1,720,997 research outputs found
Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0
No full textIndustry 4.0 and its main enabling information and communication technologies are completely changing both services and production worlds. This is especially true for the health domain, where the Internet of Things, Cloud and Fog Computing, and Big Data technologies are revolutionizing eHealth and its whole ecosystem, moving it towards Healthcare 4.0. By selectively analyzing the literature, we systematically survey how the adoption of the above-mentioned Industry 4.0 technologies (and their integration) applied to the health domain is changing the way to provide traditional services and products. In this paper, we provide (i) a description of the main technologies and paradigms in relation to Healthcare 4.0 and discuss (ii) their main application scenarios; we then provide an analysis of (iii) carried benefits and (iv) novel cross-disciplinary challenges; finally, we extract (v) the lessons learned
A sleep scheduling approach based on learning automata for WSN partialcoverage
No full textWireless sensor networks (WSNs) are currently adopted in a vast variety of domains where sensor energy consumption is a critical challenge because of the existing power constraints. Sleep scheduling approaches have recently attracted the interest of the scientific community, as they give the opportunity of turning off the redundant nodes of a network to save energy and prolong the lifetime of the network without suspending the monitoring activities performed by the WSN.
Our study focuses on the problem of partial coverage, targeting scenarios in which the continuous monitoring of a limited portion of the area of interest is enough. In this paper we present PCLA, a novel algorithm that relies on Learning Automata to implement sleep scheduling approaches. It aims at minimizing the number of sensors to activate for covering a desired portion of the region of interest preserving the connectivity among sensors. Simulation results show how PCLA can select sensors in an efficient way to satisfy the imposed constraints, thus guaranteeing good performance in terms of time complexity, working-node ratio, scalability, and WSN lifetime. Moreover, compared to the state of the art, PCLA is able to guarantee better performance
Measuring network throughput in the cloud: The case of Amazon EC2
No full textCloud providers employ sophisticated virtualization techniques and strategies for sharing resources among a large number of largely uncoordinated and mutually untrusted customers. The shared networking environment, in particular, dictates the need for mechanisms to partition network resources among virtual machines. At the same time, the performance of applications deployed over these virtual machines may be heavily impacted by the performance of the underlying network, and therefore by such mechanisms. Nevertheless, due to security and commercial reasons, providers rarely provide detailed information on network organization, performance, and mechanisms employed to regulate it. In addition, the scientific literature only provides a blurred image of the network performance inside the cloud. The few available pioneer works marginally focus on this aspect, use different methodologies, operate in few limited scenarios, or report conflicting results.
In this paper, we present a detailed analysis of the performance of the internal network of Amazon EC2, performed by adopting a non-cooperative experimental evaluation approach (i.e. not relying on provider support). Our aim is to provide a quantitative assessment of the networking performance as a function of the several variables available, such as geographic region, resource price or size. We propose a detailed methodology to perform this kind of analysis, which we believe is essential in a such complex and dynamic environment. During this analysis we have discovered and analyzed the limitations enforced by Amazon over customer traffic in terms of maximum throughput allowed. Thanks to our work it is possible to understand how the complex mechanisms enforced by the provider in order to manage its infrastructure impact the performance perceived by the cloud customers and potentially tamper with monitoring and controlling approaches previously proposed in literature. Leveraging our knowledge of the bandwidth-limiting mechanisms, we then present a clear picture of the maximum throughput achievable in Amazon EC2 network, shedding light on when and how such maximum throughput can be achieved and at which cost
A First Look at Accurate Network Traffic Generation in Virtual Environments
No full textThe generation of synthetic network traffic is necessary to several fundamental networking activities, ranging from device testing to path monitoring, with implications on security and management. While literature focused on high-rate traffic generation, for many use cases accurate traffic generation is of importance instead. These scenarios have expanded with Network Function Virtualization, Software Defined Networking, and Cloud applications, which introduce further causes for alterations of generated traffic. Such causes are described and experimentally evaluated in this work, where the generation accuracy of D-ITG, an open-source software generator, is investigated in a virtualized environment. A definition of accuracy in terms of Mean Absolute Percentage Error of the sequences of Payload Lengths (PLs) and Inter-Departure Times (IDTs) is exploited to this end. The tool is found accurate for all PLs and for IDTs greater than one millisecond, and after the correction of a systematic error, also from 100 us
A feedback-control approach for resource management in public clouds
No full textNowadays, more and more the industry and market depend on cloud-based infrastructures for delivering IT services. To this aim cloud-based infrastructures are changing continuously, increasing their complexity especially for the management of cloud resources. Control and management of resources (e.g., virtual machines, VMs) are of paramount importance to adjust resources automatically allocated to an application and for delivering quality-assured services to final users. In this paper, we propose a feedback-based control approach for the management of VMs in the AWS EC2 public cloud. First, we evaluate the proposed Gain Scheduling policy against different workloads. Second, we provide results on the robustness of the proposed Gain Scheduling policy in presence of failures. Finally, we compare our approach to state-of-the-art control approaches for cloud resources. Our results indicate that the proposed control strategy guarantees, without the need of a priori information on system dynamics or complex estimations of the operating conditions, high performance with respect to both constant and time- varying workloads as well as in spite of sudden VM failures
A Fuzzy Approach Based on Heterogeneous Metrics for Scaling Out Public Clouds
No full textThanks to resource elasticity, cloud systems allow to build high performance applications by dynamically adapting
resources to workload dynamics. In this paper, we present a novel approach for horizontally scaling cloud resources. The approach
is based on an optimized feedback control scheme that leverages fuzzy logic to self-adjust its parameters in order to cope with
unpredictable and highly time-varying public-cloud operating conditions. The proposed approach takes as input heterogeneous
monitoring metrics related to distinct aspects of interest (i.e., CPU and network load) merged through a fitness function. Therefore, it
is able to accomplish the application needs from different viewpoints. The extensive experimental evaluation performed in the Amazon
EC2 environment showed how the proposed approach is robust against a number of realistic workloads—also when VM failures
happen— and that it is flexible, as being suitable for applications with different needs. Finally, it also achieves better performance
when compared to previously proposed solutions
Integration of Cloud computing and Internet of Things: A survey
No full textCloud computing and Internet of Things (IoT) are two very different technologies that are both already part of our life. Their adoption and use are expected to be more and more pervasive, making them important components of the Future Internet. A novel paradigm where Cloud and IoT are merged together is foreseen as disruptive and as an enabler of a large number of application scenarios.
In this paper, we focus our attention on the integration of Cloud and IoT, which is what we call the CloudIoT paradigm. Many works in literature have surveyed Cloud and IoT separately and, more precisely, their main properties, features, underlying technologies, and open issues. However, to the best of our knowledge, these works lack a detailed analysis of the new CloudIoT paradigm, which involves completely new applications, challenges, and research issues. To bridge this gap, in this paper we provide a literature survey on the integration of Cloud and IoT. Starting by analyzing the basics of both IoT and Cloud Computing, we discuss their complementarity, detailing what is currently driving to their integration. Thanks to the adoption of the CloudIoT paradigm a number of applications are gaining momentum: we provide an up-to-date picture of CloudIoT applications in literature, with a focus on their specific research challenges. These challenges are then analyzed in details to show where the main body of research is currently heading. We also discuss what is already available in terms of platforms–both proprietary and open source–and projects implementing the CloudIoT paradigm. Finally, we identify open issues and future directions in this field, which we expect to play a leading role in the landscape of the Future Internet
On the performance of the wide-area networks interconnecting public-cloud datacenters around the globe
No full textAccording to current usage patterns, research trends, and latest reports, the performance of the wide-area networks interconnecting geographically distributed cloud nodes (i.e. inter-datacenter networks) is gaining more and more interest. In this paper we leverage only active approaches—thus we do not rely on information restricted to providers—and propose a deep analysis of these infrastructures for the two public-cloud leading providers: Amazon Web Services and Microsoft Azure. Our study provides an assessment of the performance of these networks as a function of the several configuration factors under the control of the customer and evidences specific cases of particular interest. The analysis of these cases and of their root causes, also related with service fees, provides insights on their impact on both the Quality of Service perceived by cloud customers and the outcomes of studies neglecting them.
Our results show that Azure inter-datacenter infrastructure performs better than Amazon’s in terms of throughput (+56% on average). On the other hand, the performance of the two providers is comparable in terms of latency, with the exception of limited specific cases. Moreover, some of the configuration factors cloud customers can leverage (such as larger more expensive VM sizes, advertised to have better network performance) may have no effect on the inter-datacenter network performance actually perceived. Counterintuitively, lower performance may even be related to higher costs for the customer. Experimental evidences show that public-cloud providers also rely on external network providers for some geographical regions, which is the cause of lower performance and higher costs. A comparison with previous works show that TCP throughput has not been improved recently, while evidences of higher link capacities have been found
Benchmarking big data architectures for social networks data processing using public cloud platforms
No full textWhen considering popular On-line Social Networks (OSN) containing heterogeneous multimedia data sources, the complexity of the underlying processing systems becomes challenging, and requires to implement application-specific but still comprehensive benchmarking. The variety of big data architectures (and of their possible realization) for both batch and streaming processing in a huge number of application domains, makes the benchmarking of these systems critical for both academic and industrial communities.
In this work, we evaluate the performance of two state-of-art big data architectures, namely Lambda and Kappa, considering OSN data analysis as reference task. In more details, we have implemented and deployed an influence analysis algorithm on the Microsoft Azure public cloud platform to investigate the impact of a number of factors on the performance obtained by cloud users. These factors comprise the type of the implemented architecture, the volume of the data to analyze, the size of the cluster of nodes realizing the architectures and their characteristics, the deployment costs, as well as the quality of the output when the analysis is subjected to strict temporal deadlines. Experimental campaigns have been carried out on the Yahoo Flickr Creative Commons 100 Million (YFCC100M). Reported results and discussions show that Lambda outperforms Kappa architecture for the class of problems investigated. Providing a variety of analyses – e.g., also investigating the impact of dataset size, scaling, cost – this paper provides useful insights on the performance of these state-of-art big data architectures that are helpful to both experts and newcomers interested in deploying big data architectures leveraging cloud platforms
The role of Information and Communication Technologies in healthcare: taxonomies, perspectives, and challenges
No full textProgress in Information and Communication Technologies (ICTs) is shaping more and more the healthcare domain. ICTs adoption provides new opportunities, as well as discloses novel and unforeseen application scenarios. As a result, the overall health sector is potentially benefited, as the quality of medical services is expected to be enhanced and healthcare costs are reduced, in spite of the increasing demand due to the aging population.
Notwithstanding the above, the scientific literature appears to be still quite scattered and fragmented, also due to the interaction of scientific communities with different background, skills, and approaches. A number of specific terms have become of widespread use (e.g., regarding ICTs-based healthcare paradigms as well as at health-related data formats), but without commonly-agreed definitions. While scientific surveys and reviews have also been proposed, none of them aims at providing a holistic view of how today ICTs are able to support healthcare. This is the more and more an issue, as the integrated application of most if not all the main ICTs pillars is the most agreed upon trend, according to the Industry 4.0 paradigm about ongoing and future industrial revolution.
In this paper we aim at shedding light on how ICTs and healthcare are related, identifying the most popular ICTs-based healthcare paradigms, together with the main ICTs backing them. Studying more than 300 papers, we survey outcomes of literature analyses and results from research activities carried out in this field. We characterize the main ICTs-based healthcare paradigms stemmed out in recent years fostered by the evolution of ICTs. Dissecting the scientific literature, we also identify the technological pillars underpinning the novel applications fueled by these technological advancements. Guided by the scientific literature, we review a number of application scenarios gaining momentum thanks to the beneficial impact of ICTs. As the evolution of ICTs enables to gather huge and invaluable data from numerous and highly varied sources in easier ways, here we also focus on the shapes that this healthcare-related data may take. This survey provides an up-to-date picture of the novel healthcare applications enabled by the ICTs advancements, with a focus on their specific hottest research challenges. It helps the interested readership (from both technological and medical fields) not to lose orientation in the complex landscapes possibly generated when advanced ICTs are adopted in application scenarios dictated by the critical healthcare domain
- …
