Kim K.,Applied Technology Internet
Computers and Operations Research | Year: 2012
We propose a new priority discipline called the T-preemptive priority discipline. Under this discipline, during the service of a customer, at every T time units the server periodically reviews the queue states of each class with different queue-review processing times. If the server finds any customers with higher priorities than the customer being serviced during the queue-review process, then the service of the customer being serviced is preempted and the service for customers with higher priorities is started immediately. We derive the waiting-time distributions of each class in the M/G/1 priority queue with multiple classes of customers under the proposed T-preemptive priority discipline. We also present lower and upper bounds on the offered loads and the mean waiting time of each class, which hold regardless of the arrival processes and service-time distributions of lower-class customers. To demonstrate the utility of the T-preemptive priority queueing model, we take as an example an opportunistic spectrum access in cognitive radio networks, where one primary (licensed) user and multiple (unlicensed) users with distinct priorities can share a communication channel. We analyze the queueing delays of the primary and secondary users in the proposed opportunistic spectrum access model, and present numerical results of the queueing analysis. © 2011 Elsevier Ltd. All rights reserved.
Fox S.,Applied Technology Internet
Pediatrics | Year: 2013
Although the majority of US adults has Internet access and gathers health information online, the Internet does not replace clinicians. People rate health professionals as their top source for technical questions such as diagnosis and treatment, but nonprofessionals (eg, friends and family) are rated higher for emotional support and quick remedies. For their most recent health issue, 21% of adults say they turned to others who have the same health condition; evidence of people's interest in connecting with and learning from each other. People living with chronic diseases (and their caregivers) are especially likely to say they look online for peer advice. They are pioneering new ways of pursuing health by banding together and sharing knowledge; so-called peer-to-peer health care. Practical tips from fellow patients and caregivers can have far-reaching implications for clinical outcomes. As a parent of a chronically ill child observed: "We all work collaboratively, but I notice that my doctor doesn't. After I've talked with my community online, I go back to him and ask, 'What do your colleagues say about this issue?' And it's clear it didn't occur to him to ask them." Clinicians might do well to look into online patient communities and consider recommending them as resources for their patients. Clinicians might look at patient networks as a model for their own collaborative learning process as well. Linking the expertise of patients, families, and clinicians holds promise for further improving care and outcomes.
Agency: Cordis | Branch: H2020 | Program: IA | Phase: ICT-19-2015 | Award Amount: 3.84M | Year: 2016
The arrival of immersive head-mounted displays to the consumer market will create a demand for immersive content of over 2 Billion euros in 2016. However, current audiovisual content is ill-suited for Immersive displays. For example, cuts between shots, which constitute the very basic fabric of traditional cinematic language, do not work well in immersive displays. ImmersiaTV will create a novel form of broadcast omnidirectional video content production and delivery that offers end-users a coherent audiovisual experience across head mounted displays, second screens and the traditional TV set, instead of having their attention divided across them. This novel kind of content will seamlessly integrate with and further augment traditional TV and second display consumer habits. ImmersiaTV will assemble an end-to-end toolset covering the entire audiovisual value chain: immersive production tools, support for omnidirectional cameras, including ultra-high definition and high dynamic range images, and adaptive content coding and delivery, and demonstrate it through 3 pilot demonstrations addressing both on-demand and live content delivery.
Agency: Cordis | Branch: H2020 | Program: IA | Phase: ICT-14-2014 | Award Amount: 8.26M | Year: 2015
Virtualisation and software networks are a major disruptive technology for communications networks, enabling services to be deployed as software functions running directly in the network on commodity hardware. However, deploying the more complex user-facing applications and services envisioned for 5G networks presents significant technological challenges for development and deployment. SONATA addresses both issues. For service development, SONATA provides service patterns and description techniques for composed services. A customised SDK is developed to boost the efficiency of developers of network functions and composed services, by integrating catalogue access, editing, debugging, and monitoring analysis tools with service packaging for shipment to an operator. For deployment, SONATA provides a novel service platform to manage service execution. The platform complements the SDK with functionality to validate service packages. Moreover, it improves on existing platforms by providing a flexible and extensible orchestration framework based on a plugin architecture. Thanks to SONATAs platform service developers can provide custom algorithms to steer the orchestration of their services: for continuous placement, scaling, life-cycle management and contextualization of services. These algorithms are overseen by executives in the service platform, ensuring trust and resolving any conflict between services. By combining rapid development and deployment in an open and flexible manner, SONATA is realising an extended DevOps model for network stakeholders. SONATA validates its approach through novel use-case-driven pilot implementations and disseminates its results widely by releasing its key SDK and platform components as open source software, through scientific publications and standards contributions, which, together, will have a major impact on incumbent stakeholders including network operators and manufacturers and will open the market to third-party developers.
Agency: Cordis | Branch: H2020 | Program: RIA | Phase: ICT-16-2015 | Award Amount: 6.31M | Year: 2016
The TOREADOR project is aimed at overcoming some major hurdles that until now have prevented many European companies from reaping the full benefits of Big Data analytics (BDA). Companies and organisations in Europe have become aware of the potential competitive advantage they could get by timely and accurate Big Data analytics, but lack the IT expertise and budget to fully exploit BDA. To overcome this hurdle, TOREADOR takes a model-based BDA-as-a-service (MBDAaaS) approach, providing models of the entire Big Data analysis process and of its artefacts. TOREADOR open, suitable-for-standardisation models will support substantial automation and commoditisation of Big Data analytics, while enabling it to be easily tailored to domain-specific customer requirements. Besides models for representing BDA, TOREADOR will deliver an architectural framework and a set of components for model-driven set-up and management of Big Data analytics processes. Once TOREADOR MBDAaaS will become widespread, price competition on Big Data services will ensue, driving costs of Big Data analytics well within reach of EU organizations (including SMEs) that do not have either in-house Big Data expertise or budget for expensive data consultancy. Activities supported and automatised by TOREADOR will include (i) planning Big Data sources preparation (ii) negotiating machine-readable Service Level Agreements for BDA detailing privacy, timing, and accuracy needs (iii) choosing data management and algorithm parallelisation strategies (iv) ensuring auditing and assessment of legal compliance (for example, to privacy regulations) of BDA enactment. TOREADOR framework will address automatically all major problems of on-demand data preparation, including handling Big Data opacity, diversity, security, and privacy compliance, and will support abstract modelling of the BDA life cycle from distributed data acquisition/storage to the design and parallel deployment of analytics and presentation of results.