A Microservices Model to Enhance the Availability of Data for Building Energy Efficiency Management Services

Jarwar, Muhammad Aslam; Ali, Sajjad; Chong, Ilyoung

doi:10.3390/en12030360

Cited by 8 publications

(7 citation statements)

References 37 publications

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“…The work by Jawar et al [20] highlights that in the energy-data management infrastructure supported by IoT, objects from various energy generation and consumption terminals in buildings produce abundant data. The authors propose using data processing microservice modelling to increase data availability and provide service capabilities through microservices for energy efficiency management.…”

Section: Contextmentioning

confidence: 99%

“…However, it can be affected by energy consumption, especially in systems that have high processing demands. The work Jawar et al [20] suggests using data processing microservices modelling to increase data availability and expose capabilities of services with microservices for energy efficiency management. Below, metric macros are discussed in more detail.…”

Section: Number Of Papersmentioning

confidence: 99%

“…Through modeling, some other works verified the reliability of the [18] [19]. Multiple studies have indicated that microservices architectures can result in high energy costs for maintaining availability [20], scalability [21] [22], performance [23], reliability [18], and sustainability [24]. Such studies bring up important issues, such as the objectives and different types of energy consumption architecture in microservices.…”

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confidence: 96%

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Energy Consumption in Microservices Architectures: A Systematic Literature Review

Araújo,

Sabino,

Lima

et al. 2023

Preprint

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Cloud computing is a paradigm that delivers technology resources on demand through service virtualization. Virtualization allows sharing of hardware by applications compartmentalized into multiple independent modules. Microservices architecture uses the ability of virtualization to adopt a software architecture approach based on building applications composed of several interdependent and loosely coupled modules. However, microservices architectures can incur large energy costs to achieve the required levels of performance and availability. Several research topics related to energy consumption in microservice architectures have already been explored, such as elasticity, reliability, performance, and availability. However, given the variety of problems and solutions, identifying research trends and gaps related to microservices is complex. We aim to systematically identify, rank, and compare existing research trends and gaps around energy consumption in microservices. This paper presents a systematic review of energy consumption in microservices architectures. This work also provides a list of references to find the mapped works, thus being able to guide searches more safely. paper selection started with 3625 papers, followed by three refinement processes in which 37 papers were selected for a full review. The remaining 37 papers were cataloged and classified according to some criteria, such as metrics, evaluation method and type of architecture. Thus, the review revealed research gaps and trends in energy consumption in microservices architectures. In addition, significant research challenges and directions were identified. The research challenges are presented based on the metrics that guide each work in this systematic review: performance, elasticity, scalability, reliability, sustainability, and availability.

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Section: Contextmentioning

confidence: 99%

Section: Number Of Papersmentioning

confidence: 99%

mentioning

confidence: 96%

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Energy Consumption in Microservices Architectures: A Systematic Literature Review

Araújo,

Sabino,

Lima

et al. 2023

Preprint

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“…The Web of Objects (WoO) is supported by inter-operable microservices and the granularity of heterogeneous objects as well as virtualization through virtual objects composites. To implement the IoT of cross-domain applications, Jarwar et al introduced a WoO enabled inter-operable micro-services architecture and demonstrated the implementation using a use case [13]. Moreover, the IoT dynamic environment behavior requires them to be able to evolve and scale over time, adopting novel technologies and various requirements.…”

Section: Introductionmentioning

confidence: 99%

A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks

2020

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In recent years, scarce water resources became one of the main problems that endanger human species existence and the advancement of any nation. In this research, smart water meters were implemented, distributed, and installed in a regional area in Cairo while data were collected at uniform intervals then sent to the cloud instantly. The solution paradigm uses an Internet of Things (IoT) based on micro-services and containers. The design incorporates real-time streaming and infrastructure performance optimization to store data. A second layer to analyze the acquired data was used to model water consumption using Long Short-Term Memory (LSTM). The designed LSTM is validated and tested to be utilized in the forecast of future water demand. Moreover, two alternative machine learning methods, namely Support Vector Regression and Random Forest commonly utilized in time series forecasting applications, were used for a comparative analysis of which LSTM has proven to be superior. The proper integration of the system elements is the key to the proposed system success. Based on the success of the designed system, it can be applicable on a national scale. That can enable the optimal management of consumers' demand and improve water infrastructure utilization. The proposed paradigm presents a testbed for various scenarios that can be used in water resources management.

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“…It fosters to analyze a very huge amount of data using microservices and VOs and CVOs ontologies [21]. The intrinsic and contextual data quality assessment processing services are distributed and scaled into many microservices to increase the availability of quality data for the semantic data applications [22,23]. The proposed model supports to analyze data quality with intrinsic and contextual metric parameters at the data acquisition, assessment and service level.…”

Section: Introductionmentioning

confidence: 99%

Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application

Jarwar

Chong²

2020

Applied Sciences

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Due to the convergence of advanced technologies such as the Internet of Things, Artificial Intelligence, and Big Data, a healthcare platform accumulates data in a huge quantity from several heterogeneous sources. The adequate usage of this data may increase the impact of and improve the healthcare service quality; however, the quality of the data may be questionable. Assessing the quality of the data for the task in hand may reduce the associated risks, and increase the confidence of the data usability. To overcome the aforementioned challenges, this paper presents the web objects based contextual data quality assessment model with enhanced classification metric parameters. A semantic ontology of virtual objects, composite virtual objects, and services is also proposed for the parameterization of contextual data quality assessment of web objects data. The novelty of this article is the provision of contextual data quality assessment mechanisms at the data acquisition, assessment, and service level for the web objects enabled semantic data applications. To evaluate the proposed data quality assessment mechanism, web objects enabled affective stress and teens’ mood care semantic data applications are designed, and a deep data quality learning model is developed. The findings of the proposed approach reveal that, once a data quality assessment model is trained on web objects enabled healthcare semantic data, it could be used to classify the incoming data quality in various contextual data quality metric parameters. Moreover, the data quality assessment mechanism presented in this paper can be used to other application domains by incorporating data quality analysis requirements ontology.

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A Microservices Model to Enhance the Availability of Data for Building Energy Efficiency Management Services

Cited by 8 publications

References 37 publications

Energy Consumption in Microservices Architectures: A Systematic Literature Review

Energy Consumption in Microservices Architectures: A Systematic Literature Review

A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks

Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application

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