2023
Conference article
Open Access
A proposal for a continuum-aware programming model: from workflows to services autonomously interacting in the compute continuum
Aldinucci M., Birke R., Brogi A., Carlini E., Coppola M., Danelutto M., Dazzi P., Ferrucci L., Forti S., Kavalionak H., Mencagli G., Mordacchini M., Pasin M., Paganelli F., Torquati M.
This paper proposes a continuum-aware programming model enabling the execution of application workflows across the compute continuum: cloud, fog and edge resources. It simplifies the management of heterogeneous nodes while alleviating the burden of programmers and unleashing innovation. This model optimizes the continuum through advanced development experiences by transforming workflows into autonomous service collaborations. It reduces complexity in positioning/interconnecting services across the continuum. A metamodel introduces high-level workflow descriptions as service networks with defined contracts and quality of service, thus enabling the deployment/management of workflows as first-class entities. It also provides automation based on policies, monitoring and heuristics. Tailored mechanisms orchestrate/manage services across the continuum, optimizing performance, cost, data protection and sustainability while managing risks. This model facilitates incremental development with visibility of design impacts and seamless evolution of applications and infrastructures. In this work, we explore this new computing paradigm showing how it can trigger the development of a new generation of tools to support the compute continuum progress.Source: COMPSAC 2023 - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, pp. 1852–1857, Torino, Italy, 23-30/06/2023
DOI: 10.1109/compsac57700.2023.00287
Metrics:
Aldinucci M., Birke R., Brogi A., Carlini E., Coppola M., Danelutto M., Dazzi P., Ferrucci L., Forti S., Kavalionak H., Mencagli G., Mordacchini M., Pasin M., Paganelli F., Torquati M.
This paper proposes a continuum-aware programming model enabling the execution of application workflows across the compute continuum: cloud, fog and edge resources. It simplifies the management of heterogeneous nodes while alleviating the burden of programmers and unleashing innovation. This model optimizes the continuum through advanced development experiences by transforming workflows into autonomous service collaborations. It reduces complexity in positioning/interconnecting services across the continuum. A metamodel introduces high-level workflow descriptions as service networks with defined contracts and quality of service, thus enabling the deployment/management of workflows as first-class entities. It also provides automation based on policies, monitoring and heuristics. Tailored mechanisms orchestrate/manage services across the continuum, optimizing performance, cost, data protection and sustainability while managing risks. This model facilitates incremental development with visibility of design impacts and seamless evolution of applications and infrastructures. In this work, we explore this new computing paradigm showing how it can trigger the development of a new generation of tools to support the compute continuum progress.Source: COMPSAC 2023 - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, pp. 1852–1857, Torino, Italy, 23-30/06/2023
DOI: 10.1109/compsac57700.2023.00287
Metrics:
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| ieeexplore.ieee.org 
| CNR ExploRA
2023
Conference article
Open Access
GNOSIS: proactive pmage placement using graph neural networks & deep reinforcement learning
Theodoropoulos T., Makris A., Psomakelis E., Carlini E., Mordacchini M., Dazzi P., Tserpes K.
The transition from Cloud Computing to a Cloud-Edge continuum brings many new exciting possibilities for interactive and data-intensive Next Generation applications, but as many challenges. Approaches and solutions that successfully worked in the Cloud space now need to be rethought for the Edge's distributed, heterogeneous and dynamic ecosystem. The placement of application images needs to be proactively devised to reduce as much as possible the image transfer time and comply with the dynamic nature and strict requirements of the applications. To this end, this paper proposes an approach based on the combination of Graph Neural Networks and actor-critic Reinforcement Learning. The approach is analyzed empirically and compared with a state-of-the-art solution. The results show that the proposed approach exhibits a larger execution times but generally better results in terms of application image placement.Source: CLOUD 2023 - IEEE 16th International Conference on Cloud Computing, pp. 120–128, Chicago, Illinois, USA, 2-8/7/2023
DOI: 10.1109/cloud60044.2023.00022
Project(s): ACCORDION
, CHARITY
Metrics:
Theodoropoulos T., Makris A., Psomakelis E., Carlini E., Mordacchini M., Dazzi P., Tserpes K.
The transition from Cloud Computing to a Cloud-Edge continuum brings many new exciting possibilities for interactive and data-intensive Next Generation applications, but as many challenges. Approaches and solutions that successfully worked in the Cloud space now need to be rethought for the Edge's distributed, heterogeneous and dynamic ecosystem. The placement of application images needs to be proactively devised to reduce as much as possible the image transfer time and comply with the dynamic nature and strict requirements of the applications. To this end, this paper proposes an approach based on the combination of Graph Neural Networks and actor-critic Reinforcement Learning. The approach is analyzed empirically and compared with a state-of-the-art solution. The results show that the proposed approach exhibits a larger execution times but generally better results in terms of application image placement.Source: CLOUD 2023 - IEEE 16th International Conference on Cloud Computing, pp. 120–128, Chicago, Illinois, USA, 2-8/7/2023
DOI: 10.1109/cloud60044.2023.00022
Project(s): ACCORDION
, CHARITY Metrics:
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2023
Conference article
Open Access
Innovation potential of the ACCORDION platform
Carlini E., Dazzi P., Tserpes K., Blasi L., Di Girolamo M., Dober D.
The seamless utilization of resources in the cloud-edge spectrum is a key driver for innovation in the ICT sector, as it supports economic growth and strengthens the industry's competitiveness while making next-application services possible with minimal investments and disruption. In this context, the EU project ACCORDION provides an innovative three-layered architecture designed as a comprehensive solution dedicated to latency-aware applications. This paper summarizes the key technological innovations of ACCORDION, highlighting their alignment with the European agenda of the ICT sector.Source: FRAME '23 - 3rd Workshop on Flexible Resource and Application Management on the EdgeAugust 2023 - colocated with HPDC '23 - 32nd International Symposium on High-Performance Parallel and Distributed Computing, pp. 33–35, Orlando, Florida, USA, 20/06/2023
DOI: 10.1145/3589010.3594887
Project(s): ACCORDION
Metrics:
Carlini E., Dazzi P., Tserpes K., Blasi L., Di Girolamo M., Dober D.
The seamless utilization of resources in the cloud-edge spectrum is a key driver for innovation in the ICT sector, as it supports economic growth and strengthens the industry's competitiveness while making next-application services possible with minimal investments and disruption. In this context, the EU project ACCORDION provides an innovative three-layered architecture designed as a comprehensive solution dedicated to latency-aware applications. This paper summarizes the key technological innovations of ACCORDION, highlighting their alignment with the European agenda of the ICT sector.Source: FRAME '23 - 3rd Workshop on Flexible Resource and Application Management on the EdgeAugust 2023 - colocated with HPDC '23 - 32nd International Symposium on High-Performance Parallel and Distributed Computing, pp. 33–35, Orlando, Florida, USA, 20/06/2023
DOI: 10.1145/3589010.3594887
Project(s): ACCORDION
Metrics:
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dl.acm.org | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
EDGELESS Project: on the road to serverless edge AI
Cicconetti C., Carlini E., Paradell A.
The EDGELESS project is set to efficiently operate serverless computing in extremely diverse computing environments, from resource-constrained edge devices to highly-virtualized cloud platforms. Automatic deployment and reconfiguration will leverage AI/ML techniques, resulting in a flexible horizontally-scalable computation solution able to fully use heterogeneous edge resources while preserving vertical integration with the cloud and the benefits of serverless and its companion programming model, i.e., Function-as-a-Service (FaaS). The system under design will be environmentally sustainable, as it will dynamically concentrate resources physically (e.g., by temporarily switching off far-edge devices) or logically (e.g., by dispatching tasks towards a specific set of nodes) at the expense of performance-tolerant applications.Source: FRAME '23 - 3rd Workshop on Flexible Resource and Application Management on the EdgeAugust 2023 - colocated with HPDC '23 - 32nd International Symposium on High-Performance Parallel and Distributed Computing, pp. 41–43, Orlando, Florida, USA, 20/06/2023
DOI: 10.1145/3589010.3594890
Metrics:
Cicconetti C., Carlini E., Paradell A.
The EDGELESS project is set to efficiently operate serverless computing in extremely diverse computing environments, from resource-constrained edge devices to highly-virtualized cloud platforms. Automatic deployment and reconfiguration will leverage AI/ML techniques, resulting in a flexible horizontally-scalable computation solution able to fully use heterogeneous edge resources while preserving vertical integration with the cloud and the benefits of serverless and its companion programming model, i.e., Function-as-a-Service (FaaS). The system under design will be environmentally sustainable, as it will dynamically concentrate resources physically (e.g., by temporarily switching off far-edge devices) or logically (e.g., by dispatching tasks towards a specific set of nodes) at the expense of performance-tolerant applications.Source: FRAME '23 - 3rd Workshop on Flexible Resource and Application Management on the EdgeAugust 2023 - colocated with HPDC '23 - 32nd International Symposium on High-Performance Parallel and Distributed Computing, pp. 41–43, Orlando, Florida, USA, 20/06/2023
DOI: 10.1145/3589010.3594890
Metrics:
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ISTI Repository | dl.acm.org | CNR ExploRA
2023
Conference article
Open Access
DATA7: a dataset for assessing resource and application management solutions at the edge
Carlini E., Coppola M., Dazzi P., Ferrucci L., Kavalionak H., Mordacchini M.
This paper presents a dataset on edge devices and mobility patterns to comprehensively understand user behaviour and devices workload in Edge computing environments. The dataset is built on top of a publicly available dataset of cellular tower locations to simulate Edge devices, and on user mobility trajectories generated by a state-of-the-art simulator based on real location maps in the area of the city of Pisa, Italy. The resulting dataset reports the amount of vehicles in the range of about 200 Edge devices for each step of the simulation. The dataset can be used for various applications in edge computing and mobility, most notably for assessing results on resource and application management solutions at the edge in a realistic environment.Source: FRAME '23 - 3rd Workshop on Flexible Resource and Application Management on the EdgeAugust 2023 - colocated with HPDC '23 - 32nd International Symposium on High-Performance Parallel and Distributed Computing, pp. 3–6, Orlando, Florida, USA, 20/06/2023
DOI: 10.1145/3589010.3595652
Project(s): ACCORDION
Metrics:
Carlini E., Coppola M., Dazzi P., Ferrucci L., Kavalionak H., Mordacchini M.
This paper presents a dataset on edge devices and mobility patterns to comprehensively understand user behaviour and devices workload in Edge computing environments. The dataset is built on top of a publicly available dataset of cellular tower locations to simulate Edge devices, and on user mobility trajectories generated by a state-of-the-art simulator based on real location maps in the area of the city of Pisa, Italy. The resulting dataset reports the amount of vehicles in the range of about 200 Edge devices for each step of the simulation. The dataset can be used for various applications in edge computing and mobility, most notably for assessing results on resource and application management solutions at the edge in a realistic environment.Source: FRAME '23 - 3rd Workshop on Flexible Resource and Application Management on the EdgeAugust 2023 - colocated with HPDC '23 - 32nd International Symposium on High-Performance Parallel and Distributed Computing, pp. 3–6, Orlando, Florida, USA, 20/06/2023
DOI: 10.1145/3589010.3595652
Project(s): ACCORDION
Metrics:
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ISTI Repository | dl.acm.org | CNR ExploRA
2023
Conference article
Open Access
EDGELESS Project: on the Road to Serverless Edge AI
Cicconetti C., Carlini E., Paradell A.
The EDGELESS project is set to efficiently operate serverless computing in extremely diverse computing environments, from resourceconstrained edge devices to highly-virtualized cloud platforms. Automatic deployment and reconfiguration will leverage AI/ML techniques, resulting in a flexible horizontally-scalable computation solution able to fully use heterogeneous edge resources while preserving vertical integration with the cloud and the benefits of serverless and its companion programming model, i.e., Function-asa-Service (FaaS). The system under design will be environmentally sustainable, as it will dynamically concentrate resources physically (e.g., by temporarily switching off far-edge devices) or logically (e.g., by dispatching tasks towards a specific set of nodes) at the expense of performance-tolerant applications.Source: FRAME 2023 - ACM 3rd Workshop on Flexible Resource and Application Management on the Edge, pp. 41–43, Orlando, USA, 20/06/2023
DOI: 10.1145/3589010.3594890
Metrics:
Cicconetti C., Carlini E., Paradell A.
The EDGELESS project is set to efficiently operate serverless computing in extremely diverse computing environments, from resourceconstrained edge devices to highly-virtualized cloud platforms. Automatic deployment and reconfiguration will leverage AI/ML techniques, resulting in a flexible horizontally-scalable computation solution able to fully use heterogeneous edge resources while preserving vertical integration with the cloud and the benefits of serverless and its companion programming model, i.e., Function-asa-Service (FaaS). The system under design will be environmentally sustainable, as it will dynamically concentrate resources physically (e.g., by temporarily switching off far-edge devices) or logically (e.g., by dispatching tasks towards a specific set of nodes) at the expense of performance-tolerant applications.Source: FRAME 2023 - ACM 3rd Workshop on Flexible Resource and Application Management on the Edge, pp. 41–43, Orlando, USA, 20/06/2023
DOI: 10.1145/3589010.3594890
Metrics:
See at:
dl.acm.org | ISTI Repository | CNR ExploRA
2023
Journal article
Open Access
SmartORC smart orchestration of resources in the compute continuum
Carlini E., Coppola M., Dazzi P., Ferrucci L., Kavalionak H., Korontanis I., Mordacchini M., Tserpes K.
The promise of the compute continuum is to present applications with a flexible and transparent view of the resources in the Internet of Things-Edge-Cloud ecosystem. However, such a promise requires tackling complex challenges to maximize the benefits of both the cloud and the edge. Challenges include managing a highly distributed platform, matching services and resources, harnessing resource heterogeneity, and adapting the deployment of services to the changes in resources and applications. In this study, we present SmartORC, a comprehensive set of components designed to provide a complete framework for managing resources and applications in the Compute Continuum. Along with the description of all the SmartORC subcomponents, we have also provided the results of an evaluation aimed at showcasing the framework's capability.Source: Frontiers in high performance computing 1 (2023). doi:10.3389/fhpcp.2023.1164915
DOI: 10.3389/fhpcp.2023.1164915
Metrics:
Carlini E., Coppola M., Dazzi P., Ferrucci L., Kavalionak H., Korontanis I., Mordacchini M., Tserpes K.
The promise of the compute continuum is to present applications with a flexible and transparent view of the resources in the Internet of Things-Edge-Cloud ecosystem. However, such a promise requires tackling complex challenges to maximize the benefits of both the cloud and the edge. Challenges include managing a highly distributed platform, matching services and resources, harnessing resource heterogeneity, and adapting the deployment of services to the changes in resources and applications. In this study, we present SmartORC, a comprehensive set of components designed to provide a complete framework for managing resources and applications in the Compute Continuum. Along with the description of all the SmartORC subcomponents, we have also provided the results of an evaluation aimed at showcasing the framework's capability.Source: Frontiers in high performance computing 1 (2023). doi:10.3389/fhpcp.2023.1164915
DOI: 10.3389/fhpcp.2023.1164915
Metrics:
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doi.org | ISTI Repository | www.frontiersin.org | CNR ExploRA
2022
Conference article
Open Access
Data models for an imaging bio-bank for colorectal, prostate and gastric cancer: the NAVIGATOR project
Berti A., Carloni G., Colantonio S., Pascali M. A., Manghi P., Pagano P., Buongiorno R., Pachetti E., Caudai C., Di Gangi D., Carlini E., Falaschi Z., Ciarrocchi E., Neri E., Bertelli E., Miele V., Carpi R., Bagnacci G., Di Meglio N., Mazzei M. A., Barucci A.
Researchers nowadays may take advantage of broad collections of medical data to develop personalized medicine solutions. Imaging bio-banks play a fundamental role, in this regard, by serving as organized repositories of medical images associated with imaging biomarkers. In this context, the NAVIGATOR Project aims to advance colorectal, prostate, and gastric oncology translational research by leveraging quantitative imaging and multi-omics analyses. As Project's core, an imaging bio-bank is being designed and implemented in a web-accessible Virtual Research Environment (VRE). The VRE serves to extract the imaging biomarkers and further process them within prediction algorithms. In our work, we present the realization of the data models for the three cancer use-cases of the Project. First, we carried out an extensive requirements analysis to fulfill the necessities of the clinical partners involved in the Project. Then, we designed three separate data models utilizing entity-relationship diagrams. We found diagrams' modeling for colorectal and prostate cancers to be more straightforward, while gastric cancer required a higher level of complexity. Future developments of this work would include designing a common data model following the Observational Medical Outcomes Partnership Standards. Indeed, a common data model would standardize the logical infrastructure of data models and make the bio-bank easily interoperable with other bio-banks.Source: BHI '22 - IEEE-EMBS International Conference on Biomedical and Health Informatics, Ioannina, Greece, 27-30/09/2022
DOI: 10.1109/bhi56158.2022.9926910
Metrics:
Berti A., Carloni G., Colantonio S., Pascali M. A., Manghi P., Pagano P., Buongiorno R., Pachetti E., Caudai C., Di Gangi D., Carlini E., Falaschi Z., Ciarrocchi E., Neri E., Bertelli E., Miele V., Carpi R., Bagnacci G., Di Meglio N., Mazzei M. A., Barucci A.
Researchers nowadays may take advantage of broad collections of medical data to develop personalized medicine solutions. Imaging bio-banks play a fundamental role, in this regard, by serving as organized repositories of medical images associated with imaging biomarkers. In this context, the NAVIGATOR Project aims to advance colorectal, prostate, and gastric oncology translational research by leveraging quantitative imaging and multi-omics analyses. As Project's core, an imaging bio-bank is being designed and implemented in a web-accessible Virtual Research Environment (VRE). The VRE serves to extract the imaging biomarkers and further process them within prediction algorithms. In our work, we present the realization of the data models for the three cancer use-cases of the Project. First, we carried out an extensive requirements analysis to fulfill the necessities of the clinical partners involved in the Project. Then, we designed three separate data models utilizing entity-relationship diagrams. We found diagrams' modeling for colorectal and prostate cancers to be more straightforward, while gastric cancer required a higher level of complexity. Future developments of this work would include designing a common data model following the Observational Medical Outcomes Partnership Standards. Indeed, a common data model would standardize the logical infrastructure of data models and make the bio-bank easily interoperable with other bio-banks.Source: BHI '22 - IEEE-EMBS International Conference on Biomedical and Health Informatics, Ioannina, Greece, 27-30/09/2022
DOI: 10.1109/bhi56158.2022.9926910
Metrics:
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ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2022
Conference article
Open Access
A novel approach to distributed model aggregation using Apache Kafka
Bano S., Carlini E., Cassarà P., Coppola M., Dazzi P., Gotta A.
Multi-Access Edge Computing (MEC) is attracting a lot of interest because it complements cloud-based approaches. Indeed, MEC is opening up in the direction of reducing both interaction delays and data sharing, called Cyber-Physical Systems (CPSs). In the near fu-ture, edge technologies will be a fundamental tool to better support time-dependent and data-intensive applications. In this context, this work explores existing and emerging platforms for MEC and human-centric applications, and proposes a suitable architecture that can be used in the context of autonomous vehicle systems.The proposed architecture will support scalable communication among sensing devices and edge/cloud computing platforms, as well as orchestrate services for computing, storage, and learning with the use of an Information-centric paradigm such as Apache KafkaSource: FRAME '22 - 2nd Workshop on Flexible Resource and Application Management on the Edge, pp. 33–36, Minneapolis, Minnesota, USA, 27/06-01/07/2022
DOI: 10.1145/3526059.3533621
Project(s): TEACHING
Metrics:
Bano S., Carlini E., Cassarà P., Coppola M., Dazzi P., Gotta A.
Multi-Access Edge Computing (MEC) is attracting a lot of interest because it complements cloud-based approaches. Indeed, MEC is opening up in the direction of reducing both interaction delays and data sharing, called Cyber-Physical Systems (CPSs). In the near fu-ture, edge technologies will be a fundamental tool to better support time-dependent and data-intensive applications. In this context, this work explores existing and emerging platforms for MEC and human-centric applications, and proposes a suitable architecture that can be used in the context of autonomous vehicle systems.The proposed architecture will support scalable communication among sensing devices and edge/cloud computing platforms, as well as orchestrate services for computing, storage, and learning with the use of an Information-centric paradigm such as Apache KafkaSource: FRAME '22 - 2nd Workshop on Flexible Resource and Application Management on the Edge, pp. 33–36, Minneapolis, Minnesota, USA, 27/06-01/07/2022
DOI: 10.1145/3526059.3533621
Project(s): TEACHING
Metrics:
See at:
ZENODO | dl.acm.org | doi.org | CNR ExploRA
2022
Conference article
Open Access
A federated cloud solution for transnational mobility data sharing
Carlini E., Chevalier T., Dazzi P., Lettich F., Perego R., Renso C., Trani S.
Nowadays, innovative digital services are massively spreading both in the public and private sectors. In this work we focus on the digital data regarding the mobility of persons and goods, which are experiencing exponential growth thanks to the significant diffusion of telecommunication infrastructures and inexpensive GPS-equipped devices. The volume, velocity, and heterogeneity of mobility data call for advanced and efficient services to collect and integrate various data sources from different data producers. The MobiDataLab H2020 project aims to deal with these challenges by introducing an efficient and highly interoperable digital framework for mobility data sharing. In particular, the project aims to propose to the mobility stakeholders (i.e., transport organising authorities, operators, industry, governments, and innovators) reproducible methodologies and sustainable tools that can foster the development of a data-sharing culture in Europe and beyond. This paper introduces the key concepts driving the design and definition of a cloud-based data-sharing federation we call the Transport Cloud platform, which represents one of the main pillars of the MobiDataLab project. Such platform aims to ensure transnational access to mobility data in a secure, efficient, and seamless way, and to ensure that FAIR principles (i.e., mobility data should be findable, accessible, interoperable, and reusable) are enforced.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 586–592, Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): ACCORDION, MobiDataLab
Carlini E., Chevalier T., Dazzi P., Lettich F., Perego R., Renso C., Trani S.
Nowadays, innovative digital services are massively spreading both in the public and private sectors. In this work we focus on the digital data regarding the mobility of persons and goods, which are experiencing exponential growth thanks to the significant diffusion of telecommunication infrastructures and inexpensive GPS-equipped devices. The volume, velocity, and heterogeneity of mobility data call for advanced and efficient services to collect and integrate various data sources from different data producers. The MobiDataLab H2020 project aims to deal with these challenges by introducing an efficient and highly interoperable digital framework for mobility data sharing. In particular, the project aims to propose to the mobility stakeholders (i.e., transport organising authorities, operators, industry, governments, and innovators) reproducible methodologies and sustainable tools that can foster the development of a data-sharing culture in Europe and beyond. This paper introduces the key concepts driving the design and definition of a cloud-based data-sharing federation we call the Transport Cloud platform, which represents one of the main pillars of the MobiDataLab project. Such platform aims to ensure transnational access to mobility data in a secure, efficient, and seamless way, and to ensure that FAIR principles (i.e., mobility data should be findable, accessible, interoperable, and reusable) are enforced.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 586–592, Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): ACCORDION
, MobiDataLab
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2022
Conference article
Open Access
A mathematical model for latency constrained self-organizing application placement in the edge
Mordacchini M., Carlini E., Dazzi P.
The highly dynamic and heterogeneous environment that characterizes the edge of the Cloud/Edge Continuum calls for new intelligent methods for tackling the needs of such a complex scenario. In particular, adaptive and self-organizing decentralized solutions have been advanced for optimizing the placement of applications at the Edge. In this paper, we propose a probabilistic mathematical model that allows to describe one of such solutions. The goal of the model is twofold: i) to make it possible to demonstrate the convergence of the proposed solution; ii) to study the impact of the self-organizing solution without the need of an actual implementation or simulation of the system, allowing to evaluate the suitability of the solution in specific contexts. The paper presents the mathematical formulation of the proposed solution as well as the validation of the proposed model against a simulation of the system.Source: FRAME: 2nd Workshop on Flexible Resource and Application Management on the Edge (colocated with HPDC 2022), pp. 29–32, Minneapolis, Minnestota, USA, 01/07/2022
DOI: 10.1145/3526059.3533620
Project(s): ACCORDION
Metrics:
Mordacchini M., Carlini E., Dazzi P.
The highly dynamic and heterogeneous environment that characterizes the edge of the Cloud/Edge Continuum calls for new intelligent methods for tackling the needs of such a complex scenario. In particular, adaptive and self-organizing decentralized solutions have been advanced for optimizing the placement of applications at the Edge. In this paper, we propose a probabilistic mathematical model that allows to describe one of such solutions. The goal of the model is twofold: i) to make it possible to demonstrate the convergence of the proposed solution; ii) to study the impact of the self-organizing solution without the need of an actual implementation or simulation of the system, allowing to evaluate the suitability of the solution in specific contexts. The paper presents the mathematical formulation of the proposed solution as well as the validation of the proposed model against a simulation of the system.Source: FRAME: 2nd Workshop on Flexible Resource and Application Management on the Edge (colocated with HPDC 2022), pp. 29–32, Minneapolis, Minnestota, USA, 01/07/2022
DOI: 10.1145/3526059.3533620
Project(s): ACCORDION
Metrics:
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ISTI Repository | dl.acm.org | doi.org | CNR ExploRA
2022
Conference article
Open Access
Network measurements with function-as-a-service for distributed low-latency edge applications
Carlini E., Kavalionak H., Dazzi P., Ferrucci L., Coppola M., Mordacchini M.
Edge computing promises to bring computation and storage close to end-users, opening exciting new areas of improvement for applications with a high level of interactivity and requiring low latency. However, these improvements require careful scheduling of applications in the correct Edge resource. This decision is generally taken by considering multiple parameters, including the network capabilities. In this paper, we discuss an approach that measures latency and bandwidth between multiple clients and Edge servers. The approach is based on recent Serverless computing technologies, and it is meant as a support to take timely and correct scheduling decisions in the Edge. We also provide the description of a proof of concept implementation of the said approach.Source: FRAME 2022 - 2nd Workshop on Flexible Resource and Application Management on the Edge (colocated with HPDC 2022), pp. 25–28, Minneapolis, Minnestota, USA, 01/07/2022
DOI: 10.1145/3526059.3533622
Project(s): ACCORDION
Metrics:
Carlini E., Kavalionak H., Dazzi P., Ferrucci L., Coppola M., Mordacchini M.
Edge computing promises to bring computation and storage close to end-users, opening exciting new areas of improvement for applications with a high level of interactivity and requiring low latency. However, these improvements require careful scheduling of applications in the correct Edge resource. This decision is generally taken by considering multiple parameters, including the network capabilities. In this paper, we discuss an approach that measures latency and bandwidth between multiple clients and Edge servers. The approach is based on recent Serverless computing technologies, and it is meant as a support to take timely and correct scheduling decisions in the Edge. We also provide the description of a proof of concept implementation of the said approach.Source: FRAME 2022 - 2nd Workshop on Flexible Resource and Application Management on the Edge (colocated with HPDC 2022), pp. 25–28, Minneapolis, Minnestota, USA, 01/07/2022
DOI: 10.1145/3526059.3533622
Project(s): ACCORDION
Metrics:
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ISTI Repository | dl.acm.org | doi.org | CNR ExploRA
2022
Conference article
Open Access
Energy and QoE aware placement of applications and data at the edge
Mordacchini M., Ferrucci L., Carlini E., Kavalionak H., Coppola M., Dazzi P.
Recent years are witnessing extensions of cyber-infrastructures towards distributed environments. The Edge of the network is gaining a central role in the agenda of both infrastructure and application providers. Following the actual distributed structure of such a computational environment, nowadays, many solutions face resource and application management needs in Cloud/Edge continua. One of the most challenging aspects is ensuring highly available computing and data infrastructures while optimizing the system's energy consumption. In this paper, we describe a decentralized solution that limits the energy consumption by the system without failing to match the users' expectations, defined as the services' Quality of Experience (QoE) when accessing data and leveraging applications at the Edge. Experimental evaluations through simulation conducted with PureEdgeSim demonstrate the effectiveness of the approach.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 109–116, Tirrenia, Pisa, Italy, 19-22/06/2022
Mordacchini M., Ferrucci L., Carlini E., Kavalionak H., Coppola M., Dazzi P.
Recent years are witnessing extensions of cyber-infrastructures towards distributed environments. The Edge of the network is gaining a central role in the agenda of both infrastructure and application providers. Following the actual distributed structure of such a computational environment, nowadays, many solutions face resource and application management needs in Cloud/Edge continua. One of the most challenging aspects is ensuring highly available computing and data infrastructures while optimizing the system's energy consumption. In this paper, we describe a decentralized solution that limits the energy consumption by the system without failing to match the users' expectations, defined as the services' Quality of Experience (QoE) when accessing data and leveraging applications at the Edge. Experimental evaluations through simulation conducted with PureEdgeSim demonstrate the effectiveness of the approach.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 109–116, Tirrenia, Pisa, Italy, 19-22/06/2022
See at:
ISTI Repository | ceur-ws.org | CNR ExploRA
2022
Conference article
Open Access
Decentralized federated learning and network topologies: an empirical study on convergence
Kavalionak H., Carlini E., Dazzi P., Ferrucci L., Mordacchini M., Coppola M.
Federated Learning is a well-known learning paradigm that allows the distributed training of machine learning models. Federated Learning keeps data in the source devices and communicates only the model's coefficients to a centralized server. This paper studies the decentralized flavor of Federated Learning. A peer-to-peer network replaces the centralized server, and nodes exchange model's coefficients directly. In particular, we look for empirical evidence on the effect of different network topologies and communication parameters on the convergence in the training of distributed models. Our observations suggest that small-world networks converge faster for small amounts of nodes, while xx are more suitable for larger setups.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 317–324, Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): TEACHING
Kavalionak H., Carlini E., Dazzi P., Ferrucci L., Mordacchini M., Coppola M.
Federated Learning is a well-known learning paradigm that allows the distributed training of machine learning models. Federated Learning keeps data in the source devices and communicates only the model's coefficients to a centralized server. This paper studies the decentralized flavor of Federated Learning. A peer-to-peer network replaces the centralized server, and nodes exchange model's coefficients directly. In particular, we look for empirical evidence on the effect of different network topologies and communication parameters on the convergence in the training of distributed models. Our observations suggest that small-world networks converge faster for small amounts of nodes, while xx are more suitable for larger setups.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 317–324, Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): TEACHING
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2022
Conference article
Open Access
A topological perspective of port networks from three years (2017-2019) of AIS Data
Carlini E., De Lira V. M., Soares A., Etemad M., Brandoli B., Matwin S.
Complex network analysis is a fundamental tool to understand non-trivial aspects of graphs and networks and is widely used in many fields. In this paper, we apply complex network techniques to study port networks, in which nodes are ports and edges are maritime lines between ports. In particular, we study the temporal evolution of several topological features of a network of ports, including connected components, shortest paths, and clustering coefficients. We built the network with three years of Automatic Identification System data from 2017 to 2019. We highlight several interesting trends and behaviors that differentiate long-range vessels from short-range vessels.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 268–275, Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): MASTER
Carlini E., De Lira V. M., Soares A., Etemad M., Brandoli B., Matwin S.
Complex network analysis is a fundamental tool to understand non-trivial aspects of graphs and networks and is widely used in many fields. In this paper, we apply complex network techniques to study port networks, in which nodes are ports and edges are maritime lines between ports. In particular, we study the temporal evolution of several topological features of a network of ports, including connected components, shortest paths, and clustering coefficients. We built the network with three years of Automatic Identification System data from 2017 to 2019. We highlight several interesting trends and behaviors that differentiate long-range vessels from short-range vessels.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 268–275, Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): MASTER
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2022
Journal article
Open Access
NAVIGATOR: an Italian regional imaging biobank to promote precision medicine for oncologic patients
Borgheresi R., Barucci A., Colantonio S., Aghakhanyan G., Assante M., Bertelli E., Carlini E., Carpi R., Caudai C., Cavallero D., Cioni D., Cirillo R., Colcelli V., Dell'Amico A., Di Gangi D., Erba P. A., Faggioni L., Falaschi Z., Gabelloni M., Gini R., Lelii L., Liò P., Lorito A., Lucarini S., Manghi P., Mangiacrapa F., Marzi C., Mazzei M. A., Mercatelli L., Mirabile A., Mungai F., Miele V., Olmastroni M., Pagano P., Paiar F., Panichi G., Pascali M. A., Pasquinelli F., Shortrede J. E., Tumminello L., Volterrani L., Neri E., On Behalf Of The Navigator Consortium Group
NAVIGATOR is an Italian regional project to boost precision medicine in oncology with the aim to make it more predictive, preventive, and personalised by advancing translational research based on quantitative imaging and integrative omics analyses. The project's goal is to develop an open imaging biobank for the collection and preservation of a large amount of standardised imaging multimodal datasets, including computed tomography, magnetic resonance imaging, and positron emission tomography data, together with the corresponding patient-related and omics-related relevant information extracted from regional healthcare services using an adapted privacy-preserving model. The project is based on an open-source imaging biobank and an open-science oriented virtual research environment (VRE). Available integrative omics and multi-imaging data of three use cases (prostate cancer, rectal cancer, and gastric cancer) will be collected. All data confined in NAVIGATOR (i.e. standard and novel imaging biomarkers, non-imaging data, health agency data) will be used to create a digital patient model, to support the reliable prediction of the disease phenotype and risk stratification. The VRE that relies on a well-established infrastructure, called D4Science.org, will further provide a multiset infrastructure for processing the integrative omics data, extracting specific radiomic signatures, and for identification and testing of novel imaging biomarkers through big data analytics and artificial intelligence.Source: European radiology experimental Online 6 (2022). doi:10.1186/s41747-022-00306-9
DOI: 10.1186/s41747-022-00306-9
Metrics:
Borgheresi R., Barucci A., Colantonio S., Aghakhanyan G., Assante M., Bertelli E., Carlini E., Carpi R., Caudai C., Cavallero D., Cioni D., Cirillo R., Colcelli V., Dell'Amico A., Di Gangi D., Erba P. A., Faggioni L., Falaschi Z., Gabelloni M., Gini R., Lelii L., Liò P., Lorito A., Lucarini S., Manghi P., Mangiacrapa F., Marzi C., Mazzei M. A., Mercatelli L., Mirabile A., Mungai F., Miele V., Olmastroni M., Pagano P., Paiar F., Panichi G., Pascali M. A., Pasquinelli F., Shortrede J. E., Tumminello L., Volterrani L., Neri E., On Behalf Of The Navigator Consortium Group
NAVIGATOR is an Italian regional project to boost precision medicine in oncology with the aim to make it more predictive, preventive, and personalised by advancing translational research based on quantitative imaging and integrative omics analyses. The project's goal is to develop an open imaging biobank for the collection and preservation of a large amount of standardised imaging multimodal datasets, including computed tomography, magnetic resonance imaging, and positron emission tomography data, together with the corresponding patient-related and omics-related relevant information extracted from regional healthcare services using an adapted privacy-preserving model. The project is based on an open-source imaging biobank and an open-science oriented virtual research environment (VRE). Available integrative omics and multi-imaging data of three use cases (prostate cancer, rectal cancer, and gastric cancer) will be collected. All data confined in NAVIGATOR (i.e. standard and novel imaging biomarkers, non-imaging data, health agency data) will be used to create a digital patient model, to support the reliable prediction of the disease phenotype and risk stratification. The VRE that relies on a well-established infrastructure, called D4Science.org, will further provide a multiset infrastructure for processing the integrative omics data, extracting specific radiomic signatures, and for identification and testing of novel imaging biomarkers through big data analytics and artificial intelligence.Source: European radiology experimental Online 6 (2022). doi:10.1186/s41747-022-00306-9
DOI: 10.1186/s41747-022-00306-9
Metrics:
See at:
eurradiolexp.springeropen.com | ISTI Repository | CNR ExploRA
2021
Contribution to conference
Open Access
Cloud and data federation in MobiDataLab
Carlini E., Dazzi P., Lettich F., Perego R., Renso C.
Today's innovative digital services dealing with the mobility of per- sons and goods produce huge amount of data. To propose advanced and efficient mobility services, the collection and aggregation of new sources of data from various producers are necessary. The overall objective of the MobiDataLab H2020 project is to propose to the mobility stakeholders (transport organising authorities, operators, industry, government and innovators) reproducible methodologies and sustainable tools that foster the development of a data-sharing culture in Europe and beyond. This short paper introduces the key concepts driving the design and definition of the Cloud and Data Federation that stands at the basis of MobiDataLab.Source: FRAME'21 - 1st Workshop on Flexible Resource and Application Management on the Edge, pp. 39–40, Virtual Event, Sweden, 25/06/2021
DOI: 10.1145/3452369.3463819
Project(s): ACCORDION
Metrics:
Carlini E., Dazzi P., Lettich F., Perego R., Renso C.
Today's innovative digital services dealing with the mobility of per- sons and goods produce huge amount of data. To propose advanced and efficient mobility services, the collection and aggregation of new sources of data from various producers are necessary. The overall objective of the MobiDataLab H2020 project is to propose to the mobility stakeholders (transport organising authorities, operators, industry, government and innovators) reproducible methodologies and sustainable tools that foster the development of a data-sharing culture in Europe and beyond. This short paper introduces the key concepts driving the design and definition of the Cloud and Data Federation that stands at the basis of MobiDataLab.Source: FRAME'21 - 1st Workshop on Flexible Resource and Application Management on the Edge, pp. 39–40, Virtual Event, Sweden, 25/06/2021
DOI: 10.1145/3452369.3463819
Project(s): ACCORDION
Metrics:
See at:
ISTI Repository | dl.acm.org | CNR ExploRA
2021
Conference article
Open Access
Inter-operability and orchestration in heterogeneous cloud/edge resources: the ACCORDION vision
Korontanis I., Tserpes K., Pateraki M., Blasi L., Violos J., Ferran D., Marin E., Kourtellis N., Coppola M., Carlini E., Ledwo? Z., Tarkowski P., Loven T., González Rozas Y., Kentros M., Dodis M., Dazzi P.
This paper introduces the ACCORDION framework, a novel frame- work for the management of the cloud-edge continuum, targeting the support of NextGen applications with strong QoE requirements. The framework addresses the need for an ever expanding and het- erogeneous pool of edge resources in order to deliver the promise of ubiquitous computing to the NextGen application clients. This endeavor entails two main technical challenges. First, to assure interoperability when incorporating heterogeneous infrastructures in the pool. Second, the management of the largely dynamic pool of edge nodes. The optimization of the delivered QoE stands as the core driver to this work, therefore its monitoring and modelling comprises a core part of the conducted work. The paper discusses the main pillars that support the ACCORDION vision, and provide a description of the three planned use case that are planned to demonstrate ACCORDION capabilities.Source: FRAME'21 - 1st Workshop on Flexible Resource and Application Management on the Edge, pp. 9–14, Virtual Event, Sweden, 25/06/2021
DOI: 10.1145/3452369.3463816
Project(s): ACCORDION, ACCORDION
Metrics:
Korontanis I., Tserpes K., Pateraki M., Blasi L., Violos J., Ferran D., Marin E., Kourtellis N., Coppola M., Carlini E., Ledwo? Z., Tarkowski P., Loven T., González Rozas Y., Kentros M., Dodis M., Dazzi P.
This paper introduces the ACCORDION framework, a novel frame- work for the management of the cloud-edge continuum, targeting the support of NextGen applications with strong QoE requirements. The framework addresses the need for an ever expanding and het- erogeneous pool of edge resources in order to deliver the promise of ubiquitous computing to the NextGen application clients. This endeavor entails two main technical challenges. First, to assure interoperability when incorporating heterogeneous infrastructures in the pool. Second, the management of the largely dynamic pool of edge nodes. The optimization of the delivered QoE stands as the core driver to this work, therefore its monitoring and modelling comprises a core part of the conducted work. The paper discusses the main pillars that support the ACCORDION vision, and provide a description of the three planned use case that are planned to demonstrate ACCORDION capabilities.Source: FRAME'21 - 1st Workshop on Flexible Resource and Application Management on the Edge, pp. 9–14, Virtual Event, Sweden, 25/06/2021
DOI: 10.1145/3452369.3463816
Project(s): ACCORDION
, ACCORDION Metrics:
See at:
ISTI Repository | ZENODO | dl.acm.org | CNR ExploRA
2021
Conference article
Open Access
Latency preserving self-optimizing placement at the edge
Ferrucci L., Mordacchini M., Coppola M., Carlini E., Kavalionak H., Dazzi P.
The Internet is experiencing a fast expansion at its edges. The wide availability of heterogeneous resources at the Edge is pivotal in the definition and extension of traditional Cloud solutions toward supporting the development of new applications. However, the dynamic and distributed nature of these resources poses new challenges for the optimization of the behaviour of the system. New decentralized and self-organizing methods are needed to face the needs of the Edge/Cloud scenario and to optimize the exploitation of Edge resources. In this paper we propose a distributed and adaptive solution that reduces the number of replicas of application services that are executed throughout the system, all the while ensuring that the latency constraints of applications are met, thus allowing to also meet the end users' QoS requirements. Experimental evaluations through simulation show the effectiveness of the proposed approach.Source: FRAME'21 - 1st Workshop on Flexible Resource and Application Management on the Edge, pp. 3–8, Virtual Event, Sweden, 25/06/2021
DOI: 10.1145/3452369.3463815
Project(s): ACCORDION, ACCORDION
Metrics:
Ferrucci L., Mordacchini M., Coppola M., Carlini E., Kavalionak H., Dazzi P.
The Internet is experiencing a fast expansion at its edges. The wide availability of heterogeneous resources at the Edge is pivotal in the definition and extension of traditional Cloud solutions toward supporting the development of new applications. However, the dynamic and distributed nature of these resources poses new challenges for the optimization of the behaviour of the system. New decentralized and self-organizing methods are needed to face the needs of the Edge/Cloud scenario and to optimize the exploitation of Edge resources. In this paper we propose a distributed and adaptive solution that reduces the number of replicas of application services that are executed throughout the system, all the while ensuring that the latency constraints of applications are met, thus allowing to also meet the end users' QoS requirements. Experimental evaluations through simulation show the effectiveness of the proposed approach.Source: FRAME'21 - 1st Workshop on Flexible Resource and Application Management on the Edge, pp. 3–8, Virtual Event, Sweden, 25/06/2021
DOI: 10.1145/3452369.3463815
Project(s): ACCORDION
, ACCORDION Metrics:
See at:
ISTI Repository | ZENODO | dl.acm.org | CNR ExploRA
2021
Conference article
Open Access
An osmotic ecosystem for data streaming applications in smart cities
Carlini E., Carnevale L., Coppola M., Dazzi P., Mencagli G., Talia D., Villari M.
Modern multi-tier Cloud-Edge-IoT computational platforms seamlessly map with the distributed and hierarchical nature of smart cities infrastructure. However, classical tools and methodologies to organise data as well as computational and network resources are poorly equipped to tackle the dynamic and heterogeneous environments of smart cities. In this paper we propose a reference architecture that aims to establish a unified approach for the orchestration of modern Cloud-Edge-IoT infrastructures and resources specifically tailored for data streaming applications in smart-cities. Stemming from the proposed reference architecture, we also discuss a series of open challenges, which we believe represent relevant research directions in the nearest future.Source: FRAME'21 - 1st Workshop on Flexible Resource and Application Management on the Edge, pp. 27–31, Virtual event, Sweden, 25/06/2021
DOI: 10.1145/3452369.3463822
Metrics:
Carlini E., Carnevale L., Coppola M., Dazzi P., Mencagli G., Talia D., Villari M.
Modern multi-tier Cloud-Edge-IoT computational platforms seamlessly map with the distributed and hierarchical nature of smart cities infrastructure. However, classical tools and methodologies to organise data as well as computational and network resources are poorly equipped to tackle the dynamic and heterogeneous environments of smart cities. In this paper we propose a reference architecture that aims to establish a unified approach for the orchestration of modern Cloud-Edge-IoT infrastructures and resources specifically tailored for data streaming applications in smart-cities. Stemming from the proposed reference architecture, we also discuss a series of open challenges, which we believe represent relevant research directions in the nearest future.Source: FRAME'21 - 1st Workshop on Flexible Resource and Application Management on the Edge, pp. 27–31, Virtual event, Sweden, 25/06/2021
DOI: 10.1145/3452369.3463822
Metrics:
See at:
ISTI Repository | dl.acm.org | CNR ExploRA