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2023 Conference article Open Access OPEN
GAM Forest explanation
Lucchese C., Orlando S., Perego R., Veneri A.
Most accurate machine learning models unfortunately produce black-box predictions, for which it is impossible to grasp the internal logic that leads to a specific decision. Unfolding the logic of such black-box models is of increasing importance, especially when they are used in sensitive decision-making processes. In thisworkwe focus on forests of decision trees, which may include hundreds to thousands of decision trees to produce accurate predictions. Such complexity raises the need of developing explanations for the predictions generated by large forests.We propose a post hoc explanation method of large forests, named GAM-based Explanation of Forests (GEF), which builds a Generalized Additive Model (GAM) able to explain, both locally and globally, the impact on the predictions of a limited set of features and feature interactions.We evaluate GEF over both synthetic and real-world datasets and show that GEF can create a GAM model with high fidelity by analyzing the given forest only and without using any further information, not even the initial training dataset.Source: EDBT 2022 - 26th International Conference on Extending Database Technology, pp. 171–182, Ioannina, Greece, 28-31/03/2023
DOI: 10.48786/edbt.2023.14
Metrics:


See at: openproceedings.org Open Access | CNR ExploRA Open Access


2022 Journal article Open Access OPEN
Dynamic hard pruning of Neural Networks at the edge of the internet
Valerio L., Nardini F. M., Passarella A., Perego R.
Neural Networks (NN), although successfully applied to several Artificial Intelligence tasks, are often unnecessarily over-parametrized. In edge/fog computing, this might make their training prohibitive on resource-constrained devices, contrasting with the current trend of decentralizing intelligence from remote data centres to local constrained devices. Therefore, we investigate the problem of training effective NN models on constrained devices having a fixed, potentially small, memory budget. We target techniques that are both resource-efficient and performance effective while enabling significant network compression. Our Dynamic Hard Pruning (DynHP) technique incrementally prunes the network during training, identifying neurons that marginally contribute to the model accuracy. DynHP enables a tunable size reduction of the final neural network and reduces the NN memory occupancy during training. Freed memory is reused by a dynamic batch sizing approach to counterbalance the accuracy degradation caused by the hard pruning strategy, improving its convergence and effectiveness. We assess the performance of DynHP through reproducible experiments on three public datasets, comparing them against reference competitors. Results show that DynHP compresses a NN up to 10 times without significant performance drops (up to 3.5% additional error w.r.t. the competitors), reducing up to 80% the training memory occupancy.Source: Journal of network and computer applications 200 (2022). doi:10.1016/j.jnca.2021.103330
DOI: 10.1016/j.jnca.2021.103330
Metrics:


See at: ISTI Repository Open Access | CNR ExploRA Restricted | www.sciencedirect.com Restricted


2022 Journal article Open Access OPEN
Distilled neural networks for efficient learning to rank
Nardini F. M., Rulli C., Trani S., Venturini R.
Recent studies in Learning to Rank have shown the possibility to effectively distill a neural network from an ensemble of regression trees. This result leads neural networks to become a natural competitor of tree-based ensembles on the ranking task. Nevertheless, ensembles of regression trees outperform neural models both in terms of efficiency and effectiveness, particularly when scoring on CPU. In this paper, we propose an approach for speeding up neural scoring time by applying a combination of Distillation, Pruning and Fast Matrix multiplication. We employ knowledge distillation to learn shallow neural networks from an ensemble of regression trees. Then, we exploit an efficiency-oriented pruning technique that performs a sparsification of the most computationally-intensive layers of the neural network that is then scored with optimized sparse matrix multiplication. Moreover, by studying both dense and sparse high performance matrix multiplication, we develop a scoring time prediction model which helps in devising neural network architectures that match the desired efficiency requirements. Comprehensive experiments on two public learning-to-rank datasets show that neural networks produced with our novel approach are competitive at any point of the effectiveness-efficiency trade-off when compared with tree-based ensembles, providing up to 4x scoring time speed-up without affecting the ranking quality.Source: IEEE transactions on knowledge and data engineering (Online) (2022): 103330. doi:10.1109/TKDE.2022.3152585
DOI: 10.1109/tkde.2022.3152585
Metrics:


See at: ieeexplore.ieee.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2022 Journal article Open Access OPEN
MAT-Index: an index for fast multiple aspect trajectory similarity measuring
De Souza A. P. R., Renso C., Perego R., Bogorny V.
The semantic enrichment of mobility data with several information sources has led to a new type of movement data, the so-called multiple aspect trajectories. Comparing multiple aspect trajectories is crucial for several analysis tasks such as querying, clustering, similarity, and classification. Multiple aspect trajectory similarity measurement is more complex and computationally expensive, because of the large number and heterogeneous aspects of space, time, and semantics that require a different treatment. Only a few works in the literature focus on optimizing all these dimensions in a single solution, and, to the best of our knowledge, none of them proposes a fast point-to-point comparison. In this article we propose the Multiple Aspect Trajectory Index, an index data structure for optimizing the point-to-point comparison of multiple aspect trajectories, considering its three basic dimensions of space, time, and semantics. Quantitative and qualitative evaluations show a processing time reduction of up to 98.1%.Source: Transactions in GIS (Print) (2022). doi:10.1111/tgis.12889
DOI: 10.1111/tgis.12889
Project(s): MASTER via OpenAIRE
Metrics:


See at: onlinelibrary.wiley.com Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2022 Conference article Closed Access
Ensemble model compression for fast and energy-efficient ranking on FPGAs
Gil-Costa V., Loor F., Molina R., Nardini F. M., Perego R., Trani S.
We investigate novel SoC-FPGA solutions for fast and energy-efficient ranking based on machine-learned ensembles of decision trees. Since the memory footprint of ranking ensembles limits the effective exploitation of programmable logic for large-scale inference tasks, we investigate binning and quantization techniques to reduce the memory occupation of the learned model and we optimize the state-of-the-art ensemble-traversal algorithm for deployment on low-cost, energy-efficient FPGA devices. The results of the experiments conducted using publicly available Learning-to-Rank datasets, show that our model compression techniques do not impact significantly the accuracy. Moreover, the reduced space requirements allow the models and the logic to be replicated on the FPGA device in order to execute several inference tasks in parallel. We discuss in details the experimental settings and the feasibility of the deployment of the proposed solution in a real setting. The results of the experiments conducted show that our FPGA solution achieves performances at the state of the art and consumes from 9 × up to 19.8 × less energy than an equivalent multi-threaded CPU implementation.Source: ECIR 2022 - 44th European Conference on IR Research, pp. 260–273, Stavanger, Norway, 10-14/04/2022
DOI: 10.1007/978-3-030-99736-6_18
Metrics:


See at: doi.org Restricted | link.springer.com Restricted | CNR ExploRA Restricted


2022 Conference article Open Access OPEN
A dependency-aware utterances permutation strategy to improve conversational evaluation
Faggioli G., Ferrante M., Ferro N., Perego R., Tonellotto N.
The rapid growth in the number and complexity of conversational agents has highlighted the need for suitable evaluation tools to describe their performance. The main evaluation paradigms move from analyzing conversations where the user explores information needs following a scripted dialogue with the agent. We argue that this is not a realistic setting: different users ask different questions (and in a diverse order), obtaining distinct answers and changing the conversation path. We analyze what happens to conversational systems performance when we change the order of the utterances in a scripted conversation while respecting temporal dependencies between them. Our results highlight that the performance of the system widely varies. Our experiments show that diverse orders of utterances determine completely different rankings of systems by performance. The current way of evaluating conversational systems is thus biased. Motivated by these observations, we propose a new evaluation approach based on dependency-aware utterance permutations to increase the power of our evaluation tools.Source: ECIR 2022 - 44th European Conference on IR Research, pp. 184–198, Stavanger, Norway, 10-14/04/2022
DOI: 10.1007/978-3-030-99736-6_13
Metrics:


See at: ISTI Repository Open Access | doi.org Restricted | link.springer.com Restricted | CNR ExploRA Restricted


2022 Conference article Open Access OPEN
The Istella22 dataset: bridging traditional and neural learning to rank evaluation
Dato D., Macavaney S., Nardini F. M., Perego R., Tonellotto N.
Neural approaches that use pre-trained language models are effective at various ranking tasks, such as question answering and ad-hoc document ranking. However, their effectiveness compared to feature-based Learning-to-Rank (LtR) methods has not yet been well-established. A major reason for this is because present LtR benchmarks that contain query-document feature vectors do not contain the raw query and document text needed for neural models. On the other hand, the benchmarks often used for evaluating neural models, e.g., MS MARCO, TREC Robust, etc., provide text but do not provide query-document feature vectors. In this paper, we present Istella22, a new dataset that enables such comparisons by providing both query/document text and strong query-document feature vectors used by an industrial search engine. The dataset consists of a comprehensive corpus of 8.4M web documents, a collection of query-document pairs including 220 hand-crafted features, relevance judgments on a 5-graded scale, and a set of 2,198 textual queries used for testing purposes. Istella22 enables a fair evaluation of traditional learning-to-rank and transfer ranking techniques on the same data. LtR models exploit the feature-based representations of training samples while pre-trained transformer-based neural rankers can be evaluated on the corresponding textual content of queries and documents. Through preliminary experiments on Istella22, we find that neural re-ranking approaches lag behind LtR models in terms of effectiveness. However, LtR models identify the scores from neural models as strong signals.Source: SIGIR '22 - 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3099–3107, Madrid, Spain, 11-15/07/2022
DOI: 10.1145/3477495.3531740
Metrics:


See at: ISTI Repository Open Access | dl.acm.org Restricted | CNR ExploRA Restricted


2022 Conference article Open Access OPEN
ILMART: interpretable ranking with constrained LambdaMART
Lucchese C., Nardini F. M., Orlando S., Perego R., Veneri A.
Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models. While most of the previous research efforts focus on creating post-hoc explanations, in this paper we investigate how to train effective and intrinsically-interpretable ranking models. Developing these models is particularly challenging and it also requires finding a trade-off between ranking quality and model complexity. State-of-the-art rankers, made of either large ensembles of trees or several neural layers, exploit in fact an unlimited number of feature interactions making them black boxes. Previous approaches on intrinsically-interpretable ranking models address this issue by avoiding interactions between features thus paying a significant performance drop with respect to full-complexity models. Conversely, ILMART, our novel and interpretable LtR solution based on LambdaMART, is able to train effective and intelligible models by exploiting a limited and controlled number of pairwise feature interactions. Exhaustive and reproducible experiments conducted on three publicly-available LtR datasets show that ILMART outperforms the current state-of-the-art solution for interpretable ranking of a large margin with a gain of nDCG of up to 8%.Source: SIGIR '22 - 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2255–2259, Madrid, Spain, 11-15/07/2022
DOI: 10.1145/3477495.3531840
Metrics:


See at: ISTI Repository Open Access | dl.acm.org Restricted | CNR ExploRA Restricted


2022 Conference article Open Access OPEN
Data models for an imaging bio-bank for colorectal, prostate and gastric cancer: the NAVIGATOR project
Berti A., Carloni G., Colantonio S., Pascali M. A., Manghi P., Pagano P., Buongiorno R., Pachetti E., Caudai C., Di Gangi D., Carlini E., Falaschi Z., Ciarrocchi E., Neri E., Bertelli E., Miele V., Carpi R., Bagnacci G., Di Meglio N., Mazzei M. A., Barucci A.
Researchers nowadays may take advantage of broad collections of medical data to develop personalized medicine solutions. Imaging bio-banks play a fundamental role, in this regard, by serving as organized repositories of medical images associated with imaging biomarkers. In this context, the NAVIGATOR Project aims to advance colorectal, prostate, and gastric oncology translational research by leveraging quantitative imaging and multi-omics analyses. As Project's core, an imaging bio-bank is being designed and implemented in a web-accessible Virtual Research Environment (VRE). The VRE serves to extract the imaging biomarkers and further process them within prediction algorithms. In our work, we present the realization of the data models for the three cancer use-cases of the Project. First, we carried out an extensive requirements analysis to fulfill the necessities of the clinical partners involved in the Project. Then, we designed three separate data models utilizing entity-relationship diagrams. We found diagrams' modeling for colorectal and prostate cancers to be more straightforward, while gastric cancer required a higher level of complexity. Future developments of this work would include designing a common data model following the Observational Medical Outcomes Partnership Standards. Indeed, a common data model would standardize the logical infrastructure of data models and make the bio-bank easily interoperable with other bio-banks.Source: BHI '22 - IEEE-EMBS International Conference on Biomedical and Health Informatics, Ioannina, Greece, 27-30/09/2022
DOI: 10.1109/bhi56158.2022.9926910
Metrics:


See at: ISTI Repository Open Access | ieeexplore.ieee.org Restricted | CNR ExploRA Restricted


2022 Conference article Open Access OPEN
A novel approach to distributed model aggregation using Apache Kafka
Bano S., Carlini E., Cassarà P., Coppola M., Dazzi P., Gotta A.
Multi-Access Edge Computing (MEC) is attracting a lot of interest because it complements cloud-based approaches. Indeed, MEC is opening up in the direction of reducing both interaction delays and data sharing, called Cyber-Physical Systems (CPSs). In the near fu-ture, edge technologies will be a fundamental tool to better support time-dependent and data-intensive applications. In this context, this work explores existing and emerging platforms for MEC and human-centric applications, and proposes a suitable architecture that can be used in the context of autonomous vehicle systems.The proposed architecture will support scalable communication among sensing devices and edge/cloud computing platforms, as well as orchestrate services for computing, storage, and learning with the use of an Information-centric paradigm such as Apache KafkaSource: FRAME '22 - 2nd Workshop on Flexible Resource and Application Management on the Edge, pp. 33–36, Minneapolis, Minnesota, USA, 27/06-01/07/2022
DOI: 10.1145/3526059.3533621
Project(s): TEACHING via OpenAIRE
Metrics:


See at: ZENODO Open Access | dl.acm.org Restricted | doi.org Restricted | CNR ExploRA Restricted


2022 Conference article Open Access OPEN
A federated cloud solution for transnational mobility data sharing
Carlini E., Chevalier T., Dazzi P., Lettich F., Perego R., Renso C., Trani S.
Nowadays, innovative digital services are massively spreading both in the public and private sectors. In this work we focus on the digital data regarding the mobility of persons and goods, which are experiencing exponential growth thanks to the significant diffusion of telecommunication infrastructures and inexpensive GPS-equipped devices. The volume, velocity, and heterogeneity of mobility data call for advanced and efficient services to collect and integrate various data sources from different data producers. The MobiDataLab H2020 project aims to deal with these challenges by introducing an efficient and highly interoperable digital framework for mobility data sharing. In particular, the project aims to propose to the mobility stakeholders (i.e., transport organising authorities, operators, industry, governments, and innovators) reproducible methodologies and sustainable tools that can foster the development of a data-sharing culture in Europe and beyond. This paper introduces the key concepts driving the design and definition of a cloud-based data-sharing federation we call the Transport Cloud platform, which represents one of the main pillars of the MobiDataLab project. Such platform aims to ensure transnational access to mobility data in a secure, efficient, and seamless way, and to ensure that FAIR principles (i.e., mobility data should be findable, accessible, interoperable, and reusable) are enforced.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 586–592, Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): ACCORDION via OpenAIRE, MobiDataLab via OpenAIRE

See at: ceur-ws.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2022 Conference article Open Access OPEN
Predicting vehicles parking behaviour for EV recharge optimization
Monteiro De Lira V., Pallonetto F., Gabrielli L., Renso C.
The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 199–206, Tirrenia, Pisa, Italy, 19-22/06/2022

See at: ceur-ws.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2022 Conference article Open Access OPEN
MAT-Builder: a system to build semantically enriched trajectories
Pugliese C., Lettich F., Renso C., Pinelli F.
The notion of multiple aspect trajectory (MAT) has been recently introduced in the literature to represent movement data that is heavily semantically enriched with dimensions (aspects) representing various types of semantic information (e.g., stops, moves, weather, traffic, events, and points of interest). Aspects may be large in number, heterogeneous, or structurally complex. Although there is a growing volume of literature addressing the modelling and analysis of multiple aspect trajectories, the community suffers from a general lack of publicly available datasets. This is due to privacy concerns that make it difficult to publish such type of data, and to the lack of tools that are capable of linking raw spatio-temporal data to different types of semantic contextual data. In this work we aim to address this last issue by presenting MAT-Builder, a system that not only supports users during the whole semantic enrichment process, but also allows the use of a variety of external data sources. Furthermore, MAT-Builder has been designed with modularity and extensibility in mind, thus enabling practitioners to easily add new functionalities. The running example provided towards the end of the paper highlights how MAT-Builder's main features allow users to easily generate multiple aspect trajectories, hence benefiting the mobility data analysis community.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 175–182, Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): MobiDataLab via OpenAIRE, MASTER via OpenAIRE

See at: ceur-ws.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2022 Report Open Access OPEN
Mobility data mining: from technical to ethical (Dagstuhl Seminar 22022)
Berendt B., Matwin S., Renso C., Meissner F., Pratesi F., Raffaeta A., Rockwell G.
This report documents the program and the outcomes of Dagstuhl Seminar 22022 "Mobility Data Analysis: from Technical to Ethical" that took place fully remote and hosted by Schloss Dagstuhl from 10-12 January 2022. An interdisciplinary team of 23 researchers from Europe, the Americas and Asia in the fields of computer science, ethics and mobility analysis discussed interactions between their topics and fields to bridge the gap between the more technical aspects to the ethics with the objective of laying the foundations of a new Mobility Data Ethics research field.Source: ISTI Research report, pp.35–66, 2022
DOI: 10.4230/dagrep.12.1.35
Project(s): MASTER via OpenAIRE
Metrics:


See at: ISTI Repository Open Access | CNR ExploRA Open Access | doi.org Restricted


2022 Conference article Open Access OPEN
AUTOMATISE: multiple aspect trajectory data mining tool library
Tortelli T., Bogorny V., Bernasconi A., Renso C.
With the rapid increasing availability of information and popularization of mobility devices, trajectories have become more complex in their form. Trajectory data is now high dimensional, and often associated with heterogeneous sources of semantic data, that are called Multiple Aspect Trajectories. The high dimensionality and heterogeneity of these data makes classification a very challenging task both in term of accuracy and in terms of efficiency. The present demo offers a tool, called AUTOMATISE, to support the user in the classification task of multiple aspect trajectories, specifically for extracting and visualizing the movelets, the parts of the trajectory that better discriminate a class. The AUTOMATISE integrates into a unique platform the fragmented approaches available in the literature for multiple aspects trajectories and, in general, for multidimensional sequence classification into a unique web-based and python library system. We illustrate the architecture and the use of the tool for offering both movelets visualization and a complete configuration of classification experimental settings.Source: MDM 2022 - 23rd IEEE International Conference on Mobile Data Management, pp. 282–285, Paphos, Cyprus, Online, 6-9/06/2022
DOI: 10.1109/mdm55031.2022.00060
Project(s): MASTER via OpenAIRE
Metrics:


See at: ISTI Repository Open Access | ieeexplore.ieee.org Restricted | CNR ExploRA Restricted


2022 Conference article Open Access OPEN
MAT-Builder: a system to build semantically enriched trajectories
Pugliese C., Lettich F., Renso C., Pinelli F.
The notion of multiple aspect trajectory (MAT) has been recently introduced in the literature to represent movement data that is heavily semantically enriched with dimensions (aspects) representing various types of semantic information (e.g., stops, moves, weather, traffic, events, and points of interest). Aspects may be large in number, heterogeneous, or structurally complex. Although there is a growing volume of literature addressing the modelling and analysis of multiple aspect tra-jectories, the community suffers from a general lack of publicly available datasets. This is due to privacy concerns that make it difficult to publish such type of data, and to the lack of tools that are capable of linking raw spatio-temporal data to different types of semantic contextual data. In this work we aim to address this last issue by presenting MAT-BUILDER, a system that not only supports users during the whole semantic enrichment process, but also allows the use of a variety of external data sources. Furthermore, MAT-BUILDER has been designed with modularity and extensibility in mind, thus enabling practitioners to easily add new functionalities to the system and set up their own semantic enrichment process. The demonstration scenario, which will be showcased during the demo session, highlights how MAT-BUILDER's main features allow users to easily generate multiple aspect trajectories, hence benefiting the mobility data analysis community.Source: MDM 2022 - 23rd IEEE International Conference on Mobile Data Management, pp. 274–277, Paphos, Cyprus, Online, 6-9/06/2022
DOI: 10.1109/mdm55031.2022.00058
Project(s): MobiDataLab via OpenAIRE, MASTER via OpenAIRE
Metrics:


See at: ISTI Repository Open Access | ieeexplore.ieee.org Restricted | CNR ExploRA Restricted


2022 Contribution to conference Open Access OPEN
Energy-efficient ranking on FPGAs through ensemble model compression (Abstract)
Gil-Costa V., Loor F., Molina R., Nardini F. M., Perego R., Trani S.
In this talk, we present the main results of a paper accepted at ECIR 2022 [1]. We investigate novel SoC-FPGA solutions for fast and energy-efficient ranking based on machine learned ensembles of decision trees. Since the memory footprint of ranking ensembles limits the effective exploitation of programmable logic for large-scale inference tasks [2], we investigate binning and quantization techniques to reduce the memory occupation of the learned model and we optimize the state-of-the-art ensemble-traversal algorithm for deployment on lowcost, energy-efficient FPGA devices. The results of the experiments conducted using publicly available Learning-to-Rank datasets, show that our model compression techniques do not impact significantly the accuracy. Moreover, the reduced space requirements allow the models and the logic to be replicated on the FPGA device in order to execute several inference tasks in parallel. We discuss in details the experimental settings and the feasibility of the deployment of the proposed solution in a real setting. The results of the experiments conducted show that our FPGA solution achieves performances at the state of the art and consumes from 9× up to 19.8× less energy than an equivalent multi-threaded CPU implementation.Source: IIR 2022 - 12th Italian Information Retrieval Workshop 2022, Tirrenia, Pisa, Italy, 19-22/06/2022

See at: ceur-ws.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2022 Conference article Open Access OPEN
A mathematical model for latency constrained self-organizing application placement in the edge
Mordacchini M., Carlini E., Dazzi P.
The highly dynamic and heterogeneous environment that characterizes the edge of the Cloud/Edge Continuum calls for new intelligent methods for tackling the needs of such a complex scenario. In particular, adaptive and self-organizing decentralized solutions have been advanced for optimizing the placement of applications at the Edge. In this paper, we propose a probabilistic mathematical model that allows to describe one of such solutions. The goal of the model is twofold: i) to make it possible to demonstrate the convergence of the proposed solution; ii) to study the impact of the self-organizing solution without the need of an actual implementation or simulation of the system, allowing to evaluate the suitability of the solution in specific contexts. The paper presents the mathematical formulation of the proposed solution as well as the validation of the proposed model against a simulation of the system.Source: FRAME: 2nd Workshop on Flexible Resource and Application Management on the Edge (colocated with HPDC 2022), pp. 29–32, Minneapolis, Minnestota, USA, 01/07/2022
DOI: 10.1145/3526059.3533620
Project(s): ACCORDION via OpenAIRE
Metrics:


See at: ISTI Repository Open Access | dl.acm.org Restricted | doi.org Restricted | CNR ExploRA Restricted


2022 Conference article Open Access OPEN
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 via OpenAIRE
Metrics:


See at: ISTI Repository Open Access | dl.acm.org Restricted | doi.org Restricted | CNR ExploRA Restricted


2022 Conference article Open Access OPEN
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

See at: ISTI Repository Open Access | ceur-ws.org Restricted | CNR ExploRA Restricted