Beijing University of Posts and Telecommunications

About: Beijing University of Posts and Telecommunications is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: MIMO & Quality of service. The organization has 39576 authors who have published 41525 publications receiving 403759 citations. The organization is also known as: BUPT.

Resource Allocation for Energy-Efficient MEC in NOMA-Enabled Massive IoT Networks

Abstract: Integrating mobile edge computing (MEC) into the Internet of Things (IoT) enables the IoT devices of limited computation capabilities and energy to offload their computation-intensive and delay-sensitive tasks to the network edge, thereby providing high quality of service to the devices. In this article, we apply non-orthogonal multiple access (NOMA) technique to enable massive connectivity and investigate how it can be exploited to achieve energy-efficient MEC in IoT networks. In order to maximize the energy efficiency for offloading, while simultaneously satisfying the maximum tolerable delay constraints of IoT devices, a joint radio and computation resource allocation problem is formulated, which takes both intra- and inter-cell interference into consideration. To tackle this intractable mixed integer non-convex problem, we first decouple it into separated radio and computation resource allocation problems. Then, the radio resource allocation problem is further decomposed into a subchannel allocation problem and a power allocation problem, which can be solved by matching and sequential convex programming algorithms, respectively. Based on the obtained radio resource allocation solution, the computation resource allocation problem can be solved by utilizing the Knapsack method. Numerical results validate our analysis and show that our proposed scheme can significantly improve the energy efficiency of NOMA-enabled MEC in IoT networks compared to the existing baselines.

A cost-aware auto-scaling approach using the workload prediction in service clouds

Abstract: Service clouds are distributed infrastructures which deploys communication services in clouds. The scalability is an important characteristic of service clouds. With the scalability, the service cloud can offer on-demand computing power and storage capacities to different services. In order to achieve the scalability, we need to know when and how to scale virtual resources assigned to different services. In this paper, a novel service cloud architecture is presented, and a linear regression model is used to predict the workload. Based on this predicted workload, an auto-scaling mechanism is proposed to scale virtual resources at different resource levels in service clouds. The auto-scaling mechanism combines the real-time scaling and the pre-scaling. Finally experimental results are provided to demonstrate that our approach can satisfy the user Service Level Agreement (SLA) while keeping scaling costs low.

A Workgroup Climate Perspective on the Relationships Among Transformational Leadership, Workgroup Diversity, and Employee Creativity:

Abstract: Previous research on the effects of workgroup diversity and workgroup functioning and outcomes has produced mixed results. We address these inconsistencies by proposing a model that considers the effects of both transformational leadership and workgroup climate on one workgroup outcome believed particularly relevant to diversity: employee creativity. Multilevel analyses of 172 individuals working in 31 teams found that innovation workgroup climate level mediated the relationship between transformational leadership and employee creativity. Furthermore, results indicated that diverse groups had higher innovation climate strength when transformational leadership was high, and that innovation climate level was more strongly related to employee creativity when innovation climate strength was high. Overall, these results suggest that transformational