Energy-Aware Real-Time Task Scheduling for Heterogeneous Multiprocessors with Particle Swarm Optimization Algorithm

Zhang, Weizhe; Xie, Hang; Cao, Boran; Cheng, Albert M. K.

doi:10.1155/2014/287475

Cited by 32 publications

(21 citation statements)

References 20 publications

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“…The specific variants of the metaheuristics were adapted from other relevant studies. For ACO, the variant proposed by Hyung and Sungho [33] was adapted while for PSO, the algorithm proposed by Zhang et al [34] was adapted. Moreover, no application of CS has been reported in the literature for the underlying problem.…”

Section: Resultsmentioning

confidence: 99%

“…Their simulation results show a considerable improvement over the genetic algorithm in terms of solution quality. There are a number of other studies that focus on scheduling real-time task on multiprocessor systems to reduce energy consumption by dynamically adjusting the processor operating frequency [28][29][30][31][32][33][34].…”

Section: Literature Reviewmentioning

confidence: 99%

“…It then randomly selects a task from the segment (line 20, 21) and assigns it to a randomly selected processor (line 22, 23) at a randomly selected voltage level (line 24,25). A chromosome it added into the initial population if it satisfies the feasibility test (line [32][33][34] to ensure that the initial population has only the feasible solutions. Figure 6 shows two chromosomes generated by the algorithm for the given DAG (the vertical dotted lines show the segment boundaries).…”

Section: Solution Encoding and Generation Of The Initial Populationmentioning

confidence: 99%

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Energy-Aware Real-Time Task Scheduling in Multiprocessor Systems Using a Hybrid Genetic Algorithm

et al. 2017

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Abstract:Minimizing power consumption to prolong battery life has become an important design issue for portable battery-operated devices such as smartphones and personal digital assistants (PDAs). On a Dynamic Voltage Scaling (DVS) enabled processor, power consumption can be reduced by scaling down the operating frequency of the processor whenever the full processing speed is not required. Real-time task scheduling is a complex and challenging problem for DVS-enabled multiprocessor systems. This paper first formulates the real-time task scheduling for DVS-enabled multiprocessor systems as a combinatorial optimization problem. It then proposes a genetic algorithm that is hybridized with the stochastic evolution algorithm to allocate and schedule real-time tasks with precedence constraints. It presents specialized crossover and perturb operations as well as a topology preserving algorithm to generate the initial population. A comprehensive simulation study has been done using synthetic and real benchmark data to evaluate the performance of the proposed Hybrid Genetic Algorithm (HGA) in terms of solution quality and efficiency. The performance of the proposed HGA has been compared with the genetic algorithm, particle swarm optimization, cuckoo search, and ant colony optimization. The simulation results show that HGA outperforms the other algorithms in terms of solution quality.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Solution Encoding and Generation Of The Initial Populationmentioning

confidence: 99%

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Energy-Aware Real-Time Task Scheduling in Multiprocessor Systems Using a Hybrid Genetic Algorithm

et al. 2017

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“…The study in [48] proposed a PSO-based algorithm, which could successfully reduce the energy cost and the time for searching feasible solutions. The authors presented an environment of heterogeneous multiprocessors, which is similar to the environment of cloud data center, and they proposed a job to the processor assignment model that would work in that environment.…”

Section: ) Predicting the Upcoming Number Of The (Vm)mentioning

confidence: 99%

Power management in virtualized data centers: state of the art

et al. 2016

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Cloud computing is an emerging technology in the field of computing that provides access to a wide range of shared resources. The rapid growth of cloud computing has led to establishing numerous data centers around the world. As data centers consume huge amounts of power, enhancing their power efficiency has become a major challenge in cloud computing. This paper surveys previous studies and researches that aimed to improve power efficiency of virtualized data centers. This survey is a valuable guide for researchers in the field of power efficiency in virtualized data centers following the cloud computing model.

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“…Various optimization methods such as, Ant Colony Optimization (ACO) (Gao et al, 2013), Particle Swarm Optimization (PSO) (Zhang et al, 2014b) and Genetic Algorithms (GA) (Portaluri et al, 2014;Dong et al, 2014) were proposed to do the process of VM placement aiming to reduce energy consumption in data centers.…”

Section: Related Workmentioning

confidence: 99%

Towards Solving the Problem of Virtual Machine Placement in Cloud Computing: A Job Classification Approach

Al-Dulaimy¹,

Zekri²,

Itani³

et al. 2016

Journal of Computer Science

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Cloud Computing is a paradigm that delivers services by providing an access to wide range of shared resources which are hosted in cloud data centers. One of the recent challenges in this paradigm is to enhance the energy efficiency in these data centers. In this study, a model that identifies common patterns for the jobs submitted to the cloud is proposed. This model is able to predict the type of the job submitted and accordingly, the set of users' jobs is classified into four subsets. Each subset contains jobs that have similar requirements. In addition to the jobs' common pattern and requirements, the users' history is considered in the jobs' type prediction model. The goal of job classification is to find a way to propose useful strategy that helps to improve power efficiency. Based on the process of jobs' classification, the best fit virtual machine is allocated to each job. Then, the virtual machines are placed on the physical machines according to a novel strategy, called Mixed Type Placement strategy. The core idea of the proposed strategy is to place virtual machines of the jobs of different types in the same physical machine whenever possible. The placement process is based on Multi Choice Knapsack Problem which is a generalization of the classical Knapsack Problem. This is because different types of jobs do not intensively use the same compute or storage resources in the physical machine. This strategy minimizes the number of active physical machines which, in turn, leads to major reduction in the total energy consumption in the data center. The total execution time and the cost of executing the jobs submitted are considered in the placement process. To evaluate the performance of the proposed strategy, the CloudSim simulator is used with a real workload trace to simulate the cloud computing environment. The results show that the proposed strategy outperform both Genetic Algorithm and Round Robin from energy efficiency perspective.

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Energy-Aware Real-Time Task Scheduling for Heterogeneous Multiprocessors with Particle Swarm Optimization Algorithm

Cited by 32 publications

References 20 publications

Energy-Aware Real-Time Task Scheduling in Multiprocessor Systems Using a Hybrid Genetic Algorithm

Energy-Aware Real-Time Task Scheduling in Multiprocessor Systems Using a Hybrid Genetic Algorithm

Power management in virtualized data centers: state of the art

Towards Solving the Problem of Virtual Machine Placement in Cloud Computing: A Job Classification Approach

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