官术网_书友最值得收藏!

GemFire clustering

At the core of vRealize Operations, 6.6 architecture is the powerful GemFire in-memory clustering and distributed cache. GemFire provides the internal transport bus, as well as the ability to balance CPU and memory consumption across all nodes through compute pooling, memory sharing, and data partitioning. With this change, it is better to then think of the Controller, Analytics, and Persistence layers as components that span nodes, rather than individual components on individual nodes:

During deployment, ensure all your vRealize Operations 6.6 nodes are configured with the same amount of vCPUs and memory. This is because, from a load balancing point of view, vRealize Operations expects all nodes to have the same amount of resources as part of the controller's round-robin load balancing.

The migration to GemFire is probably the single largest underlying architectural change from vCenter Operations Manager 5.x, and the result of moving to a distributed in-memory database has made many of the new vRealize Operations 6.x features possible, including the following:

  • Elasticity and scale: Nodes can be added on demand, allowing vRealize Operations to scale as required. This allows a single Operations Manager instance to scale to 6 extra large nodes in a cluster, which can support up to 180,000 objects and 45,000,000 metrics.
  • Reliability: When GemFire HA is enabled, a backup copy of all data is stored in both the Analytics GemFire cache and the Persistence layer.
  • Availability: Even with the GemFire HA mode disabled, in the event of a failure, other nodes take over the failed services and the load of the failed node (assuming the failure was not the master node).
  • Data partitioning: vRealize Operations leverages GemFire data partitioning to distribute data across nodes in units called buckets. A partition region will contain multiple buckets that are configured during a startup, or migrated during a rebalance operation. Data partitioning allows the use of the GemFire MapReduce function. This function is a data-aware query, that supports parallel data querying on a subset of the nodes. The result of this is then returned to the coordinator node for final processing.
主站蜘蛛池模板: 洮南市| 厦门市| 桐乡市| 望谟县| 临西县| 浮梁县| 斗六市| 神木县| 合山市| 从江县| 通化县| 云霄县| 新田县| 台北市| 丰原市| 梧州市| 江西省| 肇源县| 台南县| 民县| 奉贤区| 丁青县| 凌源市| 望城县| 通州区| 新邵县| 青铜峡市| 监利县| 同心县| 乌审旗| 柳江县| 靖州| 娄底市| 临江市| 鄂州市| 彭山县| 大城县| 庄河市| 株洲县| 大方县| 清原|