Vmware, Inc.

University of Florida Research Foundation Inc

Ruijin Zhou

Lenin Singaravelu

Tao Li

Xiaobo Huang

Amitabha Banerjee

Chien-Chia Chen

Debessay Fesehaye Kassa

Certain embodiments described herein are generally directed to improving performance of one or more machines within a system by clustering multidimensional datasets relating to the performance of the machines using inter-group dissimilarities between groups of the dataset. The method for improving performance of one or more machines within a system, includes forming a multidimensional dataset having a plurality of groups using performance related data associated with one or more machines in the system, clustering the plurality of groups into one or more clusters based on intergroup dissimilarities between the plurality of groups, identifying one or more anomalous clusters from among the one or more clusters, identifying the one or more anomalous groups in the one or more anomalous clusters, and adjusting a configuration of the system to improve the performance of the one or more machines corresponding to the one or more anomalous groups.

Group clustering using inter-group dissimilarities

Exemplary methods, apparatuses, and systems include receiving time series data for each of a plurality of performance metrics. The time series data is sorted into buckets based upon an amount of variation of time series data values for each performance metric. The time series data in each bucket is divided into first and second clusters of time series data points. The bucket having the greatest distance between clusters is used to determine a performance metric having a greatest distance between clusters. The performance metric having the greatest distance between clusters is reported as a potential root cause of a performance issue.

Detecting and remediating root causes of performance issues

A system and method for storage resource management using copy-on-write/read snapshots to redirect the I/O requests without moving the virtual disk is disclosed herein. The system includes storage pools, a virtualized server, and a centralized data center management server. The virtualized server includes a base disk and a virtualized disk having a copy-on-write and a copy-on-read snapshot process responsive to an I/O request that merges a previous snapshot stored on a first one of the plurality storage pools with the base disk stored on a second one of the plurality of storage pools, while it fetches a new snapshot to be stored on a third one of the plurality of storage pools from the previous snapshot or the base virtual disk. The system includes a performance model, employing an exponentially weighted moving average with an adjustable sliding window.

Method and apparatus for big data cloud storage resource management

Embodiments pertain to live storage migration for virtual machines. Specific embodiments can implement the migration of VM disk images without service interruption to the running workload. Specific embodiments relate to storage migration between different disk arrays. Embodiments of the subject invention relate to a method and apparatus that can enhance the efficiency of virtual machine (VM) live storage migration in heterogeneous storage environments from a multi-dimensional perspective, e.g., user experience, device wearing, and/or manageability. Specific embodiments utilize one or more of the following: adaptive storage migration strategies, or techniques, such as 1) Low Redundancy (LR), which generates a reduced, and preferably the least, amount of redundant writes; 2) Source-based Low Redundancy (SLR), which can help keep a desirable balance between IO performance and write redundancy; and 3) Asynchronous IO Mirroring (AIO), which seeks high, and preferably the highest, IO performance. Specific embodiments adaptively mix one or more of these adaptive storage migration techniques during massive VM live storage migration.

Growing community of inventors

Sunnyvale, CA, United States of America

Ruijin Zhou