Serverless computing for Internet of Things: A systematic literature review

Cassel, Gustavo André Setti; Rodrigues, Vinicius Facco; Righi, Rodrigo da Rosa; Bez, Marta Rosecler; Nepomuceno, Andressa Cruz; Costa, Cristiano André da

doi:10.1016/j.future.2021.10.020

Cited by 55 publications

(11 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is recommended for IoT manufacturers to upgrade and optimize offloading algorithms while considering a higher amount of properties, such as latency and device robustness, possibly leveraging pretrained machine learning models to enhance accuracy. Serverless computing needs some effort and is beneficial for future research because it is a significant technology for IoT applications on edge, fog and cloud layers (Cassel et al , 2022).…”

Section: Practical Implications and Recommendationsmentioning

confidence: 99%

A systematic and critical review of Internet of Things in contemporary hospitality: a roadmap and avenues for future research

Elkhwesky

Elkhwesky²

2022

IJCHM

View full text Add to dashboard Cite

Purpose Internet of Things (IoT) is a key enabler of big data and it is critical toward a new age of firm's architecture and proper decision-making for the industry. IoT adoption by the hospitality and tourism (H&T) industries is still rare and with contrasting evidence. The purpose of this study is to highlight the concept of IoT, its benefits, its challenges and its applications in the hospitality industry. Design/methodology/approach This study presents an in-depth and critical review of the 78 articles published on IoT in hospitality, spanning more than seven years (from 2015 to March 2022). The eligible 78 articles were collected from the “Web of Science database” and H&T Social Sciences Citation Index-listed journals. Findings This systematic review exhibits that IoT in hospitality has advanced in the past seven years. IoT has brought many advantages and challenges in hospitality. However, hospitality IoT research is scarce, necessitating a profound and more thorough investigation of many significant matters. Practical implications This systematic review provides significant implications and recommendations for hospitality sectors, IoT developers and manufacturers, governments and policymakers. This review ends with an in-depth research agenda with several research questions and a framework to achieve theoretical and empirical advances in future IoT research. Originality/value To the best of the authors’ knowledge, the current research is one of the first to systematically and critically review IoT with its concept, benefits, challenges and applications in a hospitality context. This major review provides a comprehensive idea of the focus of the past studies and what must be concentrated in future research.

show abstract

Section: Practical Implications and Recommendationsmentioning

confidence: 99%

A systematic and critical review of Internet of Things in contemporary hospitality: a roadmap and avenues for future research

Elkhwesky

Elkhwesky²

2022

IJCHM

View full text Add to dashboard Cite

show abstract

“…This means that the MEC infrastructure should be able to allocate the resources for function execution, a much more ine-grained operation than the traditional virtual machine or container virtualization. The literature gives special attention to the case of serverless computing to the edge for IoT [12,65]. It is possible to ind tools for serverless computing on the edge, such as OpenWhisk [103].…”

Section: Implementations and Tools Fine-tuningmentioning

confidence: 99%

On the Edge of the Deployment: A Survey on Multi-access Edge Computing

2022

View full text Add to dashboard Cite

Multi-Access Edge Computing (MEC) attracts much attention from the scientific community due to its scientific, technical, and commercial implications. In particular, the ETSI standard convergence consolidates the discussions around MEC. Still, the existing MEC practical initiatives are incomplete in their majority, hardening or invalidating their effective deployment. To fill this gap, it is essential to understand a series of experimental prototypes, implementations, and deployments. The early implementations can reveal the potential, the limitations, the related technologies, and the development tools for MEC adoption. In this context, this work first brings a discussion on existing MEC initiatives regarding the use cases they target and their vision (i.e., whether they are more network-related or more distributed systems). Second, we survey MEC practical initiatives according to their strategies, including the ETSI MEC standard. Besides, we compare the strategies according to related limitations, impact, and deployment efforts. We also survey the existing tools making MEC systems a reality. Finally, we give hints to issues yet to be addressed in practice. By bringing a better comprehension of MEC initiatives, we believe this survey will help researchers and developers design their own MEC systems or improve and simplify the usability of existing ones.

show abstract

“…They found several techniques to mitigate the challenges related to edge computing i.e.,caching, training, inference, and offloading. Setti Cassel et al [100] analyzed 60 research papers on serverless IoT devices during a systematic literature review. They find that serverless computing is a promising technology for IoT applications that can bring functions closer to the devices to reduce latency and avoid unnecessary energy and resource consumption.…”

Section: B Literature Reviews On Machine Learning and Cloud Computingmentioning

confidence: 99%

Serverless on Machine Learning: A Systematic Mapping Study

2022

View full text Add to dashboard Cite

Machine Learning Operations (MLOps) is an approach to managing the entire lifecycle of a machine learning model. It has evolved over the last years and has started attracting many people in research and businesses in the industry. It supports the development of machine learning (ML) pipelines typical in the phases of data collection, data pre-processing, building datasets, model training, hyperparameters refinement, testing, and deployment to production. This complex pipeline workflow is a tedious process of iterative experimentation. Moreover, cloud computing services provide advanced features for managing ML stages and deploying them efficiently to production. Specifically, serverless computing has been applied in different stages of the machine learning pipeline. However, to the best of our knowledge, it is missing to know the serverless suitability and benefits it can provide to the ML pipeline. In this paper, we provide a systematic mapping study of machine learning systems applied on serverless architecture that include 53 relevant studies. During this study, we focused on (1) exploring the evolution trend and the main venues; (2) determining the researchers' focus and interest in using serverless on machine learning;(3) discussing solutions that serverless computing provides to machine learning. Our results show that serverless usage is growing, and several venues are interested in the topic. In addition, we found that the most widely used serverless provider is AWS Lambda, where the primary application was used in the deployment of the ML model. Additionally, several challenges were explored, such as reducing cost, resource scalability, and reducing latency. We moreover discuss the potential challenges of adopting ML on serverless, such as respecting service level agreement, the cold start problem, security, and privacy. Finally, our contribution provides foundations for future research and applications implying machine learning in serverless computing.

show abstract

Serverless computing for Internet of Things: A systematic literature review

Cited by 55 publications

References 27 publications

A systematic and critical review of Internet of Things in contemporary hospitality: a roadmap and avenues for future research

A systematic and critical review of Internet of Things in contemporary hospitality: a roadmap and avenues for future research

On the Edge of the Deployment: A Survey on Multi-access Edge Computing

Serverless on Machine Learning: A Systematic Mapping Study

Contact Info

Product

Resources

About