In the process of encoding and decoding,erasure codes over binary fields,which just need AND operations and XOR operations and therefore have a high computational efficiency,are widely used in various fields of inform...In the process of encoding and decoding,erasure codes over binary fields,which just need AND operations and XOR operations and therefore have a high computational efficiency,are widely used in various fields of information technology.A matrix decoding method is proposed in this paper.The method is a universal data reconstruction scheme for erasure codes over binary fields.Besides a pre-judgment that whether errors can be recovered,the method can rebuild sectors of loss data on a fault-tolerant storage system constructed by erasure codes for disk errors.Data reconstruction process of the new method has simple and clear steps,so it is beneficial for implementation of computer codes.And more,it can be applied to other non-binary fields easily,so it is expected that the method has an extensive application in the future.展开更多
Given the complex nature of data centers’thermal management,which costs too many resources,processing time,and energy consumption,thermal awareness and thermal management powered by artificial intelligence(AI)are the...Given the complex nature of data centers’thermal management,which costs too many resources,processing time,and energy consumption,thermal awareness and thermal management powered by artificial intelligence(AI)are the targeted study.In addition to a few research on AI techniques and models,other strategies have also been introduced in recent years.Data center models,including cooling,thermal,power,and workload models,and their relationship are factors that need to be understood in the optimal thermal management system.Simulation approaches have been proposed to help validate new models or methods used for scheduling and consolidating processes and virtual machines(VMs),hotspot identification,thermal state estimation,and power usage change.AI-powered thermal optimization leads to improved process scheduling and consolidation of VMs and eliminates the hotspot from happening.At present,research on AI-powered thermal control is still in its infancy.This paper concludes with four issues in thermal management,which will be the scope of further research.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.61501064Sichuan Provincial Science and Technology Project under Grant No.2016GZ0122
文摘In the process of encoding and decoding,erasure codes over binary fields,which just need AND operations and XOR operations and therefore have a high computational efficiency,are widely used in various fields of information technology.A matrix decoding method is proposed in this paper.The method is a universal data reconstruction scheme for erasure codes over binary fields.Besides a pre-judgment that whether errors can be recovered,the method can rebuild sectors of loss data on a fault-tolerant storage system constructed by erasure codes for disk errors.Data reconstruction process of the new method has simple and clear steps,so it is beneficial for implementation of computer codes.And more,it can be applied to other non-binary fields easily,so it is expected that the method has an extensive application in the future.
基金supported by the National Natural Science Foundation of China(Nos.61662057 and 61672143)。
文摘Given the complex nature of data centers’thermal management,which costs too many resources,processing time,and energy consumption,thermal awareness and thermal management powered by artificial intelligence(AI)are the targeted study.In addition to a few research on AI techniques and models,other strategies have also been introduced in recent years.Data center models,including cooling,thermal,power,and workload models,and their relationship are factors that need to be understood in the optimal thermal management system.Simulation approaches have been proposed to help validate new models or methods used for scheduling and consolidating processes and virtual machines(VMs),hotspot identification,thermal state estimation,and power usage change.AI-powered thermal optimization leads to improved process scheduling and consolidation of VMs and eliminates the hotspot from happening.At present,research on AI-powered thermal control is still in its infancy.This paper concludes with four issues in thermal management,which will be the scope of further research.
