The continuous conduction mode (CCM) totem-pole bridgeless power factor correction (TPBPFC) converters are feasible because of the better reverse-recovery performance of the wide band gap (WBG) devices. However, practical applications encounter challenges such as low power density, high cost, and high-power loss. This paper proposes an improved hybrid Si/SiC CCM TPBPFC rectifier. A buck-type active power decoupling (APD) circuit is used to remove the bulky capacitor and increase the power density. A SiC-based bidirectional switch is connected between the Si-based PFC and APD bridges to achieve the soft switching of Si-based switches and decrease the power losses. The operation principle is given and then the analysis of soft switching conditions and power loss are provided. Finally, the verification of proposed circuit is experimented on a 500 W prototype and a 2% efficiency improvement is demonstrated.

The main goal of a cloud provider is to make profits by providing services to users. Existing profit optimization strategies employ homogeneous user models in which user personality is ignored, resulting in fewer profits and particularly notably lower user satisfaction that in turn, leads to fewer users and reduced profits. In this article, we propose efficient personality-aware request scheduling schemes to maximize the profit of the cloud provider under the constraint of user satisfaction. Specifically, we first model the service requests at the granularity of individual personality and propose a personalized user satisfaction prediction model based on questionnaires. Subsequently, we design a personality-guided integer linear programming (ILP)-based request scheduling algorithm to maximize the profit under the constraint of user satisfaction, which is followed by an approximate but lightweight value assessment and cross entropy (VACE)-based profit improvement scheme. The VACE-based scheme is especially tailored for applications with high scheduling resolution. Extensive simulation results show that our satisfaction prediction model can achieve the accuracy of up to 83 percent, and our profit optimization schemes can improve the profit by at least 3.96 percent as compared to the benchmarking methods while still obtaining a speedup of at least 1.68x.

Based on extended phase shift (EPS) modulation with voltage matching control and the DC blocking capacitor, an improved modulation method for dual-active-bridge (DAB) DC-DC converters with the blocking capacitor is proposed in this paper, which can realize full load range zero voltage switching (ZVS) under wide voltage range with low root-mean-square (RMS) current. In the proposed scheme, the volt-second products of all windings are kept equal to achieve the full load range ZVS, and the secondary side of the converter is switched between the full bridge mode and the half bridge mode to switch the optimal matching point of the voltage and then reduces RMS current and enlarges the ZVS region. The operation principle of the proposed method is analyzed, and ZVS region is illustrated and compared with conventional modulation to demonstrate the performance. A 1kW experimental prototype with 200V input and 100 - 400V output was built to verify the feasibility and effectiveness of the proposed method.

Fog computing is an emerging architectural paradigm for the implementation of the Internet of Things, where computation moves from cloud servers to network edges. Fog computing systems are with three characteristics: 1) low latency; 2) strong presence of real-time applications; and 3) reusability of end devices. Most existing designs of fog computing systems concentrate on reducing application processing latency, but neglect real-time requirements of applications and reusability of end devices, which may drastically degrade both functionality and quality-of-service (QoS) of applications. In this article, we investigate QoS optimization of real-time applications in fog computing systems equipped with reusable end devices and powered by hybrid energy of renewable generations and grid electricity. We propose a renewable-adaptive computation offloading approach. At the end device layer, local energy allocation schemes are designed at the application-level and component-level, where techniques of the cooperative game and mixed-integer linear programming (MILP) are leveraged, respectively. At the fog layer, the local energy allocation method is augmented to a local-remote scheduling solution by judiciously judging whether or not the computation offloading of an application needs to be triggered. The experimental results demonstrate that compared to benchmarking algorithms, our approach improves the overall and individual application QoS by up to 101.93% and 59.30%, respectively.

In recent years, the Internet of Things (IoT) has promoted many battery-powered emerging applications, such as smart home, environmental monitoring, and human healthcare monitoring, where energy management is of particular importance. Meanwhile, there is an accelerated tendency toward mobility of IoT devices, either being transported by humans or being mobile by itself. Existing energy management mechanisms for battery-powered IoT fail to consider the two significant characteristics of IoT: 1) the approximate real-time computation and 2) the mobility of IoT devices, resulting in unnecessary energy waste and network lifetime decay. In this paper, we explore mobility-aware network lifetime maximization for battery-powered IoT applications that perform approximate real-time computation under the quality-of-service (QoS) constraint. The proposed scheme is composed of offline and online stages. At offline stage, an optimal mobility-aware task schedule that maximizes network lifetime is derived by using mixed-integer linear programming technique. Redundant executions due to mobility-incurred overlapping of a single task on different IoT devices are avoided for energy savings. At online stage, a performance-guaranteed and time-efficient QoS-adaptive heuristic based on cross-entropy method is developed to adapt task execution to the fluctuating QoS requirements. Extensive simulations based on synthetic applications and real-life benchmarks have been implemented to validate the effectiveness of our proposed scheme. Experimental results demonstrate that the proposed technique can achieve up to 169.52% network lifetime improvement compared to benchmarking solutions.

The main goal of a cloud service provider is to make profits by providing services to users. Existing pricing strategies adopt a single revenue function for different types of service requests, which ignores the heterogeneity of users and leads to low profits. In this paper, we propose a heterogeneity-aware request scheduling scheme that maximizes profits of service providers by exploiting the heterogeneity of users. Specifically, we first model charge functions of requests at the granularity of individual users to capture their heterogeneity. Then an integer linear programming (ILP)-based optimal scheduling algorithm is designed to maximize profits, which is followed by an approximate but lightweight genetic algorithm (GA)-based profit improvement scheme. The GA-based scheme is particularly tailored for applications of high scheduling resolution. Extensive simulation results show that our schemes improve profits by at least 22.46% compared to benchmarking methods while achieving at least 13.51 times of speedup.