RADON: Rational Decomposition and Orchestration for Serverless Computing

Casale, G., Artač, M., van den Heuvel, WJ. et al. SICS Softw.-Inensiv. Cyber-Phys. Syst. (2019).

doi: https://doi.org/10.1007/s00450-019-00413-w

The Essential Deployment Metamodel: A Systematic Review of Deployment Automation Technologies

Wurster, M., Breitenbücher, U., Falkenthal, M. et al. SICS Softw.-Inensiv. Cyber-Phys. Syst. (2019).

doi: https://doi.org/10.1007/s00450-019-00412-x

ATOM: Model-Driven Autoscaling for Microservices

A. Gias, G. Casale, M. Woodside. ATOM: Model-Driven Autoscaling for Microservices, in Proc. of IEEE ICDCS, 11 pages, (2019).

SD: a Divergence-based Estimation Method for Service Demands in Cloud Systems

Salvatore Dipietro, & Giuliano Casale. (2019). SD: a Divergence-based Estimation Method for Service Demands in Cloud Systems.

doi: http://doi.org/10.5281/zenodo.3243604

Automated Multi-paradigm Analysis of Extended and Layered Queueing Models with LINE.

Casale, Giuliano. “Automated Multi-paradigm Analysis of Extended and Layered Queueing Models with LINE.” ICPE Companion (2019).

doi: 10.1145/3302541.3311959

Adoption, Support, and Challenges of Infrastructure-as-Code: Insights from Industry
Novel Solutions for Closed Queueing Networks with Load-Dependent Stations

G. Casale, P.G. Harrison, O.W. Hong. Novel Solutions for Closed Queueing Networks with Load-Dependent Stations, Proc. of MAMA workshop 2019.

Protecting Deployment Models in Collaborative Cloud Application Development

Yussupov, Vladimir & Falazi, Ghareeb & Falkenthal, Michael & Leymann, Frank. (2019). Protecting Deployment Models in Collaborative Cloud Application Development.

Quality-Aware DevOps Research: Where Do We Stand?

A. Alnafessah, et al.. Quality-Aware DevOps Research: Where Do We Stand?, IEEE ACCESS, 2021.

Within-Project Defect Prediction of Infrastructure-as-Code Using Product and Process Metrics

Dalla Palma, S., Di Nucci, D., Palomba, F., & Tamburri, D. A. (2021). Within-Project Defect Prediction of Infrastructure-as-Code Using Product and Process Metrics. IEEE Transactions on Software Engineering.

Toward a catalog of software quality metrics for infrastructure code

Dalla Palma, S., Di Nucci, D., Palomba, F., & Tamburri, D. A. (2020). Towards a catalogue of software quality metrics for infrastructure code. Journal of Systems and Software, 110726.

AnsibleMetrics: A Python library for measuring Infrastructure-as-Code blueprints in Ansible

Stefano Dalla Palma, Dario Di Nucci, Damian A. Tamburri, AnsibleMetrics: A Python library for measuring Infrastructure-as-Code blueprints in Ansible, SoftwareX, Volume 12, 2020, 100633, ISSN 2352-7110, https://doi.org/10.1016/j.softx.2020.100633.

Application deployment using containers with auto-scaling for microservices in cloud environment

S. N. Srirama, M. Adhikari, and S. Paul. (2020). Application Deployment using Containers with Auto-scaling for Microservices in Cloud Environment”, Journal of Network and Computer Applications, 160, 1-20.

CCoDaMiC: A framework for Coherent Coordination of Data Migration and Computation platforms

Chinmaya Kumar Dehury, Satish Narayana Srirama, Tek Raj Chhetri, CCoDaMiC: A framework for Coherent Coordination of Data Migration and Computation platforms, Future Generation Computer Systems, Volume 109, 2020, Pages 1-16, ISSN 0167-739X, https://doi.org/10.1016/j.future.2020.03.029.

Data Pipeline Architecture for Serverless Platform

Dehury C., Jakovits P., Srirama S.N., Tountopoulos V., Giotis G. (2020) Data Pipeline Architecture for Serverless Platform. In: Muccini H. et al. (eds) Software Architecture. ECSA 2020. Communications in Computer and Information Science, vol 1269. Springer, Cham. https://doi.org/10.1007/978-3-030-59155-7_18

An efficient service dispersal mechanism for fog and cloud computing using deep reinforcement learning

Dehury, C. K., & Srirama, S. N. (2020, May). An efficient service dispersal mechanism for fog and cloud computing using deep reinforcement learning. In 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID) (pp. 589-598). IEEE.

Personalized Service Delivery using Reinforcement Learning in Fog and Cloud Environment

Dehury, C. K., & Srirama, S. N. (2019, December). Personalized Service Delivery using Reinforcement Learning in Fog and Cloud Environment. In Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services (pp. 522-529).

DPTO: A Deadline and Priority-Aware Task Offloading in Fog Computing Framework Leveraging Multilevel Feedback Queueing

Adhikari, M., Mukherjee, M., & Srirama, S. N. (2020). DPTO: A Deadline and Priority-Aware Task Offloading in Fog Computing Framework Leveraging Multilevel Feedback Queueing. IEEE Internet of Things Journal, 7(7), 5773-5782

Application Offloading Strategy for Hierarchical Fog Environment Through Swarm Optimization

Adhikari, M., Srirama, S. N., & Amgoth, T. (2020). Application offloading strategy for hierarchical fog environment through swarm optimization. IEEE Internet of Things Journal, 7(5), 4317-4328.

Context-tailored Workload Model Generation for Continuous Representative Load Testing

Schulz, H., Okanovic, H., van Hoorn, A., Tuma, P.: Context-tailored Workload Model Generation for Continuous Representative Load Testing. ICPE 2021: 21-32

Scalability Assessment of Microservice Architecture Deployment Configurations: A Domain-based Approach Leveraging Operational Profiles and Load Tests

Avritzer, A., Ferme, V., Janes, A., Russo, B., van Hoorn, A., Schulz, H., Menasché, D. S., Rufino, V.:
Scalability Assessment of Microservice Architecture Deployment Configurations: A Domain-based Approach Leveraging Operational Profiles and Load Tests. J. Syst. Softw. 165: 110564 (2020)