Real-Time Multiple-Workflow Scheduling in Cloud Environments

Ma, Xiaojin; Xu, Huahu; Gao, Honghao; Bian, Minjie

doi:10.1109/tnsm.2021.3125395

Cited by 86 publications

(33 citation statements)

References 43 publications

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“…Besides, their costs are closely related to application fields, including aerospace applications, high‐precision medical equipment, and Internet applications in the financial (H. Gao et al, 2021). According to the experience of software development and testing, software testers and developers need to manually check and confirm the results of defect prediction, where manual code inspections are quite labour‐intensive (X. Ma et al, 2021; Shull et al, 2002a). Depending on the review methods, 8–20 lines of code can be inspected per minute per reviewer, where a review team consists of four or six members (Bener et al, 2015; Shull et al, 2002b).…”

Section: Discussionmentioning

confidence: 99%

Eliminating the high false‐positive rate in defect prediction through BayesNet with adjustable weight

et al. 2022

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In defect prediction, a high false‐positive rate (FPR) caused by class imbalance not only increases the workload of testing and development but also consumes unnecessary costs. Many defect models against class imbalance have been proposed to improve the accuracy of defect prediction, but their ability to reduce FPR is unclear. To solve these problems, we first proposed a BayesNet with adjustable weights, called WBN, to reduce the FPR in software defect prediction, which is an algorithm independent of data preprocessing techniques. The mechanism of our WBN is to change the sampling probability of the misclassified instances when training the defect model, making the BayesNet model focus more on false alarm instances. And then, we investigate the FPR of five mainstream defect models for solving class imbalance and select them as comparison models to test the validity of our methods. The experimental result on eight open‐source projects shows that a) our WBN, in in‐version defect prediction (IVDP) and cross‐version defect prediction (CVDP), effectively reduces FPR with means of 0.384 and 0.322, respectively; b) compared with improved subclass discriminant analysis (ISDA) that is the lowest FPR in all control models, our WBN not only reduced the FPR but maintained recall whose mean value was 0.797, whereas ISDA did not, with an average recall of only 0.397; c) our WBN, in CVDP, not only reduces FPR, but also has significant superiority over five control defect models and baseline. Besides, we also found that the class imbalance difference between the test set and the training set has an impact on CVDP performance, recommending that practitioners choose the best dataset for CVDP from the defect data of the historical version through special technology.

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Section: Discussionmentioning

confidence: 99%

Eliminating the high false‐positive rate in defect prediction through BayesNet with adjustable weight

et al. 2022

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“…[24] proposed an approach to optimizing virtual machine allocation strategy for cloud data centres and Ma et al. [25] proposed a real‐time multiple‐workflow scheduling method in a cloud environment. These methods improved the efficiency of processing big data during dealing with passenger flow source data.…”

Section: Related Workmentioning

confidence: 99%

Short‐term passenger flow forecasting using CEEMDAN meshed CNN‐LSTM‐attention model under wireless sensor network

2022

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For a long time, the accurate prediction of passenger flow can provide early warning information for various industries such as the public service industry, tourism industry, and industrial business, thus opportunely arranging passengers and providing homologous services to relieve the overloading of places and the accidents caused by overcrowding of people. In recent years, by using the wireless sensor network to sense the passenger data in advance, the technique of machine learning and neural networks has been utilized to assist the short‐term passenger flow prediction. In this study, building on convolutional neural network (CNN) and long short‐term memory network (LSTM), a complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and attention‐based CNN‐LSTM network to extract both temporal and spatial characteristics of passenger flow data, is proposed. Moreover, the problem of the inaccuracy of the noise part is properly solved by adding the CEEMDAN algorithm to the input layer. With the proposed network structure, the CNN‐LSTM network is replaced with the Conv‐LSTM network to reduce the information loss and get a further performance improvement. The result shows that 39% performance improvement can be achieved than the case with a single LSTM network, and 28% performance improvement can be achieved than the CNN‐LSTM network.

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“…In (11) and ( 12), the optimal resource allocation solutions fa * n,u,l , p * n,u,l g are determined by the values of dual variables, which cannot be derived directly. In order to obtain the optimal RB and power allocation solutions, we resort to dual decomposition method and derive the optimal resource allocation solutions by updating dual variables.…”

Section: Energy Efficient Resource Allocation Solutionsmentioning

confidence: 99%

Energy‐efficient resource allocation in heterogeneous networks under different backhaul capacity constraints

Zheng

2022

Int J Communication

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Summary In heterogeneous networks (HetNets), sharply increasing data traffic exacerbates the rapid growth in the network energy consumption and at the same time brings tremendous pressure on the backhaul links. Aiming at improving the network energy efficiency (EE) and relieving backhaul data volume traffic pressure, in this paper, we investigate energy efficient resource allocation which restricts small cell data traffic by imposing different kinds of backhaul capacity constraints (BCCs) on small cells, including small cell BCC (SBCC) and macrocell BCC (MBCC). We formulate the joint resource block (RB) and power allocation problem as a fractional programming problem. By using variable relaxation and replacement, we convert this nonconvex optimization problem into a concave–convex fractional programming problem, which can further be converted into a parametric convex problem and solved by convex optimization theory. Based on the Dinkelbach and dual decomposition methods, we propose iterative resource allocation algorithms under different BCCs, where RB and power are jointly allocated among users in each cell. Numerical results reveal that the proposed algorithms have good convergence performance and also evaluates the impact of both SBCC and MBCC on network performance.

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Real-Time Multiple-Workflow Scheduling in Cloud Environments

Cited by 86 publications

References 43 publications

Eliminating the high false‐positive rate in defect prediction through BayesNet with adjustable weight

Eliminating the high false‐positive rate in defect prediction through BayesNet with adjustable weight

Short‐term passenger flow forecasting using CEEMDAN meshed CNN‐LSTM‐attention model under wireless sensor network

Energy‐efficient resource allocation in heterogeneous networks under different backhaul capacity constraints

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