Empirical Performance and Energy Consumption Evaluation of Container Solutions on Resource Constrained IoT Gateways

Raza, Syed M.; Jeong, Jaeyeop; Kim, Moonseong; Kang, Byungseok; Choo, Hyunseung

doi:10.3390/s21041378

Cited by 11 publications

(4 citation statements)

References 31 publications

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“…The work by Ferreira et al [37] presents a middleware for energy management based on edge computing for smart homes. The work by Raza et al [38] aims to establish guidelines and identify critical factors for implementing containerized services on resource-constrained IoT devices.…”

Section: Contextmentioning

confidence: 99%

“…Hybrid Computing has proved useful for the Deployment Model of containerized services on IoT devices. The work by Raza et al [38] aimed to identify key factors in the Deployment Model of containerized services on IoT devices with limited resources. The limited resources cited are computing and energy that hinder the scalability and Deployment Model of concurrent services.…”

Section: Number Of Papersmentioning

confidence: 99%

See 1 more Smart Citation

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%

Energy Consumption in Microservices Architectures: A Systematic Literature Review

Araújo,

Sabino,

Lima

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The experiments employed a Raspberry Pi 4 Model B with a 1.5 GHz 64-bit quad-core Arm Cortex-A72 CPU and 4 GB of RAM. We chose the Raspberry Pi due to its wide use in the literature of IoT gateways [37,38].…”

Section: Execution Time and Memory Usage Analysismentioning

confidence: 99%

Missing Data Imputation in Internet of Things Gateways

2021

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In an Internet of Things (IoT) environment, sensors collect and send data to application servers through IoT gateways. However, these data may be missing values due to networking problems or sensor malfunction, which reduces applications’ reliability. This work proposes a mechanism to predict and impute missing data in IoT gateways to achieve greater autonomy at the network edge. These gateways typically have limited computing resources. Therefore, the missing data imputation methods must be simple and provide good results. Thus, this work presents two regression models based on neural networks to impute missing data in IoT gateways. In addition to the prediction quality, we analyzed both the execution time and the amount of memory used. We validated our models using six years of weather data from Rio de Janeiro, varying the missing data percentages. The results show that the neural network regression models perform better than the other imputation methods analyzed, based on the averages and repetition of previous values, for all missing data percentages. In addition, the neural network models present a short execution time and need less than 140 KiB of memory, which allows them to run on IoT gateways.

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“…This method of sensor deployment offers greater control over resource constraints. Energy consumption optimization is typically the primary metric in many deterministic deployment strategies [2][3][4]. A common example of this type is grid-based deployment, which includes hexagonal, square, and equilateral triangular patterns.…”

Section: Introductionmentioning

confidence: 99%

Resilient Localization and Coverage in the Internet of Things

Mtawa

Hassanein

2023

Electronics

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The proliferation of the Internet of Things (IoT) has revolutionized traditional services, giving rise to emerging smart infrastructures by connecting the physical and digital worlds. Sensory data is essential in IoT-based systems for providing context-aware and location-based services. Hence, the accurate localization of IoT devices is paramount. Anchor misplacement can significantly affect location information and coverage services in IoT. We study the effect of anchor misplacement in typical IoT settings where sensors are randomly deployed, can be mobile, and may belong to multiple providers. We identify sensing coverage holes formed by anchor misplacement and analyze their presence and impact. To mitigate the impact of anchor misplacement on network reliability, we propose a framework to identify the affected sensor nodes and then identify and remove misplaced anchor nodes. The validity of our approach is verified, and its effectiveness is demonstrated by several experiments with different network topologies and parameters. Our results are promising and can be utilized in multiple coverage applications, such as smart agriculture systems and habitat monitoring, regardless of the sensors or deployment types. It also sheds light on best practices and methods for a reliable design of IoT-based systems.

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Empirical Performance and Energy Consumption Evaluation of Container Solutions on Resource Constrained IoT Gateways

Cited by 11 publications

References 31 publications

Energy Consumption in Microservices Architectures: A Systematic Literature Review

Energy Consumption in Microservices Architectures: A Systematic Literature Review

Missing Data Imputation in Internet of Things Gateways

Resilient Localization and Coverage in the Internet of Things

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