FOCloud: Feature Model Guided Performance Prediction and Explanation for Deployment Configurable Cloud ApplicationsJournal Transactions on Services Computing
- International Conference on Service-Oriented Computing ICSOC 2021: Service-Oriented Computing pp 238-253
- 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA)
- Frontiers in Sustainable Cities
- 15th Symposium and Summer School On Service-Oriented Computing (SummerSoc)
An Approach to Support Automated Deployment of Applications on Heterogeneous Cloud-HPC Infrastructures2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)
- SYSTOR '21: Proceedings of the 14th ACM International Conference on Systems and Storage - June 2021 - Article No.: 24 - Pages 1 Abstract: We propose enabling continuous performance optimisation of distributed hybrid applications in heterogeneous cloud, Edge, and HPC environments by employing an intelligent re-deployment feedback loop. See the poster HERE
- SYSTOR '21: Proceedings of the 14th ACM International Conference on Systems and Storage - June 2021 - Article No.: 23 - Pages 1 Abstract: We propose to tackle the complexity of deploying and operating modern applications onto heterogeneous HPC and cloud-based systems by providing application developers and infrastructure operators with tools to abstract their application and infrastructure requirements.
- TSOS21: First workshop on trustworthy software and open source, March 23-25, 2021, Virtual Conference
- SYSTOR '19: Proceedings of the 12th ACM International Conference on Systems and Storage - May 2019 - Pages 196 Abstract:
- European Conference on Software Architecture - ECSA 2020: Software Architecture pp 247-259
- MaLTeSQuE 2019: Proceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation - August 2019 - Pages 37–42
- Journal of Grid Computing volume 19, Article number: 29 (2021)
- 4th Special Session on High-Performance Services Computing and Internet Technologies (@HPCS2020)
A simulation-based Comparison between Industrial Autoscaling Solutions and COCOS for Cloud Applications2020 IEEE International Conference on Web Services (ICWS)
- Journal Information and Software Technology Volume 127, November 2020, 106376
- ScienceDirect - Information and Software Technology Volume 137, September 2021, 106593 Abstract: Infrastructure-as-code (IaC) is the DevOps tactic of managing and provisioning software infrastructures through machine-readable definition files, rather than manual hardware configuration or interactive configuration tools.
- ACM Transactions on Software Engineering and MethodologyVolume 30Issue 1 January 2021 Article No.: 1pp 1–30
A Survey of Context-Aware Access Control Mechanisms for Cloud and Fog Networks: Taxonomy and Open Research IssuesSensors 2020, 20(9), 2464; https://doi.org/10.3390/s20092464
- ESOCC 2020: Advances in Service-Oriented and Cloud Computing pp 173-178
- Artificial Intelligence (AI) applications based on Deep Neural Networks (DNN) or Deep Learning (DL) have become popular due to their success in solving problems likeimage analysis and speech recognition. Training a DNN is computationally intensive and High Performance Computing(HPC) has been a key driver in AI growth. Virtualisation and container technology have led to the convergence of cloud and HPC infrastructure. These infrastructures with diverse hardware increase the complexity of deploying and optimising AI training workloads.
- Deep Leaning and code embedding based approach to linguistic detecting anti-patterns in Infrastructure as code. This is from SODALITE smell and defect prediction task.
- Journal of 2020 IEEE International Conference on Software Architecture (ICSA) pp. 103-113 The work presents a scalable architecture for controlling heterogenous systems. The architecture exploits containers and provides multiple levels of control (container, VM, cluster). A prototype based on Kubernetes is also presented and evaluated.
- 10th International Conference on Web Intelligence, Mining and Semantics (WIMS) This paper presents our (SODALITE) knowledge-driven approach enabling developers to identify the smells in deployment descriptions /infrastructure codes. We detect smells with SPARQL-based rules over pattern-based OWL 2 knowledge graphs capturing deployment models.
- Journal of Software: Practice and Experience Volume49, Issue5, May 2019 Pages 813-839 The work presented in the mentioned paper reflects the state of art in multi-tenant cloud applications and microservices. It supported the preparation of the SODALITE proposal. The deployment refactoring task in the SODALITE adopts and extends the findings of the paper. Moreover, SODALITE case studies use microservice-based architectures. As the paper is a journal paper, no travel expenses have been claimed.