Cloud computing

Abstract: EVCE computing is an attractive network paradigm involving seamless connections among heterogeneous vehicular contexts. It will be a trend along with EVs becoming popular in V2X. The EVs act as potential resource infrastructures referring to both information and energy interactions, and there are serious security challenges for such hybrid cloud and edge computing. Context-aware vehicular applications are identified according to the perspectives of information and energy interactions. Blockchain-inspired data coins and energy coins are proposed based on distributed consensus, in which data contribution frequency and energy contribution amount are applied to achieve the proof of work. Security solutions are presented for securing vehicular interactions in EVCE computing.

Abstract: Security is a major barrier to enterprise adoption of cloud computing. Physical co-residency with other tenants poses a particular risk, due to pervasive virtualization in the cloud. Recent research has shown how side channels in shared hardware may enable attackers to exfiltrate sensitive data across virtual machines (VMs). In view of such risks, cloud providers may promise physically isolated resources to select tenants, but a challenge remains: Tenants still need to be able to verify physical isolation of their VMs. We introduce Home Alone, a system that lets a tenant verify its VMs' exclusive use of a physical machine. The key idea in Home Alone is to invert the usual application of side channels. Rather than exploiting a side channel as a vector of attack, Home Alone uses a side-channel (in the L2 memory cache) as a novel, defensive detection tool. By analyzing cache usage during periods in which "friendly" VMs coordinate to avoid portions of the cache, a tenant using Home Alone can detect the activity of a co-resident "foe" VM. Key technical contributions of Home Alone include classification techniques to analyze cache usage and guest operating system kernel modifications that minimize the performance impact of friendly VMs sidestepping monitored cache portions. Home Alone requires no modification of existing hyper visors and no special action or cooperation by the cloud provider.

Abstract: Objective: The gold standard for diagnosing sleep disorders is polysomnography, which generates extensive data about biophysical changes occurring during sleep. We developed the National Sleep Research Resource (NSRR), a comprehensive system for sharing sleep data. The NSRR embodies elements of a data commons aimed at accelerating research to address critical questions about the impact of sleep disorders on important health outcomes. Approach: We used a metadata-guided approach, with a set of common sleep-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) annotated datasets; (2) user interfaces for accessing data; and (3) computational tools for the analysis of polysomnography recordings. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the NSRR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor. Results: The authors curated and deposited retrospective data from 10 large, NIH-funded sleep cohort studies, including several from the Trans-Omics for Precision Medicine (TOPMed) program, into the NSRR. The NSRR currently contains data on 26,808 subjects and 31,166 signal files in European Data Format. Launched in April 2014, over 3000 registered users have downloaded over 130 terabytes of data. Conclusions: The NSRR offers a use case and an example for creating a full-fledged data commons. It provides a single point of access to analysis-ready physiological signals from polysomnography obtained from multiple sources, and a wide variety of clinical data to facilitate sleep research. The NIH Data Commons (or Commons) is an ambitious vision for a shared virtual space to allow digital objects to be stored and computed upon by the scientific community. The Commons would allow investigators to find, manage, share, use and reuse data, software, metadata and workflows. It imagines an ecosystem that makes digital objects Findable, Accessible, Interoperable and Reusable (FAIR). Four components are considered integral parts of the Commons: a computing resource for accessing and processing of digital objects; a "digital object compliance model" that describes the properties of digital objects that enable them to be FAIR; datasets that adhere to the digital object compliance model; and software and services to facilitate access to and use of data. This paper describes the contributions of NSRR along several aspects of the Commons vision: metadata for sleep research digital objects; a collection of annotated sleep data sets; and interfaces and tools for accessing and analyzing such data. More importantly, the NSRR provides the design of a functional architecture for implementing a Sleep Data Commons. The NSRR also reveals complexities and challenges involved in making clinical sleep data conform to the FAIR principles. Future directions: Shared resources offered by emerging resources such as cloud instances provide promising platforms for the Data Commons. However, simply expanding storage or adding compute power may not allow us to cope with the rapidly expanding volume and increasing complexity of biomedical data. Concurrent efforts must be spent to address digital object organization challenges. To make our approach future-proof, we need to continue advancing research in data representation and interfaces for human-data interaction. A possible next phase of NSRR is the creation of a universal self-descriptive sequential data format. The idea is to break large, unstructured, sequential data files into minimal, semantically meaningful, fragments. Such fragments can be indexed, assembled, retrieved, rendered, or repackaged on-the-fly, for multitudes of application scenarios. Data points in such a fragment will be locally embedded with relevant metadata labels, governed by terminology and ontology. Potential benefits of such an approach may include precise levels of data access, increased analysis readiness with on-the-fly data conversion, multi-level data discovery and support for effective web-based visualization of contents in large sequential files.

Abstract: Edge computing paradigm has attracted many interests in the last few years as a valid alternative to the standard cloud-based approaches to reduce the interaction timing and the huge amount of data coming from Internet of Things (IoT) devices toward the Internet. In the next future, Edge-based approaches will be essential to support time-dependent applications in the Industry 4.0 context; thus, the paper proposes BodyEdge , a novel architecture well suited for human-centric applications, in the context of the emerging healthcare industry. It consists of a tiny mobile client module and a performing edge gateway supporting multiradio and multitechnology communication to collect and locally process data coming from different scenarios; moreover, it also exploits the facilities made available from both private and public cloud platforms to guarantee a high flexibility, robustness, and adaptive service level. The advantages of the designed software platform have been evaluated in terms of reduced transmitted data and processing time through a real implementation on different hardware platforms. The conducted study also highlighted the network conditions (data load and processing delay) in which BodyEdge is a valid and inexpensive solution for healthcare application scenarios.