Multi-resource collaborative optimization for adaptive virtual machine placement

The unbalanced resource utilization of physical machines (PMs) in cloud data centers could cause resource wasting, workload imbalance and even negatively impact quality of service (QoS). To address this problem, this paper proposes a multi-resource collaborative optimization control (MCOC) mechanism for virtual machine (VM) migration. It uses Gaussian model to adaptively estimate the probability that the running PMs are in the multi-resource utilization balance status. Given the estimated probability of the multi-resource utilization balance state, we propose effective selection algorithms for live VM migration between the source hosts and destination hosts, including adaptive Gaussian model-based VMs placement (AGM-VMP) algorithm and VMs consolidation (AGM-VMC) method. Experimental results show that the AGM-VMC method can effectively achieve load balance and significantly improve resource utilization, reduce data center energy consumption while guaranteeing QoS.

High-power single-mode 894 nm VCSELs operating at high temperature (> 2 mW @ 365 K)

In this study, we realize the high-power output of a single-mode 894 nm vertical-cavity surface-emitting laser (VCSEL) at high temperature. The effects of the dimensional parameters of oxide aperture and surface relief on the transverse mode and threshold gain of VCSEL were analyzed. Through collaborative optimization of the oxide aperture and relief, the VCSEL with 8 µm oxide aperture diameter and 5 µm surface relief inner diameter can operate at high temperature of 365 K with single-mode output power of 2.02 mW and side-mode suppression of 29.2 dB.

Collaborative Optimization of Electric Vehicles Based on MultiAgent Variant Roth–Erev Algorithm

With the implementation of the carbon neutral policy, the number of electric vehicles (EVs) is increasing. Thus, it is urgently needed to manage the charging and discharging behavior of EVs scientifically. In this paper, EVs are regarded as agents, and a multiagent cooperative optimization scheduling model based on Roth–Erev (RE) algorithm is proposed. The charging and discharging behaviors of EVs will influence each other. The charging and discharging strategy of one EV owner will affect the choice of others. Therefore, the RE algorithm is selected to obtain the optimal charging and discharging strategy of the EV group, with the utility function of the prospect theory proposed to describe EV owners’ different risk preferences. The utility function of the prospect theory has superior effectiveness in describing consumers’ utility. Finally, in the case of residential electricity, the effectiveness of the proposed method is verified. Compared with that of random charging, this method reduces the total EV group cost of EVs by 52.4%, with the load variance reduced by 26.4%.

Multiparameter Optimization Framework of Cyberphysical Systems: A Case Study on Energy Saving of the Automotive Engine

Multiparameter optimization of complex electromechanical systems in a physical space is a challenging task. CPS (Cyberphysical system) technology can speed up the solution of the problem based on data interaction and collaborative optimization of physical space and cyberspace. This paper proposed a general multiparameter optimization framework by combining physical process simulation and clustering genetic algorithm for the CPS application. The utility of this approach is demonstrated in the instance of automobile engine energy-saving in this paper. A 1.8-L turbocharged GDI (gasoline direct injection) engine model was established and calibrated according to the test data and physical entity. A joint simulation program combining CGA (Clustering Genetic Algorithm) with the GDI engine simulation model was set up for the engine multiparameter optimization and performance prediction in cyberspace; then, the influential mechanism of multiple factors on engine energy-saving optimization was analyzed at 2000 RPM (Revolutions Per Minute) working condition. A multiparameter optimization with clustering genetic algorithm was introduced for multiparameter optimization among physical and digital data. The trade-off between fuel efficiency, dynamic performance, and knock risk was discussed. The results demonstrated the effectiveness of the proposed method and that it can contribute to develop a novel automotive engine control strategy in the future.

Collaborative Optimization Method of Power and Efficiency for LCC-S Wireless Power Transmission System

Dynamic wireless charging enables moving equipment such as electric vehicles, robots to be charged in motion, and thus is a research hotspot. The applications in practice, however, suffer from mutual inductance fluctuation due to unavoidable environmental disturbances. In addition, the load also changes during operation, which makes the problem more complicated. This paper analyzes the impacts of equivalent load and mutual inductances variation over the system by LCC-S topology modeling utilizing two-port theory. The optimal load expression is derived. Moreover, a double-sided control strategy enabling optimal efficiency and power adjustment is proposed. Voltage conducting angles on the inverter and rectifier are introduced. The simulation and experimental results verify the proposed method.