A new evolutionary algorithm using shadow price guided operators

Shen, Gang; Yan-qing, Zhang

doi:10.1016/j.asoc.2010.06.014

Cited by 18 publications

(8 citation statements)

References 16 publications

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“…However, modern complex distributed computing systems can be made up of hundreds or thousands of various components such as computers and database virtual servers. Seh et al in [18] and [19] present a shadow price technique for improving the genetic operations in standard GA used as a scheduler in computational cloud. The configuration of the genetic operators requires an application of specially designed mutation and crossover techniques, such as partially matching or cycle crossover, and swap or rebalancing mutation mechanisms [17].…”

Section: Introductionmentioning

confidence: 99%

“…The configuration of the genetic operators requires an application of specially designed mutation and crossover techniques, such as partially matching or cycle crossover, and swap or rebalancing mutation mechanisms [17]. Seh et al in [18] and [19] present a shadow price technique for improving the genetic operations in standard GA used as a scheduler in computational cloud. They used the classical single-population GA with a modified mutation scheme.…”

Section: Introductionmentioning

confidence: 99%

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Energy efficient genetic‐based schedulers in computational grids

Kołodziej

Khan

Wang

et al. 2012

Concurrency and Computation

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In today's highly parametrized distributed computational environments, such as green grid clusters and clouds, the growing power and cooling rates are becoming the dominant part of the users' and system managers' budgets. Computational grids, owing to their sheer sizes, still require advanced methodologies and strategies for supporting the scheduling of the users' tasks and applications to the distributed resources. The efficient resource allocation becomes even more challenging when energy utilization, beyond the conventional scheduling criteria, such as Makespan, is treated as first-class additional scheduling objective. In this paper, we address the independent batch scheduling in computational grid as a bi-objective global minimization problem with Makespan and energy consumption as the main criteria. We apply the dynamic voltage and frequency scaling model for the management of the cumulative power energy utilized by the grid resources. We develop three genetic algorithms as energy-aware grid schedulers, which were empirically evaluated in three grid size scenarios in static and dynamic modes. The simulation results confirmed the effectiveness of the proposed genetic algorithm-based schedulers in the reduction of the energy consumed by the whole system and in dynamic load balancing of the resources in grid clusters, which is sufficient to maintain the desired quality level(s). systems must provide a wide range of services, high performance computing platforms, and process the large amounts of data. Moreover, a user in one locality might not be able to have control over other parts of the system. Therefore, to achieve full knowledge of the system and users profiles may be difficult, and the growing power and cooling rates are becoming the dominant part of the users' and system managers' budgets [3]. A severe increase in energy consumption in CGs may be the result of a disproportion of resource availability and resource provisioning in the light of computational demands [4]. All of the aforementioned issues will necessitate the development of intelligent resource management and scheduling techniques in grid computing. The modern grid schedulers should be able to optimize the conventional scheduling objectives simultaneously, such as Makespan, Flowtime, and Resource Utilization [5], and the new scheduling criteria, such as security requirements and energy consumed by all system's entities [6]. Energy-efficient scheduling in CGs becomes a complex endeavor owing to the multi-constraints and different optimization criteria and different priorities of the resource owners. Heuristic approaches seem to be the effective methodologies for designing energy-aware grid schedulers by trading off among various preferences and goals of the grid users, resource, service managers, and resource owners.In this paper, we address the problem of independent batch job scheduling in CGs, where tasks are processed in a batch mode and there are no dependencies among them. This scheduling scenario is very useful in illustrating many rea...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy efficient genetic‐based schedulers in computational grids

Kołodziej

Khan

Wang

et al. 2012

Concurrency and Computation

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“…In [41] and [42] Shen et al present a shadow price technique for improving the genetic operations in standard GA used as a scheduler in computational cloud. The "shadow price" for a pair task-machine is defined as an average energy consumption per instruction for the processor that can operate at different voltage levels.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical genetic-based grid scheduling with energy optimization

et al. 2012

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An optimization of power and energy consumptions is the important concern for a design of modern-day and future computing and communication systems. Various techniques and high performance technologies have been investigated and developed for an efficient management of such systems. All these technologies should be able to provide good performance and to cope under an increased workload demand in the dynamic environments such as Computational Grids (CGs), clusters and clouds. In this paper we approach the independent batch scheduling in CG as a bi-objective minimization problem with makespan and energy consumption as the scheduling criteria. We use the Dynamic Voltage Scaling (DVS) methodology for scaling and possible reduction of cumulative power energy utilized by the system resources. We develop two implementations of Hierarchical Genetic Strategy-based grid scheduler (Green-HGS-Sched) with elitist and struggle replacement mechanisms. The proposed algorithms were empirically evaluated versus single-population Genetic Algorithms (GAs) and Island GA models for four CG size scenarios in static and dynamic modes. The simulation results show that proposed scheduling methodologies fairly reduce the energy usage and can be easily adapted to the dynamically changing grid states and various scheduling scenarios.

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“…In recent years, since the TSP is a good ground for testing optimization techniques, many researchers in various fields such as artificial intelligence, biology, mathematics, physics, and operations research devote themselves to trying to find the efficient methods for solving the TSP, such as genetic algorithms (GAs) [15], ant colony optimization (ACO) [16], simulated annealing (SA) [17], neural networks (NN) [18], particle swarm optimization (PSO) [19], evolutionary algorithms (EA) [20], memetic computing [21], etc. Besides, there are many practical applications of the TSP in the real world [22,23], such as data association, vehicle routing (with the additional constraints of vehicle's route, such as capacity's vehicles), data transmission in computer networks, job scheduling, DNA sequencing, drilling of printed circuits boards, clustering of data arrays, image processing and pattern recognition, analysis of the structure of crystals, transportation and logistics.…”

Section: Related Workmentioning

confidence: 99%