Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing.

By: Indrusiak, Leando Soares
Contributor(s): Dziurzanski, Piotr | Kumar Singh, Amit
Language: English Series: River Publishers series in information science and technology: Publisher: . . Description: 1 online resource (178 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 8793519079; 9788793519077; 9781003337997; 1003337996; 9781000791266; 1000791262; 9781000794380; 1000794385Subject(s): Resource allocation | Embedded computer systems | High performance computing | Affectation des ressources | Systèmes enfouis (Informatique) | Superinformatique | COMPUTERS / Computer Architecture | SCIENCE / Energy | Embedded computer systems | High performance computing | Resource allocationGenre/Form: Electronic books.Additional physical formats: Print version:: Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing.LOC classification: T57.77 | .I537 2017ebOnline resources: Full text is available at the Directory of Open Access Books. Click here to view.
Contents:
Front Cover; Half Title Page; RIVER PUBLISHERS SERIES IN INFORMATION SCIENCE AND TECHNOLOGY; Title Page; Copyright Page; Contents; Preface; Acknowledgements; List of Figures; List of Tables; List of Algorithms; List of Abbreviations; Chapter 1 -- Introduction; 1.1 Application Domains; 1.2 RelatedWork; 1.2.1 Allocation Techniques for Guaranteed Performance; 1.2.2 Allocation Techniques for Energy-efficiency; 1.3 Challenges; 1.3.1 Load Representation; 1.3.2 Monitoring and Feedback; 1.3.3 Allocation of Modal Applications; 1.3.4 Distributed Allocation; 1.3.5 Value-based Allocation.
Chpater 2 -- Load and Resource Models2.1 RelatedWork; 2.2 Requirements; 2.2.1 Requirements on Modelling Load Structure; 2.2.1.1 Singleton; 2.2.1.2 Independent jobs; 2.2.1.3 Single-dependency jobs; 2.2.1.4 Communicating jobs; 2.2.1.5 Multi-dependency jobs; 2.2.2 Requirements on Modelling Load Temporal Behaviour; 2.2.2.1 Single appearance; 2.2.2.2 Strictly periodic; 2.2.2.3 Sporadic; 2.2.2.4 Aperiodic; 2.2.2.5 Fully dependent; 2.2.2.6 N out of M dependent; 2.2.3 Requirements on Modelling Load Resourcing Constraints; 2.2.3.1 Untyped job; 2.2.3.2 Single-typed job; 2.2.3.3 Multi-typed job.
2.2.4 Requirements on Modelling Load Characterisation2.2.4.1 Fixed load; 2.2.4.2 Probabilistic load; 2.2.4.3 Typed fixed load; 2.2.4.4 Typed probabilistic load; 2.3 An Interval Algebra for Load and Resource Modelling; 2.3.1 Modelling Load Structure; 2.3.2 Modelling Load Temporal Behaviour; 2.3.3 Modelling Load Resourcing Constraints; 2.3.4 Modelling Load Characterisation; 2.3.5 Stochastic Time; 2.4 Summary; Chapter 3 -- Feedback-Based Admission Control Heuristics; 3.1 System Model and Problem Formulation; 3.1.1 Platform Model; 3.1.2 Application Model.
3.2 Distributed Feedback Control Real-Time Allocation3.3 Experimental Results; 3.3.1 Controller Tuning; 3.3.2 Stress Tests; 3.3.3 RandomWorkloads; 3.4 Dynamic Voltage Frequency Scaling; 3.5 Applying Controllers to Steer DVFS; 3.6 Experimental Results; 3.6.1 Controller Tuning; 3.6.2 RandomWorkloads; 3.7 RelatedWork; 3.8 Summary; Chapter 4 -- Feedback-Based Allocation and Optimisation Heuristics; 4.1 System Model and Problem Formulation; 4.1.1 Application Model; 4.1.2 Platform Model; 4.1.3 Problem Formulation.
4.2 Performing Runtime Admission Control and Load Balancing to Cope with Dynamic Workloads4.3 Experimental Results; 4.3.1 Number of Executed Tasks, Rejected Tasks and Schedulability Tests; 4.3.1.1 Periodic workload; 4.3.1.2 Random workload; 4.3.2 Dynamic Slack, Setpoint and Controller Output; 4.3.2.1 Periodic workload; 4.3.2.2 Light workload; 4.3.3 Core Utilization; 4.3.4 Case Study: IndustrialWorkload Having Dependent Jobs; 4.4 RelatedWork; 4.5 Summary; Chapter 5 -- Search-Based Heuristics for Modal Application; 5.1 System Model and Problem Formulation; 5.1.1 Application Model.
Summary: The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: Load and Resource Models Admission Control Feedback-based Allocation and Optimisation Search-based Allocation Heuristics Distributed Allocation based on Swarm Intelligence Value-Based AllocationEach of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments.
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5.1.2 Platform Model.

Front Cover; Half Title Page; RIVER PUBLISHERS SERIES IN INFORMATION SCIENCE AND TECHNOLOGY; Title Page; Copyright Page; Contents; Preface; Acknowledgements; List of Figures; List of Tables; List of Algorithms; List of Abbreviations; Chapter 1 -- Introduction; 1.1 Application Domains; 1.2 RelatedWork; 1.2.1 Allocation Techniques for Guaranteed Performance; 1.2.2 Allocation Techniques for Energy-efficiency; 1.3 Challenges; 1.3.1 Load Representation; 1.3.2 Monitoring and Feedback; 1.3.3 Allocation of Modal Applications; 1.3.4 Distributed Allocation; 1.3.5 Value-based Allocation.

Chpater 2 -- Load and Resource Models2.1 RelatedWork; 2.2 Requirements; 2.2.1 Requirements on Modelling Load Structure; 2.2.1.1 Singleton; 2.2.1.2 Independent jobs; 2.2.1.3 Single-dependency jobs; 2.2.1.4 Communicating jobs; 2.2.1.5 Multi-dependency jobs; 2.2.2 Requirements on Modelling Load Temporal Behaviour; 2.2.2.1 Single appearance; 2.2.2.2 Strictly periodic; 2.2.2.3 Sporadic; 2.2.2.4 Aperiodic; 2.2.2.5 Fully dependent; 2.2.2.6 N out of M dependent; 2.2.3 Requirements on Modelling Load Resourcing Constraints; 2.2.3.1 Untyped job; 2.2.3.2 Single-typed job; 2.2.3.3 Multi-typed job.

2.2.4 Requirements on Modelling Load Characterisation2.2.4.1 Fixed load; 2.2.4.2 Probabilistic load; 2.2.4.3 Typed fixed load; 2.2.4.4 Typed probabilistic load; 2.3 An Interval Algebra for Load and Resource Modelling; 2.3.1 Modelling Load Structure; 2.3.2 Modelling Load Temporal Behaviour; 2.3.3 Modelling Load Resourcing Constraints; 2.3.4 Modelling Load Characterisation; 2.3.5 Stochastic Time; 2.4 Summary; Chapter 3 -- Feedback-Based Admission Control Heuristics; 3.1 System Model and Problem Formulation; 3.1.1 Platform Model; 3.1.2 Application Model.

3.2 Distributed Feedback Control Real-Time Allocation3.3 Experimental Results; 3.3.1 Controller Tuning; 3.3.2 Stress Tests; 3.3.3 RandomWorkloads; 3.4 Dynamic Voltage Frequency Scaling; 3.5 Applying Controllers to Steer DVFS; 3.6 Experimental Results; 3.6.1 Controller Tuning; 3.6.2 RandomWorkloads; 3.7 RelatedWork; 3.8 Summary; Chapter 4 -- Feedback-Based Allocation and Optimisation Heuristics; 4.1 System Model and Problem Formulation; 4.1.1 Application Model; 4.1.2 Platform Model; 4.1.3 Problem Formulation.

4.2 Performing Runtime Admission Control and Load Balancing to Cope with Dynamic Workloads4.3 Experimental Results; 4.3.1 Number of Executed Tasks, Rejected Tasks and Schedulability Tests; 4.3.1.1 Periodic workload; 4.3.1.2 Random workload; 4.3.2 Dynamic Slack, Setpoint and Controller Output; 4.3.2.1 Periodic workload; 4.3.2.2 Light workload; 4.3.3 Core Utilization; 4.3.4 Case Study: IndustrialWorkload Having Dependent Jobs; 4.4 RelatedWork; 4.5 Summary; Chapter 5 -- Search-Based Heuristics for Modal Application; 5.1 System Model and Problem Formulation; 5.1.1 Application Model.

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: Load and Resource Models Admission Control Feedback-based Allocation and Optimisation Search-based Allocation Heuristics Distributed Allocation based on Swarm Intelligence Value-Based AllocationEach of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments.

Leando Soares Indrusiak, Piotr Dziurzanski, Amit Kumar Singh

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