My research focuses on resource provisioning, configuration, orchestration, and application placement across the digital continuum, from cloud datacentres and edge infrastructure to endpoint devices. Through projects such as the Continuum framework, I develop methods and systems that simplify deployment in heterogeneous environments, use configuration knowledge to improve deployment decisions, and enable rapid design-space exploration of distributed applications.
As the next generation of diverse workloads like autonomous driving and augmented/virtual reality evolves, computation is shifting from cloud-based services to the edge, leading to the emergence of a cloud-edge compute continuum. This continuum promises a wide spectrum of deployment opportunities for workloads that can leverage the strengths of cloud (scalable infrastructure, high reliability), edge (energy efficient, low latencies), and endpoints (sensing, user-owned). Designing and deploying software in the continuum is complex because of the variety of available hardware, each with unique properties and trade-offs. In practice, developers have limited access to these resources, limiting their ability to create software deployments. To simplify research and development in the compute continuum, in this paper, we propose Continuum, a framework for automated infrastructure deployment and benchmarking that helps researchers and engineers to deploy and test their use cases in a few lines of code. Continuum can automatically deploy a wide variety of emulated infrastructures and networks locally and in the cloud, install software for operating services and resource managers, and deploy and benchmark applications for users with diverse configuration options. In our evaluation, we show how our design covers these requirements, allowing Continuum to be (i) highly flexible, supporting any computing model, (ii) highly configurable, allowing users to alter framework components using an intuitive API, and (iii) highly extendable, allowing users to add support for more infrastructure, applications, and more. Continuum is available at https://github.com/atlarge-research/continuum.
@inproceedings{DBLP:conf/wosp/JansenWTI23,author={Jansen, Matthijs and Wagner, Linus and Trivedi, Animesh and Iosup, Alexandru},editor={Vieira, Marco and Cardellini, Valeria and Marco, Antinisca Di and Tuma, Petr},title={Continuum: Automate Infrastructure Deployment and Benchmarking in
the Compute Continuum},booktitle={Companion of the 2023 {ACM/SPEC} International Conference on Performance
Engineering, {ICPE} 2023, Coimbra, Portugal, April 15-19, 2023},pages={181--188},publisher={{ACM}},year={2023},url={https://doi.org/10.1145/3578245.3584936},doi={10.1145/3578245.3584936},timestamp={Sat, 29 Apr 2023 19:25:34 +0200},biburl={https://dblp.org/rec/conf/wosp/JansenWTI23.bib},bibsource={dblp computer science bibliography, https://dblp.org}}
The SPEC-RG Reference Architecture for The Compute Continuum
Matthijs Jansen, Auday Al-Dulaimy, Alessandro V. Papadopoulos, Animesh Trivedi, and Alexandru Iosup
In 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2023, Bangalore, India, May 1-4, 2023, 2023
As the next generation of diverse workloads like autonomous driving and augmented/virtual reality evolves, computation is shifting from cloud-based services to the edge, leading to the emergence of a cloud-edge compute continuum. This continuum promises a wide spectrum of deployment opportunities for workloads that can leverage the strengths of cloud (scalable infrastructure, high reliability) and edge (energy efficient, low latencies). Despite its promises, the continuum has only been studied in silos of various computing models, thus lacking strong end-to-end theoretical and engineering foundations for computing and resource management across the continuum. Consequently, devel-opers resort to ad hoc approaches to reason about performance and resource utilization of workloads in the continuum. In this work, we conduct a first-of-its-kind systematic study of various computing models, identify salient properties, and make a case to unify them under a compute continuum reference architecture. This architecture provides an end-to-end analysis framework for developers to reason about resource management, workload distribution, and performance analysis. We demonstrate the utility of the reference architecture by analyzing two popular continuum workloads, deep learning and industrial IoT. We have developed an accompanying deployment and benchmarking framework and first-order analytical model for quantitative reasoning of continuum workloads. The framework is open-sourced and available at https://github.com/atlarge-research/continuum.
@inproceedings{DBLP:conf/ccgrid/JansenAPTI23,author={Jansen, Matthijs and Al{-}Dulaimy, Auday and Papadopoulos, Alessandro V. and Trivedi, Animesh and Iosup, Alexandru},editor={Simmhan, Yogesh and Altintas, Ilkay and Varbanescu, Ana Lucia and Balaji, Pavan and Prasad, Abhinandan S. and Carnevale, Lorenzo},title={The {SPEC-RG} Reference Architecture for The Compute Continuum},booktitle={23rd {IEEE/ACM} International Symposium on Cluster, Cloud and Internet
Computing, CCGrid 2023, Bangalore, India, May 1-4, 2023},pages={469--484},publisher={{IEEE}},year={2023},url={https://doi.org/10.1109/CCGrid57682.2023.00051},doi={10.1109/CCGrid57682.2023.00051},timestamp={Fri, 21 Jul 2023 22:25:52 +0200},biburl={https://dblp.org/rec/conf/ccgrid/JansenAPTI23.bib},bibsource={dblp computer science bibliography, https://dblp.org}}
