Transparent Access to Heterogeneous IoT Based on Virtual Resources

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.

Virtual resource monitoring in cloud computing

Cloud computing is a new computing model. The resource monitoring tools are immature compared to traditional distributed computing and grid computing. In order to better monitor the virtual resource in cloud computing, a periodically and event-driven push （PEP） monitoring model is proposed. Taking advantage of the push and event-driven mechanism, the model can provide comparatively adequate information about usage and status of the resources. It can simplify the communication between Master and Work Nodes without missing the important issues happened during the push interval. Besides, we develop ＂mon＂ to make up for the deficiency of Libvirt in monitoring of virtual CPU and memory.

Resource Reconstruction Algorithms for On-demand Allocation in Virtual Computing Resource Pool

Resource reconstruction algorithms are studied in this paper to solve the problem of resource on-demand allocation and improve the efficiency of resource utilization in virtual computing resource pool. Based on the idea of resource virtualization and the analysis of the resource status transition, the resource allocation process and the necessity of resource reconstruction are presented, l^esource reconstruction algorithms are designed to determine the resource reconstruction types, and it is shown that they can achieve the goal of resource on-demand allocation through three methodologies： resource combination, resource split, and resource random adjustment. The effects that the resource users have on the resource reconstruction results, the deviation between resources and requirements, and the uniformity of resource distribution are studied by three experiments. The experiments show that resource reconstruction has a close relationship with resource requirements, but it is not the same with current distribution of resources. The algorithms can complete the resource adjustment with a lower cost and form the logic resources to match the demands of resource users easily.

Resource scheduling virtualization in service-oriented future Internet architecture

Service-oriented future internet architecture（SOFIA） is a clean-slate network architecture. In SOFIA, a service request is mainly processed through service resolution and network resource allocation. To realize the network resource allocation, we reference the idea of network virtualization and propose resource scheduling virtualization. In resource scheduling virtualization, a service request is abstracted as a virtual network（VN） and the network resources are allocated by mapping the VN onto the physical network. Resource scheduling virtualization provides centralized resource scheduling control within an autonomous system（AS） and achieves better controllability compared with the distributed schemes. Besides, resource scheduling virtualization supports multi-site selection as well. Meanwhile, we propose a collection of resource scheduling algorithms based on maximum resource tree（MRT） adapting to different scenarios. According to the simulation results, the proposed algorithms show good performance on the key metrics, such as acceptance ratio, revenue, cost and utilization. Moreover, the simulation results reveal that our algorithm is more efficient than the traditional ones.

Resource Virtualization Model Using Hybrid-graph Representation and Converging Algorithm for Cloud Computing

Cloud computing can provide a great capacity for massive computing, storage as well as processing. The capacity comes from the cloud computing system itself, which can be likened to a virtualized resource pool that supports virtualization applications as well as load migration. Based on the existing technologies, the paper proposes a resource virtualization model （RVM） utilizing a hybrid-graph structure. The hybrid-graph structure can formally represent the critical entities such as private clouds, nodes within the private clouds, and resource including its type and quantity. It also provides a clear description of the logical relationship and the dynamic expansion among them as well. Moreover, based on the RVM, a resource converging algorithm and a maintaining algorithm of the resource pool which can timely reflect the dynamic variation of the private cloud and resource are presented. The algorithms collect resources and put them into the private cloud resource pools and global resource pools, and enable a real-time maintenance for the dynamic variation of resource to ensure the continuity and reliability. Both of the algorithms use a queue structure to accomplish functions of resource converging. Finally, a simulation platform of cloud computing is designed to test the algorithms proposed in the paper. The results show the correctness and the reliability of the algorithms.

EasiSMP:A Resource-Oriented Programming Framework Supporting Runtime Propagation of RESTful Resources

In order to simplify programming for building sensor networks, macro-programming methods have been pro- posed in prior work. Most of them are designed for the dedicated networks and specific scenarios where devices are mostly homogeneous. Nevertheless the methods rarely consider those shared networks which are composed of heterogeneous de- vices, e.g., sensors, actuators, mobile devices, and share resources among themselves. In this paper, we present EasiSMP, a resource-oriented programming framework for these shared networks and generic application scenarios. In this framework, the devices and their functionalities are abstracted into RESTful virtual resources （VRs） each of which is labelled by a uni- form resource identifier （URI）. The post-deployment VR can be globally accessed and reused to propagate new resource（s） at runtime. To support the resource propagation, programming primitives are proposed and a virtual resource engine （VRE） is studied. To perform evaluation, EasiSMP is deployed into a relic monitoring network. Experimental results show that programming using Ea-siSMP is concise, and the average deployment overhead is decreased by up to 27% compared with the node-level programming.