Towards energy-efficient scheduling for real-time tasks under uncertain cloud computing environment

Chen, Huangke; Zhu, Xiaomin; Guo, Hui; Zhu, Jianghan; Qin, Xiao; Wu, Jianhong

doi:10.1016/j.jss.2014.08.065

Cited by 152 publications

(66 citation statements)

References 20 publications

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“…and providing the desired level of quality of service (QoS) [5,[12][13][14][15]. Wu et al [13] proposed a task scheduling algorithm based on several quality of service (QoS) metrics.…”

Section: Literature Reviewmentioning

confidence: 99%

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Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

2017

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Abstract:In the Infrastructure-as-a-Service cloud computing model, virtualized computing resources in the form of virtual machines are provided over the Internet. A user can rent an arbitrary number of computing resources to meet their requirements, making cloud computing an attractive choice for executing real-time tasks. Economical task allocation and scheduling on a set of leased virtual machines is an important problem in the cloud computing environment. This paper proposes a greedy and a genetic algorithm with an adaptive selection of suitable crossover and mutation operations (named as AGA) to allocate and schedule real-time tasks with precedence constraint on heterogamous virtual machines. A comprehensive simulation study has been done to evaluate the performance of the proposed algorithms in terms of their solution quality and efficiency. The simulation results show that AGA outperforms the greedy algorithm and non-adaptive genetic algorithm in terms of solution quality.

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“…and providing the desired level of quality of service (QoS) [5,[12][13][14][15]. Wu et al [13] proposed a task scheduling algorithm based on several quality of service (QoS) metrics.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The second Constraint (11) specifies the real-time constraint and the last Constraint (12) specifies that a task cannot start before the completion of all of its predecessor tasks.…”

Section: Cost Functionmentioning

confidence: 99%

Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

2017

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“…Here is a combination of Nimbus and Cumulus services to create a server-provider. IaaS cloud environment, KVM/Xen virtualization, and LSTR scheduler have been used to balance load and maximize operational capacity and resource utilization [13]. In [14], dynamic load balancing is done using virtual machines for the cloud computing environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Load Balancing in Cloud Complex Systems using Adaptive Fuzzy Neural Systems

Sadeghi¹

2018

ijacsa

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Abstract-Load balancing, reliability, and traffic are among the service-oriented issues in software engineering, and cloud computing is no exception to this rule and has put many challenges ahead of experts in this field. Considering the importance of the load balancing process in cloud computing, the purpose of this paper is to provide an appropriate solution for load balancing load in complex cloud systems using an adaptive fuzzy neural system. This system consists of four layers, and a particular operation is performed on each layer. The results of the experiments show that the system has better performance in the criteria mentioned above (balancing, traffic and reliability).

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“…The authors in [40] stated the previous real time job scheduling algorithms were running in uncertain cloud environments. Those algorithms assumed that cloud computing environments were deterministic, and there were statistical pre-computed schedule decisions to be followed during the schedule execution.…”

Section: Software Level Solutionsmentioning

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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Towards energy-efficient scheduling for real-time tasks under uncertain cloud computing environment

Cited by 152 publications

References 20 publications

Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

Load Balancing in Cloud Complex Systems using Adaptive Fuzzy Neural Systems

Power management in virtualized data centers: state of the art

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