Towards Digital Transformation: Serverless Function Deployment Strategies

Hacia la transformación digital: Estrategias de despliegue de funciones Serverless

Main Article Content

Armando Cabrera-Silva
José Carrillo-Verdún
Patricio Martínez-Palacios
Daniel-Alejandro Guamán-Coronel

Abstract

Digital transformation is a great asset for companies that evolve and drive their activities towards new ways where technology is a great ally, in this evolution, cloud computing plays a key role for transformation. Each company measures its performance through a business model enabled and managed in the cloud and considers the customer experience to differentiate its strategy and opt for technological solutions that make them different from the market competitors. As a strategic tool for digital transformation, moving from technological solutions from a local environment to one of serverless computing is the next step in the evolution of software. This allows software engineers to focus on coding for services, microservices, or functions to meet time-to-market without thinking too much on the complexity to implement and maintain the infrastructure. In this work, we propose a strategy to build and implement functions using a set of serverless runtimes provided by different Cloud Service Providers (CSPs). For the validation, a guided experimentation is carried out in three scenarios, considering the performance of the workload in each runtime and the average execution time of each CSP, which are monitored through analysis and visualization tools. The performance value associated with each CSP allows defining a serverless computing (FaaS) deployment strategy.

Keywords:

Downloads

Download data is not yet available.

Article Details

Author Biographies (SEE)

Armando Cabrera-Silva, Universidad Politécnica de Madrid

Roles: supervision, investigation, writing – original draft.

José Carrillo-Verdún, Universidad Politécnica de Madrid

Roles: supervisión, metodología, escritura – revisión y edición.

Patricio Martínez-Palacios, Universidad Técnica Particular de Loja

Roles: experimental design, validation.

Daniel-Alejandro Guamán-Coronel, Universidad Técnica Particular de Loja

Roles: writing – original draft, validation.

References (SEE)

M. Smith, P. R. Saunders, L. Lyons, A Practical Guide to Microservices and containers, 2018.

W. Gottesheim, “Challenges, benefits and best practices of performance focused DevOps,” in Proceedings of the 4th International Workshop on Large-Scale Testing, 2015, p. 3. https://doi.org/10.1145/2693182.2693187

T. Kohlborn, A. Korthaus, T. Chan, M. Rosemann, “Identification and analysis of business and software services-a consolidated approach,” IEEE Transactions on Services Computing, vol. 2, no. 1, pp. 50–64, 2009. https://doi.org/10.1109/TSC.2009.6

L. Bass, P. Clements, R. Kazman, Software Architecture in Practice, vol. 2nd. 2012.

H. Lee, K. Satyam, G. Fox, “Evaluation of production serverless computing environments,” in IEEE 11th International Conference on Cloud Computing (CLOUD), 2018, pp. 442–450. https://doi.org/10.1109/CLOUD.2018.00062

J. Lewis, M. Fowler, “Microservices: a definition of this new architectural term,” 2014. https://martinfowler.com/articles/microservices.html

T. Back, V. Andrikopoulos, “Using a microbenchmark to compare function as a service solutions,” in Lecture Notes in Computer Science, Springer, 2018. https://doi.org/10.1007/978-3-319-99819-0_11

I. Baldini, P. Castro, K. Chang, P. Cheng, S. Fink, V. Ishakian, N. Mitchell, V. Muthusamy, R. Rabbah, A. Slominski, P. Suter, “Serverless computing: Current trends and open problems,” Research Advances in Cloud Computing, Springer, 2017, pp. 1–20. https://doi.org/10.1007/978-981-10-5026-8_1

D. Poccia, AWS Lambda in Action: Event-driven serverless applications. Manning Publications Co., 2016.

V. Ishakian, V. Muthusamy, A. Slominski, “Serving Deep Learning Models in a Serverless Platform,” in IEEE International Conference on Cloud Engineering (IC2E), 2018, pp. 257–262.

T. Chen, M. Li, Y. Li, M. Lin, N. Wang, M. Wang, T. Xiao, B. Xu, C. Zhang, Z. Zhang, “Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems,” in Workshop on Machine Learning Systems, 2016. https://arxiv.org/abs/1512.01274

B. Wu, F. Iandola, P. H. Jin, K. Keutzer, “Squeezedet: Unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017, pp. 129–137. https://doi.org/10.1109/CVPRW.2017.60

H. Lin, S. Jegelka, “Resnet with one-neuron hidden layers is a universal approximator,” in Advances in neural information processing systems, 2018, pp. 6169–6178. https://doi.org/10.5555/3327345.3327515

L. Wen, X. Li, L. Gao, “A transfer convolutional neural network for fault diagnosis based on ResNet-50,” Neural Computing and Applications, vol. 32, pp. 6111–6124, 2020. https://doi.org/10.1007/s00521-019-04097-w

K. Figiela, A. Gajek, A. Zima, B. Obrok, M. Malawski, “Performance evaluation of heterogeneous cloud functions,” Concurrency and Computation: Practice and Experience, vol. 30, no. 23, e4792. https://doi.org/10.1002/cpe.4792

J. Manner, M. Endreß, T. Heckel, G. Wirtz, “Cold start influencing factors in function as a service,” in IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion), 2018, pp. 181–188.

Citado por: