1,721,231 research outputs found
A Multicloud Observability Support Based on ElasticSearch for Cloud-native Smart Cities Services
Full text linkEffective communication and information sharing among different districts and cities are crucial for the management of utility flows, traffic, and emergencies in smart cities. In this scenario, a smart city requires cloud-native solutions to collect and analyze data from various sources, including traffic sensors and public transport vehicles. Thus, a multicloud observability approach is proposed to aggregate data from different localities. The solution aims to provide a complete suite for observability capable of collecting data across layers of a multicloud and integrating already existing open-source projects. Like what you’re reading
A Survey on the Use of Lightweight Virtualization in I4.0 Manufacturing Environments
Full text linkOver the past decade, in the industrial sector we have witnessed the rise of a revolutionary movement, known as Industry 4.0, that promotes the digital transformation as the key to increase the competitiveness of manufacturing factories. Among the many technologies recognized as "drivers" of such revolutionary transition, microservices stand out as a software development paradigm capable of bringing several benefits to the manufacturing process. Whilst the literature offers many examples of initiatives exploiting microservices in digitally-advanced sectors (e.g., finance, telecommunication, retailing), its potential in the industrial manufacturing is yet to be fully unleashed. We conducted an extensive literature survey in the twofold aim of bringing to the reader's attention the many benefits that the microservices paradigm may offer in industrial manufacturing settings, and drawing a picture of how light virtualization techniques are actually being exploited to achieve Industry 4.0 digitization goals. In this paper, we propose a structured analysis of the collected literature proposals which combines the benefits sought by authors when approaching to the microservices techniques and the specific scope of application of proposals. We conclude the paper highlighting the research aspects that have not been sufficiently explored in the literature and that would deserve further attention in the near future
Handling Data Handoff of AI-based Applications in Edge Computing Systems
Full text linkEdge computing aims at better supporting low-latency applications. One of its key techniques is computation offloading, the process that outsources computing tasks from resourced-constrained mobile devices and moves them to edge data centers. In this paper, we tackle an emerging problem within the umbrella of computation offloading, i.e., migration of offloaded inference tasks of Artificial Intelligence (AI) trained models. Such context tailors migration aspects of data-sensitive services where i) the value of the updates is inversely proportional to the data age and ii) outage is highly detrimental to accuracy. To tackle this challenge, we propose Mobile Edge Data-handoff (MED) a framework able to relocate inference or online training tasks from one edge datacenter to another by moving only the necessary data to minimize any accuracy drop during the process. We implemented MED in a well-known edge computing emulator, openLEON, and experimentally verified its performance with an AI-based Industry 4.0 application that forecasts the gas flow in a chemical plant. For our experiments, we use a real, open-source dataset that contains sensors readings. Collected results show that MED, employing proactive data handoff algorithms, is able to minimize the packet loss during the handoff thereby providing guarantees on the inference accuracy
Empowering Cloud Computing With Network Acceleration: A Survey
Full text linkModern interactive and data-intensive applications must operate under demanding time constraints, prompting a shift toward the adoption of specialized software and hardware network acceleration technologies. This specialization, however, poses significant scalability, flexibility, security, and economic sustainability challenges for application developers. Cloud computing holds the potential to overcome these obstacles by offering the cost-effective option to access specialized acceleration technologies through standard cloud interfaces. Nevertheless, that integration is still challenging for cloud providers. In the cloud, physical resources are hidden behind a virtualization layer, whereas acceleration technologies make applications directly interact with the hardware. To bridge this gap, recent literature explores the possibility of empowering cloud platforms with accelerated networking as a commodity, thus offering the innovative option of Network Acceleration as a Service. This survey reviews these recent research efforts by adopting popular technologies like XDP, DPDK, and RDMA as a reference. To organize the surveyed research in a comprehensive framework, we identify four key aspects that pose critical problems to the integration of acceleration options in cloud computing -access interfaces, virtualization techniques, serviceability, and security -and systematically discuss the associated challenges. Then, we present the issues to be further addressed and outline the most promising research directions for the full integration of network acceleration within next-generation cloud computing platforms
A Practical way to Handle Service Migration of ML-based Applications in Industrial Analytics
No full textNowadays, Machine learning (ML) plays a significant role in Industrial Analytics. It enables predictive analytics, and helps uncovering essential insights to transform industries. As a result, real-time data analytics has become an essential requirement for industrial engineering jobs. Edge computing enables local intelligence and real-time analytics that are key for industry processes to take autonomous decisions locally at the edge of the network. However, outages in edge datacenters can jeopardize the whole plant security. In this paper, we proposed a practical approach to effectively handling service and data migration of ML-based applications in Industrial Analytics scenarios in the presence of a lack of computing resources at the edge. We argue that in this context the value of data is inversely proportional to their age and is very important to work with fresher data. In this paper, we describe our architectural approach for service and data handoff and show a predictive diagnostics case study deployed in an edge-enabled IIoT infrastructure. We evaluate our proposed approach in terms of drop of accuracy in a well-known edge computing emulator, i.e., openLEON. The experimental results show the benefit of our solution with respect to standard techniques
Decentralised Learning in Federated Deployment Environments
Full text linkDecentralised learning is attracting more and more interest because it embodies the principles of data minimisation and focused data collection, while favouring the transparency of purpose specification (i.e., the objective for which a model is built). Cloud-centric-only processing and deep learning are no longer strict necessities to train high-fidelity models; edge devices can actively participate in the decentralised learning process by exchanging meta-level information in place of raw data, thus paving the way for better privacy guarantees. In addition, these new possibilities can relieve the network backbone from unnecessary data transfer and allow it to meet strict low-latency requirements by leveraging on-device model inference. This survey provides a detailed and up-to-date overview of the most recent contributions available in the state-of-the-art decentralised learning literature. In particular, it originally provides the reader audience with a clear presentation of the peculiarities of federated settings, with a novel taxonomy of decentralised learning approaches, and with a detailed description of the most relevant and specific system-level contributions of the surveyed solutions for privacy, communication efficiency, non-IlDness, device heterogeneity, and poisoning defense
Cloud standards: Security and interoperability issues
No full textIn the last years, the surging demand for inexpensive and scalable IT infrastructures led to the widespread adoption of Cloud computing architectures; nowadays, Cloud architectures have reached their momentum due to their inherent capacity of simplifying IT infrastructure building and maintenance by making related costs easily accountable and paid on a pay-per-use basis.
Although a general agreement about standards has still to be reached, some emerging de-facto definitions, such as the one proposed by the National Institute of Standards and Technology (NIST) underline that Cloud computing inherits from several state-of-the-art technologies, including grid computing, virtualization, Service Oriented Architectures (SOA), and utility computing. At the same time, to make the process more complex, at the current stage, Cloud providers work at the different Cloud software stack layers, namely, Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS) and they tend to adopt proprietary Cloud solutions and middleware platforms, thus producing isolated environments with many risks stemming from that isolation that can hardly obstruct further advancements of Cloud computing. This lack of proper Cloud standardization and certification processes, especially for security-related aspects, hinders the outsourcing of enterprise IT assets to third-party Cloud computing platforms because organizations are afraid of the loss of control over their Cloud-hosted assets and feel very hard and difficult to migrate from one Cloud solution to another one.
Starting from the core assumption that only a deep and broad knowledge of existing efforts can pave the way to the publication of widely-accepted future Cloud standards, this chapter aims at putting together current trends and open issues in Cloud standardization to derive an original and holistic view of the existing proposals and specifications. In particular, among the several Cloud technical areas, our analysis focuses on two main aspects, namely, security and interoperability, because they are the ones mostly covered by ongoing standardization efforts and, from both our experience and existing studies about enterprise concerns and acceptance of Cloud technologies, currently represent two of the main limiting factors for the diffusion and large adoption of Cloud. After an in-depth presentation of security and interoperability requirements and standardization issues, we overview main general frameworks and initiatives in these two areas, and then we introduce and survey all main related standards; finally, we compare surveyed standards and give future standardization directions for Cloud
Business-driven service placement for highly dynamic and distributed cloud systems
No full textThe emergence of large-scale Cloud computing environments characterized by dynamic resource pricing schemes enables valuable cost saving opportunities for service providers that could dynamically decide to change the placement of their IT service components in order to reduce their bills. However, that requires new management solutions to dynamically reconfigure IT service components placement, in order to respond to pricing changes and to control and guarantee the high-level business objectives defined by service providers. This paper proposes a novel approach based on Genetic Algorithm (GA) optimization techniques for adaptive business-driven IT service component reconfiguration. Our proposal allows to evaluate the performance of complex IT services deployments over large-scale Cloud systems in a wide range of alternative configurations, by granting prompt transitions to more convenient placements as business values and costs change dynamically. We deeply assessed our framework in a realistic scenario that consists of 2-tier service architectures with real-world pricing schemes. Collected results show the effectiveness and quantify the overhead of our solution. The results also demonstrate the suitability of business-driven IT management techniques for service components placement and reconfiguration in highly dynamic and distributed Cloud systems
Performance evaluation of communications in distributed systems and web based service architectures
No full textPerformance evaluation is still a topic that attains a lot of attention in both distributed and mobile systems as well as web based services architectures. Due to the recent advances in internet based applications as well as distributed and mobile communication systems, we are witnessing a variety of new technologies. However,these systems are becoming very large and complex at the same time. Several challenges remain to be resolved before these systems become a commodity. Guaranteeing Quality of Service (QoS)and provisioning of web-based systems as well as distributed and mobile systems and evaluating their communication performance represent among the challenging problem in the design of these systems. Quantitative analysis can be very difficult and may be intractable because of the state space explosion. New methods and tools have recently emerged for these kinds of complex systems,such as Stochastic Automata Networks, Stochastic bounds, and so forth
FlowChain: The Playground for Federated Learning in Industrial Internet of Things Environments
No full textThe Industrial Internet of Things (IIoI) lays the foundation for a new industrial revolution, the so-called Industry 4.0, in which every element, from machines to processes, is interconnected and fully automated, producing a huge amount of valuable data, crucial for making industrial processes more efficient and profitable. For this reason, it is common to run machine learning algorithms in order to extract useful information from these data. At the same time, due to bandwidth and privacy issues, it is often infeasible to transfer all these large-scale data to a centralized location where these algorithms can be performed. To address these scenarios, federated learning (FL) has been gaining ground in recent years. FL leaves the training data on the devices where they are produced and builds a global model by aggregating locally computed models, preserving user privacy and avoiding overwhelming the network with unnecessary raw data. However, there are still important challenges and limitations to the application of FL in Industry 4.0, mainly due to security issues and the fact that many solutions still suffer from single points of failure and bottlenecks. In this article, we present FlowChain, a framework that integrates FL with blockchain technology and decentralized identifiers (DIDs) to create an infrastructure that offers the possibility to easily exploit FL in Industry 4.0 scenarios. By using smart contract technology to automate the aggregation of partial models, it is possible to make FL fully decentralized. In addition, the fact that the blockchain is immutable and every transaction is verified and traceable makes FL secure. Furthermore, we exploit DIDs to be able to uniquely identify each element participating in industrial processes. This allows finer-grained control of authorizations so that only IIoT devices with a known and authorized identity can actually participate in the FL training process. Finally, we present some preliminary results that demonstrate the feasibility of the proposed approach
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