A model-driven framework for data-driven applications in serverless cloud computing

Samea, Fatima; Azam, Farooque; Rashid, Muhammad; Anwar, Muhammad Waseem; Butt, Wasi Haider; Muzaffar, Abdul Wahab

doi:10.1371/journal.pone.0237317

Cited by 20 publications

(4 citation statements)

References 43 publications

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“…These data are used by MEC/Cloud servers for knowledge building and decision-making purposes. As mentioned in [21], the core of data-driven tasks is data analysis. Noteworthy, data-driven analytic holds unlimited potential for assisting various domains of real-world scenarios such as dealing with air pollution, climate change, oil spill management, moving target tracking, healthcare monitoring, hazard analysis, real-time monitoring of stochastic damage in aerospace structures, forest fire propagation prediction, volcanic ash propagation, and traffic jams prediction could be precisely predicted according to data that have been collected by edge devises.…”

Section: Data-driven Tasksmentioning

confidence: 99%

Data-Driven Analytics Task Management Reasoning Mechanism in Edge Computing

et al. 2022

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Internet of Things (IoT) applications have led to exploding contextual data for predictive analytics and exploration tasks. Consequently, computationally data-driven tasks at the network edge, such as machine learning models’ training and inference, have become more prevalent. Such tasks require data and resources to be executed at the network edge, while transferring data to Cloud servers negatively affects expected response times and quality of service (QoS). In this paper, we study certain computational offloading techniques in autonomous computing nodes (ANs) at the edge. ANs are distinguished by limited resources that are subject to a variety of constraints that can be violated when executing analytical tasks. In this context, we contribute a task-management mechanism based on approximate fuzzy inference over the popularity of tasks and the percentage of overlapping between the data required by a data-driven task and data available at each AN. Data-driven tasks’ popularity and data availability are fed into a novel two-stages Fuzzy Logic (FL) inference system that determines the probability of either executing tasks locally, offloading them to peer ANs or offloading to Cloud. We showcase that our mechanism efficiently derives such probability per each task, which consequently leads to efficient uncertainty management and optimal actions compared to benchmark models.

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Section: Data-driven Tasksmentioning

confidence: 99%

Data-Driven Analytics Task Management Reasoning Mechanism in Edge Computing

et al. 2022

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“…Data-driven tasks refers to tasks that rely heavily on data generated by smart devices (e.g., sensors, smartphones) to build knowledge and make decisions. As mentioned in [14], the core of data-driven applications is data analysis. Various real-world applications in different domains become data-driven to make precise decisions.…”

Section: A Data-driven Tasksmentioning

confidence: 99%

Data-Driven Analytics Task Management at the Edge: A Fuzzy Reasoning Approach

Aladwani

Alghamdi

Kolomvatsos

et al. 2022

2022 9th International Conference on Future Internet of Things and Cloud (FiCloud)

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Dynamic data-driven applications such as tracking and surveillance have emerged in Internet of Things (IoT) environments. Such applications rely heavily on data generated by connected devices (e.g., sensors). Consequently, leveraging these data in building data-driven predictive analytics tasks improves the Quality of Service (QoS) and, as a result, Quality of Experience (QoE). Such data support various data-driven tasks such as regression and classification. Analytics tasks require data and resources to be executed at the edge since transferring them to the cloud negatively affects response times and QoS. However, the network edge is characterized by limited resources compared to the cloud, being the subject of constraints that are violated upon offloading data-driven tasks to improper edge nodes. We contribute with an analytics task management mechanism based on the context of the requested data, the task delay sensitivity and the VM utilization. We introduce a novel Fuzzy inference mechanism for determining whether data-driven tasks should be executed locally, offloaded to peer edge servers, or sent to cloud. We showcase how our fuzzy reasoning mechanism efficiently derives such decisions by calculating the offloading probability per task. The derived optimal actions are compared against benchmark models in Edge Computing (EC) environments.

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“…It is essential to offer a consistent user experience across different connection types because of the cross-platform characteristic in the Web environment. Also, it is critical to simplify the development process of the data-driven serverless Web application [49]. Thus, we use the method named UMLPMSC (UML Profile for Multi-Cloud Service Configuration) proposed in reference [50] to model the service configuration design requirements at high abstraction level, which is shown in Fig.…”

Section: Experiments and Analysis A System Framework And Service Configurationmentioning

confidence: 99%

A Lightweight Processing Method for Hand-Drawn Pressure-Sensitive Trajectories Oriented Toward Web-Based 3D Modeling

et al. 2021

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Digital geometry processing of hand-drawn pressure-sensitive trajectories oriented toward Web-based three-dimensional (3D) modeling becomes attractive problem. Existing practices usually focus on converting raw trajectories into 3D stroke meshes with consistent line width, which the pressure information can not been fully utilized for denoising, simplification and reflecting realistic graphics of 3D stroke mesh. Also, there are little research on the high quality of trajectory with data transmission fluency and light weight in the Web environment. We propose a lightweight processing method for hand-drawn pressure-sensitive trajectories that is oriented toward Web-based 3D modeling. An angle-chord height united trajectory simplification (ACUTS) algorithm is proposed, in which the discrete points are simplified by comprehensively considering the angle deviation and chord height deviation with the sliding window mechanism. In addition, a lightweight reconstruction algorithm based on pressure -sensitivity for discrete points (LRPDP) is also proposed, in which the pressure value can be fully utilized for simplifying the redundant points while ensuring the reconstructed 3D stroke mesh more realistic. Besides, the experimental results show that compared to some other methods, we perform best in terms of data reduction using compression ratio and reduction percentages. We also perform best in terms of high accuracy of 3D stroke mesh using mean stochastic error distance, local maximum error and total length error. Although the insufficient utilization of the pressure-sensitive information limits our research to some extents, the significance of our research is to provide a new strategy to realize Web-based 3D modeling using hand drawing mode.

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A model-driven framework for data-driven applications in serverless cloud computing

Cited by 20 publications

References 43 publications

Data-Driven Analytics Task Management Reasoning Mechanism in Edge Computing

Data-Driven Analytics Task Management Reasoning Mechanism in Edge Computing

Data-Driven Analytics Task Management at the Edge: A Fuzzy Reasoning Approach

A Lightweight Processing Method for Hand-Drawn Pressure-Sensitive Trajectories Oriented Toward Web-Based 3D Modeling

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