Self-Adaptation: MAPE-K and Control Theory
Danny Weyns & Stepan Shevtsov
www.lnu.se/adaptwise
Grenoble, France
November 4, 2014
Linnaeus University Sweden
Växjö Campus
AdaptWise research team focusing on engineering self-adaptive systems
2
Research setting
•  Software engineers have to deal with uncertainties
resulting from incomplete knowledge at design time, e.g.
•  - Dynamic operating conditions that are difficult to predict
•  - Parts enter and leave the system at will
•  - Changing goals
•  How to engineer such systems and guarantee the required
system goals?
Self-adaptation
•  System maintains a model of itself at runtime
•  System reasons about itself and adapts to realize system goals
•  Architecture-based self-adaptation
•  - Separation between domain concerns & adaptation concerns
•  - Adaptation concerns are handled by a feedback loop
•  - Feedback loop ≈ set of components that share knowledge
Model architecture-based self-adaptation
Software system
effect
input
Environment
Non-controllable software,
hardware, network, physical context
Model architecture-based self-adaptation
Self-adaptive software system
Managing system
monitor
monitor
adapt
Managed system
Controllable software
effect
input
Environment
Non-controllable software,
hardware, network, physical context
Model architecture-based self-adaptation
Self-adaptive software system
A
M
P
K
monitor
monitor
Managing
system
E
adapt
Managed system
Controllable software
effect
input
Environment
Non-controllable software,
hardware, network, physical context
MAPE-K
- M: Monitor
- A: Analyze
- P: Plan
- E: Execute
- K: Knowledge
Research focus
•  Principles of architecture-based self-adaptation
•  - FORMS: Unifying reference model for self-adaptation
•  - Do External Feedback Loops Improve the Design of SelfAdaptive Systems?
•  Rigorous modeling and verification
•  - ActivFORMS: Active formal models for self-adaptation
•  - Unifying approach for self-adaptation that integrates
architecture-based approach with control-theoretic approach
Research focus
•  Principles of architecture-based self-adaptation
•  - FORMS: Unifying reference model for self-adaptation
•  - Do External Feedback Loops Improve the Design of SelfAdaptive Systems?
•  Assurances to handle uncertainty
Today
•  - ActivFORMS: Active formal models for self-adaptation
•  - Unifying approach for self-adaptation that integrates
architecture-based approach with control-theoretic approach
ActivFORMS: Starting points
Assurances for self-adaption
- One prominent approach is using formal models
- Main focus on K of MAPE-K
- Main focus on uncertainty wrt model parameters
Assurance of adaptation capabilities by MAPE functions?
- E.g., does analysis detect errors correctly?
- Are adaptations performed in order of selected plan?
How to provide assurances with changing/new goals?
- Requires support for adaptations of adaptation functions
ActivFORMS: Active formal models for selfadaptation
Formal model of complete MAPE-K loop
Model is directly executed to adapt the managed system
Model directly supports online verification of goal
satisfaction/violation
Goals and MAPE-K models can be changed at runtime to
support changing/new goals
Approach
Active Model
MAPE-K behaviors modeled as network of timed automata
Adaptation requirements formulated in timed computation
tree logic
Model can be verified offline (e.g., using Uppaal tool)
PlanDisableLane
planningDisableLane[RiD]?
!dependOnLane()
PlanDisableLane
initiatePlan()
PlanDisable
Plan
PlanReady
PlanWait
planningDisableLane[RiD]?
dependOnLane()
addPlanStepWait()
!dependOnLane()
initiatePlan()
planned()
Figure 6: Input triggered execution
execute[RiD]!
PlanDisable
PlanWait
addPlanStepDisable()
dependOnLane()
addPlanStepWait()
Plan
PlanReady
planned()
Figure 4: Plan automaton of a robot
execute[RiD]!
from verification only hold to the extent that the implementation of
the managed system conforms to the model of the system that is
addPlanStepDisable()
used
for verification. Such conformance can be tested, for example
with model-based testing techniques [21]. We illustrate verification
with an example for the robotic system.
Monitoring(1).RequestToDisableLane
&& knowledge[1].disabledLane == Lane_cd
--> Execution(1).DisableLane
&& knowledge[1].disabledLane == Lane_cd
The goal allows verifying that when the monitor behavior of
Figure 7: Time triggered execution
triggered either by input from the managed system or the environ2
Virtual Machine
Can load and execute active model
Interaction with managed system and environment through
probes and effectors
Allows verification of goal satisfaction/violation
Goals and MAPE-K models can be changed at runtime to
support changing/new goals
Goal management
Goal model with associated
adaptation models
Goal monitor monitors goal
satisfaction
Goal adaptor satisfies current goals and
adapt active model when needed
Goal manager supports inspection active
model and updating models
Case study
Goal Management Interface
Goal Management Interface
Show executing model
Goal Management Interface
Updating goals and models
Goal Management Interface
Update model
Discussion
Active formal model paves the way for “handling continuous change”
Goal model and associated adaptation models
- Support changing goals and adding new goals
- Results in small models allowing efficient verification
We have applied the approach successfully to different case studies
Various opportunities for future research remain, including:
- Support for probabilistic models
- Runtime support for model checking
- Coordination between active models
Research focus
•  Principles of architecture-based self-adaptation
•  - FORMS: Unifying reference model for self-adaptation
•  - Do External Feedback Loops Improve the Design of SelfAdaptive Systems?
•  Rigorous modeling and verification
•  - ActivFORMS: Active formal models for self-adaptation
•  - Unifying approach for self-adaptation that integrates
architecture-based approach with control-theoretic approach
Unifying approach for self-adaptation that integrates
architecture-based approach with control-theoretic approach
Control theory provides a mature mathematical framework
to design and analyze feedback loops
Has been used to control computing systems
Focus primarily on controlling resources of computing
systems (CPU, memory, bandwidth, etc.)
Our aim is to control software
Motivation for controlling software
How do control-based solutions relate to MAPE-K based
solutions?
Are they complementary?
Or does control theory provide a foundational theoretical
framework to software adaptation?
Unifying approach for self-adaptation that integrates
architecture-based approach with control-theoretic approach
Control in service-based
system: e.g.,
- Dynamically replace service that
goes down with alternative
- Change service with better quality
properties
- Change the workflow at runtime to
improve QoS, etc.
Variety of challenges
How does the (mathema-cal) model of so2ware look like? For which types of adapta-ons can we control so2ware and for which not? How is a setpoint/input defined? What are the “knobs” to adapt so2ware? What guarantees can be provided? How do typical so2ware adapta-on proper-es relate to typical proper-es of controllers? What are the benefits and limita-ons of applying control theory to so2ware systems? Current work
Systema-c literature review of using control theory to adapt so2ware Focus on applica-on layer and suppor-ng service layer (not 1 to 1 mapping to resources (e.g. a driver or virtual machine) Research ques-ons we plan to address -­‐ RQ1: What is the focus of research on control-­‐based adapta-on in so2ware systems? -­‐ RQ2: What quali-es are affected by adap-ng so2ware using principles from control theory? -­‐ RQ3: How are the so2ware systems with feedback controllers modeled and constructed? In parallel, we are studying how to apply a control-­‐based approach to adapt the architecture of a service based system Bibliography •  D. Weyns, S. Malek, J. Andersson, FORMS: Unifying Reference Model for Formal Specifica-on of Distributed Self-­‐Adap-ve Systems, ACM Transac-ons on Autonomous and Adap-ve Systems, TAAS, 7(1), 2012 •  D. Weyns, U. I2ikhar and J. Söderlund, Do External Feedback Loops Improve the Design of Self-­‐Adap-ve Systems? A Controlled Experiment, So2ware Engineering for Adap-ve and Self-­‐Managing Systems, SEAMS 2013 •  M. Usman I2ikhar, Danny Weyns: Ac-vFORMS: ac-ve formal models for self-­‐adapta-on, Interna-onal Symposium in So2ware Engineering of Adap-ve and Self-­‐Managing Systems, SEAMS 2014 •  D. Weyns, S. Shevtsov, S. Pllana: Providing Assurances for Self-­‐Adapta-on in a Mobile Digital Storytelling Applica-on Using Ac-vFORMS, Interna-onal Conference on Self-­‐Adap-ve and Self-­‐Organizing Systems, SASO 2014 •  Papers available via: hcp://homepage.lnu.se/staff/daweaa/publica-ons/publica-ons.htm