Ant Colony Optimization Inspired Swarm Optimization for Grid Task Scheduling

Chen, Ruey-Maw; Shen, Yin-Mou; Wang, Ching-Te

doi:10.1109/is3c.2016.122

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…Static scheduling algorithms are usually based on metaheuristic algorithms such as generic algorithm (GA), ant colony optimization (ACO), and bee colony algorithm. (e.g., [30], [31] [32], [34]) Job shop scheduling (e.g., [18]- [21]) is an active area of task scheduling research, where the schedule for a set of production jobs is planned before the production and this scheduling can be done with static scheduling schemes. Dynamic scheduling is performed when the applications are mapped in an on-line fashion, (e.g., when tasks' arrival are unpredictable) and are mapped as they arrive (the workload is not known a priori).…”

Section: Introductionmentioning

confidence: 99%

A Method to Construct Task Scheduling Algorithms for Heterogeneous Multi-Core Systems

Kim

2019

IEEE Access

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The use of heterogeneous multicore processors (HMP) is spreading rapidly from data centers to large-scale deployment in smartphones because they give greater flexibility to adapt to power constraints and performance needs. In this study, we show that an intelligent task scheduler is critical for improving the performance and energy efficiency in an HMP environment. We assume that the tasks are independent in the environment, with hard real-time constraints and multicore systems, where the processors can be manipulated to change the clock cycle speed and power levels. Tasks are assumed to arrive aperiodically where the tasks are applications from the SPEC CPU 2006 benchmark suite. In the evaluation, we used a real system comprising of two multicore processors, which supported on-the-fly dynamic voltage/frequency scaling. We extracted several important components from previously proposed algorithms and combined them to construct algorithms with better performance. Our results showed that some of the best combinations reduced the energy consumption and achieved a better completion rate in the environment. In addition, a method is proposed for calculating the upper-bound of the task completion rate and energy consumption so that there is a guide as to how near the results are to the optimal performance. INDEX TERMS Task scheduling, energy-aware scheduling, heterogeneous multi-core, real-time scheduling, dynamic voltage/frequency scaling.

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Section: Introductionmentioning

confidence: 99%

A Method to Construct Task Scheduling Algorithms for Heterogeneous Multi-Core Systems

Kim

2019

IEEE Access

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“…Genetic algorithms may be faster than PSO and restrict the reproduction of weak solutions, but their crossover and mutation operations result as incompatible with our optimization problem [29], since it is formed by load duration curves, with individuals defined by non-independent characteristics [30]. Ant colony algorithms, despite of being based on swarm behavior [31] as in the case of PSO, assure convergence in problems where source and destination are predefined and specific [32], unlike in the congestion threshold determination problem proposed here. The objective of this article is threefold: (1) To determine optimal technical thresholds to prevent congestion in distribution network assets, such as MV/LV power transformers and LV feeders; (2) to contribute with an optimization methodology not based on subjective previous experience and replicable in any kind of network, by employing clustering and multi objective particle swarm optimization (MOPSO); and (3) to apply the methodology to a real dataset obtained from Smartcity Malaga Living Lab, an area with more than 15,000 real end users, where 750 sensors installed in 56 MV/LV secondary substations are measuring current, voltage, power, and energy every 5 min [33].…”

Section: Introductionmentioning

confidence: 99%

Data Analytics-Based Multi-Objective Particle Swarm Optimization for Determination of Congestion Thresholds in LV Networks

2019

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A growing presence of distributed energy resources (DER) and the increasingly diverse nature of end users at low-voltage (LV) networks make the operation of these grids more and more challenging. Particularly, congestion and voltage management strategies for LV grids have usually been limited to some elemental criteria based on human experience, asset oversizing, or grid reinforcement. However, with the current massive deployment of sensors in modern LV grids, new approaches are feasible for distribution network assets operation. This article proposes a multi-objective particle swarm optimization (MOPSO) approach, combined with data analytics through affinity propagation clustering, for congestion threshold determination in LV grids. A real case study from the smart grid of Smartcity Malaga Living Lab is used to illustrate the proposed approach. Within this approach, distribution system operators (DSOs) can take decisions in order to prevent situations of risk or potential failure at LV grids.

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“…In other words, workflow scheduling has to simultaneously solve two subproblems in task-resource matching and those unrelated to tasks' priorities. Most scheduling problems are NP-complete, and many heuristic and metaheuristic algorithms have been proposed to solve NP problems, such as the ant colony optimization (ACO) [3], genetic algorithm (GA) [6], simulated annealing (SA) [5], and particle swarm optimization (PSO) [10]. Among them, PSO carries the advantages of easy implementation, requiring fewer parameters and having a faster convergence speed; therefore, PSO is often used to solve scheduling problems in fields aside from a grid or cloud computing, such as course timetabling problems [11], flowshop problems [12], and vehicle routing problems (VRP) [13].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Greedy-Based Particle Swarm Optimization for Workflow Application in Grid

Chen¹,

Shen²

2018

Particle Swarm Optimization With Applications

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The workflow application is a common grid application. The objective of a workflow application is to complete all the tasks within the shortest time, i.e., minimal makespan. A job scheduler with a high-efficient scheduling algorithm is required to solve workflow scheduling based on grid information. Scheduling problems are NP-complete problems, which have been well solved by metaheuristic algorithms. To attain effective solutions to workflow application, an algorithm named the stochastic greedy PSO (SGPSO) is proposed to solve workflow scheduling; a new velocity update rule based on stochastic greedy is suggested. Restated, a stochastic greedy-driven search guidance is provided to particles. Meanwhile, a stochastic greedy probability (SGP) parameter is designed to help control whether the search behavior of particles is exploitation or exploration to improve search efficiency. The advantages of the proposed scheme are retaining exploration capability during a search, reducing complexity and computation time, and easy to implement. Retaining exploration capability during a search prevents particles from getting trapped on local optimums. Additionally, the diversity of the proposed SGPSO is verified and analyzed. The experimental results demonstrate that the SGPSO proposed can effectively solve workflow class problems encountered in the grid environment.

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Ant Colony Optimization Inspired Swarm Optimization for Grid Task Scheduling

Cited by 9 publications

References 11 publications

A Method to Construct Task Scheduling Algorithms for Heterogeneous Multi-Core Systems

A Method to Construct Task Scheduling Algorithms for Heterogeneous Multi-Core Systems

Data Analytics-Based Multi-Objective Particle Swarm Optimization for Determination of Congestion Thresholds in LV Networks

Stochastic Greedy-Based Particle Swarm Optimization for Workflow Application in Grid

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