Dynamic resource allocation heuristics that manage tradeoff between makespan and robustness
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Summary
A model for quantifying robustness in a dynamic heterogeneous computing environment where task execution time estimates are known to contain errors is developed and applied to two different problem environments.
- Type
- article
- Published
- 2007-10-01
- Cited by
- 37
- References
- 36
- Access
- Open access
- OpenAlex
- https://openalex.org/W1965430003
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:15613582
Keywords
Robustness (evolution), Computer science, Heuristics, Distributed computing, Mathematical optimization
References
- Guest Editor's Introduction: Heterogeneous Processing
- Representing Task and Machine Heterogeneities for Heterogeneous Computing Systems
- Computer and job-shop scheduling theory
- How to Solve It: Modern Heuristics
- QoS and Contention-Aware Multi-Resource Reservation
- Utilization-Based Techniques for Statically Mapping Heterogeneous Applications onto the HiPer-D Heterogeneous Computing System
- Dynamic mapping in a heterogeneous environment with tasks having priorities and multiple deadlines
- Reactive scheduling: improving the robustness of schedules and restricting the effects of shop floor disturbances by fuzzy reasoning
- Robustness Measures and Robust Scheduling for Job Shops
- A distributed heterogeneous supercomputing management system
- β-Robust scheduling for single-machine systems with uncertain processing times
- Dynamic Mapping of a Class of Independent Tasks onto Heterogeneous Computing Systems
- A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems
- Matchup Scheduling with Multiple Resources, Release Dates and Disruptions
- The Grid 2: Blueprint for a New Computing Infrastructure
- Static allocation of resources to communicating subtasks in a heterogeneous ad hoc grid environment
- Heuristic Algorithms for Scheduling Independent Tasks on Nonidentical Processors
- Segmented min-min: a static mapping algorithm for meta-tasks on heterogeneous computing systems
- Application of Lagrangian receding horizon techniques to resource management in ad hoc grid environments
- Task matching and scheduling in heterogeneous systems using simulated evolution
Cited by
- Iterative Techniques for Maximizing Stochastic Robustness of a Static Resource Allocation in Periodic Sensor Driven Clusters
- Linear programming based resource management for heterogeneous computing systems
- Optimizing availability and QoS of heterogeneous distributed system based on residual lifetime in uncertain environment
- Multi-objective robust static mapping of independent tasks on grids
- Robust resource allocation in a cluster based imaging system
- Statistical measures for quantifying task and machine heterogeneities
- 3E: Energy-efficient elastic scheduling for independent tasks in heterogeneous computing systems
- Robust task scheduling for volunteer computing systems
- Heuristics for Robust Resource Allocation of Satellite Weather Data Processing on a Heterogeneous Parallel System
- Robust sequential resource allocation in heterogeneous distributed systems with random compute node failures
- Robust data placement in urgent computing environments
- Robust CDN replica placement techniques
- Improvement on Scheduling Dependent Tasks for Grid Applications
- Time Utility Functions for Modeling and Evaluating Resource Allocations in a Heterogeneous Computing System
- Performance-Driven Load Balancing with a Primary-Backup Approach for Computational Grids with Low Communication Cost and Replication Cost
- Measuring the Robustness of Resource Allocations in a Stochastic Dynamic Environment
- Robustness of Heuristic Resource Allocation Techniques in Grid Computing System
- A hybrid Branch-and-Bound and evolutionary approach for allocating strings of applications to heterogeneous distributed computing systems
- Models and Heuristics for Robust Resource Allocation in Parallel and Distributed Computing Systems
- Linear Programming-Based Affinity Scheduling of Independent Tasks on Heterogeneous Computing Systems
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