On the use of big data frameworks for big service composition

Sellami, Mokhtar; Mezni, Haithem; Hacid, Mohand Said

doi:10.1016/j.jnca.2020.102732

Cited by 21 publications

(31 citation statements)

References 28 publications

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“…Marcel et al 21 presented a generalized multi‐objective differential evolution for QoS‐aware web service composition. Sellami et al 2 proposed a fuzzy Relational Concept Analysis (fuzzy RCA) method to build the big service composition. Gharbi et al 22 introduced a RCA and composite particle swarm optimization (CPSO) algorithm to solve big service composition.…”

Section: Related Workmentioning

confidence: 99%

“…The Fourth Industrial Revolution (or Industry 4.0) is the ongoing automation of traditional manufacturing and industrial practices, using these modern smart technologies. Big services are collections of interrelated web services across the virtual and physical domains of Industry 4.0, processing Big Data 1,2 . Big services are integrated from various domains to develop a composite service that addresses the requirements of a customer 3 …”

Section: Introductionmentioning

confidence: 99%

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QoS‐aware big service composition using distributed co‐evolutionary algorithm

Dutta

Jatoth

Gangadharan

et al. 2021

Concurrency and Computation

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Big services are collections of interrelated web services across virtual and physical domains, processing Big Data. Existing service selection and composition algorithms fail to achieve the global optimum solution in a reasonable time. In this paper, we design an efficient quality of service-aware big service composition methodology using a distributed co-evolutionary algorithm. In our proposed model, we develop a distributed NSGA-III for finding the optimal Pareto front and a distributed multi-objective Jaya algorithm for enhancing the diversity of solutions. The distributed co-evolutionary algorithm finds the near-optimal solution in a fast and scalable way. K E Y W O R D Sbig services, distributed co-evolutionary algorithm, distributed Jaya, distributed NSGA-III, quality of service, service composition INTRODUCTIONWith the onset of cloud computing, Internet of Things (IoT), and machine learning/ deep learning, manufacturing industries are undergoing a drastic change. Modern industries are incorporating these new technologies to achieve better efficiency in manufacturing products and services, with an improvement in quality control and a reduction in production costs. The Fourth Industrial Revolution (or Industry 4.0) is the ongoing automation of traditional manufacturing and industrial practices, using these modern smart technologies. Big services are collections of interrelated web services across the virtual and physical domains of Industry 4.0, processing Big Data. 1,2 Big services are integrated from various domains to develop a composite service that addresses the requirements of a customer. 3 Consider a fictitious company that manufactures electric vehicles. The company uses IoT, cloud computing, and machine learning/ deep learning to test for and predict possible failures and for tweaking or designing new engine parts, suspension units, and many more. Let us consider that the company has two divisions: design analysis unit and production unit. The design analysis unit analyzes the performances of the electric vehicles and suggests new design models. The lessons learned from the design analysis unit are incorporated in the design and manufacturing of electric vehicles by the production unit. Assume that both the design analysis unit and the production unit use a similar workflow. We can imagine two separate sets of service composition requirements: The design analysis unit requires high performance in terms of response time and throughput, high security, and with no restrictions on cost. The production unit has to keep the cost low and maintain the desirable quality of service (QoS) parameters and changes with respect to supply and demand throughout the year.In this scenario, we need to execute the service composition process repeatedly whenever there is a change in the requirements, with certain changes in the fitness function. This becomes computationally expensive as the number of services grows, being classified as NP-hard. 4,5 Co-evolutionary approaches offer promising solutions in this case and man...

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

QoS‐aware big service composition using distributed co‐evolutionary algorithm

Dutta

Jatoth

Gangadharan

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Big data management also presents challenges to service composition. M. Sellami et al [33] proposed a scalable approach for big service composition by considering both the quality of reused services (QoS) and the quality of their consumed data sources (QoD). The work can be divided into four steps: (1) quantifying the data breaches using L-Severity metrics; (2) building a repository of big services into a lattice family; (3) clustering services and data sources based on various criteria; (4) parsing the lattice family to select and compose high-quality and secure big services in a parallel fashion.…”

Section: Related Workmentioning

confidence: 99%

An Energy-Aware Multi-Target Service Composition Method in a Multi-Cloud Environment

Yin

Hao

2020

IEEE Access

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Service composition is always employed to enhance function by composing atomic services that reside in different clouds together. In a multi-cloud environment (MCE), the time and energy consumption of service composition may differ because of the dynamic of the network. The network varies between clouds, or even between composite requests and clouds. A cloud provides a limited variety of services, which makes a composite request that requires multiple clouds to jointly compose all atomic services together. All of this makes it more difficult than ever to implement energy-aware service composite in a MCE. In this paper, we model service composition in the MCE and propose an energy-aware multiple targets service composition method for executing atomic services in a composite request. Since the proposed method gets the scheduling by searching all possible mappings between service request blocks (a set of requests to multiple atomic services) and clouds, we call it "All-Search". To reduce the complexity of All-Search, we propose "Itersplit", a heuristics algorithm that can achieve multiple targets. The performance of All-Search, Cloud-SEnergy, and Itersplit is tested through simulations. The results in multiple aspects indicate that Itersplit performs better than other algorithms when we take Itersplit-20%.

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“…To implement big data, architectures, frameworks, and processes of data analytics have been applied in many information systems research studies (Campos, Sharma, Gabiria, Jantunen, & Baglee, 2017;Fahmideh & Beydoun, 2019;Li, Zhang, & Wang, 2013;Sellami, Mezni, & Hacid, 2020). This paper aims to study and analyse big data architecture for a public hospital in Thailand.…”

Section: Introductionmentioning

confidence: 99%

The Analysis of Big Data Architecture for Healthcare Service, a Case Study of a Public Hospital

Noonpakdee

2022

IJMKL

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Big data is one of the promising technologies which helps the healthcare sector improve patient experience, reduce the cost of treatment, predict epidemics, and provide competitive healthcare services. This research aims to study and analyse big data architecture for a public hospital in Thailand.Study design/methodology/approach: This research employs a qualitative approach. For data collection and analysis, document analysis is applied as a qualitative method to obtain empirical data. In addition, in-depth interviews of the IT manager, doctor, and staff are conducted to confirm the big data architecture and examine issues in a case study public hospital.Findings: According to the study, big data architecture for the public hospital comprises data acquisition, data integration, data analytics, data visualisation, a health data centre on a cloud, and data governance.Originality/value: The architecture components and analysis can be applied as a guideline for big data processing and analytics to create a solution for implementing big data in healthcare effectively.

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On the use of big data frameworks for big service composition

Cited by 21 publications

References 28 publications

QoS‐aware big service composition using distributed co‐evolutionary algorithm

QoS‐aware big service composition using distributed co‐evolutionary algorithm

An Energy-Aware Multi-Target Service Composition Method in a Multi-Cloud Environment

The Analysis of Big Data Architecture for Healthcare Service, a Case Study of a Public Hospital

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