control properties, leveraging their formalization in temporal logics. We illustrate the different steps of the mapping on an exemplar case in the cloud computing domain and conclude with identifying open challenges in the area.

Abstract: Self-adaptive systems are required to adapt its behaviour in the face of changes in their environment and goals. Such a requirement is typically achieved by developing a system as a closed-loop system following a Monitor-AnalysePlan-Act (MAPE) scheme. MAPE loops are a mechanism that allows systems to monitor their state and produce changes aiming to guarantee that the goals are met. In practice it is often the case that to achieve their desired goals, self-adaptive systems must combine a number of MAPE loops with different responsibilities and at different abstraction levels. Higher-level goals require decision-level mechanisms to produce a plan in terms of the high-level system actions to be performed. Various mechanisms have been proposed and developed for automatically generating decision-level plans (e.g., event-based controller synthesis), providing guarantees about the satisfaction of hard goals (e.g., providing a certain level of service), and supporting improvements in soft goals (e.g., doing this in an efficient or cost-effective manner). These decisions are often made at a time scale of seconds to minutes. Lower-level goals, on the other hand, typically require control mechanisms that sense the state of the system and environment and react at a fine time granularity of milliseconds. Solutions to this problem are typically based on classical control theory techniques such as discrete-time control. A successful adaptive system, then, must find ways to integrate these multiple levels of control, leading to an important question of how best to do that, and what concepts. Additionally, concepts from classical control theory (typically applied at low levels of control) can also be useful in understanding higher-level control. Recently the software engineering community has begun to study the application of control theory and the formal guarantees it provides in the context of software engineering. For example, the 2014 Dagstuhl Seminar “Control Theory meets Software Engineering”, is an example of such recent interest. That seminar discussed a variety of possible applications of control theory to software engineering problems.

Also, and perhaps more relevant, is the first CASaS Shonan seminar held in 2016. The seminar focused on formal guarantees that can be provided in self-adaptive systems via the use of control theory (e.g., event-based controller synthesis and discrete-time control). The seminar was a success in many respects. It had over 30 attendees from more than 10 countries. The seminar was an active gathering of outstanding researchers in both control theory and software engineering, and provided a forum in which discussions on the connections between control theory and software engineering for self-adaptive systems could be held. Most of the attendees expressed their intention to continue studying and discussing the relation between control theory and software engineering, which was highlighted as key to address with the requirements of self-adaptive systems. As in the first edition we expected to involve a group of active researchers in key areas such as Self-Adaptive Systems, Control theory, Game theory, Software Engineering, and Requirements Engineering, creating an ideal environment to discuss current and future applications and possibilities of control theory as a mechanism to provide formal guarantees for self-adaptive systems (e.g., convergence, safety, stability). Encouraged by the success of the first CASaS, we expected to have a number of participants from a wide variety of research areas to further explore the benefits of incorporating the application capabilities and formal framework provided by control theory to self-adaptive systems. Among the research questions that we expected to discuss are: How to coordinate multiple levels of adaptive control? What kinds of properties from classical control theory can be applied at higher levels to guarantee certain properties? To what extent does the domain and contest of use influence the design of a control regime for adaptation? In what ways can AI techniques of planning and machine learning be applied to adaptive systems? How can one deal with uncertainty in a systematic fashion? How can control theory inform our decisions about ways to incorporate humans into self-adaptive systems? We envisaged the 5-day meeting to be organised in two main parts. During the first day, participants presented their background and what they are interested in, and there were three lectures about continuous control, discrete-event control, and hybrid approach were given. Then, for the remaining four days, we identified and discussed the most relevant topics selected by the participants in working groups. In the end, we decided to discuss about two topics: “cooperation and coordination” and “properties”. The first topic is concerned with ways to incorporate components with ‘’classical” control implementation into larger systems, which will typically be a mixture of discrete and continuous control, and may need to adapt at an architectural level at run time in response to environmental conditions. The second topic is concerned with ways to formalize properties that are used in control theory in terms that would be useful for systems that reason in terms of discrete control. We divided into two groups, discussed the topics, and created draft reports about the discussion. These reports were further edited and improved, and now constitute the main body of this report.