Research on the photoluminescence of spectral broadening by rapid thermal annealing on InAs/GaAs quantum dots

Photoluminescence (PL) test was conducted to investigate the effect of rapid thermal annealing (RTA) on the opticalperformance of self-assembled InAs/GaAs quantum dots (QDs) at the temperatures of 16 and 300 K. It was found that after RTAtreatment, the PL spectrum of the QDs sample had a large blue-shift and significantly broadened at 300 K. Compared with theas-grown InAs QDs sample, the PL spectral width has increased by 44.68 meV in the InAs QDs sample RTA-treated at800 ℃. The excitation power-dependent PL measurements showed that the broadening of the PL peaks of the RTA-treatedInAs QDs should be related to the emission of the ground state (GS) of different-sized InAs QDs, the InAs wetting layer (WL)and the In0.15Ga0.85As strain reduction layer (SRL) in the epitaxial InAs/GaAs layers.

QoS-aware simulation job scheduling algorithm in virtualized cloud environment

Cloud computing is attracting an increasing number of simulation applications running in the virtualized cloud data center.These applications are submitted to the cloud in the form of simulation jobs.Meanwhile,the management and scheduling of simulation jobs are playing an essential role to offer efficient and high productivity computational service.In this paper,we design a management and scheduling service framework for simulation jobs in two-tier virtualization-based private cloud data center,named simulation execution as a service(SimEaaS).It aims at releasing users from complex simulation running settings,while guaranteeing the QoS requirements adaptively.Furthermore,a novel job scheduling algorithm named adaptive deadline-aware job size adjustment(ADaSA)algorithm is designed to realize high job responsiveness under QoS requirement for SimEaaS.ADaSA tries to make full use of the idle fragmentation resources by tuning the number of requested processes of submitted jobs in the queue adaptively,while guaranteeing that jobs’deadline requirements are not violated.Extensive experiments with trace-driven simulation are conducted to evaluate the performance of our ADaSA.The results show that ADaSA outperforms both cloud-based job scheduling algorithm KCEASY and traditional EASY in terms of response time(up to 90%)and bounded slow down(up to 95%),while obtains approximately equivalent deadline-missed rate.ADaSA also outperforms two representative moldable scheduling algorithms in terms of deadline-missed rate(up to 60%).