Efficient Cache Management Model for Cooperative Reasoning in Wide Area Surveillance

Information-centric networking (ICN) is gradually becoming an alternative approach to the conventional Internet architecture through the distribution of enlightening information (named as content) on the Internet. Given the growing Internet traffic, the adapted information-centric architecture is expected to more favourably serve the user’s need of accessing data. In this paper, we proposed a novel framework for cache management in ICN that jointly considers the caching strategy and network coding. For the proposed cache management framework, we first evaluated a new Centrality-measures to assess the variation in the behaviour of each node in the network and to identify such behaviour. Subsequently, we developed an efficient random network coding-based cache management algorithm to obtain a support for caching and multicasting in the ICN paradigm. Extensive simulations were made, and related results showed that our proposed scheme has better performance in terms of cache hit ratio, average download time, server hit reduction ratio, and traffic overhead compared with other schemes in our simulated scenarios.

Information-Centric Network (ICN) emerged as a new Internet paradigm to address the problems arising from the mismatch between the TCP/IP host-to-host transmission pattern and content-oriented access mode. However, the current network infrastructure does not support ICN. ICN needs to be deployed incrementally on existing networks, and its coexistence with IP is inevitable. Software-Defined Network (SDN) is an effective solution for achieving flexible and controlled deployment. In this paper, we design a compatible format for IP packets and NDN (Named Data Networking, Typical representatives of ICN) packets based on MPLS. On this basis, a network architecture for IP and NDN coexistence, called SDNDN (Software-Defined Named Data Network), is designed in combination with SDN. With data plane programmable capabilities, functionally extended switches enable parsing, processing, and forwarding of NDN packets; algorithms such as route optimization and cache management are implemented based on a global view. Experimental results indicate that SDNDN can realize the coexistence of ICN and IP networks and has performance advantages such as reduced content retrieval time. Besides, IP and NDN in the coexistence network are suited to different traffic models, which facilitates flexible network slicing.

It is a paradigm shift in internet architecture from receiver centric model to the information centric networking (ICN) model to improve the end-users' latency experience. With exponential increment in number of users and data volume, efficient usage of the nodes (caches) and content forwarding methodology are key issues in an ICN architecture. In ICN intra-domain routers have storage capacity and they can act as temporary content provider, therefore it reduces average hop count and correspondingly average delay for content download. In this article, we formulate the content management issues in a cache having finite storage capability and propose an efficient content management policy that metamorphoses a node to a self-sustained cache. We address how content packets are stored in buffer of the cache before processing and how they suffer from several finite propagation delays during the processing in a cache. We simulate our proposition with deploying real-life network environment and calculate user experience metrics like average latency, throughput and good-put. From simulation results we observe that our proposed model outperforms the existing state-of-the-art on-path caching policies.

This research represents a novel contribution regarding the application of digital technology to the management and cultural promotion of industrial heritage. The study answers questions about the level of digital transformation of certain preselected buildings and areas of great historical and technical interest. It includes an extensive bibliographic review and analyzes different variables linked to webpages, which are the main source of information for visitors, and studies the level of digitization using a survey of the technical managers. The results are valuable because they offer an original profile of selected industrial heritage sites, characterized by an important connection between visitors, visited spaces, and available resources; the interaction of these three elements with the surrounding territory, fostering a new competitive capacity; the projection of each place in a modern and attractive way; and the commitment to an efficient and sustainable local management model. The results provide a fresh look at the technological changes embodied by new uses in old industrialization sites. In addition, the performed analysis could easily be applied and operationally compared in other different heritage environments.

IT infrastructures have been widely deployed in datacentres by cloud service providers for Infrastructure as a Service (IaaS) with Virtual Machines (VMs). With the rapid development of cloud-based tools and techniques, IaaS is changing the current cloud infrastructure to meet the customer demand. In this paper, an efficient management model is presented and evaluated using our unique Trans-Atlantic high-speed optical fibre network connecting three datacentres located in Coleraine (Northern Ireland), Dublin (Ireland) and Halifax (Canada). Our work highlights the design and implementation of a management system that can dynamically create VMs upon request, process live migration and other services over the high-speed inter-networking Datacentres (DCs). The goal is to provide an efficient and intelligent on-demand management system for virtualization that can make decisions about the migration of VMs and get better utilisation of the network.

Motivated by the “clean-slate” opportunity of the evolving Smart Grid, we propose an Information Centric Networking (ICN) approach for the home communications fabric and create an ICN-based secure publish-subscribe system to support home energy management. We design a secure group communication protocol with efficient key management specifically embedded in ICN for home data privacy. To validate the approach, we enhance an open-source ICN implementation and build a proof-of-concept Smart Home testbed.

An overview of innovative cancer drug clinical development in China

Project management is an important task in business although project is not just confined to business. Due to the uncertainty of the various variables involved in a project, over several past decades research is going on in the search for an efficient project management model. Although numerous crisp models are easily implementable, the potential of fuzzy models are huge. In the case of software development, the variables involved are highly dynamic. In this paper, we propose a ranking based fuzzy model that can prioritize various activities. We use a popular crisp model to test the effectiveness of the fuzzy model proposed. Simulation is done through Java Server Pages (JSP). There is considerable computational and managerial advantage in implementing the fuzzy model.

ArPi Lab: A Low-cost Remote Laboratory for Control Education

As there is a huge requirement for IT resources in business world, cloud computing is used by various businesses to satisfy their requirements. Many businesses outsource their IT needs to cloud and get benefited from cloud services. Cloud represents datacenter which has full infrastructure that provides virtualized services. The main challenge in cloud datacenter is energy consumption by server and computer and cooling equipment in cloud datacenter. Cloud datacenter emits huge amount of carbon dioxide which affect the environment acts against green atmosphere. This paper attempts to reduce the energy consumption of computing resources in cloud datacenter. Architecture has different entities which are described in the concerned sections. The proposed architecture saves energy by clustering similar request from the clients and also switches off ideal resource in cloud datacenter. The proposed architecture is an efficient energy consumption resource management model for cloud datacenter.

New opportunities and challenges arise in power system operations due to the energy transition from fossil fuels to renewable energy resources coupled with the liberalization of electricity markets. These opportunities appear in the form of energy flexibility, and the uncertainty of renewable generation challenges power system security of supply. This paper presents an efficient energy management model that considers available flexibility from active industrial networks connected to the power distribution grid. Installation of storage units in the industrial grid provides flexibility. The goal is to solve a multi-objective optimal power flow problem to reduce system costs and carbon emissions. In the proposed two-fold approach, Tchebycheff's decomposition method breaks down the multi-objective problem into scalar subproblems, which are then singularly minimized using a distributed gradient projection algorithm. Distributed computation helps retain the data privacy of each participant. The algorithm is applied to modified IEEE radial test network to demonstrate achieved cost benefits and carbon footprint reduction.

Grid computing is the next generation platform that maximizes computing performance of system as using numerous computing resources on computer networks. Grid computing not only shares computing resources but also efficiently utilizes available computing resources. Therefore, grid computing needs resource management policies that handle and manage grid computing resources. This paper reviews existing resource management models based on economic principles such as auction model and commodity market model. This paper proposes an efficient resource management model, which is called the grid resource trade network model, and presents its bidding algorithms for grid users and grid providers. The proposed model reduces idle resources, improves resource utilization, and provides effective resource distribution. Empirical results show usefulness and efficiency of the grid resource trade network model.

Review, Classification and Comparative Analysis of Maintenance Management Models

In this paper, we investigate the concept of reputation to improve the resistance of vehicular networks against malicious and misbehaving vehicles. We propose a robust reputation management system, which consists of a model for reputation calculation and a credibility model to enhance network efficiency. The reputation score or value reflects the behavior of a vehicle towards other vehicles and network services (selfish, cooperative, malicious, misbehaving, etc.); while the credibility of vehicles is used to determine whether a reputation score given by a vehicle is correct to deal with malicious vehicles that use reputation calculation to spoil the network operation. We first describe how the reputation score of each vehicle is determined. Then, we introduce a non-cooperative game, where each vehicle aims to maximize its credibility. The effectiveness of the proposed model is demonstrated through extensive simulations.

Neurons in navigational brain regions provide information about position, orientation, and speed relative to environmental landmarks. These cells also change their firing patterns (“remap”) in response to changing contextual factors such as environmental cues, task conditions, and behavioral state, which influence neural activity throughout the brain. How can navigational circuits preserve their local computations while responding to global context changes? To investigate this question, we trained recurrent neural network models to track position in simple environments while at the same time reporting transiently-cued context changes. We show that these combined task constraints (navigation and context inference) produce activity patterns that are qualitatively similar to population-wide remapping in the entorhinal cortex, a navigational brain region. Furthermore, the models identify a solution that generalizes to more complex navigation and inference tasks. We thus provide a simple, general, and experimentally-grounded model of remapping as one neural circuit performing both navigation and context inference.