Load balancing is vital for the efficient and long-term operation of cloud data centers.With virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing an...Load balancing is vital for the efficient and long-term operation of cloud data centers.With virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consolidation.However,it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring instability.Therefore,we provide a new approach,called Prepartition,for load balancing.It partitions a VM request into a few sub-requests sequentially with start time,end time and capacity demands,and treats each sub-request as a regular VM request.In this way,it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal,which supports the resource allocation in a fine-grained manner.Simulations with real-world trace and synthetic data show that our proposed approach with offline version(PrepartitionOff)scheduling has 10%–20%better performance than the existing load balancing baselines under several metrics,including average utilization,imbalance degree,makespan and Capacity_makespan.We also extend Prepartition to online load balancing.Evaluation results show that our proposed approach also outperforms state-of-the-art online algorithms.展开更多
We attempt to interpret the cosmic-ray positron excess by injection from the nearby pulsar Geminga,assuming a two-zone diffusion scenario and an injection spectrum with a low energy cutoff.Since the high energy positr...We attempt to interpret the cosmic-ray positron excess by injection from the nearby pulsar Geminga,assuming a two-zone diffusion scenario and an injection spectrum with a low energy cutoff.Since the high energy positrons and electrons from Geminga can induceγrays via inverse Compton scattering,we take into account the extendedγ-ray observations around Geminga from HAWC for∼10 TeV and from Fermi-LAT forO(10)GeV.According to the extendedγ-ray observation claimed by an analysis of Fermi-LAT data,we find that Geminga could explain the positron excess for a 30%energy conversion efficiency into positrons and electrons.However,based on the constraint on the extendedγrays given by another Fermi-LAT analysis,positrons from Geminga would be insufficient to account for the positron excess.Further robust analysis of Fermi-LAT data for the extendedγrays would be crucial to determine whether Geminga can explain the positron excess or not.展开更多
基金supported by Shenzhen Industrial Application Projects of undertaking the National Key Research and Development Program of China under Grant No.CJGJZD20210408091600002the National Natural Science Foundation of China under Grant No.62102408Shenzhen Science and Technology Program under Grant No.RCBS20210609104609044.
文摘Load balancing is vital for the efficient and long-term operation of cloud data centers.With virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consolidation.However,it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring instability.Therefore,we provide a new approach,called Prepartition,for load balancing.It partitions a VM request into a few sub-requests sequentially with start time,end time and capacity demands,and treats each sub-request as a regular VM request.In this way,it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal,which supports the resource allocation in a fine-grained manner.Simulations with real-world trace and synthetic data show that our proposed approach with offline version(PrepartitionOff)scheduling has 10%–20%better performance than the existing load balancing baselines under several metrics,including average utilization,imbalance degree,makespan and Capacity_makespan.We also extend Prepartition to online load balancing.Evaluation results show that our proposed approach also outperforms state-of-the-art online algorithms.
基金supported in part by the National Natural Science Foundation of China under Grants No. 11875327 and No. 11805288the Fundamental Research Funds for the Central Universities+1 种基金the Sun YatSen University Science Foundationsupported by the Program for Innovative Talents and Entrepreneur in Jiangsu。
文摘We attempt to interpret the cosmic-ray positron excess by injection from the nearby pulsar Geminga,assuming a two-zone diffusion scenario and an injection spectrum with a low energy cutoff.Since the high energy positrons and electrons from Geminga can induceγrays via inverse Compton scattering,we take into account the extendedγ-ray observations around Geminga from HAWC for∼10 TeV and from Fermi-LAT forO(10)GeV.According to the extendedγ-ray observation claimed by an analysis of Fermi-LAT data,we find that Geminga could explain the positron excess for a 30%energy conversion efficiency into positrons and electrons.However,based on the constraint on the extendedγrays given by another Fermi-LAT analysis,positrons from Geminga would be insufficient to account for the positron excess.Further robust analysis of Fermi-LAT data for the extendedγrays would be crucial to determine whether Geminga can explain the positron excess or not.