Towards a systematic approach to the dynamic adaptation of structured parallel computations using model predictive control

Adaptiveness is an essential feature for distributed parallel applications executed on dynamic environments like Grids and Clouds. Being adaptive means that parallel components can change their configuration at run-time (by modifying their parallelism degree or switching to a different parallel vari...

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Bibliographic Details
Published in:Cluster computing Vol. 17; no. 4; pp. 1443 - 1463
Main Authors: Mencagli, Gabriele, Vanneschi, Marco
Format: Journal Article
Language:English
Published: Boston Springer US 01.12.2014
Springer
Springer Nature B.V
Subjects:
Adaptation
Adaptive control
Applied sciences
Cloud computing
Computer centers
Computer Communication Networks
Computer Science
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Control stability
Control theory
Control theory. Systems
Cost analysis
Exact sciences and technology
Heuristic
Mathematical models
Operating Systems
Optimal control
Optimization
Parallel programming
Power
Predictive control
Processor Architectures
Quality of service
Reconfiguration
Software
System theory
Performance Modeling
Structured Parallel Programming
Model Predictive Control
Adaptiveness
Cloud computing
Parallel algorithm
Stability
Methodology
Operating cost
Systematic review
Distributed system
Modeling
Adaptive method
Workload
Model predictive control
Parallel programming
Optimal control
Distributed algorithm
Parallelism
Parallel computation
Feasibility
Control theory
Optimal control (mathematics)
Service quality
Reconfiguration
ISSN:1386-7857, 1573-7543
Online Access:Get full text
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Summary:Adaptiveness is an essential feature for distributed parallel applications executed on dynamic environments like Grids and Clouds. Being adaptive means that parallel components can change their configuration at run-time (by modifying their parallelism degree or switching to a different parallel variant) to face irregular workload or to react to uncontrollable changes of the execution platform. A critical problem consists in the definition of adaptation strategies able to select optimal reconfigurations (minimizing operating costs and reconfiguration overhead) and achieve the stability of control decisions (avoiding unnecessary reconfigurations). This paper presents an approach to apply Model Predictive Control (a form of optimal control studied in Control Theory) to adaptive parallel computations expressed according to the Structured Parallel Programming methodology. We show that predictive control is amenable to achieve stability and optimality by relying on the predictability of structured parallelism patterns and the possibility to express analytical cost models of their QoS metrics. The approach has been exemplified on two case-studies, providing a first assessment of its potential and feasibility.
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ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-014-0346-3