A Survey on Vertical and Horizontal Scaling Platforms for Big Data Analytics

Ali, Ahmed Hussein; Abdullah, Mahmood Zaki

doi:10.30880/ijie.2019.11.06.015

Cited by 41 publications

(23 citation statements)

References 33 publications

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“…With extensibility comes scalability. Big Data architectures support horizontal scaling [39]. Since the functionality was split into microservices, with the dependencies and components being adopted, not only the architecture is horizontally and vertically scalable, but also distinct parts of it can be adjusted to the respective circumstances and needs [40].…”

Section: Advantages and Challengesmentioning

confidence: 99%

Towards a Concept for Building a Big Data Architecture with Microservices

et al. 2021

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Microservices and Big Data are renowned hot topics in computer science that have gained a lot of hype. While the use of microservices is an approach that is used in modern software development to increase flexibility, Big Data allows organizations to turn today’s information deluge into valuable insights. Many of those Big Data architectures have rather monolithic elements. However, a new trend arises in which monolithic architectures are replaced with more modularized ones, such as microservices. This transformation provides the benefits from microservices such as modularity, evolutionary design and extensibility while maintaining the old monolithic product’s functionality. This is also valid for Big Data architectures. To facilitate the success of this transformation, there are certain beneficial factors. In this paper, those aspects will be presented and the transformation of an exemplary Big Data architecture with somewhat monolithic elements into a microservice favoured one is outlined.

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Section: Advantages and Challengesmentioning

confidence: 99%

Towards a Concept for Building a Big Data Architecture with Microservices

et al. 2021

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“…To maximize data processing efficiency, it requires the stacking of several independent devices. Usually, separate computers run different operating systems [12].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Auto-Scaled Service-Cloud-Based Predictive Workload Modeling and Analysis for Smart Campus System

et al. 2021

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The internet of things is an emerging technology used in cloud computing and provides many services of the cloud. The cloud services users mostly suffer from service delays and disruptions due to service cloud resource management based on vertical and horizontal scalable systems. Adding more resources to a single cloud server is called vertical scaling, and an increasing number of servers is known as horizontal scaling. The service-bursts significantly impact the vertical scaled environment where the scaleup degrades the service quality and users' trust after reaching the server's maximum capacity. Besides, the horizontally scaled environment, though being resilient, is cost-inefficient. It is also hard to detect and manage bursts online to sustain application efficiency for complex workloads. Burst detection in real-time workloads is a complicated issue because even in the presence of auto-scaling methods, it can dramatically degrade the application's efficiency. This research study presents a new bursts-aware auto-scaling approach that detects bursts in dynamic workloads using resource estimation, decision-making scaling, and workload forecasting while reducing response time. This study proposes a hybrid auto-scaled service cloud model that ensures the best approximation of vertical and horizontal scalable systems to ensure Quality of Service (QoS) for smart campus-based applications. This study carries out the workload prediction and auto-scaling employing an ensemble algorithm. The model pre-scales the scalable vertical system by leveraging the service-load predictive modeling using an ensemble classification of defined workload estimation. The prediction of the upcoming workload helped scale-up the system, and auto-scaling dynamically scaled the assigned resources to many users' service requests. The proposed model efficiently managed service-bursts by addressing load balancing challenges through horizontal auto-scaling to ensure application consistency and service availability. The study simulated the smart campus environment model to monitor the timestamped diverse service-requests appearing with different workloads.INDEX TERMS Auto-scaling, cloud computing, horizontal scalability, internet of things, predictive modeling, quality of service (QoS), smart campus, vertical scalability, workloads

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“…Among the major signs of BC are changed and increase in the increase normal size and shape of the breast, pain in the breast, inflammation of the affected or all parts of the breast, varying breast skin colors, presence of a lump beneath the arm area, etc. [2][3][4]. The World Health Organization (WHO) [5] reported that there are around 1.2 million cases of BC diagnosed in women every year.…”

Section: Introductionmentioning

confidence: 99%

A new model for large dataset dimensionality reduction based on teaching learning-based optimization and logistic regression

et al. 2020

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One of the human diseases with a high rate of mortality each year is breast cancer (BC). Among all the forms of cancer, BC is the commonest cause of death among women globally. Some of the effective ways of data classification are data mining and classification methods. These methods are particularly efficient in the medical field due to the presence of irrelevant and redundant attributes in medical datasets. Such redundant attributes are not needed to obtain an accurate estimation of disease diagnosis. Teaching learning-based optimization (TLBO) is a new metaheuristic that has been successfully applied to several intractable optimization problems in recent years. This paper presents the use of a multi-objective TLBO algorithm for the selection of feature subsets in automatic BC diagnosis. For the classification task in this work, the logistic regression (LR) method was deployed. From the results, the projected method produced better BC dataset classification accuracy (classified into malignant and benign). This result showed that the projected TLBO is an efficient features optimization technique for sustaining data-based decision-making systems.

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A Survey on Vertical and Horizontal Scaling Platforms for Big Data Analytics

Cited by 41 publications

References 33 publications

Towards a Concept for Building a Big Data Architecture with Microservices

Towards a Concept for Building a Big Data Architecture with Microservices

Hybrid Auto-Scaled Service-Cloud-Based Predictive Workload Modeling and Analysis for Smart Campus System

A new model for large dataset dimensionality reduction based on teaching learning-based optimization and logistic regression

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