2024
Journal article
Open Access
Springald: GPU-Accelerated Window-Based Aggregates over Out-of-Order Data Streams
Mencagli G., Dazzi P., Coppola M.
An increasing number of application domains require high-throughput processing to extract insights from massive data streams. The Data Stream Processing (DSP) paradigm provides formal approaches to analyze structured data streams considered as special, unbounded relations. The most used class of stateful operators in DSP are the ones running sliding-window aggregation, which continuously extracts insights from the most recent portion of the stream. This article presents Springald, an efficient sliding-window operator leveraging GPU devices. Springald, incorporated in the WindFlow parallel library, processes out-of-order data streams with watermarks propagation. These two features-GPU processing and out-of-orderliness-make Springald a novel contribution to this research area. This article describes the methodology behind Springald, its design and implementation. We also provide an extensive experimental evaluation to understand the behavior of Springald deeply, and we showcase its superior performance against state-of-the-art competitors.Source: IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, vol. 35 (issue 9), pp. 1657-1671
DOI: 10.1109/tpds.2024.3431611
Project(s): NOUS
Metrics:
Mencagli G., Dazzi P., Coppola M.
An increasing number of application domains require high-throughput processing to extract insights from massive data streams. The Data Stream Processing (DSP) paradigm provides formal approaches to analyze structured data streams considered as special, unbounded relations. The most used class of stateful operators in DSP are the ones running sliding-window aggregation, which continuously extracts insights from the most recent portion of the stream. This article presents Springald, an efficient sliding-window operator leveraging GPU devices. Springald, incorporated in the WindFlow parallel library, processes out-of-order data streams with watermarks propagation. These two features-GPU processing and out-of-orderliness-make Springald a novel contribution to this research area. This article describes the methodology behind Springald, its design and implementation. We also provide an extensive experimental evaluation to understand the behavior of Springald deeply, and we showcase its superior performance against state-of-the-art competitors.Source: IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, vol. 35 (issue 9), pp. 1657-1671
DOI: 10.1109/tpds.2024.3431611
Project(s): NOUS
Metrics:
See at:
IEEE Transactions on Parallel and Distributed Systems 
| CNR IRIS | ieeexplore.ieee.org | Archivio della Ricerca - Università di Pisa 
| Archivio della Ricerca - Università di Pisa | Archivio della Ricerca - Università di Pisa | CNR IRIS
2024
Conference article
Open Access
TEACHING platform for human-centric autonomous applications: design and overview
De Caro V., Chronis C., Coppola M., Lomonaco V., Gallicchio C., Tserpes K., Bacciu D.
The TEACHING project enhances AI applications in pervasive environments via Humanistic Intelligence, fostering synergy between humans and Cyber-Physical Systems of Systems (CPSoS). Here, we present the TEACHING Platform, a microservice-based framework providing the technological advancements to represent humans and CPSoS as containerized software models that interact to mutually empower each other.DOI: 10.1145/3625549.3658813
Project(s): TEACHING
Metrics:
De Caro V., Chronis C., Coppola M., Lomonaco V., Gallicchio C., Tserpes K., Bacciu D.
The TEACHING project enhances AI applications in pervasive environments via Humanistic Intelligence, fostering synergy between humans and Cyber-Physical Systems of Systems (CPSoS). Here, we present the TEACHING Platform, a microservice-based framework providing the technological advancements to represent humans and CPSoS as containerized software models that interact to mutually empower each other.DOI: 10.1145/3625549.3658813
Project(s): TEACHING
Metrics:
See at:
dl.acm.org | CNR IRIS | doi.org | Archivio della Ricerca - Università di Pisa | CNR IRIS
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.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.DOI: 10.1109/compsac57700.2023.00287
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
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.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.DOI: 10.1145/3589010.3595652
Project(s): ACCORDION
Metrics:
See at:
dl.acm.org | CNR IRIS | ISTI Repository | CNR IRIS | CNR IRIS
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, vol. 1
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, vol. 1
DOI: 10.3389/fhpcp.2023.1164915
Metrics:
See at:
doi.org | CNR IRIS | ISTI Repository | www.frontiersin.org | CNR IRIS
2023
Conference article
Open Access
TEACHING: A Computing Toolkit for Building Efficient Autonomous appliCations Leveraging Humanistic INtelliGence
Bacciu D, Tserpes K, Coppola M, Macher G, Gallicchio C, Veledar O, Anaxagorou Am, Dazzi P
TEACHING proposes a distributed, trustworthy AI integrating continuous human feedback, supporting CPSoS application design and deployment. TEACHING envisions an intelligent environment, empowering humans through cybernetic assistance. It advances autonomous safety-critical systems, improving safety, reliability and acceptability through human-centred design and formal validation crossing paradigms. TEACHING brings humans and AI together, enabling participatory development, optimisation and oversight.DOI: 10.1145/3589010.3594886
Project(s): TEACHING
Metrics:
Bacciu D, Tserpes K, Coppola M, Macher G, Gallicchio C, Veledar O, Anaxagorou Am, Dazzi P
TEACHING proposes a distributed, trustworthy AI integrating continuous human feedback, supporting CPSoS application design and deployment. TEACHING envisions an intelligent environment, empowering humans through cybernetic assistance. It advances autonomous safety-critical systems, improving safety, reliability and acceptability through human-centred design and formal validation crossing paradigms. TEACHING brings humans and AI together, enabling participatory development, optimisation and oversight.DOI: 10.1145/3589010.3594886
Project(s): TEACHING
Metrics:
See at:
dl.acm.org | CNR IRIS | ISTI Repository | doi.org | CNR IRIS
2023
Conference article
Open Access
AI-Toolkit: a microservices architecture for low-code decentralized machine intelligence
Lomonaco V., Caro V. D., Gallicchio C., Carta A., Sardianos C., Varlamis I., Tserpes K., Coppola M., Marmpena M., Politi S., Schoitsch E., Bacciu D.
Artificial Intelligence and Machine Learning toolkits such as Scikit-learn, PyTorch and Tensorflow provide today a solid starting point for the rapid prototyping of R&D solutions. However, they can be hardly ported to heterogeneous decentralised hardware and real-world production environments. A common practice involves outsourcing deployment solutions to scalable cloud infrastructures such as Amazon SageMaker or Microsoft Azure. In this paper, we proposed an open-source microservices-based architecture for decentralised machine intelligence which aims at bringing R&D and deployment functionalities closer following a low-code approach. Such an approach would guarantee flexible integration of cutting-edge functionalities while preserving complete control over the deployed solutions at negligible costs and maintenance efforts.DOI: 10.1109/icasspw59220.2023.10193222
DOI: 10.5281/zenodo.8091679
DOI: 10.5281/zenodo.8091680
Project(s): TEACHING
Metrics:
Lomonaco V., Caro V. D., Gallicchio C., Carta A., Sardianos C., Varlamis I., Tserpes K., Coppola M., Marmpena M., Politi S., Schoitsch E., Bacciu D.
Artificial Intelligence and Machine Learning toolkits such as Scikit-learn, PyTorch and Tensorflow provide today a solid starting point for the rapid prototyping of R&D solutions. However, they can be hardly ported to heterogeneous decentralised hardware and real-world production environments. A common practice involves outsourcing deployment solutions to scalable cloud infrastructures such as Amazon SageMaker or Microsoft Azure. In this paper, we proposed an open-source microservices-based architecture for decentralised machine intelligence which aims at bringing R&D and deployment functionalities closer following a low-code approach. Such an approach would guarantee flexible integration of cutting-edge functionalities while preserving complete control over the deployed solutions at negligible costs and maintenance efforts.DOI: 10.1109/icasspw59220.2023.10193222
DOI: 10.5281/zenodo.8091679
DOI: 10.5281/zenodo.8091680
Project(s): TEACHING
Metrics:
See at:
ZENODO | ZENODO | CNR IRIS | ieeexplore.ieee.org | doi.org | GitHub | Archivio della Ricerca - Università di Pisa | CNR IRIS | CNR IRIS
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 KafkaDOI: 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 KafkaDOI: 10.1145/3526059.3533621
Project(s): TEACHING
Metrics:
See at:
ZENODO | dl.acm.org | doi.org | CNR IRIS | CNR IRIS
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.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.DOI: 10.1145/3526059.3533622
Project(s): ACCORDION
Metrics:
See at:
dl.acm.org | CNR IRIS | ISTI Repository | doi.org | CNR IRIS | CNR IRIS
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: CEUR WORKSHOP PROCEEDINGS, 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: CEUR WORKSHOP PROCEEDINGS, pp. 109-116. Tirrenia, Pisa, Italy, 19-22/06/2022
See at:
ISTI Repository | ceur-ws.org | CNR IRIS | CNR IRIS
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: CEUR WORKSHOP PROCEEDINGS, 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: CEUR WORKSHOP PROCEEDINGS, pp. 317-324. Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): TEACHING
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2022
Other
Open Access
Driving AI-IoT design towards the UN Sustainable Development Goals (SDGs)
Tulone D, Samuel G, Tibuzzi A, Coppola M, Charter M, Gemma P, Henz P, Gonzales R, Chessa S, Catarci T
Final technical report from the ITU-T Focus Group on Environmental Efficiency for Artificial Intelligence and other Emerging Technologies (FG-AI4EE), discussing the need for integrating and harmonizing environmental, social models and sustainability needs when designing AI-IoT based solutions. The report highlights (I) current barriers hampering the adoption of a comprehensive path that addresses all three needs, (II) the risks stemming from single-path sustainability approaches, and (III) provides suggestions for future work that can foster and promote the adoption of a more comprehensive process of designing sustainable AI-IoT systems.Project(s): TEACHING, ACCORDION
Tulone D, Samuel G, Tibuzzi A, Coppola M, Charter M, Gemma P, Henz P, Gonzales R, Chessa S, Catarci T
Final technical report from the ITU-T Focus Group on Environmental Efficiency for Artificial Intelligence and other Emerging Technologies (FG-AI4EE), discussing the need for integrating and harmonizing environmental, social models and sustainability needs when designing AI-IoT based solutions. The report highlights (I) current barriers hampering the adoption of a comprehensive path that addresses all three needs, (II) the risks stemming from single-path sustainability approaches, and (III) provides suggestions for future work that can foster and promote the adoption of a more comprehensive process of designing sustainable AI-IoT systems.Project(s): TEACHING
, ACCORDION
See at:
CNR IRIS | www.itu.int | CNR IRIS | CNR IRIS
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.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.DOI: 10.1145/3452369.3463816
Project(s): ACCORDION
, ACCORDION Metrics:
See at:
dl.acm.org | CNR IRIS | ISTI Repository | ZENODO | CNR IRIS | CNR IRIS
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.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.DOI: 10.1145/3452369.3463815
Project(s): ACCORDION
, ACCORDION Metrics:
See at:
dl.acm.org | CNR IRIS | ISTI Repository | ZENODO | CNR IRIS | CNR IRIS
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.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.DOI: 10.1145/3452369.3463822
Metrics:
See at:
dl.acm.org | CNR IRIS | ISTI Repository | CNR IRIS | CNR IRIS
2021
Other
Open Access
ACCORDION D4.1 - Edge/Cloud continuum management framework report (I)
Taleb T, Violos J, Tsanakas S, Pagoulatou T, Theodoropoulos T, Coppola M, Dazzi P, Ferrucci L, Diego F, Marin E, Kourtelis N
This deliverable provides the first report summarizing the scientific advancements, during the first year of the project, achieved by WP4 Tasks. Work Package (WP) 4, dubbed Edge/Cloud continuum management framework, is organized around 6 Tasks is to develop a framework that efficiently manages the deployment and runtime of ACCORDION applications on the continuum.Project(s): ACCORDION
Taleb T, Violos J, Tsanakas S, Pagoulatou T, Theodoropoulos T, Coppola M, Dazzi P, Ferrucci L, Diego F, Marin E, Kourtelis N
This deliverable provides the first report summarizing the scientific advancements, during the first year of the project, achieved by WP4 Tasks. Work Package (WP) 4, dubbed Edge/Cloud continuum management framework, is organized around 6 Tasks is to develop a framework that efficiently manages the deployment and runtime of ACCORDION applications on the continuum.Project(s): ACCORDION
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2021
Other
Open Access
ACCORDION D7.2 - Dissemination & exploitation activities (I)
Tserpes K, Di Girolamo M, Violos J, Lipa B, Paterkai M, Loven T, Tarkowski P, Taleb T, Nadir Z, Kourtelis N, Schmidt S, Ferran D, Dazzi P, Rapisarda B, Coppola M, Vakalellis M
ACCORDION project aims at unlocking the full potential of a big class of applications that are too latency- sensitive, or data-dependent, to be moved to the public cloud. ACCORDION couples efficient, decentralized and AI-based solutions for cloud and edge resource federation with novel approaches for application definition management and generation at runtime. This deliverable provides a broad overview of the communication, dissemination and exploitation activities implemented within the ACCORDION project within the first 18 months of the project and a detailed market analysis. A detailed exploitation strategy for the project, as well as exploitation plans for individual partners, was presented in the previous deliverable D7.1. Based on those plans, in this deliverable we carry out an evaluation of the ACCORDION project's dissemination and exploitation activities made so far.Project(s): ACCORDION
Tserpes K, Di Girolamo M, Violos J, Lipa B, Paterkai M, Loven T, Tarkowski P, Taleb T, Nadir Z, Kourtelis N, Schmidt S, Ferran D, Dazzi P, Rapisarda B, Coppola M, Vakalellis M
ACCORDION project aims at unlocking the full potential of a big class of applications that are too latency- sensitive, or data-dependent, to be moved to the public cloud. ACCORDION couples efficient, decentralized and AI-based solutions for cloud and edge resource federation with novel approaches for application definition management and generation at runtime. This deliverable provides a broad overview of the communication, dissemination and exploitation activities implemented within the ACCORDION project within the first 18 months of the project and a detailed market analysis. A detailed exploitation strategy for the project, as well as exploitation plans for individual partners, was presented in the previous deliverable D7.1. Based on those plans, in this deliverable we carry out an evaluation of the ACCORDION project's dissemination and exploitation activities made so far.Project(s): ACCORDION
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CNR IRIS | ISTI Repository | CNR IRIS
2021
Conference article
Open Access
TEACHING - Trustworthy autonomous cyber-physical applications through human-centred intelligence
Bacciu D., Akarmazyan S., Armengaud E., Bacco M., Bravos G., Calandra C., Carlini E., Carta A., Cassarà P., Coppola M., Davalas C., Dazzi P., Degennaro M. C., Di Sarli D., Dobaj J., Gallicchio C., Girbal S., Gotta A., Groppo R., Lomonaco V., Macher G., Mazzei D., Mencagli G., Michail D., Micheli A., Peroglio R., Petroni S., Potenza R., Pourdanesh F., Sardianos C., Tserpes K., Tagliabò F., Vatl J., Varlamis I., Veledar O.
This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum. TEACHING puts forward a human-centred vision leveraging the physiological, emotional, and cognitive state of the users as a driver for the adaptation and optimization of the autonomous applications. It does so by building a distributed, embedded and federated learning system complemented by methods and tools to enforce its dependability, security and privacy preservation. The paper discusses the main concepts of the TEACHING approach and singles out the main AI-related research challenges associated with it. Further, we provide a discussion of the design choices for the TEACHING system to tackle the aforementioned challenges.DOI: 10.1109/coins51742.2021.9524099
DOI: 10.5281/zenodo.5293769
DOI: 10.5281/zenodo.5293768
Project(s): TEACHING
Metrics:
Bacciu D., Akarmazyan S., Armengaud E., Bacco M., Bravos G., Calandra C., Carlini E., Carta A., Cassarà P., Coppola M., Davalas C., Dazzi P., Degennaro M. C., Di Sarli D., Dobaj J., Gallicchio C., Girbal S., Gotta A., Groppo R., Lomonaco V., Macher G., Mazzei D., Mencagli G., Michail D., Micheli A., Peroglio R., Petroni S., Potenza R., Pourdanesh F., Sardianos C., Tserpes K., Tagliabò F., Vatl J., Varlamis I., Veledar O.
This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum. TEACHING puts forward a human-centred vision leveraging the physiological, emotional, and cognitive state of the users as a driver for the adaptation and optimization of the autonomous applications. It does so by building a distributed, embedded and federated learning system complemented by methods and tools to enforce its dependability, security and privacy preservation. The paper discusses the main concepts of the TEACHING approach and singles out the main AI-related research challenges associated with it. Further, we provide a discussion of the design choices for the TEACHING system to tackle the aforementioned challenges.DOI: 10.1109/coins51742.2021.9524099
DOI: 10.5281/zenodo.5293769
DOI: 10.5281/zenodo.5293768
Project(s): TEACHING
Metrics:
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arXiv.org e-Print Archive | CNR IRIS | ieeexplore.ieee.org | CNR IRIS | CNR IRIS | xplorestaging.ieee.org
2021
Conference article
Open Access
Impact of network topology on the convergence of decentralized federated learning systems
Kavalionak H, Carlini E, Dazzi P, Ferrucci L, Mordacchini M, Coppola M
Federated learning is a popular framework that enables harvesting edge resources' computational power to train a machine learning model distributively. However, it is not always feasible or profitable to have a centralized server that controls and synchronizes the training process. In this paper, we consider the problem of training a machine learning model over a network of nodes in a fully decentralized fashion. In particular, we look for empirical evidence on how sensitive is the training process for various network characteristics and communication parameters. We present the outcome of several simulations conducted with different network topologies, datasets, and machine learning models.DOI: 10.1109/iscc53001.2021.9631460
Project(s): TEACHING
Metrics:
Kavalionak H, Carlini E, Dazzi P, Ferrucci L, Mordacchini M, Coppola M
Federated learning is a popular framework that enables harvesting edge resources' computational power to train a machine learning model distributively. However, it is not always feasible or profitable to have a centralized server that controls and synchronizes the training process. In this paper, we consider the problem of training a machine learning model over a network of nodes in a fully decentralized fashion. In particular, we look for empirical evidence on how sensitive is the training process for various network characteristics and communication parameters. We present the outcome of several simulations conducted with different network topologies, datasets, and machine learning models.DOI: 10.1109/iscc53001.2021.9631460
Project(s): TEACHING
Metrics:
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CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
2021
Conference article
Open Access
Self- organizing energy-minimization placement of QoE-constrained services at the edge
Mordacchini M, Ferrucci L, Carlini E, Kavalionak H, Coppola M, Dazzi P
The wide availability of heterogeneous resources at the Edgeof the network is gaining a central role in defining and developing newcomputing paradigms for both the infrastructures and the applications.However, it becomes challenging to optimize the system's behaviour, dueto the Edge's highly distributed and dynamic nature. Recent solutionspropose new decentralized, self-adaptive approaches to face the needs ofthis scenario. One of the most challenging aspect is related to the opti-mization of the system's energy consumption. In this paper, we proposea fully decentralized solution that limits the energy consumed by thesystem, without failing to match the users expectations, defined as theservices' Quality of Experience (QoE.). Specifically, we propose a schemewhere the autonomous coordination of entities at Edge is able to reducethe energy consumption by reducing the number of instances of the ap-plications executed in system. This result is achieve without violatingthe services' QoE, expressed in terms of latency. Experimental evalua-tions through simulation conducted with PureEdgeSim demonstrate theeffectiveness of the approachDOI: 10.1007/978-3-030-92916-9_11
Project(s): ACCORDION
Metrics:
Mordacchini M, Ferrucci L, Carlini E, Kavalionak H, Coppola M, Dazzi P
The wide availability of heterogeneous resources at the Edgeof the network is gaining a central role in defining and developing newcomputing paradigms for both the infrastructures and the applications.However, it becomes challenging to optimize the system's behaviour, dueto the Edge's highly distributed and dynamic nature. Recent solutionspropose new decentralized, self-adaptive approaches to face the needs ofthis scenario. One of the most challenging aspect is related to the opti-mization of the system's energy consumption. In this paper, we proposea fully decentralized solution that limits the energy consumed by thesystem, without failing to match the users expectations, defined as theservices' Quality of Experience (QoE.). Specifically, we propose a schemewhere the autonomous coordination of entities at Edge is able to reducethe energy consumption by reducing the number of instances of the ap-plications executed in system. This result is achieve without violatingthe services' QoE, expressed in terms of latency. Experimental evalua-tions through simulation conducted with PureEdgeSim demonstrate theeffectiveness of the approachDOI: 10.1007/978-3-030-92916-9_11
Project(s): ACCORDION
Metrics:
See at:
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