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2016
Journal article
Restricted
Making puzzles green and useful for adaptive identity management in large-scale distributed systems
Cordeiro W. L. D. C., Santos F. R., Barcelos M. P., Gaspary L. P., Kavalionak H., Guerrieri A., Montresor A.
Various online systems offer a lightweight process for creating accounts (e.g., confirming an e-mail address), so that users can easily join them. With minimum effort, however, an attacker can subvert this process, obtain a multitude of fake accounts, and use them for malicious purposes. Puzzle-based solutions have been proposed to limit the spread of fake accounts, by establishing a price (in terms of computing resources) per identity requested. Although effective, they do not distinguish between requests coming from presumably legitimate users and potential attackers, and also lead to a significant waste of energy and computing power. In this paper, we build on adaptive puzzles and complement them with waiting time to introduce a green design for lightweight, long-term identity management; it balances the complexity of assigned puzzles based on the reputation of the origin (source) of identity requests, and reduces energy consumption caused by puzzle-solving. We also take advantage of lessons learned from massive distributed computing to come up with a design that makes puzzle-processing useful. Based on a set of experiments, we show that our solution provides significant energy savings and makes puzzle-solving a useful task, while not compromising effectiveness in limiting the spread of fake accounts.Source: Computer networks (1999) 95 (2016): 97–114. doi:10.1016/j.comnet.2015.12.005
DOI: 10.1016/j.comnet.2015.12.005
Metrics:
Cordeiro W. L. D. C., Santos F. R., Barcelos M. P., Gaspary L. P., Kavalionak H., Guerrieri A., Montresor A.
Various online systems offer a lightweight process for creating accounts (e.g., confirming an e-mail address), so that users can easily join them. With minimum effort, however, an attacker can subvert this process, obtain a multitude of fake accounts, and use them for malicious purposes. Puzzle-based solutions have been proposed to limit the spread of fake accounts, by establishing a price (in terms of computing resources) per identity requested. Although effective, they do not distinguish between requests coming from presumably legitimate users and potential attackers, and also lead to a significant waste of energy and computing power. In this paper, we build on adaptive puzzles and complement them with waiting time to introduce a green design for lightweight, long-term identity management; it balances the complexity of assigned puzzles based on the reputation of the origin (source) of identity requests, and reduces energy consumption caused by puzzle-solving. We also take advantage of lessons learned from massive distributed computing to come up with a design that makes puzzle-processing useful. Based on a set of experiments, we show that our solution provides significant energy savings and makes puzzle-solving a useful task, while not compromising effectiveness in limiting the spread of fake accounts.Source: Computer networks (1999) 95 (2016): 97–114. doi:10.1016/j.comnet.2015.12.005
DOI: 10.1016/j.comnet.2015.12.005
Metrics:
See at:
Computer Networks 
| www.sciencedirect.com | CNR ExploRA
2016
Conference article
Closed Access
NATCloud: Cloud-assisted NAT-traversal service
Kavalionak H., Payberah A. H., Dowling J., Montresor A.
Although over the last decade large efforts have been done to design efficient peer-to-peer (P2P) protocols, very few of them have taken into account the problem of firewalls and network address translators (NAT). Most of the existing P2P systems do not work properly when a high percentage of nodes are behind NAT. While a few P2P systems tackled the NAT problem, all of them employ third party nodes to establish a connection towards nodes behind NAT, and these may become bottlenecks, menacing the health of the entire system. A possible solution to this problem is to rent ex- tra resources from the cloud. This paper presents NATCLOUD, a cloud-assisted NAT-traversal service, where rented cloud resources are added on demand to the overlay, as third party nodes, to help other nodes to make connections to nodes behind NAT. We show the feasibility of integrating our approach with existing gossip-based peer sampling services and evaluate our solution by simulations, conducting extensive experiments under different network conditions.Source: 31st Annual ACM Symposium on Applied Computing, SAC 2016, pp. 508–513, Pisa, Italy, 04-08/04/2016
DOI: 10.1145/2851613.2851640
Metrics:
Kavalionak H., Payberah A. H., Dowling J., Montresor A.
Although over the last decade large efforts have been done to design efficient peer-to-peer (P2P) protocols, very few of them have taken into account the problem of firewalls and network address translators (NAT). Most of the existing P2P systems do not work properly when a high percentage of nodes are behind NAT. While a few P2P systems tackled the NAT problem, all of them employ third party nodes to establish a connection towards nodes behind NAT, and these may become bottlenecks, menacing the health of the entire system. A possible solution to this problem is to rent ex- tra resources from the cloud. This paper presents NATCLOUD, a cloud-assisted NAT-traversal service, where rented cloud resources are added on demand to the overlay, as third party nodes, to help other nodes to make connections to nodes behind NAT. We show the feasibility of integrating our approach with existing gossip-based peer sampling services and evaluate our solution by simulations, conducting extensive experiments under different network conditions.Source: 31st Annual ACM Symposium on Applied Computing, SAC 2016, pp. 508–513, Pisa, Italy, 04-08/04/2016
DOI: 10.1145/2851613.2851640
Metrics:
See at:
dl.acm.org | doi.org | CNR ExploRA
2021
Report
Open Access
ACCORDION D3.1 - Edge infrastructure pool framework report (I)
Psomakelis E., Blasi L., Vailati A., Kavalionak H., Korontanis I., Huici F., Dazzi P.
This deliverable provides the first report summarizing the scientific advancements achieved during the project, by the WP3 tasks. The achievements, risks and challenges are presented both at a high level, presenting the outcome of the WP3 tasks (called ACCORDION Minicloud VIM) as a unified component, and at a lower level, presenting the components that comprise the ACCORDION Minicloud VIM. For each component we can clearly identify the progress through the first year of the project, the challenges and problems encountered and the plans for the second year of the project.Source: ISTI Project report, ACCORDION, D3.1, 2021
Project(s): ACCORDION
Psomakelis E., Blasi L., Vailati A., Kavalionak H., Korontanis I., Huici F., Dazzi P.
This deliverable provides the first report summarizing the scientific advancements achieved during the project, by the WP3 tasks. The achievements, risks and challenges are presented both at a high level, presenting the outcome of the WP3 tasks (called ACCORDION Minicloud VIM) as a unified component, and at a lower level, presenting the components that comprise the ACCORDION Minicloud VIM. For each component we can clearly identify the progress through the first year of the project, the challenges and problems encountered and the plans for the second year of the project.Source: ISTI Project report, ACCORDION, D3.1, 2021
Project(s): ACCORDION
See at:
ISTI Repository 
| CNR ExploRA
2021
Report
Open Access
ACCORDION D3.2 - Edge infrastructure pool framework implementation (I)
Korontanis I., Psomakelis V., Kavalionak H., Di Girolamo M., Vailati A., Huici F., Dazzi P.
This document is the accompanying report documenting the software components that are released as part of ACCORDION Deliverable D3.2 and explains how to install and use them. The report includes the description of the first implementation provided by ACCORDION for the Edge infrastructure pool framework (or Edge minicloud), developed by the tasks inside WP3. The implemented minicloud model can include only resources located in a single site and typically owned by a single provider. The developed framework, representing one of the key innovations realized by ACCORDION, puts together the different components that altogether implement the functionalities requested to deploy and operate federated miniclouds inside the ACCORDION environment. It allows to locate and assign edge resources to the client applications of ACCORDION, ensuring they are duly registered, tracked, monitored and that the system is able to react whenever needed to ensure the quality of service to users of client applications. The framework implements the architectural guidelines and requirements set by WP2, will be further integrated with the higher (orchestration) layer components developed by WP4 and, once integrated, will run the Pilot use case applications proposed by WP6.Source: ISTI Project report, ACCORDION, D3.2, 2021
Project(s): ACCORDION
Korontanis I., Psomakelis V., Kavalionak H., Di Girolamo M., Vailati A., Huici F., Dazzi P.
This document is the accompanying report documenting the software components that are released as part of ACCORDION Deliverable D3.2 and explains how to install and use them. The report includes the description of the first implementation provided by ACCORDION for the Edge infrastructure pool framework (or Edge minicloud), developed by the tasks inside WP3. The implemented minicloud model can include only resources located in a single site and typically owned by a single provider. The developed framework, representing one of the key innovations realized by ACCORDION, puts together the different components that altogether implement the functionalities requested to deploy and operate federated miniclouds inside the ACCORDION environment. It allows to locate and assign edge resources to the client applications of ACCORDION, ensuring they are duly registered, tracked, monitored and that the system is able to react whenever needed to ensure the quality of service to users of client applications. The framework implements the architectural guidelines and requirements set by WP2, will be further integrated with the higher (orchestration) layer components developed by WP4 and, once integrated, will run the Pilot use case applications proposed by WP6.Source: ISTI Project report, ACCORDION, D3.2, 2021
Project(s): ACCORDION
See at:
ISTI Repository | CNR ExploRA
2015
Conference article
Restricted
DICE: A distributed protocol for camera-aided video surveillance
Kavalionak H., Gennaro C., Amato G., Meghini C.
Video surveillance systems have become an indispensable tool for the security and organization of public and private areas. Most of the current commercial video surveillance systems rely on a classical client/server architecture to perform person and object recognition. In order to support the more complex and advanced video surveillance systems proposed in the last years, companies are required to invest resources in order to maintain the servers dedicated to the recognition tasks. In this work we propose a novel distributed protocol that exploits the computational capabilities of the surveillance devices (i.e. cameras) to perform the recognition of the person. The cameras fall back to a centralized server if their hardware capabilities are not enough to perform the recognition. By means of simulations, we show that our algorithm is able to reduce up to 50% the load of the server with no negative impact on the quality of the surveillance service.Source: IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pp. 477–484, Liverpool, UK, 26-28/10/2015
DOI: 10.1109/cit/iucc/dasc/picom.2015.68
Metrics:
Kavalionak H., Gennaro C., Amato G., Meghini C.
Video surveillance systems have become an indispensable tool for the security and organization of public and private areas. Most of the current commercial video surveillance systems rely on a classical client/server architecture to perform person and object recognition. In order to support the more complex and advanced video surveillance systems proposed in the last years, companies are required to invest resources in order to maintain the servers dedicated to the recognition tasks. In this work we propose a novel distributed protocol that exploits the computational capabilities of the surveillance devices (i.e. cameras) to perform the recognition of the person. The cameras fall back to a centralized server if their hardware capabilities are not enough to perform the recognition. By means of simulations, we show that our algorithm is able to reduce up to 50% the load of the server with no negative impact on the quality of the surveillance service.Source: IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pp. 477–484, Liverpool, UK, 26-28/10/2015
DOI: 10.1109/cit/iucc/dasc/picom.2015.68
Metrics:
See at:
doi.org | ieeexplore.ieee.org | CNR ExploRA
2018
Conference article
Open Access
Toward Decentralised Consensus and Offloading for Area Coverage in a Fleet of Drones
Kavalionak H., Carlini E., Cassarà P., Meghini C.
A precise and dynamic visual coverage of a given area is an essential task in many smart contexts, ranging from civil communities to military applications. Due to the last years advancement in hardware miniaturization and efficiency, area coverage is often performed with a combination of static and moving devices, such as unmanned aerial vehicles (drones). Drones are useful to cope with the highly unpredictability and dynamicity of environments, but require specific and efficient solutions toward and efficient area coverage. In this paper we proposes an initial work toward a drone-based approach for the task of area coverage. In particular, we focus our analysis on the following points: (i) decentralized consensus for movement planning, and (ii) the integration of cloud computing infrastructures and technologies for computation offloading, both for image analysis and movement planning.Source: 9th International Conference on Wireless and Satellite Systems, WiSATS 2017, pp. 96–105, Oxford, United Kingdom, 14-15/09/2017
DOI: 10.1007/978-3-319-76571-6_10
Metrics:
Kavalionak H., Carlini E., Cassarà P., Meghini C.
A precise and dynamic visual coverage of a given area is an essential task in many smart contexts, ranging from civil communities to military applications. Due to the last years advancement in hardware miniaturization and efficiency, area coverage is often performed with a combination of static and moving devices, such as unmanned aerial vehicles (drones). Drones are useful to cope with the highly unpredictability and dynamicity of environments, but require specific and efficient solutions toward and efficient area coverage. In this paper we proposes an initial work toward a drone-based approach for the task of area coverage. In particular, we focus our analysis on the following points: (i) decentralized consensus for movement planning, and (ii) the integration of cloud computing infrastructures and technologies for computation offloading, both for image analysis and movement planning.Source: 9th International Conference on Wireless and Satellite Systems, WiSATS 2017, pp. 96–105, Oxford, United Kingdom, 14-15/09/2017
DOI: 10.1007/978-3-319-76571-6_10
Metrics:
See at:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2020
Conference article
Open Access
Dynamic Wi-Fi RSSI normalization in unmapped locations
Kavalionak H., Tosato M., Barsocchi P., Nardini F. M.
With the growing availability of open access WLAN networks, we assisted to the increase of marketing services that are based on the data collected from the WLAN access points. The identification of visitors of a commercial venue using WLAN data is one of the issues to create successful marketing products. One of the ways to separate visitors is to analyse the RSSI of the mobile devices signals coming to various access points at the venue. Nevertheless, the indoor signal distortion makes RSSI based methods unreliable. In this work we propose the algorithm for the WLAN based RSSI normalization in uncontrolled environments. Our approach is based on the two steps, where at first based on the collected data we detect the devices whose RSSI can be taken as a basic one. At the second step the algorithm allows based on the previously detected basic RSSI to normalize the received signal from mobile devices. We provide the analysis of a real dataset of WLAN probes collected in several real commercial venues in Italy.Source: EDBT/ICDT 2020 Joint Conference, Copenhagen, Denmark, 30th March - 2nd April, 2020
Kavalionak H., Tosato M., Barsocchi P., Nardini F. M.
With the growing availability of open access WLAN networks, we assisted to the increase of marketing services that are based on the data collected from the WLAN access points. The identification of visitors of a commercial venue using WLAN data is one of the issues to create successful marketing products. One of the ways to separate visitors is to analyse the RSSI of the mobile devices signals coming to various access points at the venue. Nevertheless, the indoor signal distortion makes RSSI based methods unreliable. In this work we propose the algorithm for the WLAN based RSSI normalization in uncontrolled environments. Our approach is based on the two steps, where at first based on the collected data we detect the devices whose RSSI can be taken as a basic one. At the second step the algorithm allows based on the previously detected basic RSSI to normalize the received signal from mobile devices. We provide the analysis of a real dataset of WLAN probes collected in several real commercial venues in Italy.Source: EDBT/ICDT 2020 Joint Conference, Copenhagen, Denmark, 30th March - 2nd April, 2020
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2020
Report
Open Access
ACCORDION - D2.2 State of the art report (I)
Korontanis I., Carlini E., Kavalionak H., Dazzi P., Psomakelis V., Blasi L., Nadir Z., Violos J., Toro A., Russo M., Fabregas E. M., Vasios G., Tserpes K., Zadtootaghaj S., Lipa B., Pateraki M., Di Girolamo M.
The present document is the result of the collaborative effort of all ACCORDION partners participating to Task 2.2. The document offers a review of the state of the art for a series of topics strictly related to the work performed in the ACCORDION project. There is actually a strict correlation between the topics analyzed in this document and the Tasks that are part of the three research Work Packages of ACCORDION (WP3, WP4, and WP5). The main part of this document is section 2, in which all the state of the art analysis results have been reported. Section 2 has a subsection for each of the topics researched in the project, which includes: a description of the objectives, a list of outcomes expected from the research work, and an analysis of the state of the art. Section 2.1 (Resource monitoring & characterization) reports on monitoring, characterization and classification of Edge resources, identifying Prometheus, TOSCA and the automatic creation of taxonomies, respectively, as the best solutions for each of the three fields. Section 2.2 (Resource indexing & discovery) focuses on discussing solutions and data structures for organizing data in Resource Discovery Services. Section 2.3 (Edge storage, availability, reliability and performance) presents the advantages and disadvantages of both block and object storage, and then discusses some solutions, identifying OpenStack and MinIO as the most promising ones, even if not completely suitable. Some open research issues are also summarized. Section 2.4 (Pooling Edge resources), after listing some orchestration challenges typical of Edge computing and the techniques to cope with them, reports on several solutions to be considered as possible baselines for the ACCORDION Minicloud. Section 2.5 (??-based network orchestration) first lists the main machine learning techniques, then explores both Federated Learning techniques and further evolutions such as Meta-Learning Framework and Multi-Agent Reinforcement Learning. Section 2.6 (Resilience policies & mechanisms over heterogeneous edge resources) starts by discriminating between reactive and proactive protection strategies and describing some of them. Then other Fault Tolerance approaches are explored both reported in the literature and adopted in common distributed computing frameworks (Openstack, Cloudstack, Kubernetes, Openshift, and Mesos). Finally techniques for movement behaviour and resource utilization prediction are analysed, with a particular focus on the the LSTM model for Neural Networks. The conclusion is that the most promising solution to efficiently adapt the deep learning topologies for the fault tolerance needs is the hyper parameter optimization approach. Section 2.7 (Techniques for secure Edge application development & deployment) offers an analysis of the most common types of security attacks (Distributed Denial-of-Service, Malware Injection, and Authentication-based attacks) and their related countermeasures, along with some threat modelling methods, while DevSecOps methods and tools are also described. Section 2.8 (Privacy preserving mechanisms) starts by analysing Machine Learning techniques with a focus on privacy preserving ones, and then lists a number of works analysing how cookie synchronization techniques adopted for web advertising can expose users to privacy leaks. Section 2.9 (Application model for automatic deployment / migration of components) looks for application description models suitable for ACCORDION, i.e. with a machine-processable syntax, able to represent resource capacity requirements, containerization, and recovery policies. Three available solutions, TOSCA, Juju charms and CAMP, are compared along with the projects that are using them. Furthermore, tools supporting the three above solutions are described, and works researching the interoperability among the solutions are also analysed. Section 2.10 (Modelling and assessing QoE for NextGen applications) reports on different types of objective models that can be used to estimate the Quality of Experience (QoE) perceived by users of multimedia applications, and about the latest ITU-T Recommendations on QoE models and methodologies that can be applied to Next Generation Applications. For the ACCORDION project it has been decided to follow the standardized approach to build models for QoE assessment of ACCORDION applications. Section 2.11 (DevOps tools to automate Edge applications' deployment) sets the context and reports the starting points for the evaluation of Continuous Integration and Continuous Deployment tools. The identified state-of-the art solutions are Jenkins for the CI/CD pipeline and Kubernetes as the runtime deployment environment. Section 2.12 (Collaborative VR), starting from the general requirements for Virtual Reality applications, reports considerations about the still limited power of the available HMDs and discusses the trade-offs conditioning the possibility to offload computation from the end devices to the Edge. Finally Section 2.13 (Resource federation models) describes the main features of the federation model proposed by the H2020 5GeX project and lists the additional constraints and issues raised by an Edge providers' federation, which have to be further investigated. Not all project's research Tasks have a related section in this document about their main topics, yet. Monitoring the State of the Art is an ongoing activity in ACCORDION, and the next version of this document foreseen in M22 will improve the State of the Art analysis by adding further details, covering more topics and reporting on possible new approaches that appeared in the meantime.Source: ISTI Project report, ACCORDION, D2.2, 2020
Project(s): ACCORDION
Korontanis I., Carlini E., Kavalionak H., Dazzi P., Psomakelis V., Blasi L., Nadir Z., Violos J., Toro A., Russo M., Fabregas E. M., Vasios G., Tserpes K., Zadtootaghaj S., Lipa B., Pateraki M., Di Girolamo M.
The present document is the result of the collaborative effort of all ACCORDION partners participating to Task 2.2. The document offers a review of the state of the art for a series of topics strictly related to the work performed in the ACCORDION project. There is actually a strict correlation between the topics analyzed in this document and the Tasks that are part of the three research Work Packages of ACCORDION (WP3, WP4, and WP5). The main part of this document is section 2, in which all the state of the art analysis results have been reported. Section 2 has a subsection for each of the topics researched in the project, which includes: a description of the objectives, a list of outcomes expected from the research work, and an analysis of the state of the art. Section 2.1 (Resource monitoring & characterization) reports on monitoring, characterization and classification of Edge resources, identifying Prometheus, TOSCA and the automatic creation of taxonomies, respectively, as the best solutions for each of the three fields. Section 2.2 (Resource indexing & discovery) focuses on discussing solutions and data structures for organizing data in Resource Discovery Services. Section 2.3 (Edge storage, availability, reliability and performance) presents the advantages and disadvantages of both block and object storage, and then discusses some solutions, identifying OpenStack and MinIO as the most promising ones, even if not completely suitable. Some open research issues are also summarized. Section 2.4 (Pooling Edge resources), after listing some orchestration challenges typical of Edge computing and the techniques to cope with them, reports on several solutions to be considered as possible baselines for the ACCORDION Minicloud. Section 2.5 (??-based network orchestration) first lists the main machine learning techniques, then explores both Federated Learning techniques and further evolutions such as Meta-Learning Framework and Multi-Agent Reinforcement Learning. Section 2.6 (Resilience policies & mechanisms over heterogeneous edge resources) starts by discriminating between reactive and proactive protection strategies and describing some of them. Then other Fault Tolerance approaches are explored both reported in the literature and adopted in common distributed computing frameworks (Openstack, Cloudstack, Kubernetes, Openshift, and Mesos). Finally techniques for movement behaviour and resource utilization prediction are analysed, with a particular focus on the the LSTM model for Neural Networks. The conclusion is that the most promising solution to efficiently adapt the deep learning topologies for the fault tolerance needs is the hyper parameter optimization approach. Section 2.7 (Techniques for secure Edge application development & deployment) offers an analysis of the most common types of security attacks (Distributed Denial-of-Service, Malware Injection, and Authentication-based attacks) and their related countermeasures, along with some threat modelling methods, while DevSecOps methods and tools are also described. Section 2.8 (Privacy preserving mechanisms) starts by analysing Machine Learning techniques with a focus on privacy preserving ones, and then lists a number of works analysing how cookie synchronization techniques adopted for web advertising can expose users to privacy leaks. Section 2.9 (Application model for automatic deployment / migration of components) looks for application description models suitable for ACCORDION, i.e. with a machine-processable syntax, able to represent resource capacity requirements, containerization, and recovery policies. Three available solutions, TOSCA, Juju charms and CAMP, are compared along with the projects that are using them. Furthermore, tools supporting the three above solutions are described, and works researching the interoperability among the solutions are also analysed. Section 2.10 (Modelling and assessing QoE for NextGen applications) reports on different types of objective models that can be used to estimate the Quality of Experience (QoE) perceived by users of multimedia applications, and about the latest ITU-T Recommendations on QoE models and methodologies that can be applied to Next Generation Applications. For the ACCORDION project it has been decided to follow the standardized approach to build models for QoE assessment of ACCORDION applications. Section 2.11 (DevOps tools to automate Edge applications' deployment) sets the context and reports the starting points for the evaluation of Continuous Integration and Continuous Deployment tools. The identified state-of-the art solutions are Jenkins for the CI/CD pipeline and Kubernetes as the runtime deployment environment. Section 2.12 (Collaborative VR), starting from the general requirements for Virtual Reality applications, reports considerations about the still limited power of the available HMDs and discusses the trade-offs conditioning the possibility to offload computation from the end devices to the Edge. Finally Section 2.13 (Resource federation models) describes the main features of the federation model proposed by the H2020 5GeX project and lists the additional constraints and issues raised by an Edge providers' federation, which have to be further investigated. Not all project's research Tasks have a related section in this document about their main topics, yet. Monitoring the State of the Art is an ongoing activity in ACCORDION, and the next version of this document foreseen in M22 will improve the State of the Art analysis by adding further details, covering more topics and reporting on possible new approaches that appeared in the meantime.Source: ISTI Project report, ACCORDION, D2.2, 2020
Project(s): ACCORDION
See at:
ISTI Repository | CNR ExploRA
2020
Report
Open Access
ACCORDION D2.3 - Architecture design (I)
Tserpes K., Kousiouris G., Xydis S., Korodanis I., Kafatari T., Dazzi P., Carlini E., Kavalionak H., Ferrucci L., Huici F., Fabregas E. M., Kourtellis N., Diego A. F., Violos I., Psomakelis E., Kami?ski M., Lipa B., Loven T., Gonzalez Rozad Y., Toro A., Russo M., Blasi L., Vailati A., Di Girolamo M., Zinelaabidine N., Ledwo? Z., Zadtootaghaj S., Pateraki M., Fotis S., Kallipolitis L., Vantolas S.
This deliverable provides an account of the work done for the specification of the ACCORDION ecosystem architecture, which is mainly comprised of two artefacts: the infrastructure to support the cloud-edge continuum and the platform to manage the resources and the applications hosted in that infrastructure. The main outcomes of the work described in this deliverable are: a) the definition of components that comprise the architecture as well as their interactions, and; b) the assignment of the implementation of those components to specific project Tasks and partners. The deliverable describes the process for reaching to those outcomes, starting with the delineation of the scope of the described artefacts and working down to their specifics. The main approach used is the analysis of the platform use case scenarios. In practice, we analyze the scenarios and extract the required functionality. Then we cluster and map this functionality to components. Finally, we schematically present the interactions of those components using block and sequence diagrams. These outcomes are then validated against the use case requirements, especially those linked to the applications operation itself. This is meant to provide a proof-of-concept that the ACCORDION architecture is able to support the ACCORDION applications.Source: ISTI Project report, ACCORDION, D2.3, 2020
Project(s): ACCORDION
Tserpes K., Kousiouris G., Xydis S., Korodanis I., Kafatari T., Dazzi P., Carlini E., Kavalionak H., Ferrucci L., Huici F., Fabregas E. M., Kourtellis N., Diego A. F., Violos I., Psomakelis E., Kami?ski M., Lipa B., Loven T., Gonzalez Rozad Y., Toro A., Russo M., Blasi L., Vailati A., Di Girolamo M., Zinelaabidine N., Ledwo? Z., Zadtootaghaj S., Pateraki M., Fotis S., Kallipolitis L., Vantolas S.
This deliverable provides an account of the work done for the specification of the ACCORDION ecosystem architecture, which is mainly comprised of two artefacts: the infrastructure to support the cloud-edge continuum and the platform to manage the resources and the applications hosted in that infrastructure. The main outcomes of the work described in this deliverable are: a) the definition of components that comprise the architecture as well as their interactions, and; b) the assignment of the implementation of those components to specific project Tasks and partners. The deliverable describes the process for reaching to those outcomes, starting with the delineation of the scope of the described artefacts and working down to their specifics. The main approach used is the analysis of the platform use case scenarios. In practice, we analyze the scenarios and extract the required functionality. Then we cluster and map this functionality to components. Finally, we schematically present the interactions of those components using block and sequence diagrams. These outcomes are then validated against the use case requirements, especially those linked to the applications operation itself. This is meant to provide a proof-of-concept that the ACCORDION architecture is able to support the ACCORDION applications.Source: ISTI Project report, ACCORDION, D2.3, 2020
Project(s): ACCORDION
See at:
ISTI Repository | CNR ExploRA
2021
Report
Open Access
D6.3 ACCORDION system implementation (I)
Carlini E., Vailati A., Rola J., Ferrucci L., Kavalionak H., Tserpes K., Ferrann D.
This document covers the implementation activities put in place in the ACCORDION consortium to enable a smooth integration of the distributed ACCORDION platform. The first part of the document describes the three core components of the platform, while the second part describes the first integration testing performed on such components.Source: ISTI Project report, ACCORDION, D6.3, 2021
Project(s): ACCORDION
Carlini E., Vailati A., Rola J., Ferrucci L., Kavalionak H., Tserpes K., Ferrann D.
This document covers the implementation activities put in place in the ACCORDION consortium to enable a smooth integration of the distributed ACCORDION platform. The first part of the document describes the three core components of the platform, while the second part describes the first integration testing performed on such components.Source: ISTI Project report, ACCORDION, D6.3, 2021
Project(s): ACCORDION
See at:
ISTI Repository | CNR ExploRA
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
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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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2017
Conference article
Restricted
A prediction-based distributed tracking protocol for video surveillance
Kavalionak H., Carlini E., Lulli A., Gennaro C., Amato G., Meghini C., Ricci L.
Video surveillance is an important security enforcement operation in many contexts, from large public areas to private smart homes and smart buildings. Today's video surveillance systems are much more than mere recording storages, as the advancement in classification and recognition allow for an immediate target recognition without the intervention of human operators. These smart video surveillance systems usually rely on a central server as the main coordination of recognition and tracking, which can represent a performance or economical bottleneck. In this paper, our contribution focuses on a decentralized protocol with the aim of eliminating such bottleneck. Our protocol organizes the distribution of a classification library among the cameras involved, which also participate actively to the target recognition phase. The protocol minimizes the network overhead towards the centralized server while keeping high the speed of recognition making use of a system to predict the movements of the targets. We tested the protocol by means of simulations, exploiting a realistic indoor human mobility model.Source: ICNSC 2017 - IEEE 14th International Conference on Networking, Sensing and Control, pp. 140–145, Calabria, Italy, 16-18 May, 2017
DOI: 10.1109/icnsc.2017.8000081
Metrics:
Kavalionak H., Carlini E., Lulli A., Gennaro C., Amato G., Meghini C., Ricci L.
Video surveillance is an important security enforcement operation in many contexts, from large public areas to private smart homes and smart buildings. Today's video surveillance systems are much more than mere recording storages, as the advancement in classification and recognition allow for an immediate target recognition without the intervention of human operators. These smart video surveillance systems usually rely on a central server as the main coordination of recognition and tracking, which can represent a performance or economical bottleneck. In this paper, our contribution focuses on a decentralized protocol with the aim of eliminating such bottleneck. Our protocol organizes the distribution of a classification library among the cameras involved, which also participate actively to the target recognition phase. The protocol minimizes the network overhead towards the centralized server while keeping high the speed of recognition making use of a system to predict the movements of the targets. We tested the protocol by means of simulations, exploiting a realistic indoor human mobility model.Source: ICNSC 2017 - IEEE 14th International Conference on Networking, Sensing and Control, pp. 140–145, Calabria, Italy, 16-18 May, 2017
DOI: 10.1109/icnsc.2017.8000081
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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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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:
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2020
Conference article
Open Access
Edge-Based Video Surveillance with Embedded Devices
Kavalionak H., Gennaro C., Amato G., Vairo C., Perciante C., Meghini C., Falchi F., Rabitti F.
Video surveillance systems have become indispensable tools for the security and organization of public and private areas. In this work, we propose a novel distributed protocol for an edge-based face recogni-tion system that takes advantage of the computational capabilities of the surveillance devices (i.e., cameras) to perform person recognition. The cameras fall back to a centralized server if their hardware capabili-ties are not enough to perform the recognition. We evaluate the proposed algorithm via extensive experiments on a freely available dataset. As a prototype of surveillance embedded devices, we have considered a Rasp-berry PI with the camera module. Using simulations, we show that our algorithm can reduce up to 50% of the load of the server with no negative impact on the quality of the surveillance service.Source: 28th Symposium on Advanced Database Systems (SEBD), pp. 278–285, Villasimius, Sardinia, Italy, 21-24/06/2020
Kavalionak H., Gennaro C., Amato G., Vairo C., Perciante C., Meghini C., Falchi F., Rabitti F.
Video surveillance systems have become indispensable tools for the security and organization of public and private areas. In this work, we propose a novel distributed protocol for an edge-based face recogni-tion system that takes advantage of the computational capabilities of the surveillance devices (i.e., cameras) to perform person recognition. The cameras fall back to a centralized server if their hardware capabili-ties are not enough to perform the recognition. We evaluate the proposed algorithm via extensive experiments on a freely available dataset. As a prototype of surveillance embedded devices, we have considered a Rasp-berry PI with the camera module. Using simulations, we show that our algorithm can reduce up to 50% of the load of the server with no negative impact on the quality of the surveillance service.Source: 28th Symposium on Advanced Database Systems (SEBD), pp. 278–285, Villasimius, Sardinia, Italy, 21-24/06/2020
See at:
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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:
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ISTI Repository | ZENODO | dl.acm.org | CNR ExploRA
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.Source: ISCC 2021 - 26th IEEE Symposium on Computers and Communications, Athens, Greece, 05-08/09/2021
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.Source: ISCC 2021 - 26th IEEE Symposium on Computers and Communications, Athens, Greece, 05-08/09/2021
DOI: 10.1109/iscc53001.2021.9631460
Project(s): TEACHING
Metrics:
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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 approachSource: GECON 2021: 18th International Conference on Economics of Grids, Clouds, Systems and Services, pp. 133–142, Virtual Event, Rome, 21-23/09/2021
DOI: 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 approachSource: GECON 2021: 18th International Conference on Economics of Grids, Clouds, Systems and Services, pp. 133–142, Virtual Event, Rome, 21-23/09/2021
DOI: 10.1007/978-3-030-92916-9_11
Project(s): ACCORDION
Metrics:
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