Alocação de Recursos na Nuvem de Dispositivos
Resumo
Em uma Nuvem de Dispositivos ou Nuvem das Coisas, os recursos disponíveis na Nuvem nem sempre são capazes de atender às restrições de latência de aplicações críticas devido à grande distância entre a Nuvem e os dispositivos que originam os dados. A adoção de computação na Borda pode auxiliar a Nuvem a fornecer serviços que atendam a tais requisitos temporais. Contudo, dois fatores afetam o desempenho no nível de Borda: a heterogeneidade das aplicações e a incerteza da taxa de chegada das requisições. Neste contexto, propomos dois mecanismos: o Gerenciador de Fila, que redistribui as requisições das filas dos Nós de Borda, permitindo reduzir o tempo de espera de uma requisição; e o Alocador de Recursos, que possibilita a colaboração entre os Nós de Borda por meio de encaminhamento de requisições.
Palavras-chave:
Nuvem de Dispositivos, Alocação de Recursos, Roubo de Trabalho, Computação na Borda, Computação na Névoa, Computação na Nuvem, Internet das Coisas, IoT
Referências
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Beraldi, Roberto, e Hussein Alnuweiri. “Exploiting Power-of-Choices for Load Balancing in Fog Computing.” 2019 IEEE ICFC. 2019. 80-86.
Blumofe, Robert D., e Charles E. Leiserson. “Scheduling Multithreaded Computations by Work Stealing.” J. ACM (ACM) 46 (1999): 720-748.
Fernando, Niroshinie, Seng W. Loke, e Wenny Rahayu. “Computing with Nearby Mobile Devices: A Work Sharing Algorithm for Mobile Edge-Clouds.” IEEE Trans. on Cloud Computing, 2019: 329-343.
Lera, I., C. Guerrero, e C. Juiz. “YAFS: A Simulator for IoT Scenarios in Fog Computing.” IEEE Access 7 (2019): 91745-91758.
Nan, Yucen, Wei Li, Wei Bao, Flavia C. Delicato, Paulo F. Pires, e Albert Y. Zomaya. “A dynamic tradeoff data processing framework for delay-sensitive applications in Cloud of Things systems.” Journal of Parallel and Distributed Computing 112 (2018): 53-66.
Noreikis, Marius, Yu Xiao, e Yuming Jiang. “Edge Capacity Planning for Real Time Compute-Intensive Applications.” 2019 IEEE ICFC. 2019. 175-184.
Santos, I. L., L. Pirmez, F. C. Delicato, S. U. Khan, e A. Y. Zomaya. “Olympus: The Cloud of Sensors.” IEEE Cloud Computing 2 (3 2015): 48-56.
Santos, Igor L., et al. “Zeus: A resource allocation algorithm for the cloud of sensors.” Future Generation Computer Systems 92 (2019): 564-581.
Santos, Igor, Flávia Delicato, Paulo Pires, Marcelo Alves, Ana Oliveira, e Tiago Calmon. “Data-centric resource management in edge-cloud systems for the iot.” Conference: Very Large Internet of Things (VLIOT 2019). 2019. 29-46.
Shekhar, S., e A. Gokhale. “Dynamic Resource Management Across Cloud-Edge Resources for Performance-Sensitive Applications.” 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). 2017. 707-710.
Wang, Shangguang, Yali Zhao, Jinlinag Xu, Jie Yuan, e Ching-Hsien Hsu. “Edge server placement in mobile edge computing.” Journal of Parallel and Distributed Computing 127 (2019): 160-168.
Yousefpour, A., G. Ishigaki, R. Gour, e J. P. Jue. “On Reducing IoT Service Delay via Fog Offloading.” IEEE Internet of Things Journal 5 (4 2018): 998-1010.
Zhao, Zhuoran, Kamyar Mirzazad Barijough, e Andreas Gerstlauer. “DeepThings: Distributed Adaptive Deep Learning Inference on Resource-Constrained IoT Edge Clusters.” IEEE Trans. on Computer-Aided Design of Int. Circuits and Systems, 2018: 2348-2359.
Basili, Victor R. Software Modeling and Measurement: The Goal/Question/Metric Paradigm. Tech. rep., Univ. of Maryland at College Park, 1992.
Beraldi, Roberto, e Hussein Alnuweiri. “Exploiting Power-of-Choices for Load Balancing in Fog Computing.” 2019 IEEE ICFC. 2019. 80-86.
Blumofe, Robert D., e Charles E. Leiserson. “Scheduling Multithreaded Computations by Work Stealing.” J. ACM (ACM) 46 (1999): 720-748.
Fernando, Niroshinie, Seng W. Loke, e Wenny Rahayu. “Computing with Nearby Mobile Devices: A Work Sharing Algorithm for Mobile Edge-Clouds.” IEEE Trans. on Cloud Computing, 2019: 329-343.
Lera, I., C. Guerrero, e C. Juiz. “YAFS: A Simulator for IoT Scenarios in Fog Computing.” IEEE Access 7 (2019): 91745-91758.
Nan, Yucen, Wei Li, Wei Bao, Flavia C. Delicato, Paulo F. Pires, e Albert Y. Zomaya. “A dynamic tradeoff data processing framework for delay-sensitive applications in Cloud of Things systems.” Journal of Parallel and Distributed Computing 112 (2018): 53-66.
Noreikis, Marius, Yu Xiao, e Yuming Jiang. “Edge Capacity Planning for Real Time Compute-Intensive Applications.” 2019 IEEE ICFC. 2019. 175-184.
Santos, I. L., L. Pirmez, F. C. Delicato, S. U. Khan, e A. Y. Zomaya. “Olympus: The Cloud of Sensors.” IEEE Cloud Computing 2 (3 2015): 48-56.
Santos, Igor L., et al. “Zeus: A resource allocation algorithm for the cloud of sensors.” Future Generation Computer Systems 92 (2019): 564-581.
Santos, Igor, Flávia Delicato, Paulo Pires, Marcelo Alves, Ana Oliveira, e Tiago Calmon. “Data-centric resource management in edge-cloud systems for the iot.” Conference: Very Large Internet of Things (VLIOT 2019). 2019. 29-46.
Shekhar, S., e A. Gokhale. “Dynamic Resource Management Across Cloud-Edge Resources for Performance-Sensitive Applications.” 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). 2017. 707-710.
Wang, Shangguang, Yali Zhao, Jinlinag Xu, Jie Yuan, e Ching-Hsien Hsu. “Edge server placement in mobile edge computing.” Journal of Parallel and Distributed Computing 127 (2019): 160-168.
Yousefpour, A., G. Ishigaki, R. Gour, e J. P. Jue. “On Reducing IoT Service Delay via Fog Offloading.” IEEE Internet of Things Journal 5 (4 2018): 998-1010.
Zhao, Zhuoran, Kamyar Mirzazad Barijough, e Andreas Gerstlauer. “DeepThings: Distributed Adaptive Deep Learning Inference on Resource-Constrained IoT Edge Clusters.” IEEE Trans. on Computer-Aided Design of Int. Circuits and Systems, 2018: 2348-2359.
Publicado
07/12/2020
Como Citar
XAVIER, Tiago Cariolano de Souza; DELICATO, Flávia Coimbra; PIRES, Paulo de Figueiredo; MACEDO, Evandro Luiz Cardoso; SANTOS, Igor Leão; AMORIM, Claudio Luis.
Alocação de Recursos na Nuvem de Dispositivos. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 38. , 2020, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 323-336.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2020.12292.
