Mobile Cloud for Personalized Any-Media Services

In this paper, we define mobile cloud computing and describe how it can be used for delivering advanced any-media services to both nomadic and mobile users. We focus on service delivery that is localized and personalized and suggest that virtualization and tighter cross-layer communication allows for convergence and seamless transition of services. These are also creating new and never-before seen ways of developing and delivering personalized any-media services. We discuss current paradigms for implementing cloud-based any-media services that generate revenue. Future research topics and requirements for evolving network and service elements are also discussed.

Moving from Vision to Action: Case of Shenzhen Tax Service

Local tax services of the State Taxation Administration of China(STA),as direct service providers to taxpayers and fee-payers,can contribute to the cooperation of China with other Belt and Road Initiative(BRI)jurisdictions under the framework of the Belt and Road Initiative Tax Administration Cooperation Mechanism(BRITACOM).Guided by the STA,Shenzhen Tax Service(STS)is actively engaging in the development of the BRITACOM in an effort to achieve its vision,i.e.,building a growth-friendly tax environment.The STS aims to establish a win-win cooperation mechanism that could serve as a model among other local services.This article focuses on three major issues,i.e.,the significance,the actions and achievements as well as the future prospects of the STS to facilitate the long-term growth of the BRITACOM.

Coordinated target localization base on pseudo measurement for clustered space robot

This paper presents a coordinated target localization method for clustered space robot.According to the different measuring capabilities of cluster members,the master-slave coordinated relative navigation strategy for target localization with respect to slavery space robots is proposed;then the basic mathematical models,including coordinated relative measurement model and cluster centralized dynamics,are established respectively.By employing the linear Kalman flter theorem,the centralized estimator based on truth measurements is developed and analyzed frstly,and with an intention to inhabit the initial uncertainties related to target localization,the globally stabilized estimator is designed through introduction of pseudo measurements.Furthermore,the observability and controllability of stochastic system are also analyzed to qualitatively evaluate the convergence performance of pseudo measurement estimator.Finally,on-orbit target approaching scenario is simulated by using semi-physical simulation system,which is used to verify the convergence performance of proposed estimator.During the simulation,both the known and unknown maneuvering acceleration cases are considered to demonstrate the robustness of coordinated localization strategy.

Load Feedback-Based Resource Scheduling and Dynamic Migration-Based Data Locality for Virtual Hadoop Clusters in OpenStack-Based Clouds

With cloud computing technology becoming more mature, it is essential to combine the big data processing tool Hadoop with the Infrastructure as a Service（Iaa S） cloud platform. In this study, we first propose a new Dynamic Hadoop Cluster on Iaa S（DHCI） architecture, which includes four key modules： monitoring,scheduling, Virtual Machine（VM） management, and VM migration modules. The load of both physical hosts and VMs is collected by the monitoring module and can be used to design resource scheduling and data locality solutions. Second, we present a simple load feedback-based resource scheduling scheme. The resource allocation can be avoided on overburdened physical hosts or the strong scalability of virtual cluster can be achieved by fluctuating the number of VMs. To improve the flexibility, we adopt the separated deployment of the computation and storage VMs in the DHCI architecture, which negatively impacts the data locality. Third, we reuse the method of VM migration and propose a dynamic migration-based data locality scheme using parallel computing entropy. We migrate the computation nodes to different host（s） or rack（s） where the corresponding storage nodes are deployed to satisfy the requirement of data locality. We evaluate our solutions in a realistic scenario based on Open Stack.Substantial experimental results demonstrate the effectiveness of our solutions that contribute to balance the workload and performance improvement, even under heavy-loaded cloud system conditions.