@TechReport{2017:Shonan:CASAS,
AUTHOR = {Garlan, David and D’Ippolito, Nicolas and Tei, Kenji},
TITLE = {The 2nd Controlled Adaptation of Self-Adaptive Systems Workshop (CASaS2017)},
YEAR = {2017},
MONTH = {24-28 July},
NUMBER = {NII-2017-10},
INSTITUTION = {National Institute of Informatics},
PDF = {http://acme.able.cs.cmu.edu/pubs/uploads/pdf/No.2017-10-1.pdf},
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.},
KEYWORDS = {Control Theory, Self-adaptation}
}
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