A Pretreatment Workflow Scheduling Approach for Big Data Applications in Multicloud Environments

Lin, Bing; Guo, Wenzhong; Xiong, Naixue; Chen, Guolong; Vasilakos, Athanasios V.; Zhang, Hong

doi:10.1109/tnsm.2016.2554143

Cited by 130 publications

(58 citation statements)

References 34 publications

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“…(3) Both w and R are modified, results of the shortest Euclidean distance in the same range are first selected, and the one with the minimum cost of these results is the final result. When the result contains multiple types of results, that is, it includes condition(a): only narrowing R, and condition(b): narrowing R and modifying w. We obtained two final solutions according to (2) and (3), respectively.…”

Section: Narrowing the Orthogonal Rangementioning

confidence: 99%

“…The main steps are: (1) Expand R to R , and then calculate whether w ∈ SP(R ). (2) If so, the why-not question is solved. If not, we adopt the MWR algorithm.…”

Section: Effectivenessmentioning

confidence: 99%

“…However, the data themselves have no value. After collection, storage [1], processing [2] and analysis, they generate value. For example, in intelligent communication systems, users use wireless sensors [3][4][5][6][7] and mobile devices to collect data, and then analyze the data to provide decision-making basis for programs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Researching Why-Not Questions in Skyline Query Based on Orthogonal Range

Sun

Liang

et al. 2020

Electronics

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This paper aims to answer “why-not” questions in skyline queries based on the orthogonal query range (i.e., ORSQ). These queries retrieve skyline points within a rectangular query range, which improves query efficiency. Answering why-not questions in ORSQ can help users analyze query results and make decisions. We discuss the causes of why-not questions in ORSQ. Then, we outline how to modify the why-not point and the orthogonal query range so that the why-not point is included in the result of the skyline query based on the orthogonal range. When the why-not point is in the orthogonal range, we show how to modify the why-not point and narrow the orthogonal range. We also present how to expand the orthogonal range when the why-not point is not in the orthogonal range. We effectively combine query refinement and data modification techniques to produce meaningful answers. The experimental results demonstrate that the proposed algorithms have high-quality explanations for why-not questions in ORSQ in the real and synthetic datasets.

show abstract

Section: Narrowing the Orthogonal Rangementioning

confidence: 99%

“…The main steps are: (1) Expand R to R , and then calculate whether w ∈ SP(R ). (2) If so, the why-not question is solved. If not, we adopt the MWR algorithm.…”

Section: Effectivenessmentioning

confidence: 99%

See 1 more Smart Citation

Researching Why-Not Questions in Skyline Query Based on Orthogonal Range

Sun

Liang

et al. 2020

Electronics

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show abstract

“…Mohan [7] proposed a scheduling algorithm in heterogeneous cloud computing environment which minimize the makespan of workflow. B. Lin [8] proposed a scheduling algorithm for big data application in Cloud environments. Guo-Ning and Ting-Lei [9] represented an optimized algorithm for task scheduling based on Hybrid Genetic Algorithms.…”

Section: Related Workmentioning

confidence: 99%

A Branch and Bound Algorithm for Workflow Scheduling

Toan

Lộc

2018

JST

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Abstract. Nowadays, people are connected to the Internet and use different Cloud solutions to store, process and deliver data. The Cloud consists of a collection of virtual servers that promise to provision on-demand computational and storage resources when needed. Workflow data is becoming an ubiquitous term in both science and technology and there is a strong need for new tools and techniques to process and analyze large-scale complex datasets that are growing exponentially. Scientific workflow is a sequence of connected tasks with large data transfer from parent task to children tasks. Workflow scheduling is the activity of assigning tasks to execution on servers and satisfying resource constraints and this is an NP-hard problem. In this paper, we propose a scheduling algorithm for workflow data that is derived from the Branch and Bound Algorithm.

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“…The massive Internet of things (IoTs) [1] which provide big data for cloud computing [2][3][4][5][6] and traditional networks [7] have been applied to many fields in reality. As an important part of the IoT [8,9], the wireless sensor network (WSN) [10][11][12] has the capabilities of data collection [13][14][15], data storage, and wireless communication [16,17].…”

Section: Introductionmentioning

confidence: 99%

An algorithm to optimize deployment of charging base stations for WRSN

Wan

Cheng

et al. 2019

J Wireless Com Network

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Optimizing deployment of charging base stations in wireless rechargeable sensor networks can considerably reduce the cost. Previously, the charging base stations are simply installed at some fixed special points (e.g., the grid points) after partitioning the area distributed by sensor nodes. In this paper, a new algorithm of planning the charging base stations is proposed based on the greedy algorithm and the location relationship of the sensor nodes. The proposed algorithm exploits the local search ability and avoids falling into an exponential increase of the number of the charging base stations (i.e., combinatorial explosion). The simulated results show that the proposed algorithm can result in less number and flexible deployed locations of the charging base stations. In addition, this algorithm provides a novel solution for the point coverage problems.

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A Pretreatment Workflow Scheduling Approach for Big Data Applications in Multicloud Environments

Cited by 130 publications

References 34 publications

Researching Why-Not Questions in Skyline Query Based on Orthogonal Range

Researching Why-Not Questions in Skyline Query Based on Orthogonal Range

A Branch and Bound Algorithm for Workflow Scheduling

An algorithm to optimize deployment of charging base stations for WRSN

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