Adaptive model predictive control of autonomic distributed parallel computations with variable horizons and switching costs

Summary Autonomic computing is a paradigm for building systems capable of adapting their operation when external changes occur, such as workload variations, load surges and changes in the resource availability. The optimal configuration in terms of the number of computing resources assigned to each...

Full description

Saved in:
Bibliographic Details
Published in:Concurrency and computation Vol. 28; no. 7; pp. 2187 - 2212
Main Author: Mencagli, G.
Format: Journal Article
Language:English
Published: Blackwell Publishing Ltd 01.05.2016
Subjects:
autonomic computing
Computation
Costs
Decision making
distributed cooperative optimization
Horizon
Mathematical models
model predictive control
parallel computing
Predictive control
Reconfiguration
Strategy
ISSN:1532-0626, 1532-0634
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Summary Autonomic computing is a paradigm for building systems capable of adapting their operation when external changes occur, such as workload variations, load surges and changes in the resource availability. The optimal configuration in terms of the number of computing resources assigned to each component must be automatically adjusted to the new environmental conditions. To accomplish the execution goals with the desired Quality of Service, decision‐making strategies should be in charge of selecting the best reconfigurations by taking into account metrics like performance, efficiency (avoiding wasting resources), number and frequency of reconfigurations, and their amplitude (performing minimal modifications of the current configuration). This paper presents a decision‐making strategy that merges the potential of Model Predictive Control with a cooperative optimization framework. After a description of our approach, we investigate the effect of different switching costs to model the resource allocation problem. We use a control method in which our proactive decision‐making strategy (designed to use future prediction horizons) is made adaptive itself by dynamically changing the horizon length on the basis of the prediction errors. Simulations have been used to exemplify our approach and to discuss the effectiveness of the variable‐horizon strategy in achieving the best trade‐offs between reconfiguration metrics. Copyright © 2015 John Wiley & Sons, Ltd.
Bibliography:ArticleID:CPE3495
istex:0C0BA90F391060C1797001C769AF81F308311416
ark:/67375/WNG-J2DSJR3V-1
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.3495