On revisiting energy and performance in microservices applications: A cloud elasticity-driven approach

Nardin, Igor Fontana De; Righi, Rodrigo da Rosa; Lopes, Thiago Roberto Lima; Costa, Cristiano André da; Yeom, Heon Y.; Köstler, Harald

doi:10.1016/j.parco.2021.102858

Cited by 11 publications

(8 citation statements)

References 17 publications

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“…Elasticity is a metric that efficiently adjusts resource usage as work demand changes, thereby improving energy efficiency. The works Fontana by Nardin et al [46] and Ferreira et al [37] explore how to improve energy consumption by periodically manipulating the most appropriate amount of resources to run an application. Both works are focused on providing a model to efficiently manage resources and minimize energy consumption.…”

Section: Number Of Papersmentioning

confidence: 99%

“…Microservices architectures allow you to better manage resources through service decomposition. Although, elasticity adjustment is not a simple task and requires several factors, such as user experience, application, parameter architecture, and modelling [46,53,54]. Resource management needs to be implemented efficiently and consistently to ensure that the application can adapt to changing load demands efficiently.…”

Section: Energy Consumptionmentioning

confidence: 99%

“…By utilizing microservices, applications can be distributed across different cloud providers independently, while creating the impression that they are deployed as a unified service. This has led to the increased adoption of microservices, particularly within large corporations [12,13]. Prominent organizations like Netflix, Amazon, and eBay have successfully employed microservices in their applications.…”

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confidence: 99%

“…However, microservices architectures also come with their fair share of challenges, particularly concerning energy efficiency and sustainability. These architectures can impose significant energy costs, which can pose a significant problem [12,13]. The demand for high energy consumption to keep microservices systems available can be a considerable drawback.…”

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confidence: 99%

“…The study by Bouaouda et al[42] discusses energy consumption performance in a data centre using the CloudSim simulator, emphasizing placing containers on hosts, comparing the performance of two algorithms to minimize energy consumption in Cloud systems.Elasticity is a metric that efficiently adjusts resource usage as work demand changes, thereby improving energy efficiency. The works Fontana by Nardin et al[46] and Ferreira et al[37] explore how to improve energy consumption by periodically manipulating the most appropriate amount of resources to run an application. Both works are focused on providing a model to efficiently manage resources and minimize energy consumption.…”

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confidence: 99%

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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: Number Of Papersmentioning

confidence: 99%

Section: Energy Consumptionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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