Blockchain has made an impact on today’s technology by revolutionizing the financial industry through utilization of cryptocurrencies using decentralized control. This has been followed by extending Blockchain to span several other industries and applications for its capabilities in verification. With the current trend of pursuing the decentralized Internet, many methods have been proposed to achieve decentralization considering different aspects of the current Internet model ranging from infrastructure and protocols to services and applications. This paper investigates Blockchain’s capacities to provide a robust and secure decentralized model for Internet. The paper conducts a critical review on recent Blockchain-based methods capable for the decentralization of the future Internet. We identify and investigate two research aspects of Blockchain that provides high impact in realizing the decentralized Internet with respect to current Internet and Blockchain challenges while keeping various design in considerations. The first aspect is the consensus algorithms that are vital components for decentralization of the Blockchain. We identify three key consensus algorithms including PoP, Paxos, and PoAH that are more adequate for reaching consensus for such tremendous scale Blockchain-enabled architecture for Internet. The second aspect that we investigated is the compliance of Blockchain with various emerging Internet technologies and the impact of Blockchain on those technologies. Such emerging Internet technologies in combinations with Blockchain would help to overcome Blockchain’s established flaws in a way to be more optimized, efficient and applicable for Internet decentralization.
Problem statement:The car users expect more and more accessories available in their cars, but the accessories available needed manage by driver manually and not properly manage by smart system. All these accessories are able to control by user manually using different and standalone controllers. Besides, the controller itself uses RF technology which is not existed in mobile devices. So there is lack of a comprehensive and integrated system to manage, control and monitor all the accessories inside the vehicle by using a personal mobile phone. Design and development of an integrated system to manage and control all kind of inter vehicle accessories, improving the efficiency and functionality of inter vehicle communications for the car users. Approach: The proposed system was based on Microcontroller, Bluetooth and Java technology and in order to achieve the idea of an intelligence car with ability to uses personal mobile hand phone as a remote interface. Development strategies for this innovation are includes two phases: (1) java based application platform-designed and developed for smart phones and PDAs (2) hardware design and implementation of the receiver sidecompatible smart system to managing and interconnection between all inside accessories based on monitoring and controlling mechanisms by Bluetooth media. Results: The designed system included hardware and software and the completed prototype had tested successfully on the real vehicles. During the testing stage, the components and devices were connected and implemented on the vehicle and the user by installing the system interface on a mobile phone is able to monitor and manage the vehicle accessories, the efficiency, adaptively and range of functionality of the system has proved with the various car accessories. Conclusion: This study involved design a new system to decrease the hot temperature inside a car that affecting the health of the car driver and the car driver is able to control some of the car accessories by using mobile phone. Once the car was equipped with the Bluetooth module and control system, the car accessories is able to connect with microcontroller and control by the mobile application.
The next generation of many-core enabled large-scale computing systems relies on thousands of billions of heterogeneous processing cores connected to form a single computing unit. In such large-scale computing environments, resource management is one of the most challenging, and complex issues for efficient resource sharing and utilization, particularly as we move toward Future ManyCore Systems (FMCS). This work proposes a novel resource management scheme for future peta-scale many-core-enabled computing systems, based on hybrid adaptive resource discovery, called ElCore. The proposed architecture contains a set of modules which will dynamically be instantiated on the nodes in the distributed system on demand. Our approach provides flexibility to allocate the required set of resources for various types of processes/applications. It can also be considered as a generic solution (with respect to the general requirements of large scale computing environments) which brings a set of interesting features (such as auto-scaling, multitenancy, multi-dimensional mapping, etc,.) to facilitate its easy adaptation to any distributed technology (such as SOA, Grid and HPC many-core). The achieved evaluation results assured the significant scalability and the high-quality resource mapping of the proposed resource discovery and management over highly heterogeneous, hierarchical and dynamic computing environments with respect to several scalability and efficiency aspects while supporting flexible and complex queries with guaranteed discovery results accuracy. The simulation results prove that, using our approach, the mapping between processes and resources can be done with high level of accuracy which potentially leads to a significant enhancement in the overall system performance.
This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstr act Future large scale systems will execute novel operating systems running across many chips with many cores. In this highly distributed environment, resource discovery is an important building block. Resource discovery aims to match the application's demands to the existing (distributed) resources, by discovering and f nding resources at run-time, and then selecting the best resource that matches the application running requirements. The main contribution of this paper is the design and evolution of a highly scalable, highly f exible, resource discovery model for such heterogeneous environments. The model is based on self-organizing processing resources in the system according to a hierarchical resource description where each group of resources has a local directory that collects and keeps the information of the underlying resource members (cores) in dif erent layers. Operationally, at each layer, it consists of a peer-to-peer architecture of modules that, by interacting with each other, provide a global view of the resource availability in a large, dynamic and heterogeneous distributed environment. The proposed resource discovery model provides the adaptability and f exibility to perform complex querying by supporting a large set of signif cant querying features (such as multi-dimensional, range and aggregate querying) while supporting exact and partial matching, both for static and dynamic object contents. The paper demonstrates by simulation how the proposed model can deal with issues such as scalability, ef ciency and adaptability of resource discovery in future many-core systems which are the major challenges in the current state of the art. The simulation show that the proposed resource discovery model can be applied to arbitrary scales of dynamicity, both in terms of complexity and of scale, positioning this proposal as a good architecture for future many-core systems. Permanent repository link
This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractIn recent years, Jungle Computing has emerged as a distributed computing paradigm based on simultaneous combination of various hierarchical and distributed computing environments which are composed by large number of heterogeneous resources. In such a computing environment, the resources and the underlying computation and communication infrastructures are highly-hierarchical and heterogeneous. This creates a lot of difficulty and complexity for finding the proper resources in a precise way in order to run a particular job on the system efficiently. This paper proposes Hybrid Adaptive Resource Discovery (HARD), a novel efficient and highly scalable resource-discovery approach which is built upon a virtual hierarchical overlay based on self-organization and self-adaptation of processing resources in the system, where the computing resources are organized into distributed hierarchies according to a proposed hierarchical multi-layered resource description model. The proposed approach supports distributed query processing within and across hierarchical layers by deploying various distributed resource discovery services and functionalities in the system which are implemented using different adapted algorithms and mechanisms in each level of hierarchy. The proposed approach addresses the requirements for resource discovery in Jungle Computing environments such as high-hierarchy, high-heterogeneity, high-scalability and dynamicity. Simulation results show significant scalability and efficiency of the proposed approach over highly heterogeneous, hierarchical and dynamic computing environments.Keywords: distributed operation systems, many-core systems, P2P, resource management, grid computing, DHT
The data science and AI community has gathered around the world to support tackling the climate change problem in different domains. This research aims to work on the air quality through emissions and pollutant concentration data along with vegetation information. Authorities especially in urban cities like London have been very vigilant in monitoring these different aspects of air quality and reliable sources of big data are available in this domain. This study aims to mine and collate this information spread all over the place in different formats into usable knowledge base on which further data analysis and powerful Machine Learning approaches can be built to extract strong evidences useful in building better policies around climate change. Keywords: Data mining and analysis • Data pre-processing • Air quality • Climate change • Urban planning and machine learning • Geographic information systems
This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThe architecture design of peta-scale computing systems is complex and presents lots of difficulties to designs, as current tools lack support for relevant features of future scenarios. Novel systems must be designed with great care and tools, such as manycore architecture simulators, must be adapted accordingly. However, current simulation tools are very slow, often specific-purposeoriented, suffer from various issues and are rarely able to simulate thousands of cores. The emergence of peta-scale systems and the upcoming manycore era brings nevertheless new challenges to computing systems and architectures, adding further difficulties and requirements on the development of the corresponding simulators. Furthermore, the design of architecture simulators for manycore systems involve methods and techniques from various interdisciplinary research areas, which in turn brings more challenges in different aspects. As system complexity grows, the growth of the simulation capacity is being outpaced (reaching the so called simulation wall). In this paper, we present the challenges for simulating future large scale manycore environments, and we investigate the adequacy of current modeling and simulation tools, methodologies and techniques. The aim of this work is to highlight how current approaches can best deal with the identified problems, smoothing the challenges of research in future peta-scale systems.
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