languages   The PCC is expressed as a decimal number, the sign of which gives us the correlation coefficient: +1 for a direct positive correlation, -1 for a direct negative correlation, 0 for no correlation.
The magnitude of the correlation coefficient gives us the strength of the correlation (the higher the number, the stronger the correlation).

The PCC can be used to assess the association between two variables, and is computed by multiplying the numbers that correspond to the marginal distributions of the two variables, dividing this product by the corresponding number of observations, and then squaring this product.
The Pearson product-moment correlation coefficient (PPMCC or simply PCC) is calculated as a proportion. This is the best measure of a linear relationship between two variables, and one of the most often used measures of association.

The PCC can be used to assess the association between two variables. When a correlation exists, the probability that these variables are unrelated is zero.

The PCC is represented by a number between -1 and +1. A value of +1 or -1 indicates a perfect positive or negative linear relationship, respectively. A value close to 0 indicates no linear relationship.

Pearson’s Product-Moment Correlation Coefficient (PPMCC or simply PCC) can be used for calculating a correlation between two variables.
Pearson’s correlation coefficient is used to determine the degree of association of two variables: how well they are related and whether they can be considered to be independent of each other.
The value of the Pearson correlation coefficient can vary between -1 and +1. The value of the coefficient is very high when the variables are strongly related, and very low when they are independent.

The correlation coefficient has a specific formula.
Let x and y be the samples of two variables, each with n observations.
Let x1, x2, x3, x4…xn be the values of variable x, and y1, y2, y3, y4…yn be the values of variable y.
Then the correlation coefficient is computed as
corr(x,y)=∑j=1n(xj-x̅)(yj-x̅)/n
The correlation coefficient ranges between -1 and +1, where +1 indicates that all points lie on one straight line, -1 means that all points lie on a straight line perpendicular to the line, and zero means that there is no linear relationship between d82f892c90

– The program listens for the Vkernel event “vms/datastore/latency/reached” in order to identify
the latency time.
– The user can then analyze which datastore and VMs have the highest latency.
= Requirements =
“source /usr/share/vkernel/bin/vk cli” to obtain the cli);
otherwise, “vk get vms latency” will not be executed.
– For the program to display the storage device of the virtual machine with the highest
latency, this host/datastore pair must be in the same datastore pool;
otherwise, it won’t be displayed.
– The program does not depend on any other vkernel agent, but it uses the event “vms/datastore/add/reached” to
find out the top five hosts/datastores that are in the same datastore pool and in the same datastore pool as
the virtual machine of interest.
= Installation =
– Execute the following commands from your bash shell:
git clone
cd storageview
./setup.sh
Verify that the installation succeeded
– Make sure that the kernel and agent services are running.
= Working =
StorageVIEW is a desktop tool that provides near instant visibility into the top five host/datastore pairs and their associated VMs with the highest latency.
With the help of StorageVIEW you’ll be able to discover the highest latency host/datastore pairs.
KEYMACRO Description:
– The program listens for the Vkernel event “vms/datastore/latency/reached” in order to identify
the latency time.
– The user can then analyze which datastore and VMs have the highest latency.
= Requirements =