Papers by Antonio Corradi
IEEE Access
Several classes of advanced Internet of Things (IoT) applications, e.g., in the industrial manufa... more Several classes of advanced Internet of Things (IoT) applications, e.g., in the industrial manufacturing domain, call for Quality of Service (QoS) management to guarantee/control performance indicators, even in presence of many sources of ''stochastic noise'' in real deployment environments, from scarcely available bandwidth in a time window to concurrent usage of virtualized processing resources. This paper proposes a novel IoT-oriented middleware that i) considers and coordinates together different aspects of QoS monitoring, control, and management for different kinds of virtualized resources (from networking to processing) in a holistic way, and ii) specifically targets deployment environments where edge cloud resources are employed to enable the Serverless paradigm in the cloud continuum. The reported experimental results show how it is possible to achieve the desired QoS differentiation by coordinating heterogeneous mechanisms and technologies already available in the market. This demonstrates the feasibility of effective QoS-aware management of virtualized resources in the cloud-to-things continuum when considering a Serverless provisioning scenario, which is completely original in the related literature to the best of our knowledge.
IEEE Transactions on Parallel and Distributed Systems, Dec 31, 2023
The widespread adoption of Internet of Things (IoT) motivated the emergence of mixed workload sce... more The widespread adoption of Internet of Things (IoT) motivated the emergence of mixed workload scenarios in smart cities, where fast arriving geo-referenced massive amounts data streams need to be joined with archive tables, at scale. This aims at enriching streams with descriptive attributes that enable deeper insightful analytics. More applications are now relying on finding, in real-time, to which geographical region each data streaming spatially-tagged tuple belongs. This problem requires a computationally intensive stream-static join operation, where one side of join is a dynamic stream while the other is a diskresident static table. Even with emergence of some libraries that solve this problem in static-static fashion, their adoption for live scenarios is challenging because join operations are expensive in real-time. In addition, the time-varying nature of fluctuation and skewness in the geospatial data loads arriving online calls for an approximate solution that can trade-off QoS constraints in a way which ensures that the system survives sudden spikes in data loads. In this paper, we present SpatialSSJP, an adaptive spatialaware approximate query processing system that specifically focuses on stream-static joins in a way that guarantees achieving an agreed set of Quality-of-Service goals and maintains geostatistics of stateful online aggregations over stream-static join results. SpatialSSJP employs a state-of-art stratified-like sampling design to select well-balanced representative geospatial data stream samples and serve them to a stream-static geospatial join operator downstream. We implemented a prototype atop Spark Structured Streaming. Our extensive evaluations on big real datasets show that our system can survive and mitigate harsh join workloads and outperform state-of-art baselines by significant magnitudes, without risking rigorous error bounds in terms of the accuracy of the output results. SpatialSSJP achieves a relative accuracy gain against plain Spark joins of approximately 10% in worst cases but reaching up to 50% in best case scenarios.
Computer Networks, Jun 1, 2022
Serverless computing is an emerging proposition in the cloud offering landscape that promotes a h... more Serverless computing is an emerging proposition in the cloud offering landscape that promotes a higher level of abstraction, further decoupling software operations from the underlying hardware. Often recognized as an economically driven computational approach, the serverless model relies on the execution of short-lived stateless functions, enabling a fine-grained accounting and control of resources. In this context, function composition represents an appealing feature, allowing the composition of two or more functions to create tailored processing pipelines, incentivizing modularity and reusability of functions, while paving the way to application-specific run-time optimizations. This work presents DIFFUSE: a DIstributed and decentralized platForm enabling Function composition in Serverless Environments. DIF-FUSE embodies an innovative infrastructural support, enabling the efficient and transparent composition of functions by relying on pluggable middleware support, serving as a conveyor of messages among the platform components. Broadening the deployment spectrum of our proposal, we assess different middleware solutions, each presenting distinct delivery profiles, evidencing the tradeoffs that emerge.
The technological progress is leading to an increase of instrument sensitivity in the field of ro... more The technological progress is leading to an increase of instrument sensitivity in the field of rotational spectroscopy. A direct consequence of such a progress is an increasing amount of data produced by instruments, for which the currently available analysis software is becoming limited and inadequate. In order to improve data analysis performance, parallel computing techniques and distributed computing technologies like Cloud and High Performance Computing (HPC) can be exploited. Despite the availability of computer resources, neither Cloud nor HPC have been fully investigated for identifying unknown target spectra in rotational spectrum. This paper proposes the design and implementation of a Highly Scalable AUTOFIT (HS-AUTOFIT), an enhanced version of a fitting tool for broadband rotational spectra that is capable of exploiting the resources offered by multiple computing nodes. Compared to the old program version, the new one is capable of scaling on multiple computing nodes, thus guaranteeing higher accuracy of the fit function and consistent boost of execution time. The result of tests conducted in real Cloud and HPC environments show that HS-AUTOFIT is a viable solution for the analysis of huge amount of data in the addressed scientific field.
Computer Communications, May 1, 2020
Communication infrastructures are rapidly evolving to support 5G enabling lower latency, high rel... more Communication infrastructures are rapidly evolving to support 5G enabling lower latency, high reliability, and scalability of the network and of the service provisioning. An important element of the 5G vision is Multi-access Edge Computing (MEC), that leverages the availability of powerful and low-cost middle boxes, i.e., MEC nodes, statically deployed at suitable edges of the network to extend the centralized cloud backbone. At the same time, after almost a decade of research, Mobile CrowdSensing (MCS) has established the technology able to collect sensing data on the environment by using personal devices, usually smartphones, as powerful sensing-andcommunication platforms. Even though, mutual benefits due to the integration of MEC and Mobile CrowdSensing (MCS) are still largely unexplored. In this paper, we address and analyze the potential of the synergic use of MCS and MEC by thoroughly assessing various strategies for the selection of both traditional Fixed MEC (FMEC) edges as well as human-enabled Mobile MEC (M2EC) edges to support the collection of mobile CrowdSensing data. Collected results quantitatively show the effectiveness of the proposed optimization strategies in elastically scaling the load at edge nodes according to runtime provisioning needs.
Frontiers in Sustainable Cities
The recent COVID-19 pandemic in Italy has highlighted several critical issues in the management p... more The recent COVID-19 pandemic in Italy has highlighted several critical issues in the management process of infected people. At the health level, the management of the COVID-19 positive was mainly delegated to the regional authorities and centrally monitored by the State. Despite requested common activities (such as diagnosis of virus positivity, active surveillance of infected people and contact tracing), Regional Health Departments were able to issue specific directives in their territories and establish priority levels for each activity according to the specific needs related to the emergency in their area. The development of novel digital tools for the management of infected people become an urgent necessity to foster more organized and integrated solutions, able to quickly process large amounts of data. Mobile Crowdsensing methodologies could effectively facilitate needed lateral interviewing activities as well as the monitoring of crowds in environments with a high concentratio...
Indoor location is crucial for enabling the provisioning of novel location-based services in seve... more Indoor location is crucial for enabling the provisioning of novel location-based services in several areas. There are many different technologies that could be used in order to implement a reliable indoor location service, such as, among others, Beacon with Bluetooth Low Energy, Ultrasound wave system with special receivers. The choice between these technologies must take into account multiple parameters (e.g., user-centrality, proactivity,..), and should be driven by not only industrial relevance but also cost-effectiveness issues. In this paper we claim that Beacon with Bluetooth Low Energy is the best solution for indoor localization, and we present an extensive comparison between two frameworks that are the most relevant solutions interacting with beacon technology: Apple Core Location Framework and Estimote Monitoring. The comparison results offer a clear vision of which framework has to be used in order to implement a reliable and cost-effective indoor location-based service.
Proceedings of the Conference on Information Technology for Social Good, 2021
The steady deployment of IoT is paving the road toward concrete implementations of the smart city... more The steady deployment of IoT is paving the road toward concrete implementations of the smart city concept, allowing public/private institutions to sense and model (near) real-time digital replicas of physical processes and environments. This Digital Twin (DT) could be used proactively, as a decision support system, providing insights into possible optimizations of processes in a smart city context. In this article, we present the design and main building blocks of a DT solution for the Urban Facility Management (UFM) process in the metropolitan area of Bologna, Italy. The Interactive Planning Platform for city District Adaptive Maintenance Operations (IPPODAMO) is a proof of concept solution consisting of a (distributed) multi-layer geographical system, fed with heterogeneous data sources originating from different urban data providers. The data are subject to continuous refinements and algorithmic processes, used to quantify and predict near-to-long term evolution of the urban activity level, exploited for planning purposes, scheduling urban maintenance operations and interventions.
Journal of Network and Systems Management, 2020
The high abundance of IoT devices have caused an unprecedented accumulation of avalanches of geor... more The high abundance of IoT devices have caused an unprecedented accumulation of avalanches of georeferenced IoT spatial data that if could be analyzed correctly would unleash important information. This can feed decision support systems for better decision making and strategic planning regarding important aspects of our lives that depend heavily on LBSs. Several spatial data management systems for IoT data in Cloud has recently gained momentum. However, the literature is still missing a comprehensive survey that conceptualize a convenient framework that classify those frameworks under appropriate categories. In this survey paper, we focus on the management of big geospatial data that are generated by IoT data sources. We also define a conceptual framework and match the woks of the recent literature with it. We then identify future research frontiers in the field depending on the surveyed works.
IEEE Access, 2020
Depending on the Internet as the main source of information regarding all aspects of our life is ... more Depending on the Internet as the main source of information regarding all aspects of our life is becoming a trend. People seek relevant information, suggestions, and recommendations in an overloaded online world and through social ties regarding their daily activities, including places to visit and restaurants to try new food. The wide variety of choices that are available online causes information overloading, which thereby complicates the selection process. Traditional recommender systems are mostly dependent on a conventional model that is based on user-item-rating interaction without considering contextual information. We claim that new generations of recommendation systems able to exploit context in an innovative and efficient way is important and may statistically yield more significant rating predictions. However, only few research works have focused on how to effectively and efficiently exploit context metadata in Deep Learning (DL)-based recommendations. The main reason lies, perhaps most significantly, in the fact that most current DL algorithms are not intrinsically designed to incorporate contextual tags. In this paper, we provide a significant contribution for filling this gap by designing a hybrid algorithm that retrofits and repurposes a prefiltering contextual incorporation method and feeds the new dimension to a DL-based neural collaborative filtering method, thus preserving and recovering the benefits of both without their limitations. The paper also reports quantitative results that show that our method outperforms the baselines by statistically significant margins.
IEEE Access, 2019
The Multi-access Edge Computing (MEC) and Fog Computing paradigms are enabling the opportunity to... more The Multi-access Edge Computing (MEC) and Fog Computing paradigms are enabling the opportunity to have middleboxes either statically or dynamically deployed at network edges acting as local proxies with virtualized resources for supporting and enhancing service provisioning in edge localities. However, migration of edge-enabled services poses significant challenges in the edge computing environment. In this paper, we propose an edge computing platform architecture that supports service migration with different options of granularity (either entire service/data migration, or proactive applicationaware data migration) across heterogeneous edge devices (either MEC-based servers or resource-poor Fog devices) that host virtualized resources (Docker Containers). The most innovative elements of the technical contribution of our work include i) the possibility to select either an application-agnostic or an applicationaware approach, ii) the possibility to choose the appropriate application-aware approach (e.g., based on data access frequencies), iii) an automatic edge services placement support with the aim of finding a more effective placement with low energy consumption, and iv) the in-lab experimentation of the performance achieved over rapidly deployable environments with resource-limited edges such as Raspberry Pi devices.
IEEE Access, 2018
These days, ubiquitous computing has radically changed the way users access and interact with ser... more These days, ubiquitous computing has radically changed the way users access and interact with services and content on the Internet: novel smart mobile devices and broadband wireless communication channels allow users to seamlessly access them anytime and anywhere. Middleware infrastructures to support ubiquitous computing need to support an extremely dynamic and ever-changing scenario, where novel contents/services, devices, formats, and media channels become available. Service-oriented architectures and service composition techniques have proven to be the key in designing flexible and extensible platforms that are able to reliably support ubiquitous computing. However, current trends in service composition for ubiquitous computing tend to be either too formal and, therefore, poorly used by average final users, or too vertical and poorly flexible and extensible. This paper proposes novel service composition middleware for ubiquitous computing that relies on a translucent composition model to achieve a flexible, extensible, highlyavailable, but also easily understandable and usable platform. The proposed system has been widely tested, benchmarked, and deployed on a number of different and heterogeneous ubiquitous scenarios.
IEEE Transactions on Industrial Informatics, 2019
1 -The widespread adoption of Information and Communication Technologies (ICT) is profoundly chan... more 1 -The widespread adoption of Information and Communication Technologies (ICT) is profoundly changing manufacturing. Several Internet-of-Things (IoT) and Industry 4.0 solutions deployed in production environments have pushed for standardization efforts, most notably Reference Architecture Model Industrie 4.0 (RAMI 4.0), typically focusing on smart factory environments. However, ICT evolution is also enabling novel smart appliance scenarios, where relatively cheap machines, connected and integrated, are deployed outside the typical industrial environment with a wide range of stakeholders involved. The paper reports about a real-world use case composed of more than 12000 ice cream machines connected worldwide and shows how, anticipating the state-of-the-art, the underlying design of the ICT platform presents many interesting similarities with RAMI 4.0. The integration of appliances in a smart value chain enables to develop novel services for different stakeholders, ranging from ice cream manufacturer and maintenance technicians to ice cream shop owners and final consumers. The important synergies with RAMI 4.0 and the extensive onthe-field validation make the proposed solution a compelling reference application, from which to draw useful and generally applicable guidelines for the development of future Industry 4.0 smart appliance platforms.
International Journal of Cloud Computing, 2017
Pervasive and Mobile Computing, 2018
Fog computing has emerged to support the requirements of IoT applications that could not be met b... more Fog computing has emerged to support the requirements of IoT applications that could not be met by today's solutions. Different initiatives have been presented to drive the development of fog, and much work has been done to improve certain aspects. However, an in-depth analysis of the different solutions, detailing how they can be integrated and applied to meet specific requirements, is still required. In this work, we present a unified architectural model and a new taxonomy, by comparing a large number of solutions. Finally, we draw some conclusions and guidelines for the development of IoT applications based on fog.
Sensors (Basel, Switzerland), Jan 9, 2018
The relevance of effective and efficient solutions for vehicle traffic surveillance is widely rec... more The relevance of effective and efficient solutions for vehicle traffic surveillance is widely recognized in order to enable advanced strategies for traffic management, e.g., based on dynamically adaptive and decentralized traffic light management. However, most related solutions in the literature, based on the powerful enabler of cooperative vehicular communications, assume the complete penetration rate of connectivity/communication technologies (and willingness to participate in the collaborative surveillance service) over the targeted vehicle population, thus making them not applicable nowadays. The paper originally proposes an innovative solution for cooperative traffic surveillance based on vehicular communications capable of: (i) working with low penetration rates of the proposed technology and (ii) of collecting a large set of monitoring data about vehicle mobility in targeted areas of interest. The paper presents insights and lessons learnt from the design and implementation ...
2016 IEEE Symposium on Computers and Communication (ISCC), 2016
IEEE Communications Magazine, 2016
Mobile Crowdsensing (MCS) enables collective data harvesting actions by coordinating citizens wil... more Mobile Crowdsensing (MCS) enables collective data harvesting actions by coordinating citizens willing to contribute data collected via their sensor-rich smartphones that so represent sources of valuable sensing information in urban environments nowadays. One of the biggest challenges in a real long-running MCS system lies in the capacity not only to attract new volunteers, but also and most importantly, to leverage existing social ties between volunteers to keep them involved so to build long-lasting MCS communities. In addition, the advent of highly-performing devices and ad-hoc communication technologies can help to further amplify the effect of sensing actions in proximity of the volunteer devices. The paper originally describes how to exploit these sociotechnical networking aspects to increase the performance of MCS campaigns in the ParticipAct living lab, an ongoing MCS real-world experiment that involved about 170 students of the University of Bologna for more than two years. The paper also reports some significant experimental results to quantify the effectiveness of the proposed techniques.
IEEE Transactions on Cloud Computing, 2016
Nowadays, most users carry high computing power mobile devices where speech recognition is certai... more Nowadays, most users carry high computing power mobile devices where speech recognition is certainly one of the main technologies available in every modern smartphone, although battery draining and application performance (resource shortage) have a big impact on the experienced quality. Shifting applications and services to the cloud may help to improve mobile user satisfaction as demonstrated by several ongoing efforts in the mobile cloud area. However, the quality of speech recognition is still not sufficient in many complex cases to replace the common hand written text, especially when prompt reaction to short-term provisioning requests is required. To address the new scenario, this paper proposes a mobile cloud infrastructure to support the extraction of semantics information from speech recognition in the Social Care domain, where carers have to speak about their patients conditions in order to have reliable notes used afterward to plan the best support. We present not only an architecture proposal, but also a real prototype that we have deployed and thoroughly assessed with different queries, accents, and in presence of load peaks, in our experimental mobile cloud Platform as a Service (PaaS) testbed based on Cloud Foundry.
Computer Networks, 2015
Future network architectures will be completely reshaped by the emerging Network Function Virtual... more Future network architectures will be completely reshaped by the emerging Network Function Virtualization (NFV) paradigm, and telco operators will likely deploy flexible infrastructures based on the cloud, offering programmable connectivity services and computing/storage facilities in the form of Virtual Data Centers (VDCs). This paper introduces and discusses some challenging technical issues associated with this promising approach, with a special focus on the exploitation of industry-relevant cloud platforms, such as OpenStack, and on network-aware optimal placement problem of entire VDCs, taking into account multiple virtual (Virtual Machines -VMs, virtual networks, …) and physical (host capacities, network topology and capacity, …) resources and constraints. The proposed placement, computed for real production OpenStack deployments, not only satisfies the predicted communication for all VDCs deployed atop the same physical data center, but also accounts for the (real) computing overhead due to intense communication load of VMs co-located on the same physical host, such as in the case of Virtual Network Functions (VNF) embedding, typically neglected by similar existing works.
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Papers by Antonio Corradi