QL-HEFT: a novel machine learning scheduling scheme base on cloud computing environment

Tong, Zhao; Deng, Xiaomei; Chen, Hongjian; Mei, Jing; Liu, Hong

doi:10.1007/s00521-019-04118-8

Cited by 85 publications

(55 citation statements)

References 43 publications

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“…Although cloud computing [20,21] is considered an efficient computing model, it does not handle timesensitive tasks very well. In addition, more data are generated at the edge of the network.…”

Section: What Is a Smart City?mentioning

confidence: 99%

A survey on algorithms for intelligent computing and smart city applications

Tong

Feng

Yan

et al. 2021

Big Data Min. Anal.

Self Cite

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With the rapid development of human society, the urbanization of the world's population is also progressing rapidly. Urbanization has brought many challenges and problems to the development of cities. For example, the urban population is under excessive pressure, various natural resources and energy are increasingly scarce, and environmental pollution is increasing, etc. However, the original urban model has to be changed to enable people to live in greener and more sustainable cities, thus providing them with a more convenient and comfortable living environment. The new urban framework, the smart city, provides excellent opportunities to meet these challenges, while solving urban problems at the same time. At this stage, many countries are actively responding to calls for smart city development plans. This paper investigates the current stage of the smart city. First, it introduces the background of smart city development and gives a brief definition of the concept of the smart city. Second, it describes the framework of a smart city in accordance with the given definition. Finally, various intelligent algorithms to make cities smarter, along with specific examples, are discussed and analyzed.

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Section: What Is a Smart City?mentioning

confidence: 99%

A survey on algorithms for intelligent computing and smart city applications

Tong

Feng

Yan

et al. 2021

Big Data Min. Anal.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Task scheduling and resource allocation are two sequential processes of cloud computing system [9]. The essence of task scheduling is to sort different task streams from different users.…”

Section: System Modelmentioning

confidence: 99%

“…According to the above explanation of priority constraints and the morphological characteristics of Fig. 2, the task flow with priority constraints can be represented by DAG [9]. DAG can be defined as:…”

Section: Dag Priority Constraintmentioning

confidence: 99%

“…According to [9], parameters and variables are defined as follows: T represents the set of all tasks, where T = T i |i = 1, 2, ..., n , in which T i represents a task in DAG, n is the number of tasks. E is the set of edges between tasks, where E = E ij = T i , T j |T i , T j ∈ T, ij , in which E ij is the edge between T i and.…”

Section: Dag Priority Constraintmentioning

confidence: 99%

“…Therefore, HEFT is improved to have multi-objective optimization scheduling capabilities. According to [9], the rank value is composed of calculation cost and communication cost, both of which only consider the factors of makespan index. Consider incorporating energy consumption and QoS into the calculation of rank, as shown in (11):…”

Section: Eheft-rmentioning

confidence: 99%

See 2 more Smart Citations

EHEFT-R: multi-objective task scheduling scheme in cloud computing

Zhang

Sun

2021

Complex Intell. Syst.

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In cloud computing, task scheduling and resource allocation are the two core issues of the IaaS layer. Efficient task scheduling algorithm can improve the matching efficiency between tasks and resources. In this paper, an enhanced heterogeneous earliest finish time based on rule (EHEFT-R) task scheduling algorithm is proposed to optimize task execution efficiency, quality of service (QoS) and energy consumption. In EHEFT-R, ordering rules based on priority constraints are used to optimize the quality of the initial solution, and the enhanced heterogeneous earliest finish time (HEFT) algorithm is used to ensure the global performance of the solution space. Simulation experiments verify the effectiveness and superiority of EHEFT-R.

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A cooperative multi‐agent offline learning algorithm to scheduling IoT workflows in the cloud computing environment

Gholami

Rezvan

2022

Concurrency and Computation

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Regarding the problem of workflow scheduling in cloud environments, users want the workflow to be processed at a suitable time while cloud providers want to increase resource utilization. This article proposes a cooperative multi-agent offline learning algorithm called CMOL for minimizing makespan and energy consumption. This algorithm schedules a workflow that is represented by a directed acyclic graph (DAG) and assigns them to virtual machines (VMs). Multiple parallel agents interact and cooperate based on an algorithm in three steps of research, improvement, and selection to meet the imposed constraints of deadline and energy. Depending on the number of DAG levels, there is the same number of specialist agents who use strategies to create a Pareto feasible solution and simultaneously gain experience in the first two steps. The parallel agents exploit the extracted knowledge to improve the solution obtained by ensembling their experience in the selection step. To compare the efficiency of CMOL, two algorithms based on multi-agent systems and one algorithm based on single-agent are developed. The performance of the four algorithms is investigated on different real-world workflows and compared on various sizes. Computational results reveal the competitiveness of CMOL and its relative superiority compared with others.

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QL-HEFT: a novel machine learning scheduling scheme base on cloud computing environment

Cited by 85 publications

References 43 publications

A survey on algorithms for intelligent computing and smart city applications

A survey on algorithms for intelligent computing and smart city applications

EHEFT-R: multi-objective task scheduling scheme in cloud computing

A cooperative multi‐agent offline learning algorithm to scheduling IoT workflows in the cloud computing environment

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