High Performance Architecture for NSGA-II

Dominguez, Josue; Montiel, Oscar; Sepúlveda, Roberto; Medina, Nataly

doi:10.1007/978-3-642-33021-6_35

Cited by 3 publications

(2 citation statements)

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“…f . Evolutionary algorithms usually find good approximations, that is, a set of solutions whose objective vectors are not too far away from the optimal objective vectors [33]. x / D OEf 1 .…”

Section: Multiobjective Optimization With Non-dominated Sorting Genetmentioning

confidence: 99%

“…For this reason, a number of stochastic search strategies such as evolutionary algorithms have been developed. Evolutionary algorithms usually find good approximations, that is, a set of solutions whose objective vectors are not too far away from the optimal objective vectors [33]. The NSGA-II algorithm is an improved version of the NSGA algorithm [3].…”

Section: Multiobjective Optimization With Non-dominated Sorting Genetmentioning

confidence: 99%

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Performance and energy aware scheduling simulator for HPC: evaluating different resource selection methods

Gómez-Martín¹,

Vega‐Rodríguez

González-Sánchez³

2015

Concurrency and Computation

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Today, in an energy-aware society, job scheduling is becoming an important task for computer engineers and system analysts that may lead to a performance per Watt trade-off of computing infrastructures. Thus, new algorithms, and a simulator of computing environments, may help information and communications technology and data center managers to make decisions with a solid experimental basis. There are several simulators that try to address performance and, somehow, estimate energy consumption, but there are none in which the energy model is based on benchmark data that have been countersigned by independent bodies such as the Standard Performance Evaluation Corporation. This is the reason why we have implemented a performance and energy-aware scheduling (PEAS) simulator for high-performance computing. Furthermore, to evaluate the simulator, we propose an implementation of the non-dominated sorting genetic algorithm-II (NSGA-II) algorithm, a fast and elitist multiobjective genetic algorithm, for the resource selection. With the help of the PEAS simulator, we have studied if it is possible to provide an intelligent job allocation policy that may be able to save energy and time without compromising performance. The results of our simulations show a great improvement in response time and power consumption. In most of the cases, NSGA-II performs better than other 'intelligent' algorithms like multiobjective heterogeneous earliest finish time and clearly outperforms the first-fit algorithm. We demonstrate the usefulness of the simulator for this type of studies and conclude that the superior behavior of multiobjective algorithms makes them recommended for use in modern scheduling systems. PEAS SIMULATOR FOR HPC: EVALUATING DIFFERENT RESOURCE SELECTION METHODS 5437 applications, wall clock time requirements in high-performance computing (HPC). A thorough study of actual workloads can increase the energy efficiency of information technology infrastructure.There have been several works that study the impact of dynamic voltage and frequency scaling combined with a power-aware algorithm to save energy [1], and some others about how to balance and save energy in clusters with virtual machines [2]. The innovation of the simulator presented in this article is the inclusion of a power and performance benchmark from the Standard Performance Evaluation Corporation (SPEC). This benchmark is used to model the energy consumption and the performance of a cluster of servers, or a supercomputer, when running a set of tasks with a job scheduler. Also, the implementation of the non-dominated sorting genetic algorithm-II (NSGA-II) algorithm [3], a fast and elitist multiobjective genetic algorithm, has been included in the simulator. It will study if it is possible to provide an intelligent job allocation policy, which may be able to save energy and time, without compromising performance. We found that there is a vast area of research in which multiobjective algorithms can help in the efficient management of computing infrastructures. Rela...

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Section: Multiobjective Optimization With Non-dominated Sorting Genetmentioning

confidence: 99%

Section: Multiobjective Optimization With Non-dominated Sorting Genetmentioning

confidence: 99%