Vmware, Inc.

Lei Lu

Xiaoyun Zhu

Pradeep Padala

Sahan Bamunavita Gamage

Varun S Lingaraju

Rajesh Venkatasubramanian

Aashish Parikh

Zhelong Pan

Parth Shah

Rean Griffith

Charan K Singh

Xiaoyuan Lu

Bing Hu

Hua Wang

Anne Holler

Wenyu Zhang

Maarten Wiggers

Bolt Zhang

Wang Qiong

Pradeep Ramachandra

Various examples are disclosed for performing cross-cluster load balancing. In some aspects, a workload is selected for cross-cluster migration. A destination cluster is identified for a migration of the workload from a source cluster to the destination cluster. A cross-cluster migration recommendation is generated to migrate the workload from the source cluster to the destination cluster.

Cross-cluster load balancer

Aspects of managing inventory for data transport connections within a virtualized computing environment are described. A virtualized management system managing a cluster of host devices obtains a data transport capacity parameter and an aggregate memory consumption value from respective host devices. The virtualized management system further identifies an update status associated with each of the host devices. In response to receiving a data transport connection request, the virtualized management system selects a host from the cluster of hosts to satisfy the data transport connection request based at least in part on the upgrade status, data transport capacity parameter and aggregate memory consumption value.

Inventory management for data transport connections in virtualized environment

Systems and methods for reducing network cost in a hyper-converged infrastructure are disclosed. The network cost of workload pairs can be assessed. Migration of the workloads can be considered to reduce the network cost and improve the network efficiency of the hyper-converged infrastructure.

Network costs for hyper-converged infrastructures

An automatic scaling system and method for reducing state space in reinforced learning for automatic scaling of a multi-tier application uses a state decision tree that is updated with new states of the multi-tier application. When a new state of the multi-tier application is received, the new state is placed in an existing node of the state decision tree only if a first attribute of the new state is same as a first attribute of any state contained in the existing node and a second attribute of the new state is sufficiently similar to a second attribute of each existing state contained in the existing node based on a similarity measurement of the second attribute of each state contained in the existing node with the second attribute of the new state.

System and method for reducing state space in reinforced learning by using decision tree classification

A system and method for managing resources in a distributed computer system that includes at least one resource pool for a set of virtual machines (VMs) utilizes a set of desired individual VM-level resource settings that corresponds to target resource allocations for observed performance of an application running in the distributed computer system. The set of desired individual VM-level resource settings are determined by constructing a model for the observed application performance as a function of current VM-level resource allocations and then inverting the function to compute the target resource allocations in order to meet at least one user-defined service level objective (SLO). The set of desired individual VM-level resource settings are used to determine final RP-level resource settings for a resource pool to which the application belongs and final VM-level resource settings for the VMs running under the resource pool, which are then selectively applied.

Performance-driven resource management in a distributed computer system

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Sunnyvale, CA, United States of America

Lei Lu