Decision making in autonomic computing systems: comparison of approaches and techniques

Authors:
Martina Maggio;Henry Hoffmann;Marco D. Santambrogio;Anant Agarwal;Alberto Leva
Affiliations:
Politecnico di Milano & Massachusetts Institute of Technology, Milan, Italy;Massachusetts Institute of Technology, Cambridge, USA;Politecnico di Milano & Massachusetts Institute of Technology, Milan, Italy;Massachusetts Institute of Technology, Cambridge, USA;Politecnico di Milano, Milan, Italy
Venue:
Proceedings of the 8th ACM international conference on Autonomic computing
Year:
2011

Citing 17
Cited 4

System identification (2nd ed.): theory for the user

System identification (2nd ed.): theory for the user
Managing energy and server resources in hosting centers

SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
The Vision of Autonomic Computing

Computer
Dynamically Tuning Processor Resources with Adaptive Processing

Computer
Feedback Control of Computing Systems

Feedback Control of Computing Systems
Research challenges of autonomic computing

Proceedings of the 27th international conference on Software engineering
Feedback Control Architecture and Design Methodology for Service Delay Guarantees in Web Servers

IEEE Transactions on Parallel and Distributed Systems
Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies

IEEE Internet Computing
Feedback Control-Based QoS Guarantees in Web Application Servers

HPCC '08 Proceedings of the 2008 10th IEEE International Conference on High Performance Computing and Communications
A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation

ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
Coordinated management of multiple interacting resources in chip multiprocessors: A machine learning approach

Proceedings of the 41st annual IEEE/ACM International Symposium on Microarchitecture
Applying genetic algorithms to decision making in autonomic computing systems

ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Dynamic Multicore Resource Management: A Machine Learning Approach

IEEE Micro
Applying control theory in the real world: experience with building a controller for the .NET thread pool

ACM SIGMETRICS Performance Evaluation Review
Application heartbeats: a generic interface for specifying program performance and goals in autonomous computing environments

Proceedings of the 7th international conference on Autonomic computing
Statistical machine learning makes automatic control practical for internet datacenters

HotCloud'09 Proceedings of the 2009 conference on Hot topics in cloud computing

Self-aware computing in the Angstrom processor

Proceedings of the 49th Annual Design Automation Conference
Comparison of Decision-Making Strategies for Self-Optimization in Autonomic Computing Systems

ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special Section: Extended Version of SASO 2011 Best Paper
Actor-based runtime model of adaptable feedback control loops

Proceedings of the 7th Workshop on Models@run.time
Mammoth: autonomic data processing framework for scientific state-transition applications

Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

Autonomic computing systems adapt themselves thousands of times a second, to accomplish their goal despite changing environmental conditions and demands. The literature reports many decision mechanisms, but in most realizations a single one is applied. This paper compares some state-of-the-art decision making approaches, applied to a self-optimizing autonomic system that allocates resources to a software application providing performance feedback at run-time, via the Application Heartbeat framework. The investigated decision mechanisms range from heuristics to control theory and machine learning: results are compared by means of case studies using standard benchmarks.