The new artificial intelligence(AI)era heavily depends on three converging forces:the advance of AI algorithms,the availability of big data,and the popularity of high performance computing platforms.Data-driven intell...The new artificial intelligence(AI)era heavily depends on three converging forces:the advance of AI algorithms,the availability of big data,and the popularity of high performance computing platforms.Data-driven intelligence,or data intelligence,is a new form of AI technologies that leverages the power of big data and advanced learning algorithm.展开更多
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
文摘The new artificial intelligence(AI)era heavily depends on three converging forces:the advance of AI algorithms,the availability of big data,and the popularity of high performance computing platforms.Data-driven intelligence,or data intelligence,is a new form of AI technologies that leverages the power of big data and advanced learning algorithm.
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.
