Marica Amadeo | Mediterranean University of Reggio Calabria (original) (raw)
Papers by Marica Amadeo
The increasing volume of Internet traffic is pushing the Internet Service Providers to deploy dis... more The increasing volume of Internet traffic is pushing the Internet Service Providers to deploy distributed caching services at the network edge, close to the end users, in order to speed up the data retrieval and reduce the bandwidth demands. In parallel, centralized paradigms like Software Defined Networking (SDN) are considered to optimize network management while supporting a variety of network applications like routing, load balancing and caching. In this paper, we extend the SDN control plane to support a novel content caching strategy. We consider a softwarised edge network domain where SDN nodes, augmented with storage capabilities, cache incoming data with the twofold target of limiting the retrieval delay and the inter-domain traffic. The caching decision is taken in a centralized mode by the SDN Controller, according to a newly defined content-driven closeness centrality metric, which identifies the importance of the SDN nodes as cachers based on their proximity to the majority of the clients requesting the most popular contents. Simulation results show the superiority of the solution in terms of higher cache hits and reduced latency, when compared against benchmark caching strategies.
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IEEE 4th 5G World Forum (5GWF), 2021
Edge computing is a prominent solution to support compute-intensive interactive applications whic... more Edge computing is a prominent solution to support compute-intensive interactive applications which, on the one hand, can hardly run on resource-constrained consumer devices and, on the other hand, may suffer from running in the cloud due to the strict delay constraints. The availability of network nodes with heterogeneous capabilities in the distributed edge infrastructure makes the computing task allocation decision a challenge. The straightforward approach of offloading the computation task to the edge node that is the nearest to the data source may lead to performance inefficiencies. Indeed, such edge node may easily get overloaded, thus failing to ensure low-latency task execution. A more judicious strategy is required which accounts for the edge nodes' processing capabilities and for the queuing delay accumulated when tasks wait before being executed. In this paper, we propose a novel optimal computing task allocation strategy aimed at minimizing the network resources usage, while bounding the execution latency at the edge node acting as the task executor. We formulate the optimal task allocation through an integer linear programming problem, assuming an edge infrastructure managed through software-defined networking. Achieved results show that the proposal meets the targeted objectives under all the considered simulation settings and significantly outperforms other benchmark solutions.
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ICC, 2022
Federated learning (FL) is gaining momentum as a prominent solution to perform training procedure... more Federated learning (FL) is gaining momentum as a prominent solution to perform training procedures without the need to move sensitive end-user data to a centralized third party server. In FL, models are locally trained at distributed enddevices, acting as clients, and only model updates are transferred from the clients to the aggregator, which is in charge of global model aggregation. Although FL can ensure better privacy preservation than centralized machine learning (ML), it exhibits still some concerns. First, clients need to be properly discovered and selected to ensure that highly accurate models are built. Second, huge models may still require to be exchanged from the aggregator to all the selected clients, incurring a not negligible network footprint. To tackle such issues, in this paper, we propose a framework built upon in-network caching, multicast and namebased data delivery, natively provided by the Named Data Networking (NDN) paradigm, in order to support client discovery and aggregator-clients data exchange. Benefits of the proposal are showcased when compared to a conventional application-layer solution.
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Thanks to recent advancements in edge computing, the traditional centralized cloud-based approach... more Thanks to recent advancements in edge computing, the traditional centralized cloud-based approach to deploy Artificial Intelligence (AI) techniques will be soon replaced or complemented by the so-called edge AI approach. By pushing AI at the network edge, close to the large amount of raw input data, the traffic traversing the core network as well as the inference latency can be reduced. Despite such neat benefits, the actual deployment of edge AI across distributed nodes raises novel challenges to be addressed, such as the need to enforce proper addressing and discovery procedures, to identify AI components, and to chain them in an interoperable manner. Named Data Networking (NDN) has been recently argued as one of the main enablers of network and computing convergence, which edge AI should build upon. However, the peculiarities of such a new paradigm entails to go a step further. In this paper we disclose the potential of NDN to support the orchestration of edge AI. Several motivations are discussed, as well as the challenges which serve as guidelines for progress beyond the state of the art in this topic.
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IEEE CCNC, 2022
With the outbreak of COVID-19, and its terrible and fast spread among communities, contact tracin... more With the outbreak of COVID-19, and its terrible and fast spread among communities, contact tracing methods have become crucial to protect people. However, conventional mechanisms, for instance based on the manual search of close contacts, lack of high efficiency due to the high time consumption. The research community is therefore exploring new ways to track contagious diseases by exploiting modern communications paradigms and technologies. In this paper, we propose a new contact tracing method based on Online Social Network (OSN) platforms. In our design, contact detection occurs in real-time by means of traditional proximity approaches. Then, a likely future contact forecast is notified through OSN communities.
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IEEE Transactions on Green Communications and Networking, 2021
In-network caching is one of the main pillars of the Named Data Networking (NDN) paradigm, where ... more In-network caching is one of the main pillars of the Named Data Networking (NDN) paradigm, where every Internet router, in the path between data sources and consumers, can cache incoming content packets. Multiple strategies have been designed for caching Internet of Things (IoT) data streamed by resource-constrained devices in edge domains and wireless sensor networks, while the benefits of IoT data caching at Internet-scale, including both edge and core network segments, have not been fully disclosed. In this work, we propose and analyse a novel probabilistic Internet-scale caching design for IoT data, which jointly accounts for the content popularity and lifetime. In the considered scenario, IoT contents are requested by remote consumers and delivered by crossing multiple edge and core network segments of the NDN-based future Internet. The proposal is composed of two distinct reactive caching strategies, a coordinated and an autonomous one, to be implemented in the edge and core domain, respectively. Achieved results show that the proposal outperforms state-of-the-art solutions by providing, among others, the highest cache hit ratio and the shortest number of hops. Such performances testify a lower pressure on energyconstrained devices and on the network infrastructure, overall contributing to the sustainability of the IoT ecosystem.
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Elsevier COMNET, 2021
In-network caching, natively enabled by Named Data Networking (NDN), is effective to speed up con... more In-network caching, natively enabled by Named Data Networking (NDN), is effective to speed up content retrieval in vehicular environments, where a large variety of contents are typically transient, i.e., they expire after a certain amount of time, and may exhibit different popularity profiles. Lifetime and popularity play a crucial role in the content caching decision: intuitively, caching a popular content with long lifetime can be more useful than caching an unpopular one that is ready to expire and then to be dropped from the content store. At the same time, making nearby nodes caching different contents and, therefore, improving the caching diversity, can be crucial to get better delivery performance over the broadcast wireless medium. In this paper, we devise a novel distributed caching strategy where vehicles autonomously decide which content is to be locally cached according to the content residual lifetime, its popularity and the perceived availability of the same content in the neighbourhood. The target is to cache with higher probability more popular contents with a longer lifetime, which are not already cached by a nearby node, thus improving the caching diversity in the neighbourhood. As a result, vehicles can find the majority of distinct fresh and popular contents nearby, without flooding the network with content requests that have to reach the original source. Performance evaluation shows that the conceived solution outperforms representative benchmark schemes, by guaranteeing, among others, the shortest content retrieval time and the lowest network traffic load.
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IEEE TNSM, 2021
With more than 75 billions of objects connected by 2025, Internet of Things (IoT) is the catalyst... more With more than 75 billions of objects connected by 2025, Internet of Things (IoT) is the catalyst for the digital revolution, contributing to the generation of big amounts of (tran-sient) data, which calls into question the storage and processing performance of the conventional cloud. Moving storage resources at the edge can reduce the data retrieval latency and save core network resources, albeit the actual performance depends on the selected caching policy. Existing edge caching strategies mainly account for the content popularity as crucial decision metric and do not consider the transient feature of IoT data. In this paper, we design a caching orchestration mechanism, deployed as a network application on top of a software-defined networking Controller in charge of the edge infrastructure, which accounts for the nodes' storage capabilities, the network links' available bandwidth, and the IoT data lifetime and popularity. The policy decides which IoT contents have to be cached and in which node of a distributed edge deployment with limited storage resources, with the ultimate aim of minimizing the data retrieval latency. We formulate the optimal content placement through an Integer Linear Programming (ILP) problem and propose a heuristic algorithm to solve it. Results show that the proposal outperforms the considered benchmark solutions in terms of latency and cache hit probability, under all the considered simulation settings.
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IEEE TNSM, 2019
Pushed by the challenging demands of fifth generation (5G) use cases and the recent advancements ... more Pushed by the challenging demands of fifth generation (5G) use cases and the recent advancements in virtualization technologies, edge network devices are rapidly evolving from simple forwarders to softwarized infrastructures augmented with computing and storage capabilities. Such a trend blurs the distinction between IT and telco domains and paves the way for the integrated management of network and computing resources. In this paper, we focus on the interplay of the Software Defined Networking (SDN) and Named Data Networking (NDN) paradigms as key drivers for the orchestration of computing services in softwarized edge infrastructures. We propose a new framework that brings out the best of the SDN centralized intelligence to take "smart" decisions and inject rules for service allocation (e.g., retrieve input data, execute a function over them), and the best of the adaptive NDN forwarding plane and its native in-network caching to request and deliver services by name. Within the framework, we devise a service allocation strategy that aims at selecting as an executor, among the potential candidates, the one which is able to guarantee the shortest service provisioning delay for each service request, while accounting for the network topology, the links status, and the available computing resources in the edge nodes. Performance evaluations testify to the superiority of our proposal against benchmarking solutions from the literature under different operation settings.
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AdHocNow, 2020
Thanks to recent advancements in edge computing, the traditional centralized cloud-based approach... more Thanks to recent advancements in edge computing, the traditional centralized cloud-based approach to deploy Artificial Intelligence (AI) techniques will be soon replaced or complemented by the so-called edge AI approach. By pushing AI at the network edge, close to the large amount of raw input data, the traffic traversing the core network as well as the inference latency can be reduced. Despite such neat benefits, the actual deployment of edge AI across distributed nodes raises novel challenges to be addressed, such as the need to enforce proper addressing and discovery procedures, to identify AI components, and to chain them in an interoperable manner. Named Data Networking (NDN) has been recently argued as one of the main enablers of network and computing convergence, which edge AI should build upon. However, the peculiarities of such a new paradigm entails to go a step further. In this paper we disclose the potential of NDN to support the orchestration of edge AI. Several motivations are discussed, as well as the challenges which serve as guidelines for progress beyond the state of the art in this topic.
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IEEE ICC, 2020
Software Defined Networking (SDN) and Named Data Networking (NDN) have been recently advocated as... more Software Defined Networking (SDN) and Named Data Networking (NDN) have been recently advocated as complementary paradigms to improve content distribution in the next-generation Internet. On the one hand, SDN offers a centralized control plane that can optimize routing decisions; on the other, the distinctive features at the NDN data plane, such as name-based delivery, in-network caching, and stateful forwarding, simplify data dissemination. In the integrated design, when a request cannot be handled locally at the NDN data plane in the Forwarding Information Base (FIB), the SDN Controller is contacted to inject the forwarding rule. Decisions such as which rules need to be stored in the node and for how long deeply affect the packet forwarding performance. This paper debates about the issues related to forwarding rules in the FIBs of SDN-controlled NDN nodes, by specifically accounting for their name-based nature, representing a key novelty compared to legacy SDN implementations. Quantitative results are reported to showcase the impact of crucial parameters, like the content popularity, the content requests rate, the table size, on the FIB performance in terms of valuable metrics (e.g., hit ratio, rejected requests, incurred signaling with the Controller).
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IEEE INFOCOM Workshops, 2020
Among the distinctive features of the Named Data Networking (NDN) paradigm, in-network caching pl... more Among the distinctive features of the Named Data Networking (NDN) paradigm, in-network caching plays a crucial role in improving data delivery performance. At the network edge, the benefits of in-network caching can be remarkable in terms of reduced network traffic and user-perceived access latency. Multiple strategies have been designed for caching static Internet contents in NDN routers, while less attention has been devoted to transient Internet of Things (IoT) data, which expire after a certain amount of time. In this work, we introduce a novel distributed and autonomous caching strategy where NDN nodes take decisions by considering the popularity of IoT data and their lifetime. The target is to cache the most popular data with the highest residual lifetime in order to maximize the cache hit ratio at the edge. Performance evaluation assessed with the ndnSIM network simulator shows that the proposed solution outperforms existing schemes available in the literature by guaranteeing, among others, the highest cache hit ratio and the shortest content retrieval time.
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IEEE TNSM, 2019
Edge computing is a key paradigm to offload the core network and effectively process massive Inte... more Edge computing is a key paradigm to offload the core network and effectively process massive Internet of Things (IoT) raw data without sending them to the cloud. This paradigm normally relies on a set of purpose-built and pre-planned servers, which host storage and processing resources to provide IoT services close to the data sources, thus saving core network resources and offloading the remote cloud infrastructure. In this paper, we propose to turn the network edge into a dynamic, distributed computing environment that supports the provisioning of IoT services, by exploiting the recent evolution of Named Data Networking (NDN), supporting both name-based data retrieval and computation. Specific name structure and novel NDN forwarding mechanisms are designed; a distributed strategy is also engineered to select the service executor among edge nodes, with the objectives to (i) limit the raw IoT data traffic crossing the network, and (ii) allocate the service execution according to the nodes' available processing resources. Numerical analysis shows that the performance of the proposed framework approaches the one of the optimal solution of a formulated Integer Linear Programming problem. System-level ndnSIM simulations confirm that the proposal also outperforms the considered state-of-the-art benchmark solutions in terms of service provisioning time.
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IEEE MMTC Communications - Frontiers, 2019
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Information-centric networking (ICN) is a promising solution for content delivery in multi-hop wi... more Information-centric networking (ICN) is a promising solution for content delivery in multi-hop wireless networks. In these environments, the ICN forwarding strategies typically rely on broadcasting, which facilitates content distribution by taking advantage of the shared medium, but brings about undesirable side effects. To avoid the broadcast-related issues of packet redundancy and unreliability due its unacknowledged mode, a few recent proposals advocated unicasting as the communication mode to resort to, once the content provider has been discovered. However, unicasting may suffer from connectivity breakages due to node mobility and harsh propagation conditions, and, generally, limits the data sharing capability of the wireless medium. In this paper, we design a robust ICN-based forwarding strategy, called Ad Hoc Dynamic Unicast (ADU), that wisely harnesses unicast and broadcast primitives on top of IEEE 802.11-based wireless networks. ADU relies on unicasting for content dissemination after the provider discovery, and promptly falls back to broadcast to find a new content provider in case of a link failure notified by the Medium Access Control (MAC) layer. Extensive simulations in two different multi-hop wireless scenarios, i.e., mobile and vehicular ad hoc networks, under different load settings, assess the ADU performance against benchmark ICN forwarding schemes, representative of broadcast-based and unicast-based solutions. Results show that ADU outperforms them in all circumstances, proving its better responsiveness to link failures, which guarantees a shorter content retrieval delay and a lower message overhead and energy consumption.
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—Processing high volumes of raw Internet of Things (IoT) data at the network edge is becoming a p... more —Processing high volumes of raw Internet of Things (IoT) data at the network edge is becoming a popular solution to guarantee lower latency interactions compared to the traditional computing in the remote cloud. The synergy between networking and computing domains is today enabled by innovative paradigms such as Named Data Networking (NDN) and its recent extensions, which support the request of computing services " by name " and their distributed execution right inside the network nodes, instead of being deployed in purpose-built servers. In this paper, we extend the NDN architecture to turn the network edge into a dynamic computing environment for running user applications relying on IoT data streams processing and analytics. Novel naming and forwarding mechanisms are defined to properly guide service requests towards edge computing nodes, as close as possible to the IoT data sources, in order to offer low latency and avoid that raw IoT data flood the network. Through simulations with ndnSIM, we analyse the performance of our proposal in terms of volume of data traffic and service delivery time.
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Fifth generation (5G) systems are designed as flexible and programmable multi-service networking ... more Fifth generation (5G) systems are designed as flexible and programmable multi-service networking and computing infrastructures, with improved throughput, latency, and reliability performance. In this paper, we discuss our view about the role of Information-Centric Networking (ICN) in 5G systems and present some architectural perspectives for the ICN-5G deployment. The proposed architecture relies on a hybrid approach, which makes the best of the centralized software-defined networking and the distributed ICN delivery mechanism to effectively suit the mobility of users, contents and virtualized network/service functions. Its viability is showcased in a representative use case, against a legacy non-ICN approach.
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IEEE ICNSC 2017
—Today, the novel Cloud of Things (CoT) paradigm, where Cloud and Internet of Things (IoT) techno... more —Today, the novel Cloud of Things (CoT) paradigm, where Cloud and Internet of Things (IoT) technologies are merged together, is foreseen as a promising enabler of many real-life application scenarios, like the smart home. However, several issues are still debated in the design of CoT systems, including how to effectively manage the heterogeneity of IoT devices and how to support robust and low-latency communications between the cloud and the physical world. In this paper, we present a novel CoT platform that solves such challenges in the smart home domain by leveraging two groundbreaking concepts: Information Centric Networking (ICN) and Fog Computing. The proposal, called ICN-isapiens, is a three-layered architecture where an intermediate (Fog) layer, consisting of smart home servers (HSs), is introduced between the physical world and the remote cloud, to support real-time services and hide the heterogeneity of IoT devices. Communication at the physical layer consists of name-based ICN primitives, which facilitate the network configuration and enable simple and effective interactions between HSs and IoT devices. As proof of concept, an experimental testbed is presented and some application examples are described to showcase the advanced capabilities of ICN-isapiens.
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—The technological advances in mobile devices are pushing cloud computing to the network edge, wh... more —The technological advances in mobile devices are pushing cloud computing to the network edge, where services like data storage and processing can be offered by mobile devices locally with improved quality. In this letter, we identify Named Data Networking (NDN) as a key enabler to support " by design " the peculiarities of decentralized edge clouds at the network layer. We extend NDN beyond its original scope of content retrieval facilitator, by letting names to address, not only " contents " , but also " cloud services " and enhancing the semantics of NDN primitives to efficiently and reliably support both the provider discovery and the service provisioning phases. An early evaluation is performed to showcase the benefits of the proposal, also when compared to a traditional TCP/IP-based approach.
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The European Telecommunications Standards Institute (ETSI) recently released a set of specificati... more The European Telecommunications Standards Institute (ETSI) recently released a set of specifications for a reference architecture to globally access resources provided by machine-to-machine (M2M) devices over heterogeneous technologies in an interoperable way through a RESTful interface. Resources are named through Uniform Resource Identifiers (URIs) at the application layer and typically reachable at the network layer through IP connectivity. Such an approach can be used also to access extremely resource-constrained M2M devices , provided that lightweight interactions with a gateway, remotely exposing their resources, are granted. Among potential alternatives to support communication between the gateway and such constrained devices, we investigate the Information-Centric Networking (ICN) paradigm, gaining momentum in the future Internet research arena. It differs from the host-centric IP networking in that it cares for the content to retrieve instead of the device hosting it. By directly using content names at the network layer and a receiver-driven communication , ICN well fits the requirements of many M2M applications that are information-centric in nature and rely on a publish-subscribe service model. In this paper, we propose an ICN-based solution to be deployed on top of constrained M2M devices whose named resources are exposed at a wide area scope by an M2M gateway. The proposal aims at ensuring easy interoperability with ETSI M2M specifications, thus allowing remote applications to access the resources of ICN-enabled nodes. To showcase the viability of our proposal, a test-bed has been deployed leveraging low-cost devices for home automation. Experimental results confirm a good trade-off between device resources consumption , easiness of implementation and effectiveness of communication.
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The increasing volume of Internet traffic is pushing the Internet Service Providers to deploy dis... more The increasing volume of Internet traffic is pushing the Internet Service Providers to deploy distributed caching services at the network edge, close to the end users, in order to speed up the data retrieval and reduce the bandwidth demands. In parallel, centralized paradigms like Software Defined Networking (SDN) are considered to optimize network management while supporting a variety of network applications like routing, load balancing and caching. In this paper, we extend the SDN control plane to support a novel content caching strategy. We consider a softwarised edge network domain where SDN nodes, augmented with storage capabilities, cache incoming data with the twofold target of limiting the retrieval delay and the inter-domain traffic. The caching decision is taken in a centralized mode by the SDN Controller, according to a newly defined content-driven closeness centrality metric, which identifies the importance of the SDN nodes as cachers based on their proximity to the majority of the clients requesting the most popular contents. Simulation results show the superiority of the solution in terms of higher cache hits and reduced latency, when compared against benchmark caching strategies.
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IEEE 4th 5G World Forum (5GWF), 2021
Edge computing is a prominent solution to support compute-intensive interactive applications whic... more Edge computing is a prominent solution to support compute-intensive interactive applications which, on the one hand, can hardly run on resource-constrained consumer devices and, on the other hand, may suffer from running in the cloud due to the strict delay constraints. The availability of network nodes with heterogeneous capabilities in the distributed edge infrastructure makes the computing task allocation decision a challenge. The straightforward approach of offloading the computation task to the edge node that is the nearest to the data source may lead to performance inefficiencies. Indeed, such edge node may easily get overloaded, thus failing to ensure low-latency task execution. A more judicious strategy is required which accounts for the edge nodes' processing capabilities and for the queuing delay accumulated when tasks wait before being executed. In this paper, we propose a novel optimal computing task allocation strategy aimed at minimizing the network resources usage, while bounding the execution latency at the edge node acting as the task executor. We formulate the optimal task allocation through an integer linear programming problem, assuming an edge infrastructure managed through software-defined networking. Achieved results show that the proposal meets the targeted objectives under all the considered simulation settings and significantly outperforms other benchmark solutions.
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ICC, 2022
Federated learning (FL) is gaining momentum as a prominent solution to perform training procedure... more Federated learning (FL) is gaining momentum as a prominent solution to perform training procedures without the need to move sensitive end-user data to a centralized third party server. In FL, models are locally trained at distributed enddevices, acting as clients, and only model updates are transferred from the clients to the aggregator, which is in charge of global model aggregation. Although FL can ensure better privacy preservation than centralized machine learning (ML), it exhibits still some concerns. First, clients need to be properly discovered and selected to ensure that highly accurate models are built. Second, huge models may still require to be exchanged from the aggregator to all the selected clients, incurring a not negligible network footprint. To tackle such issues, in this paper, we propose a framework built upon in-network caching, multicast and namebased data delivery, natively provided by the Named Data Networking (NDN) paradigm, in order to support client discovery and aggregator-clients data exchange. Benefits of the proposal are showcased when compared to a conventional application-layer solution.
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Thanks to recent advancements in edge computing, the traditional centralized cloud-based approach... more Thanks to recent advancements in edge computing, the traditional centralized cloud-based approach to deploy Artificial Intelligence (AI) techniques will be soon replaced or complemented by the so-called edge AI approach. By pushing AI at the network edge, close to the large amount of raw input data, the traffic traversing the core network as well as the inference latency can be reduced. Despite such neat benefits, the actual deployment of edge AI across distributed nodes raises novel challenges to be addressed, such as the need to enforce proper addressing and discovery procedures, to identify AI components, and to chain them in an interoperable manner. Named Data Networking (NDN) has been recently argued as one of the main enablers of network and computing convergence, which edge AI should build upon. However, the peculiarities of such a new paradigm entails to go a step further. In this paper we disclose the potential of NDN to support the orchestration of edge AI. Several motivations are discussed, as well as the challenges which serve as guidelines for progress beyond the state of the art in this topic.
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IEEE CCNC, 2022
With the outbreak of COVID-19, and its terrible and fast spread among communities, contact tracin... more With the outbreak of COVID-19, and its terrible and fast spread among communities, contact tracing methods have become crucial to protect people. However, conventional mechanisms, for instance based on the manual search of close contacts, lack of high efficiency due to the high time consumption. The research community is therefore exploring new ways to track contagious diseases by exploiting modern communications paradigms and technologies. In this paper, we propose a new contact tracing method based on Online Social Network (OSN) platforms. In our design, contact detection occurs in real-time by means of traditional proximity approaches. Then, a likely future contact forecast is notified through OSN communities.
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IEEE Transactions on Green Communications and Networking, 2021
In-network caching is one of the main pillars of the Named Data Networking (NDN) paradigm, where ... more In-network caching is one of the main pillars of the Named Data Networking (NDN) paradigm, where every Internet router, in the path between data sources and consumers, can cache incoming content packets. Multiple strategies have been designed for caching Internet of Things (IoT) data streamed by resource-constrained devices in edge domains and wireless sensor networks, while the benefits of IoT data caching at Internet-scale, including both edge and core network segments, have not been fully disclosed. In this work, we propose and analyse a novel probabilistic Internet-scale caching design for IoT data, which jointly accounts for the content popularity and lifetime. In the considered scenario, IoT contents are requested by remote consumers and delivered by crossing multiple edge and core network segments of the NDN-based future Internet. The proposal is composed of two distinct reactive caching strategies, a coordinated and an autonomous one, to be implemented in the edge and core domain, respectively. Achieved results show that the proposal outperforms state-of-the-art solutions by providing, among others, the highest cache hit ratio and the shortest number of hops. Such performances testify a lower pressure on energyconstrained devices and on the network infrastructure, overall contributing to the sustainability of the IoT ecosystem.
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Elsevier COMNET, 2021
In-network caching, natively enabled by Named Data Networking (NDN), is effective to speed up con... more In-network caching, natively enabled by Named Data Networking (NDN), is effective to speed up content retrieval in vehicular environments, where a large variety of contents are typically transient, i.e., they expire after a certain amount of time, and may exhibit different popularity profiles. Lifetime and popularity play a crucial role in the content caching decision: intuitively, caching a popular content with long lifetime can be more useful than caching an unpopular one that is ready to expire and then to be dropped from the content store. At the same time, making nearby nodes caching different contents and, therefore, improving the caching diversity, can be crucial to get better delivery performance over the broadcast wireless medium. In this paper, we devise a novel distributed caching strategy where vehicles autonomously decide which content is to be locally cached according to the content residual lifetime, its popularity and the perceived availability of the same content in the neighbourhood. The target is to cache with higher probability more popular contents with a longer lifetime, which are not already cached by a nearby node, thus improving the caching diversity in the neighbourhood. As a result, vehicles can find the majority of distinct fresh and popular contents nearby, without flooding the network with content requests that have to reach the original source. Performance evaluation shows that the conceived solution outperforms representative benchmark schemes, by guaranteeing, among others, the shortest content retrieval time and the lowest network traffic load.
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IEEE TNSM, 2021
With more than 75 billions of objects connected by 2025, Internet of Things (IoT) is the catalyst... more With more than 75 billions of objects connected by 2025, Internet of Things (IoT) is the catalyst for the digital revolution, contributing to the generation of big amounts of (tran-sient) data, which calls into question the storage and processing performance of the conventional cloud. Moving storage resources at the edge can reduce the data retrieval latency and save core network resources, albeit the actual performance depends on the selected caching policy. Existing edge caching strategies mainly account for the content popularity as crucial decision metric and do not consider the transient feature of IoT data. In this paper, we design a caching orchestration mechanism, deployed as a network application on top of a software-defined networking Controller in charge of the edge infrastructure, which accounts for the nodes' storage capabilities, the network links' available bandwidth, and the IoT data lifetime and popularity. The policy decides which IoT contents have to be cached and in which node of a distributed edge deployment with limited storage resources, with the ultimate aim of minimizing the data retrieval latency. We formulate the optimal content placement through an Integer Linear Programming (ILP) problem and propose a heuristic algorithm to solve it. Results show that the proposal outperforms the considered benchmark solutions in terms of latency and cache hit probability, under all the considered simulation settings.
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IEEE TNSM, 2019
Pushed by the challenging demands of fifth generation (5G) use cases and the recent advancements ... more Pushed by the challenging demands of fifth generation (5G) use cases and the recent advancements in virtualization technologies, edge network devices are rapidly evolving from simple forwarders to softwarized infrastructures augmented with computing and storage capabilities. Such a trend blurs the distinction between IT and telco domains and paves the way for the integrated management of network and computing resources. In this paper, we focus on the interplay of the Software Defined Networking (SDN) and Named Data Networking (NDN) paradigms as key drivers for the orchestration of computing services in softwarized edge infrastructures. We propose a new framework that brings out the best of the SDN centralized intelligence to take "smart" decisions and inject rules for service allocation (e.g., retrieve input data, execute a function over them), and the best of the adaptive NDN forwarding plane and its native in-network caching to request and deliver services by name. Within the framework, we devise a service allocation strategy that aims at selecting as an executor, among the potential candidates, the one which is able to guarantee the shortest service provisioning delay for each service request, while accounting for the network topology, the links status, and the available computing resources in the edge nodes. Performance evaluations testify to the superiority of our proposal against benchmarking solutions from the literature under different operation settings.
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AdHocNow, 2020
Thanks to recent advancements in edge computing, the traditional centralized cloud-based approach... more Thanks to recent advancements in edge computing, the traditional centralized cloud-based approach to deploy Artificial Intelligence (AI) techniques will be soon replaced or complemented by the so-called edge AI approach. By pushing AI at the network edge, close to the large amount of raw input data, the traffic traversing the core network as well as the inference latency can be reduced. Despite such neat benefits, the actual deployment of edge AI across distributed nodes raises novel challenges to be addressed, such as the need to enforce proper addressing and discovery procedures, to identify AI components, and to chain them in an interoperable manner. Named Data Networking (NDN) has been recently argued as one of the main enablers of network and computing convergence, which edge AI should build upon. However, the peculiarities of such a new paradigm entails to go a step further. In this paper we disclose the potential of NDN to support the orchestration of edge AI. Several motivations are discussed, as well as the challenges which serve as guidelines for progress beyond the state of the art in this topic.
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IEEE ICC, 2020
Software Defined Networking (SDN) and Named Data Networking (NDN) have been recently advocated as... more Software Defined Networking (SDN) and Named Data Networking (NDN) have been recently advocated as complementary paradigms to improve content distribution in the next-generation Internet. On the one hand, SDN offers a centralized control plane that can optimize routing decisions; on the other, the distinctive features at the NDN data plane, such as name-based delivery, in-network caching, and stateful forwarding, simplify data dissemination. In the integrated design, when a request cannot be handled locally at the NDN data plane in the Forwarding Information Base (FIB), the SDN Controller is contacted to inject the forwarding rule. Decisions such as which rules need to be stored in the node and for how long deeply affect the packet forwarding performance. This paper debates about the issues related to forwarding rules in the FIBs of SDN-controlled NDN nodes, by specifically accounting for their name-based nature, representing a key novelty compared to legacy SDN implementations. Quantitative results are reported to showcase the impact of crucial parameters, like the content popularity, the content requests rate, the table size, on the FIB performance in terms of valuable metrics (e.g., hit ratio, rejected requests, incurred signaling with the Controller).
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IEEE INFOCOM Workshops, 2020
Among the distinctive features of the Named Data Networking (NDN) paradigm, in-network caching pl... more Among the distinctive features of the Named Data Networking (NDN) paradigm, in-network caching plays a crucial role in improving data delivery performance. At the network edge, the benefits of in-network caching can be remarkable in terms of reduced network traffic and user-perceived access latency. Multiple strategies have been designed for caching static Internet contents in NDN routers, while less attention has been devoted to transient Internet of Things (IoT) data, which expire after a certain amount of time. In this work, we introduce a novel distributed and autonomous caching strategy where NDN nodes take decisions by considering the popularity of IoT data and their lifetime. The target is to cache the most popular data with the highest residual lifetime in order to maximize the cache hit ratio at the edge. Performance evaluation assessed with the ndnSIM network simulator shows that the proposed solution outperforms existing schemes available in the literature by guaranteeing, among others, the highest cache hit ratio and the shortest content retrieval time.
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IEEE TNSM, 2019
Edge computing is a key paradigm to offload the core network and effectively process massive Inte... more Edge computing is a key paradigm to offload the core network and effectively process massive Internet of Things (IoT) raw data without sending them to the cloud. This paradigm normally relies on a set of purpose-built and pre-planned servers, which host storage and processing resources to provide IoT services close to the data sources, thus saving core network resources and offloading the remote cloud infrastructure. In this paper, we propose to turn the network edge into a dynamic, distributed computing environment that supports the provisioning of IoT services, by exploiting the recent evolution of Named Data Networking (NDN), supporting both name-based data retrieval and computation. Specific name structure and novel NDN forwarding mechanisms are designed; a distributed strategy is also engineered to select the service executor among edge nodes, with the objectives to (i) limit the raw IoT data traffic crossing the network, and (ii) allocate the service execution according to the nodes' available processing resources. Numerical analysis shows that the performance of the proposed framework approaches the one of the optimal solution of a formulated Integer Linear Programming problem. System-level ndnSIM simulations confirm that the proposal also outperforms the considered state-of-the-art benchmark solutions in terms of service provisioning time.
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IEEE MMTC Communications - Frontiers, 2019
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Information-centric networking (ICN) is a promising solution for content delivery in multi-hop wi... more Information-centric networking (ICN) is a promising solution for content delivery in multi-hop wireless networks. In these environments, the ICN forwarding strategies typically rely on broadcasting, which facilitates content distribution by taking advantage of the shared medium, but brings about undesirable side effects. To avoid the broadcast-related issues of packet redundancy and unreliability due its unacknowledged mode, a few recent proposals advocated unicasting as the communication mode to resort to, once the content provider has been discovered. However, unicasting may suffer from connectivity breakages due to node mobility and harsh propagation conditions, and, generally, limits the data sharing capability of the wireless medium. In this paper, we design a robust ICN-based forwarding strategy, called Ad Hoc Dynamic Unicast (ADU), that wisely harnesses unicast and broadcast primitives on top of IEEE 802.11-based wireless networks. ADU relies on unicasting for content dissemination after the provider discovery, and promptly falls back to broadcast to find a new content provider in case of a link failure notified by the Medium Access Control (MAC) layer. Extensive simulations in two different multi-hop wireless scenarios, i.e., mobile and vehicular ad hoc networks, under different load settings, assess the ADU performance against benchmark ICN forwarding schemes, representative of broadcast-based and unicast-based solutions. Results show that ADU outperforms them in all circumstances, proving its better responsiveness to link failures, which guarantees a shorter content retrieval delay and a lower message overhead and energy consumption.
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—Processing high volumes of raw Internet of Things (IoT) data at the network edge is becoming a p... more —Processing high volumes of raw Internet of Things (IoT) data at the network edge is becoming a popular solution to guarantee lower latency interactions compared to the traditional computing in the remote cloud. The synergy between networking and computing domains is today enabled by innovative paradigms such as Named Data Networking (NDN) and its recent extensions, which support the request of computing services " by name " and their distributed execution right inside the network nodes, instead of being deployed in purpose-built servers. In this paper, we extend the NDN architecture to turn the network edge into a dynamic computing environment for running user applications relying on IoT data streams processing and analytics. Novel naming and forwarding mechanisms are defined to properly guide service requests towards edge computing nodes, as close as possible to the IoT data sources, in order to offer low latency and avoid that raw IoT data flood the network. Through simulations with ndnSIM, we analyse the performance of our proposal in terms of volume of data traffic and service delivery time.
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Fifth generation (5G) systems are designed as flexible and programmable multi-service networking ... more Fifth generation (5G) systems are designed as flexible and programmable multi-service networking and computing infrastructures, with improved throughput, latency, and reliability performance. In this paper, we discuss our view about the role of Information-Centric Networking (ICN) in 5G systems and present some architectural perspectives for the ICN-5G deployment. The proposed architecture relies on a hybrid approach, which makes the best of the centralized software-defined networking and the distributed ICN delivery mechanism to effectively suit the mobility of users, contents and virtualized network/service functions. Its viability is showcased in a representative use case, against a legacy non-ICN approach.
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IEEE ICNSC 2017
—Today, the novel Cloud of Things (CoT) paradigm, where Cloud and Internet of Things (IoT) techno... more —Today, the novel Cloud of Things (CoT) paradigm, where Cloud and Internet of Things (IoT) technologies are merged together, is foreseen as a promising enabler of many real-life application scenarios, like the smart home. However, several issues are still debated in the design of CoT systems, including how to effectively manage the heterogeneity of IoT devices and how to support robust and low-latency communications between the cloud and the physical world. In this paper, we present a novel CoT platform that solves such challenges in the smart home domain by leveraging two groundbreaking concepts: Information Centric Networking (ICN) and Fog Computing. The proposal, called ICN-isapiens, is a three-layered architecture where an intermediate (Fog) layer, consisting of smart home servers (HSs), is introduced between the physical world and the remote cloud, to support real-time services and hide the heterogeneity of IoT devices. Communication at the physical layer consists of name-based ICN primitives, which facilitate the network configuration and enable simple and effective interactions between HSs and IoT devices. As proof of concept, an experimental testbed is presented and some application examples are described to showcase the advanced capabilities of ICN-isapiens.
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—The technological advances in mobile devices are pushing cloud computing to the network edge, wh... more —The technological advances in mobile devices are pushing cloud computing to the network edge, where services like data storage and processing can be offered by mobile devices locally with improved quality. In this letter, we identify Named Data Networking (NDN) as a key enabler to support " by design " the peculiarities of decentralized edge clouds at the network layer. We extend NDN beyond its original scope of content retrieval facilitator, by letting names to address, not only " contents " , but also " cloud services " and enhancing the semantics of NDN primitives to efficiently and reliably support both the provider discovery and the service provisioning phases. An early evaluation is performed to showcase the benefits of the proposal, also when compared to a traditional TCP/IP-based approach.
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The European Telecommunications Standards Institute (ETSI) recently released a set of specificati... more The European Telecommunications Standards Institute (ETSI) recently released a set of specifications for a reference architecture to globally access resources provided by machine-to-machine (M2M) devices over heterogeneous technologies in an interoperable way through a RESTful interface. Resources are named through Uniform Resource Identifiers (URIs) at the application layer and typically reachable at the network layer through IP connectivity. Such an approach can be used also to access extremely resource-constrained M2M devices , provided that lightweight interactions with a gateway, remotely exposing their resources, are granted. Among potential alternatives to support communication between the gateway and such constrained devices, we investigate the Information-Centric Networking (ICN) paradigm, gaining momentum in the future Internet research arena. It differs from the host-centric IP networking in that it cares for the content to retrieve instead of the device hosting it. By directly using content names at the network layer and a receiver-driven communication , ICN well fits the requirements of many M2M applications that are information-centric in nature and rely on a publish-subscribe service model. In this paper, we propose an ICN-based solution to be deployed on top of constrained M2M devices whose named resources are exposed at a wide area scope by an M2M gateway. The proposal aims at ensuring easy interoperability with ETSI M2M specifications, thus allowing remote applications to access the resources of ICN-enabled nodes. To showcase the viability of our proposal, a test-bed has been deployed leveraging low-cost devices for home automation. Experimental results confirm a good trade-off between device resources consumption , easiness of implementation and effectiveness of communication.
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Link: https://www.mdpi.com/journal/futureinternet/special\_issues/Smart\_Environments
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