End-to-end networks vs named data network: A critical evaluation

Named data networking or information centric networking is the newest networking paradigm that gives foremost place to the contents in identification and dissemination.On the other hand, the end to end networking paradigm on which the Internet is currently built on places heavy emphasis on devices that make the architecture. The current Internet suffers from many shortcomings due to the misplaced emphasis. In order to overcome some of these deficiencies, researchers and developers have come up with patches and work around that have made the Internet more complex than it ought to be. Named data networking is a clean slate approach in building a network architecture overcoming all the current deficiencies and make it future safe. Several researchers have carried out comparative studies between named data networking and end to end networking. But these studies concentrate only on the features and capabilities of the networking paradigms. This is the first attempt at quantifying the performance the networking architectures experimentally. The authors in this paper present the results of the comparative study carried out experimentally in a simulated environment based on the final throughput. The results have been presented in a graphical form for easy visualization of results

Statistically enhanced multi-dimensional trust computing mechanism for cloud computing

Quality of service plays an important role in distributed systems. Cloud computing has been the recent entrant to the distributed system market and has revolutionized computing by changing the way computing resources are accessed and paid for. Users can access cloud services and pay only for the usage similar to utilities. Trust computing systems can be employed for identifying the service providers quality in terms of their adherence to the committed in SLAs. Several trust computing mechanisms have been proposed in literature based on various algorithms and functions. Almost all of them are based on a single performance parameter and modify the trust scores monotonously when performance deviations are reported. This paper proposes a trust computing mechanism that statistically validates the attribute monitored before modifying the trust scores. Hence the proposed mechanism is protected from momentary fluctuations in system performances. The experiments conducted show that the trust scores computed using the proposed mechanism are more representative of the long term system performance than the ones that were computed without the validation of the inputs. Copyrigh

How Fog Computing Can Support Latency/Reliability\u2010sensitive IoT Applications: An Overview and a Taxonomy of State\u2010of\u2010the\u2010art Solutions

The Internet of Things (IoT) paradigm is leading to the deployment of an extensive number of smart devices capable of assisting companies and people in their daily activities. For this paradigm to be effective, these devices must exchange a huge amount of information and be coordinated in unpredictable, dynamic, and very complex scenarios. So far, cloud computing has centralized data storage and offered coordination of devices. However, as the number of deployed smart devices increases and the requirements of IoT solutions are more stringent, cloud computing hardly meets them. Fog computing has emerged as a middle layer between end\u2010devices and cloud environments to support the requirements of IoT applications that cannot be met by the current edge\u2010cloud model. A great effort has been devoted during the past few years to the development of this fog vision. Most of these solutions focused on improving specific characteristics, but not on supporting all the key requirements of an IoT solution. Thus, a deep investigation of these solutions to understand how they can be connected and coordinated to meet these necessities is essential. In this paper, we distinguish the most vital necessities that IoT solutions present to accomplish a right operation. Also, by analyzing the available solutions, we propose a novel global architectural model for fog computing meeting the recognized demands. We also provide a novel scientific taxonomy for breaking down the overviewed solutions. We conclude by analyzing the most essential recommendations in Fog computing for IoT, thereby distinguishing open issues and research frontiers that must be prioritized in order to have a totally developed fog computing environment, ready to meet the IoT solutions' prerequisites