1,720,988 research outputs found
Value-of-Information Middlewares for Fog and Edge Computing
No full textFog and Edge Computing aim to deliver low-latency, immersive, and powerful services by processing information close to both devices and users. This is well suited for IoT applications in Smart City, where IoT gateways, Cloudlets, Base Stations, and other computational nodes can process (part of) the data generated by the multitude of IoT sensors directly at the edge of the network. However, the implementation of Fog and Edge Computing is challenging because it requires to deal with a (limited number of) constrained devices, dynamic services' requirements, and heterogeneous network conditions. Differently from the Cloud, where computational resources are supposed to be unlimited, Fog and Edge services should be capable to adapt to scarce and constrained resources and deal with the deluge of IoT data. To facilitate the adoption of Fog and Edge Computing this thesis proposes innovative middlewares capable of providing comprehensive solutions to address the highly dynamic characteristics of these environments. These middlewares provide functions to allocate and distribute Fog and Edge services among the available computational devices, monitor the status of the environment, and promptly modify their configuration.
To deal with the IoT data deluge this thesis investigates the interesting criterion of Value-of-Information (VoI). Originally born as an extension of Shannon's Information Theory for decision making science, researchers have studied VoI as an information management tool to select and prioritize information processing and dissemination. For this purpose, this thesis proposes the adoption of information management policies allowing the definition of service components, composable software modules that can be chained to create larger and more complex services. In addition, the middlewares presented in this thesis leverage the promising concept of VoI to select only the most valuable piece of information for processing and dissemination and to scale computational workload in an automated and lossiness fashion. This would enable to reduce the computational and network load and to propose innovative methodologies to optimize the available resources.
The research efforts presented in this thesis are the results of the collaboration with international institutes and a research period at the Florida Institute for Human and Machine Cognition (IHMC), FL, USA.Con i termini Fog ed Edge Computing si indicano dei paradigmi computazionali che, spostando l'elaborazione dei dati IoT nelle prossimità sia dei dispositivi che degli utenti, mirano a fornire servizi a bassa latenza, immersivi e real-time. Fog ed Edge Computing trovano applicazione in contesti Smart Cities, dove è possibile sfruttare la capacità computazionale di gateway IoT, Cloudlet e Base Station per elaborare parte dei dati generati dall'IoT direttamente ai margini della rete. L'adozione dei paradigmi di Fog ed Edge Computing è tuttavia complessa in quanto pone una serie di sfide tra cui il processamento dell’enorme mole di dati generati dall’IoT, la presenza di un numero limitato di dispositivi altamente eterogenei e con capacità computazionali scarse, requisiti di servizio altamente dinamici e reti con caratteristiche eterogenee. Per garantire i requisiti stringenti di bassa latenza, soluzioni per Fog ed Edge Computing devono essere in grado di utilizzare al meglio le scarse risorse a disposizione, gestendole al meglio. Se questi paradigmi sono oggetto di ampie ricerche, vi è la necessità di investigare soluzioni innovative che consentano di gestire l’enorme mole dati IoT e permettere una concreta applicazione di Fog ed Edge Computing.
Questa tesi propone middleware innovativi in grado di fornire soluzioni complete per fronteggiare al meglio le caratteristiche altamente dinamiche di scenari Smart Cities, fornendo metodologie e strumenti per allocare e distribuire servizi tra le risorse a disposizione, monitorare lo stato delle risorse e modificare prontamente la loro configurazione. Come criterio innovativo per la prioritizzazione dei dati IoT per processamento e disseminazione, questa tesi adotta il concetto di Value-of-Information (VoI), nato come estensione della Teoria dell'Informazione di Shannon e applicato in ambiti decisionali. A tal fine, questa tesi propone politiche di gestione delle informazioni che consentono di realizzare servizi modulari e facilmente (ri-)componibili e tecniche di ottimizzazione innovative che ben si adattano a questi servizi. Inoltre, i middleware presentati in questa tesi integrano il concetto di VoI sia a livello di servizio che a livello di gestione per selezionare le informazioni più preziose per l'elaborazione e la diffusione, riducendo così il carico computazionale e garantendo una gestione ottimale dei dispositivi e della rete.
Le ricerche presentate in questa tesi sono il risultato della collaborazione con istituti di ricerca internazionali e di un periodo di ricerca trascorso presso il Florida Institute for Human and Machine Cognition (IHMC), FL, USA
A Chaos Engineering Approach for Improving the Resiliency of IT Services Configurations
No full textTesting the resiliency of complex IT services deployed in hybrid Cloud scenarios is a challenging task that requires expensive and possibly destructive operations. An interesting approach lies in Chaos Engineering, a set of practices to test the resiliency of software systems running in a production environment. However, Chaos Engineering is an expensive practice that requires the setup of complicated operations that further increase the complexity of management operations. To reduce this complexity, Chaos Engineering can benefit from the adoption of non-destructive approaches such as the definition of realistic digital twins. A digital twin is a virtual replica of a real-system on which experimenting with management configurations. This paper embraces this research avenue by extending our previous efforts to integrate Chaos Engineering techniques into an IT services management framework called ChaosTwin. ChaosTwin leverages novel methodologies and tools capable of identifying and promptly react to unexpected failures. Finally, to implement autonomous fault management, ChaosTwin defines scaling and migration policies that can quickly explore for more resilient placements of software components in case of system failures. We believe that ChaosTwin can provide useful guidance to service providers in finding cost-effective service configurations capable of minimizing the negative effects of unpredictable events
The Evolution of Kubernetes Management: Introducing the KubeTwin Framework
No full textThe current trend in the management of complex and highly distributed microservices architecture is to leverage container-based orchestration tools such as Kubernetes. Kubernetes provides several capabilities that help service providers to deal with essential aspects of service provisioning such as service replication, scalability, and cluster federation. However, finding a suitable configuration for a Kubernetes application and optimizing its deployment in complex scenarios like Compute Continuum ones can be a very time-consuming and challenging operation. To simplify this process, this research project introduces the KubeTwin framework, a Digital Twin-based solution to evaluate the behavior and the impact of Kubernetes applications in a shorter computation time. Due to an accurate virtual representation of a Kubernetes application, KubeTwin provides a plethora of functionalities that can help providers to assess their ecosystems by employing different mechanisms that span from performance optimization to chaos engineering. Results shown by the first experimental model evaluations demonstrate the strength of this project and encourage for future major efforts
Value-of-Information Middleware Solutions for Fog and Edge Computing
No full textFog and Edge Computing aim to deliver low-latency, immersive, and powerful services by processing information close to both devices and users. This is well suited for IoT applications in Smart City, where IoT gateways, Cloudlets, Base Stations, and other computational nodes can process (part of) the data generated by the multitude of IoT sensors directly at the edge of the network. However, the implementation of Fog and Edge Computing is challenging because it requires to deal with a (limited number of) constrained devices, dynamic services’ requirements, and heterogeneous network conditions. Differently from the Cloud, where computational resources are supposed to be unlimited, Fog and Edge services should be capable to adapt to scarce and constrained resources and deal with the deluge of IoT data. To facilitate the adoption of Fog and Edge Computing this work proposes middleware solutions that leverage Value-of-Information (VoI) as interesting criterion to select only the most valuable piece of information for processing and dissemination and to scale computational workload in an automated fashion
Generic Architecture for Edge Computing Based on SPF for Military HADR Operations
No full textInternet-of-Things (IoT) devices have led to ubiquitous, remote and autonomous computing at the edge of the networks. These devices offload sensing, actuation and processing tasks away from the core of the network. The concept of Smart Cities tries to leverage Edge Computing based on IoT technologies for remote and distributed computing. Sieve, Process and Forward (SPF) is a Value-of-Information (VoI) based Fog as a Service (FaaS) solution for dynamic IoT applications in Smart City scenarios. The military has been looking to utilize the SPF platform for Edge Computing to assist in Human Assistance and Disaster Recovery (HADR) operations. A recent NATO IST 147 RTG demonstration proved the validity of SPF, but also highlighted the need of extending the current architecture to support specific use-case scenarios for HADR systems. This paper tries to propose a generic architecture based on SPF to enable interoperability between military C2 (Command and Control) and core computing systems to support future HADR operations in Smart City environments
A Data Mesh Approach for Enabling Data-Centric Applications at the Tactical Edge
No full textEffectively managing, sharing, and analyzing large volumes of data in real time is essential for making informed decisions, predicting potential threats, and adapting to changes on the battlefield. It therefore represents a critical capability for military operations. However, implementing effective analytics at the tactical edge requires to address the challenges of Denied, Degraded, Intermittent, and Limited (DDIL) networks, particularly in terms of bandwidth and processing capability constraints. Additionally, implementing such a system presents other challenges such as ensuring the security, trustworthiness, integrity, and privacy of data in motion and at rest. The accurate analysis of the vast amounts of data generated at the tactical edge requires a dedicated IT infrastructure shareable designed to operate in an unpredictable and changing environment while still ensuring the availability and reliability of the data. To address these challenges, we propose a middleware architecture based on a data mesh approach, which is designed to adapt to the demands of modern tactical networks by providing a secure and efficient data-centric storage solution for (big) data. Our proposed middleware is based on a distributed domain-driven approach to serve “data as a product”, facilitating data management and analysis, and providing a flexible and robust solution for developing data-driven services. “This paper was originally presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by the Information Systems Technology (IST) Panel, IST-200RSY — the ICMCIS, held in Skopje, North Macedonia, 16–17 May 2023
ChaosTwin: A Chaos Engineering and Digital Twin Approach for the Design of Resilient IT Services
No full textChaos Engineering represents an interesting software engineering methodology to improve the resilience of a complex IT system operating in a live production environment by injecting simulated faults, observing the system reaction, and devising mitigating solutions. However, Chaos Engineering is an expensive practice with a high setup and operation overhead and it often focuses on the evaluation of the system behavior from a relatively narrow technical perspective instead of a more comprehensive business level one. To enlarge the audience of Chaos Engineering there is the need for novel solutions that can give service providers the tools to deal with the deployment and testing of complex IT services. To fill this gap, this paper presents ChaosTwin, a novel solution exploring an innovative approach to apply Chaos Engineering to a digital-twin, i.e., a virtual representation of a physical object or a system. By creating realistic digital twin of an IT service, injecting faults on the digital twin and evaluating how different service configuration and fault management strategies would perform from a business level perspective, ChaosTwin provides useful guidance to service providers in finding cost-effective service configurations that can minimize the negative effects of unpredictable events. Experimental results, collected from the evaluation of a realistic case study, demonstrate how ChaosTwin is capable of minimizing both the associated costs and the effects of injected Chaos faults
Phileas: A Simulation-based Approach for the Evaluation of Value-based Fog Services
No full textEfficient design for Fog Computing applications need to consider the optimal use of the available resources at the edge and the Cloud, switching from one other depending on the current status, execution price, and user requirements. This is a complex task, as the optimization needs to consider the distributed and dynamic nature of the environment. There is the need to support investigation efforts by enabling researchers to experiment with Fog environments in a controlled and reproducible fashion. Unfortunately, most simulators do not implement a service model suited for Fog computing applications. This paper presents Phileas, a simulator that supports the definition of Fog services. Phileas allows to reenact the behavior of Fog services and evaluate different service policies and allocation strategies
Supporting the Development of Next-generation Fog Services
No full textFog Computing is a recent and compelling paradigm that proposes to run information-processing services at the edge of the network. While interesting standardization efforts are being currently pursued by many organizations, most of them focus on management and orchestration functions and primarily propose the adoption of programming models designed for Cloud applications in the Fog. Instead, Fog Computing applications would significantly benefit from innovative solutions that adopt an acceptable lossiness perspective and manage information processing and dissemination in a dynamic and integrated fashion. This paper presents an overview of the opportunities and challenges in Fog Computing application development, proposes an innovative holistic Software Defined Networking (SDN) approach based on an information-centric and value-based service model to support those applications, and presents an overview of the Holistic Analytics and Networking (HAN) SDN architecture that we are developing to realize this ambitious vision
Value is King: The MECForge Deep Reinforcement Learning Solution for Resource Management in 5G and Beyond
No full textAbstractMulti-access edge computing (MEC) is a key enabler to fulfill the promises of a new generation of immersive and low-latency services in 5G and Beyond networks. MEC represents a defining function of 5G, offering significant computational power at a reduced latency, allowing to augment the capabilities of user equipments while preserving their battery life. However, the demands generated by a plethora of innovative and concurrent IT services requiring high quality of service and quality of experience levels will likely overwhelm the—albeit considerable—resources available in 5G and Beyond scenarios. To take full advantage of its potential, MEC needs to be paired with innovative resource management solutions capable of effectively addressing the highly dynamic aspects of the scenario and of properly considering the heterogeneous and ever-changing nature of next generation IT services, prioritizing the assignment of resources in a highly dynamic and contextual fashion. This calls for the adoption of Artificial Intelligence based tools, implementing self-* approaches capable of learning the best resource management strategy to adapt to the ever changing conditions. In this paper, we present MECForge, a novel solution based on deep reinforcement learning that considers the maximization of total value-of-information delivered to end-user as a coherent and comprehensive resource management criterion. The experimental evaluation we conducted in a simulated but realistic environment shows how the Deep Q-Network based algorithm implemented by MECForge is capable of learning effective autonomous resource management policies that allocate service components to maximize the overall value delivered to the end-users.</jats:p
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