In data center, applications of big data analytics pose a big challenge to massive storage systems. It is signif- icant to achieve high availability, high performance and high scalability for PB-scale or EB-scale stor...In data center, applications of big data analytics pose a big challenge to massive storage systems. It is signif- icant to achieve high availability, high performance and high scalability for PB-scale or EB-scale storage systems. Meta- data server （MDS） cluster architecture is one of the most effective solutions to meet the requirements of applications in data center. Workload migration can achieve load balance and energy saving of duster systems. In this paper, a hybrid workload migration mechanism of MDS cluster is proposed and named as HWM. In HWM, workload of MDS is classi- fied into two categories： metadata service and state service, and they can be migrated rapidly from a source MDS to a target MDS in different ways. Firstly, in metadata service migration, all the dirty metadata of one sub file system is flushed to a shared storage pool by the source MDS, and then is loaded by the target MDS. Secondly, in state service mi- gration, all the states of that sub file system are migrated from source MDS to target MDS through network at file granular- ity, and then all of the related structures of these states are reconstructed in target MDS. Thirdly, in the process of work- load migration, instead of blocking client requests, the source MDS can decide which MDS will respond to each request according to the operation type and the migration stage. The proposed mechanism is implemented in the Blue Whale MDS cluster. The performance measurements show that the HWM mechanism is efficient to migrate the workload of a MDS cluster system and provides low-latency access to metadata and states.展开更多
Edge data centers(EDCs)have been widely developed recently to supply delay-sensitive computing services,which impose prohibitively increasing electricity costs for EDC operators.This paper presents a new spatiotempora...Edge data centers(EDCs)have been widely developed recently to supply delay-sensitive computing services,which impose prohibitively increasing electricity costs for EDC operators.This paper presents a new spatiotemporal reallocation(STR)method for energy management in EDCs.This method uses spare resources,including servers and energy storage systems(ESSs)within EDCs to reduce energy costs based on both spatial and temporal features of spare resources.This solution:1)reallocates flexible workload between EDCs within one cluster;and 2)coordinates the electricity load of data processing,ESSs and distributed energy resources(DERs)within one EDC cluster to gain benefits from flexible electricity tariffs.In addition,this paper for the first time develops a Bit-Watt transformation to simplify the STR method and represent the relationship between data workload and electricity consumption of EDCs.Case studies justifying the developed STR method delivers satisfying cost reductions with robustness.The STR method fully utilized both spatial and temporal features of spare resources in EDCs to gain benefits from 1)varying electricity tariffs,and 2)maximumly consuming DER generation.展开更多
文摘In data center, applications of big data analytics pose a big challenge to massive storage systems. It is signif- icant to achieve high availability, high performance and high scalability for PB-scale or EB-scale storage systems. Meta- data server （MDS） cluster architecture is one of the most effective solutions to meet the requirements of applications in data center. Workload migration can achieve load balance and energy saving of duster systems. In this paper, a hybrid workload migration mechanism of MDS cluster is proposed and named as HWM. In HWM, workload of MDS is classi- fied into two categories： metadata service and state service, and they can be migrated rapidly from a source MDS to a target MDS in different ways. Firstly, in metadata service migration, all the dirty metadata of one sub file system is flushed to a shared storage pool by the source MDS, and then is loaded by the target MDS. Secondly, in state service mi- gration, all the states of that sub file system are migrated from source MDS to target MDS through network at file granular- ity, and then all of the related structures of these states are reconstructed in target MDS. Thirdly, in the process of work- load migration, instead of blocking client requests, the source MDS can decide which MDS will respond to each request according to the operation type and the migration stage. The proposed mechanism is implemented in the Blue Whale MDS cluster. The performance measurements show that the HWM mechanism is efficient to migrate the workload of a MDS cluster system and provides low-latency access to metadata and states.
文摘Edge data centers(EDCs)have been widely developed recently to supply delay-sensitive computing services,which impose prohibitively increasing electricity costs for EDC operators.This paper presents a new spatiotemporal reallocation(STR)method for energy management in EDCs.This method uses spare resources,including servers and energy storage systems(ESSs)within EDCs to reduce energy costs based on both spatial and temporal features of spare resources.This solution:1)reallocates flexible workload between EDCs within one cluster;and 2)coordinates the electricity load of data processing,ESSs and distributed energy resources(DERs)within one EDC cluster to gain benefits from flexible electricity tariffs.In addition,this paper for the first time develops a Bit-Watt transformation to simplify the STR method and represent the relationship between data workload and electricity consumption of EDCs.Case studies justifying the developed STR method delivers satisfying cost reductions with robustness.The STR method fully utilized both spatial and temporal features of spare resources in EDCs to gain benefits from 1)varying electricity tariffs,and 2)maximumly consuming DER generation.
