Scheduling with processing set restrictions : a survey

2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

AND THE STATE-OF-THE-ART

Our goal in this article is to give an overview of the broad are

Multi-processor job scheduling with genetic algorithms.

by Hoi Wing, Yung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 56-60).Abstracts in English and Chinese.List of Figures --- p.vList of Tables --- p.viChapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview --- p.1Chapter 1.2 --- Literature Review --- p.3Chapter 1.2.1 --- On the Fixed Multiprocessor Job Scheduling Problems --- p.6Chapter 1.2.2 --- On the Nonfixed Multiprocessor Job Scheduling Problems --- p.8Chapter 1.3 --- Problem Formulation --- p.12Chapter 1.4 --- Organization of the Thesis --- p.13Chapter 2 --- Genetic Algorithms --- p.15Chapter 2.1 --- Basic Concepts --- p.15Chapter 2.2 --- Main components --- p.17Chapter 3 --- A New Genetic Algorithm --- p.24Chapter 3.1 --- Coding --- p.25Chapter 3.1.1 --- Simple Example --- p.28Chapter 3.2 --- Similarity of Chromosomes --- p.30Chapter 3.3 --- Fitness Evaluation --- p.33Chapter 3.4 --- Configurations --- p.35Chapter 3.4.1 --- Parent Selection --- p.35Chapter 3.4.2 --- Multipoint Crossover --- p.36Chapter 3.4.3 --- Multipoint Mutation --- p.38Chapter 3.4.4 --- Replacement Step --- p.38Chapter 3.4.5 --- Termination Criterion --- p.39Chapter 4 --- Experimental Results --- p.41Chapter 4.1 --- Total Weighted Completion Time --- p.41Chapter 4.1.1 --- Lee and Cai's Algorithm --- p.42Chapter 4.1.2 --- Computational Results --- p.44Chapter 4.1.3 --- On the Problem of Minimizing the Total Completion Time --- p.46Chapter 4.2 --- Makespan --- p.48Chapter 4.2.1 --- Mahesh's Algorithms and Linn & Chen's Algorithm --- p.48Chapter 4.2.2 --- Computational Results --- p.52Chapter 5 --- Conclusion --- p.54Bibliography --- p.5

Real-Time Systems: An Introduction and the State-of-the-Art

This encyclopedia article gives an overview of the broad area of real-time systems. This task is daunting because real-time systems are everywhere, and yet no generally accepted definition differentiates real-time systems from non-real-time systems

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

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 Allocation Each of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments.Note.-- EUR 6,000 BPC fee funded by the EC FP7 Post-Grant Open Access Pilo

Sharing Non-Processor Resources in Multiprocessor Real-Time Systems

Computing devices are increasingly being leveraged in cyber-physical systems, in which computing devices sense, control, and interact with the physical world. Associated with many such real-world interactions are strict timing constraints, which if unsatisfied, can lead to catastrophic consequences. Modern examples of such timing constraints are prevalent in automotive systems, such as airbag controllers, anti-lock brakes, and new autonomous features. In all of these examples, a failure to correctly respond to an event in a timely fashion could lead to a crash, damage, injury and even loss of life. Systems with imperative timing constraints are called real-time systems, and are broadly the subject of this dissertation. Much previous work on real-time systems and scheduling theory assumes that computing tasks are independent, i.e., the only resource they share is the platform upon which they are executed. In practice, however, tasks share many resources, ranging from more overt resources such as shared memory objects, to less overt ones, including data buses and other hardware and I/O devices. Accesses to some such resources must be synchronized to ensure safety, i.e., logical correctness, while other resources may exhibit better run-time performance if accesses are explicitly synchronized. The goal of this dissertation was to develop new synchronization algorithms and associated analysis techniques that can be used to synchronize access to many classes of resources, while improving the overall resource utilization, specifically as measured by real-time schedulability. Towards that goal, the Real-Time Nested Locking Protocol (RNLP), the first multiprocessor real-time locking protocol that supports lock nesting or fine-grained locking is proposed and analyzed. Furthermore, the RNLP is extended to support reader/writer locking, as well as k-exclusion locking. All presented RNLP variants are proven optimal. Furthermore, experimental results demonstrate the schedulability-related benefits of the RNLP. Additionally, three new synchronization algorithms are presented, which are specifically motivated by the need to manage shared hardware resources to improve real-time predictability. Furthermore, two new classes of shared resources are defined, and the first synchronization algorithms for them are proposed. To analyze these new algorithms, a novel analysis technique called idleness analysis is presented, which can be used to incorporate the effects of blocking into schedulability analysis.Doctor of Philosoph

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

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

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

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