Real time pipelined system design through simulated annealing

Coli, M.; Palazzari, P.

doi:10.1016/s1383-7621(96)00034-3

Cited by 31 publications

(17 citation statements)

References 4 publications

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“…These heuristics are designed using different design principles and with different motivation. On this basis we can roughly classify these heuristics into: list scheduling (e.g., HEFT [3] including its variations [19], HBMCT [4], GDL(DLS) [20], PCT [21], CPOP [3], ETF [22], BIL [23]), workflow-based scheduling (e.g., WBA [24], ILS [25]), guided search scheduling (e.g., GA [26], SA [27]), clustering based scheduling(e.g., Triplet [28] Figure 1: The HEFT Heuristic task duplication based scheduling (e.g., TDS [29], STDS [30], LDBS [31]). An extensive list of references of DAG scheduling heuristics can also be found in [12].…”

Section: Background and Related Workmentioning

confidence: 99%

A Monte-Carlo Approach for Full-Ahead Stochastic DAG Scheduling

Zheng

Sakellariou

2012

2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops &Amp; PhD Forum

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Abstract-In most heterogeneous computing systems, there is a need for solutions that can cope with the unavoidable uncertainty in individual task execution times, when scheduling DAGs. When such uncertainties occur, static DAG scheduling approaches may suffer, and some rescheduling may be necessary. Assuming that the uncertainty in task execution times is modelled in a stochastic manner, then we may be able to use this information to improve static DAG scheduling considerably. In this paper, a novel DAG scheduling approach is proposed to solve this stochastic scheduling problem, based on a Monte-Carlo method. The approach is built on the top of a classic static scheduling heuristic and evaluated through extensive simulation. Empirical results show that a significant improvement on average application performance can be achieved by the proposed approach at a reasonable execution time cost.

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Section: Background and Related Workmentioning

confidence: 99%

A Monte-Carlo Approach for Full-Ahead Stochastic DAG Scheduling

Zheng

Sakellariou

2012

2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops &Amp; PhD Forum

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“…SA Simulated Annealing (SA) is an iterative technique that considers only one possible mapping for each meta-task at a time. Simulated annealing uses a procedure that probabilistically allows poorer solutions to be accepted to attempt to obtain a better search of the solution space based on a system temperature [15].…”

Section: Maximum Standard Deviationmentioning

confidence: 99%

A New Heuristic Approach for Scheduling Independent Tasks on Heterogeneous Computing Systems

Rafsanjani¹,

Bardsiri²

2012

IJMLC

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Abstract-Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous computing systems. The problem of mapping meta-tasks to a machine is shown to be NP-complete. The NP-complete problem can be solved only using heuristic approach. There are a number of heuristic algorithms that were tailored to deal with scheduling of independent tasks. Different criteria can be used for evaluating the efficiency of scheduling algorithms. The most important of them are makespan, flowtime and resource utilization. In this paper, a new heuristic algorithm for scheduling meta-tasks in heterogeneous computing system is presented. The proposed algorithm improves the performance in both makespan and effective utilization of resources by reducing the idle time of the machine. The performance analyses show that the proposed algorithm has a better resource utilization rate and reduced makespan than the other known algorithms.

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“…Simulated Annealing Simulated annealing [9,18] also uses chromosomes to encode the mapping of tasks to machines. However, unlike genetic algorithms, which deal with a population of chromosomes, simulated annealing only considers one mapping at a time and uses a mutation process on that chromosome to improve its makespan.…”

Section: Output the Best Solutionmentioning

confidence: 99%