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Transparent Computing (TC) is becoming a promising paradigm in network computing era. Although many researchers believe that TC model has a high requirement for the communication bandwidth, there is no research on the communication bandwidth boundary or resource allocation, which impedes the development of TC. This paper focuses on studying an efficient transparent computing resource allocation model in an economic view. First, under the quality of experiments (QoE) ensured, the utility function of clients and transparent computing providers (TCPs) is constructed. After that, the demand boundary of communication bandwidth is analyzed under the ideal transparent computing model. Based on the above analyses, a resource allocation scheme based on double-sided combinational auctions (DCA) is proposed so that the resource can be shared by both the service side and the client side with the welfare of the whole society being maximized. Afterward, the results scheduled in different experimental scenarios are given, which verifies the effectiveness of the proposed strategy. Overall, this work provides an effective resource allocation model for optimizing the performance of TC.

In mobile transparent computing, a large number of concurrent data requests from heterogeneous clients via the network need to be processed in a timely fashion, and servers have to repeatedly fetch (search and read) the data from storage, which may cause numerous I/O costs. Generally, disk access speed is more limited than memory; therefore, massive I/O operations at servers may become a bottleneck for the system, and transport delay of the total network caused by the limitation of wireless bandwidth and stability may lead to poorer user experience. Hence, caching method plays a significant role in performance improvement of transparent computing systems. In this paper, we propose a block-level caching optimization method for the server and client by analyzing the system bottleneck in mobile transparent computing. We first analyze the storage format of the data file and the three-layer structure in the server according to the characteristics of requesting data from the client to the server and propose a block-level cache based on the access time and access frequency for the server. Second, considering the restriction of bandwidth and stability of the wireless network, we analyze network boot processes from the client’s startup and propose a client block-level cache optimization combined with local storage access technology. Finally, experimental results demonstrate that the server block-level cache optimization can effectively reduce the amount of server disk I/O, improving the concurrent ability of the server. In addition, the client block-level cache can significantly increase startup speed of the client, reduce network traffic and improve user experience.

Transparent Computing (TC) has become a promising paradigm in the network computing era, the appearance of Transparent Computing have a real impact on the industry. A growing number of cars on the road, in order to reduce road congestion, improve efficiency, and increase travelers satisfaction, IoV (Internet of Vehicles) arises at the proper time. RFID as an excellent sensing equipment is widely used in the IoV. Tags can collect information separately. The surrounding readers transmit the information to the central processing unit by using the Transparent Computing technique. Data sharing is the premise of IoV, drivers can share their information, in the meantime, they do not want to expose their own privacy information, so anonymity and untraceability is very important. Unfortunately, current applicable or proposed protocols fail to satisfy the security, efficiency and usability. In this paper, we propose a mutual authentication protocol to protect driver’s privacy, TC is used in our protocol, so we can make full use of client hardware, improve the quality of user experience. The last but not least, because the existence of OS-level monitoring and attack tracing, system security increase greatly.

Although cloud computing has made significant achievement, it still faces many challenges, such as bad interactive performance and unsatisfying user experience over a long-haul wide-area or wireless network. To address these challenges, we proposed a software-defined stream-based code scheduling framework according to the concept of transparent computing. This framework uses the idea of code streaming to decouple the computation and storage of software codes; this idea also leverages the input/output virtualization technique to support legacy operating systems and application software in a feasible and effective way. The software-defined code scheduling framework allows the computation or storage to be adaptively carried out at appropriate machines with the assistance of performance and capacity monitoring facilities. Thus, the framework can improve application performance and user experiences by executing software codes on a nearer or better machine. We developed a pilot system to investigate the advantages of the proposed framework. Preliminary experimental results show that our approach can achieve better performance than current cloud computing-based systems.

The paradigm of the Internet of Things (IoT) and edge computing brings a number of heterogeneous devices to the network edge for monitoring and controlling the environment. For reacting to events dynamically and automatically in the environment, rule-enabled IoT edge platforms operate the deployed service scenarios at the network edge, based on filtering events to perform control actions. However, due to the heterogeneity of the IoT edge networks, deploying a consistent rule context for operating a consistent rule scenario on multiple heterogeneous IoT edge platforms is difficult because of the difference in protocols and data formats. In this paper, we propose a transparent rule enablement, based on the commonization approach, for enabling a consistent rule scenario in heterogeneous IoT edge networks. The proposed IoT Edge Rule Agent Platform (IERAP) deploys device proxies to share consistent rules with IoT edge platforms without considering the difference in protocols and data formats. Therefore, each device proxy only considers the translation of the corresponding platform-specific and common formats. Also, the rules are deployed by the corresponding device proxy, which enables rules to be deployed to heterogeneous IoT edge platforms to perform the consistent rule scenario without considering the format and underlying protocols of the destination platform.

Rule-enabled Internet of Things (IoT) systems operate autonomous and dynamic service scenarios through real-time events and actions based on deployed rules. For handling the increasing events and actions in the IoT networks, the computational ability can be distributed and deployed to the edge of networks. However, operating a consistent rule to provide the same service scenario in heterogeneous IoT networks is difficult because of the difference in the protocols and rule models. In this paper, we propose a transparent rule deployment approach based on the rule translator by integrating the interworking proxy to IoT platforms for operating consistent service scenarios in heterogeneous IoT networks. The rule-enabled IoT architecture is proposed to provide functional blocks in the layers of the client, rule service, IoT service, and device. Additionally, the interworking proxy is used for translating and transferring rules between IoT platforms in different IoT networks. Based on the interactions between the IoT platforms, the same service scenarios are operated in the IoT environment. Moreover, the integrated interworking proxy enables the heterogeneity of IoT frameworks in the IoT platform. Therefore, rules are deployed on IoT platforms transparently, and consistent rules are operated in heterogeneous IoT networks without considering the underlying IoT frameworks.

The Internet of Things (IoT) has received a lot of attention, especially in industrial scenarios. One of the typical applications is the intelligent mine, which actually constructs the Six-Hedge underground systems with IoT platforms. Based on a case study of the Six Systems in the underground metal mine, this paper summarizes the main challenges of industrial IoT from the aspects of heterogeneity in devices and resources, security, reliability, deployment and maintenance costs. Then, a novel resource service model for the industrial IoT applications based on Transparent Computing (TC) is presented, which supports centralized management of all resources including operating system (OS), programs and data on the server-side for the IoT devices, thus offering an effective, reliable, secure and cross-OS IoT service and reducing the costs of IoT system deployment and maintenance. The model has five layers: sensing layer, aggregation layer, network layer, service and storage layer and interface and management layer. We also present a detailed analysis on the system architecture and key technologies of the model. Finally, the efficiency of the model is shown by an experiment prototype system.

The Internet of Things (IoT) inspires industries to deploy a massive number of connected devices to provide smart and ubiquitous services to influence our daily life. Edge computing leverages sufficient computation and storage at the edge of the network to enable deploying complex functions closer to the environment using Internet-connected devices. According to the purpose of the environment including privacy level, domain functionality, network scale and service quality, various environment-specific services can be provided through heterogeneous applications with sensors and actuators based on edge computing. However, for providing user-friendly service scenarios based on the transparent access to heterogeneous devices in edge computing, a consistent interface shall be provided to deliver services from edge computing to clients. In this paper, we propose transparent computing based on virtual resources to access heterogeneous IoT devices without considering the underlying network configuration at the edge of the networks. For supporting transparent access to different edge computing environments through a consistent interface, the virtual resource of edge gateway is proposed to bridge the Internet and devices which are deployed on the edge of the network. The proposed edge gateway exposes the services of the Internet of Things devices to the Internet using virtual resources that represent the resources of physical devices. The virtual resources provide a consistent interface to enable clients to access devices in edge computing without considering underlying protocols. The virtual resource is generated by the resource directory in the edge gateway through the registration of a device. Based on the device registration, the device information is stored in the gateway to link virtual resources and devices for translating messages according to the destination protocols and identifying physical devices that are represented by virtual resources. Moreover, through collaboration with the service provider, the function of device discovery and monitoring is provided to clients.

In cloud computing, user share data while sharing data providing security and protecting the data from a unauthorized users is still a demanding problem, so we implement a multilevel data security popular a cloud computing in order to preserve the information using multi-security levels, and provide sufficient multilevel access and validate authorized identity. The recommended scheme is dynamic in multilevel data security, adaptable to certified resource share in, and protected against different malignant strike. Experimentation result checks the feasibleness. The processed data have multiple security level and access permission.

Combining data from multiple sources is a means of enabling unified and comprehensive description of objects in high-dimensional space and helping unlock the potential value of such data. In recent years, more and more studies have focused on this field of research. However, challenges posed by separately stored data and comprehension barriers about different systems hinder the integration of data from different sources. To overcome these problems, this paper proposes a Transparent Data as a Service framework, a novel approach combining Transparent Computing and Representational State Transfer (REST) Web Services based on Linked Data. This framework is capable of integrating data from different sources and offering data services in a transparent way. That is, consumers use data services without the need to know details of where or how the data are stored. Our framework is transparent on three levels: transparent data resource integration, transparent data fusion and transparent data service provision. The Data Model Pool and Data Resource Pool are able to evolve as new data models and datasets are generated in the provision of data services. Finally, we demonstrate the feasibility of the framework by implementing a prototype system.