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GENERIC AUTONOMIC
SERVICE MANAGEMENT
IN COMPLEX SYSTEMS
PhD candidate:
Nabila Belhaj*
Advisors:
Djamel Belaïd*
Samir Tata†
*CNRS/SAMOVAR, Telecom SudParis
† Almaden Research Center, IBM Research, San Jose, CA, USA
GENERIC AUTONOMIC
SERVICE MANAGEMENT
IN COMPLEX SYSTEMS
AGENDA 1. INTRODUCTION
1.1. Context
1.2. Contribution objectives
1.3. Related work
2. CONTRIBUTIONS
2.1. Framework Description
2.1.1. Structure of the Autonomic container
2.1.2. Structure of the Analysis component
2.1.3. Mapping decision process to MDP problem
2.2. Use Case Study
2.3. Evaluation
3. CONCLUSION AND WORK IN
PROGRESS
4. PUBLICATIONS
1.1. CONTEXT
1.2. CONTRIBUTION OBJECTIVES
1.3. RELATED WORK
INTRODUCTION
GENERIC AUTONOMIC SERVICE MANAGEMENT IN COMPLEX SYSTEMS
JOURNEE FUTUR & RUPTURES – 15 FEVRIER 2018
5 CONTEXT
► Complex Applications
Complex structure and increasing size
Heterogeneous composition of distributed and interracting components
Highly dynamic deployment contexts
Tedious management tasks
Corrective
Actions
GENERIC AUTONOMIC SERVICE MANAGEMENT IN COMPLEX SYSTEMS
JOURNEE FUTUR & RUPTURES – 15 FEVRIER 2018
► Autonomic Computing Systems (ACS): Self-properties
Self-Reconfiguring
Adapting itself to
changing environments
“on the fly”.
Self-Optimizing
Optimizing system
performance and resource
utilization.
Self-Healing
Discovering, diagnosing
and acting for disruption
prevention.
Self-Protecting
Identifying and
anticipating unauthorized
accesses to protect from
attacks.
Extensive knowledge on context
dynamics to hand-code strategies!
Sophisticated ACS are supposed to lear from
their past experiences to make better decisions
CONTEXT 6
GENERIC AUTONOMIC SERVICE MANAGEMENT IN COMPLEX SYSTEMS
JOURNEE FUTUR & RUPTURES – 15 FEVRIER 2018
CONTRIBUTION OBJECTIVES
1. Enhance traditional ACS with sophisticated learning behavior
2. Render self-adaptive the decision making for component-based
applications
3. Optimization of learning performance
4. Make autonomic loops collaborate for a consistent decision
making in the overall system
7
GENERIC AUTONOMIC SERVICE MANAGEMENT IN COMPLEX SYSTEMS
JOURNEE FUTUR & RUPTURES – 15 FEVRIER 2018
8 RELATED WORK
Approaches
Assessment criteria
1. Enhancement
of MAPE-K
behavior
2. Dynamic
decision
policy
3. Optimization
of learning
performance
4. Richer
context
Raghavendra, R. et al., 2008 - - - -
Gueye, S.M.K. et al., 2013 - - - -
Cano, J. et al., 2013 - - - -
Li, Z., Parashar, M, 2005 - - - -
Tesauro, G.,2007 - X - -
Rao, J. et al., 2011 - X - -
Wang, H. et al., 2011 - X - -
Wang, H. et al., 2016 - X - X
Our proposal X X X X
2.1. FRAMEWORK DESCRIPTION
2.1.1. STRUCTURE OF THE AUTONOMIC
CONTAINER
2.1.2. STRUCTURE OF THE ANALYSIS
COMPONENT
2.1.3. MAPPING DECISION PROCESS TO
MDP PROBLEM
2.2. USE CASE STUDY
2.3. EVALUATION
CONTRIBUTIONS
GENERIC AUTONOMIC SERVICE MANAGEMENT IN COMPLEX SYSTEMS
JOURNEE FUTUR & RUPTURES – 15 FEVRIER 2018
FRAMEWORK DESCRIPTION 10
► Structure of the Autonomic Container
GENERIC AUTONOMIC SERVICE MANAGEMENT IN COMPLEX SYSTEMS
JOURNEE FUTUR & RUPTURES – 15 FEVRIER 2018
BACKGROUND: REINFORCEMENT LEARNING (RL) 11
State: s
Reward: r
Action: a
► Markov Decision Process (MDP): • S: finite set of states,
• A: finite set of actions,
• P( ): transition probability between states
• R( ): reward function on state transitions
► Main goal: Find the optimal decision
policy that maximizes the long-
run sum of reward signals.
st st’
at
r(st,at)
GENERIC AUTONOMIC SERVICE MANAGEMENT IN COMPLEX SYSTEMS
JOURNEE FUTUR & RUPTURES – 15 FEVRIER 2018
FRAMEWORK DESCRIPTION 12
► Structure of the Analysis component
GENERIC AUTONOMIC SERVICE MANAGEMENT IN COMPLEX SYSTEMS
JOURNEE FUTUR & RUPTURES – 15 FEVRIER 2018
MAPPING DECISION PROCESS TO MDP PROBLEM 13
► State Space:
: runtime value of metric of
: should comply with
: objective value for metric
►Action Space:
: elementary management service
: composition of local and/or remote
: orchestration of local and/or remote
GENERIC AUTONOMIC SERVICE MANAGEMENT IN COMPLEX SYSTEMS
JOURNEE FUTUR & RUPTURES – 15 FEVRIER 2018
MAPPING DECISION PROCESS TO MDP PROBLEM 14
► Reward Function
GENERIC AUTONOMIC SERVICE MANAGEMENT IN COMPLEX SYSTEMS
JOURNEE FUTUR & RUPTURES – 15 FEVRIER 2018
15 MAPPING DECISION PROCESS TO MDP PROBLEM
► Online RL decision making
GENERIC AUTONOMIC SERVICE MANAGEMENT IN COMPLEX SYSTEMS
JOURNEE FUTUR & RUPTURES – 15 FEVRIER 2018
USE CASE STUDY 16
► Shopping Application
Metrics objective values:
State vector:
GENERIC AUTONOMIC SERVICE MANAGEMENT IN COMPLEX SYSTEMS
JOURNEE FUTUR & RUPTURES – 15 FEVRIER 2018
EVALUATION 17
► Evaluation criterion of the Framework
Transformation overhead of the application
Self-adaptivity to context changes,
SLA guarantee,
Performance optimization of learning phase
► Context dynamics:
Workload (i.e., concurrent service calls) variations:
o Light, Medium, Heavy
Random triggering of service unavailability
►Experiments:
One-step online learning
Multi-step online learning
GENERIC AUTONOMIC SERVICE MANAGEMENT IN COMPLEX SYSTEMS
JOURNEE FUTUR & RUPTURES – 15 FEVRIER 2018
EVALUATION 18
►Transformation overhead of the application
GENERIC AUTONOMIC SERVICE MANAGEMENT IN COMPLEX SYSTEMS
JOURNEE FUTUR & RUPTURES – 15 FEVRIER 2018
EVALUATION 19
►Self-adaptivity to context changes
GENERIC AUTONOMIC SERVICE MANAGEMENT IN COMPLEX SYSTEMS
JOURNEE FUTUR & RUPTURES – 15 FEVRIER 2018
EVALUATION 20
►SLA guarantee and Performance optimization
GENERIC AUTONOMIC SERVICE MANAGEMENT IN COMPLEX SYSTEMS
JOURNEE FUTUR & RUPTURES – 15 FEVRIER 2018
EVALUATION 21
► Convergence speed: one-step vs. multi-step online learning
CONCLUSION AND
WORK IN PROGRESS
GENERIC AUTONOMIC SERVICE MANAGEMENT IN COMPLEX SYSTEMS
JOURNEE FUTUR & RUPTURES – 15 FEVRIER 2018
CONCLUSION 23
►Improve the decision making process of a traditional MAPE-
K loop
►Replace the common use of inflexible hand-coded strategies
for being knowledge-intensive and inadequate to dynamically
changing contexts
►Design of sophisticated and better performing autonomic
systems that learn based on their past experiences
►Dynamically compute a decision policy that suits the context
dynamics
GENERIC AUTONOMIC SERVICE MANAGEMENT IN COMPLEX SYSTEMS
JOURNEE FUTUR & RUPTURES – 15 FEVRIER 2018
WORK IN PROGRESS 24
►Developping collaboration algorithms to make remote
autonomic containers collaborate
►Make Analyzes components collaborate for a global consistent
learning of decision policies
►Propose mechanisms of conflict detection and conflict
resolution for the Analyzes decisions.
Thank you for your attention
Questions are welcome
JOURNEE FUTUR & RUPTURES – 15 FEVRIER 2018
26
►Published papers:
Nabila Belhaj, Djamel Belaïd, Hamid Mukhtar, Self-adaptive Decision Making for the
Management of Component-Based Applications. In: On The Move, 25rd International
Conference on Cooperative Information Systems (CoopIS), Rhodes, Greece, 2017. A-Rank.
Nabila Belhaj, Imen Ben Lahmar, Mohamed Mohamed, Djamel Belaïd, Collaborative
Autonomic Management of Distributed Component-Based Applications. In: On The Move,
23rd International Conference on Cooperative Information Systems (CoopIS), Rhodes,
Greece, 2015. A-Rank.
Nabila Belhaj, Imen Ben Lahmar, Mohamed Mohamed, Djamel Belaïd, Collaborative
Autonomic Container for the Management of Component-Based Applications. In: IEEE
24th International Conference on Enabling Technologies: Infrastructures for Collaborative
Enterprises (WETICE), Larnaca, Cyprus, 2015. B-Rank.
► Submitted papers:
Nabila Belhaj, Djamel Belaïd, Hamid Mukhtar, Framework for Building Self-Adaptive
Component Applications based on Reinforcement Learning. In: 30th International
Conference on Advanced Information Systems Engineering (CAISE), Tallinn, Estonia,
2018. A-Rank.
PUBLICATIONS