Vmware, Inc.

Aashish Parikh

Pradeep Padala

Ganesha Shanmuganathan

Jeevitha Kirubanandam

Mustafa Uysal

Parth Shah

Keith Farkas

Anne Holler

Xiaoyun Zhu

Duncan Epping

Puneet Zaroo

Rean Griffith

Lei Lu

Rohit Bhoj

Badhrinath Sampathkumar

Zhefu Jiang

A system and method for autoscaling a multi-tier application, that has components executing on a plurality of tiers of a virtual data center, allocates resources to each of the plurality of tiers based on cost and performance. An application performance is determined, and a new application performance is estimated based at least partially on an application reservation and an application limit. An optimized utility of the application is calculated based on the cost to execute the application, the application reservation, and the application limit. A scaling factor for each tier is then determined to scale up or down a number of virtual machines operating in each of the tiers.

Automated scaling of application in virtual data centers

A resource management system and method for performing resource management operations in a distributed computer system uses predicted resource demands for a specified time in the future to change a resource management option of a resource management analysis algorithm prior to the specified time so that the resource management analysis algorithm can be executed in anticipation of the predicted resource demands at the specified time.

Adaptive resource management of a cluster of host computers using predicted data

A management server and method for performing resource management operations in a distributed computer system takes into account information regarding multi-processor memory architectures of host computers of the distributed computer system, including information regarding Non-Uniform Memory Access (NUMA) architectures of at least some of the host computers, to make a placement recommendation to place a client in one of the host computers.

NUMA-based client placement

A system and method for performing remote resource allocation analyses on distributed computer systems utilizes a snapshot of a target distributed computer system obtained at a distributed computer system, which is transmitted to a remote resource allocation module so that a remote resource allocation analysis can be performed on the snapshot of the target distributed computer system. The snapshot includes configurations and resource usage information of at least some components of the target distributed computer system.

Remote service for executing resource allocation analyses for distributed computer systems

Disclosed are aspects of resource allocation diagnosis for distributed computer systems. In one example, a user interface creates a user-modified version of a snapshot of a distributed computing system. A hypothetical resource allocation is determined for the user-modified version of the snapshot. The hypothetical resource allocation is calculated based on at least one of load balancing and resource scheduling. The hypothetical resource allocation for the user-modified version of the snapshot is implemented in the distributed computing system.

Resource allocation diagnosis on distributed computer systems

Aspects of performing power management operations in a distributed computer system are described. In some aspects, predicted demand data is generated for clients executed in a cluster of host computers. The predicted demand data is based on observed resource demands of the clients. A power management setting for a time period is determined. The power management setting is based on the predicted demand data. A host computer is caused to power-down or power-up in order to apply the power management setting.

Adaptive power management of a cluster of host computers using predicted data

A universal resource management system and method for performing resource management operations for different computing environments uses a universal snapshot of the different computing environments to perform a resource management analysis to produce at least one recommended action for the different computing environments. The universal snapshot is created using state information collected from the different computing environments. The recommended action is then implemented in at least one of the different computing environments.

System and method for performing universal resource management operations for different virtualized computing environments

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

Disclosed are aspects of resource allocation diagnosis for distributed computer systems. In one example, a current snapshot of a distributed computing system is created. A current resource allocation of the distributed computing system is computed using the current snapshot of the distributed computing system. A modified snapshot is generated using the current snapshot. The modified snapshot includes a user modification. A hypothetical resource allocation is computed using the modified snapshot. A user interface includes the current resource allocation and the hypothetical resource allocation.

A decision tree analysis system and method for navigating a multi-dimensional decision tree uses acceptable alternative child nodes of a target child node to select an end child node for a parent node, where the parent node and the final child node define a single step of a navigation path for the multi-dimensional decision tree. The acceptable alternative child nodes are based on an acceptance delta parameter for a particular attribute, which defines a value range about an attribute value of the target child node within which a child node is determined to be an acceptable alternative child node of the target child node.

System and method for navigating multi-dimensional decision trees using acceptable alternate nodes

A power management system and method for performing power management operations in a distributed computer system uses predicted resource demands for a specified time in the future to change a power management option of a power management analysis algorithm prior to the specified time so that the power management analysis algorithm can be executed in anticipation of the predicted resource demands at the specified time.

Systems and methods for finding solutions exhaustively in distributed load balancing are provided. A plurality of virtual machines (VMs) is in communication with a virtual machine management server (VMMS). The VMMS is configured to generate a matrix that represents a mapping of a plurality of virtual machines (VMs) to a plurality of hosts and to calculate a first imbalance metric of the matrix. The VMMS is also configured to identify a plurality of candidate migrations the VMs. The VMMS searches through the solution space efficiently and can perform an exhaustive search to find the optimal solution. For each candidate migration, the VMMS is configured to alter the matrix to represent the candidate migration and to calculate a candidate imbalance metric based on the altered matrix. The VMMS is also configured to determine which candidate migration to perform based at least in part on the candidate imbalance metric for each candidate migration and the first imbalance metric.

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Cupertino, CA, United States of America

Aashish Parikh