# Dataset

VMAgent is constructed based on one month real VM scheduling dataset called Huawei-East-1 from HUAWEI Cloud. The Huawei-East-1 is placed in our repository.

## Data Format

The data format is concluded below

Field

Type

Description

vmid

int

The virtual machine ID

cpu

int

Number of CPU cores

memory

int

Number of Memory GBs

time

int

Relative time in seconds

type

int

0 denotes creation while 1 denotes deleteion

## Statistical Analysis

The statsical information of the dataset is listed below.

Number of VM types

Number of creation requests

Number of deletion requests

Time duration

Server location

15

125430

116313

30 Days

East China

To gain better understanding of the cpu and memory distribution, we plot the histograms of the cpu and memory.

More than 2/3 requests only consumes 1U and less than 2G. We also plot the statiscs of the (cpu, mem) request:

The 1U1G,1U2G, 2U4G and 4U8G constitues the main body of the requests.

We also visualize the dynamic of virtual machine during the month:

Although there exists deletion request, the number of alive virtual machines increses from 0 to more than 8000. It should be noted that, even in the one month, the VM’s dynamic is highly related to the time. Increase, Flux, Increase, Flux happens through the one month.

We also visualize the allocated cpu and memory dynamic above. They can be helpful in constructing domain knowledge.

## Naive Baselines performance

Another way to describe the dataset is measuring performance of naive baselines in the dataset. We adopt First-Fit and Best-Fit as the naive baselines and conduct experiments on different settings.

We conduct fading and recovering experiments with 5, 20, 50 servers and each server has 40 cpu and 90 memeory.

Scenario

Number of servers

Method

Number of Allocations

Terminated CPU Rate

Terminated MEM Rate

5

BestFit

$$211.7 \pm 30$$

$$91.6\% \pm 9.4\%$$

$$83.6\% \pm 9.2\%$$

FirstFit

$$224.5 \pm 28$$

$$98.3\% \pm 1.9\%$$

$$90.0\% \pm 1.9\%$$

20

BestFit

$$735.1 \pm 83$$

$$63.5\% \pm 29.2\%$$

$$35.7\% \pm 21.9\%$$

FirstFit

$$888.0 \pm 65$$

$$91.6\% \pm 8.5\%$$

$$64.7 \pm 5.6\%$$

50

BestFit

$$1674.5 \pm 28$$

$$91.6\% \pm 1.1\%$$

$$84.3 \pm 1.0\%$$

FirstFit

$$2298.3 \pm 19$$

$$95.5\% \pm 0.7\%$$

$$91.5\% \pm 0.5\%$$

Recovering

5

BestFit

$$221.1 \pm 29$$

$$96.3\% \pm 5.6\%$$

$$88.1\% \pm 5.7\%$$

FirstFit

$$222.7 \pm 27$$

$$97.2\% \pm 3.4\%$$

$$89.0\% \pm 3.4\%$$

20

BestFit

$$850.0 \pm 13$$

$$99.1\% \pm 0$$

$$95.8\% \pm 0$$

FirstFit

$$926.1 \pm 10$$

$$98.7\% \pm 0.5\%$$

$$96.5\% \pm 0.3\%$$

50

BestFit

$$1829.6 \pm 37$$

$$92.8\% \pm 1.4\%$$

$$88.8\% \pm 0.2\%$$

FirstFit

$$2301.7 \pm 19$$

$$95.0\% \pm 0.5\%$$

$$91.1\% \pm 0.4\%$$