Vmware, Inc.

Chien-Chia Chen

Lenin Singaravelu

Shilpi Agarwal

Ayyappan Veeraiyan

Jin Heo

Ruijin Zhou

Xiaobo Huang

Dongdong Deng

In one embodiment, a method selects a percentage of a plurality hosts that are coupled together via a network fabric and calculates a number of workloads needed for the percentage of hosts based on a benchmark test to run. A plurality of data compute nodes are configured on one or more host pairs in the percentage of the plurality of hosts to send and receive the number of workloads through the network fabric to perform the benchmark test. A set of measurements is received for sending and receiving the workloads through the network fabric using the plurality of data compute nodes. The method increases the percentage of the plurality of hosts until the set of measurements fails a criteria or the percentage of the plurality of hosts is all of the plurality of hosts.

Control of a computing system to perform network fabric benchmark measurements

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

Some embodiments provide a queue management system that efficiently and dynamically manages multiple queues that process traffic to and from multiple virtual machines (VMs) executing on a host. This system manages the queues by (1) breaking up the queues into different priority pools with the higher priority pools reserved for particular types of traffic or VM (e.g., traffic for VMs that need low latency), (2) dynamically adjusting the number of queues in each pool (i.e., dynamically adjusting the size of the pools), (3) dynamically reassigning a VM to a new queue based on one or more optimization criteria (e.g., criteria relating to the underutilization or overutilization of the queue).

Traffic and load aware dynamic queue management

A tuning engine for a virtualized computing system is described that periodically collect performance metrics from the virtualized computing system, and detects whether a change in system state has occurred based on the collected metrics. The tuning engine may determine whether the virtualized computing system is densely virtualized, and accordingly modify operations and configuration settings of various components in charge of handling networking for the virtualized computing system.

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Mountain View, CA, United States of America

Chien-Chia Chen