COBEATS (COntainers Bio-inspired Enhanced AuToscaling System) is a software to test the proposal of using bio-inspired cells in autoscaling systems based on software containers.
This software introduces a set of bio-inspired algorithms aimed at supporting auto-scaling in Container Orchestration Platforms.
A key feature of cloud computing is elasticity, where applications can dynamically adjust the computing and storage resources. On the one hand, vertical elasticity involves increasing or decreasing the amount of computing and memory resources of a single computing entity. On the other hand, horizontal elasticity involves running on a distributed fleet of computing nodes that can grow and shrink according to the values of certain metrics, such as the average CPU usage across the available nodes. This simulation software studies the impact of integrating bio-inspired approaches for dynamic distributed auto-scaling on container orchestration platforms.
To run this code only (tested on Python 3.5.2), you need to install the following libraries:
- mathplotlib 2.2.3
- numpy 1.15.1
- pandas 0.23.4
- statsmodels 0.9.0
- sklearn 0.19.2
Some python libraries was configured using Anaconda.
localhost: /home/instalation_dir$ conda list -n desa
Name Version Build Channel
blas 1.0 mkl anaconda
ca-certificates 2019.1.23 0 anaconda
certifi 2019.3.9 py37_0 anaconda
intel-openmp 2019.3 199 anaconda
libedit 3.1.20181209 hc058e9b_0 anaconda
libffi 3.2.1 h4deb6c0_3 anaconda
libgcc-ng 8.2.0 hdf63c60_1 anaconda
libgfortran-ng 7.3.0 hdf63c60_0 anaconda
libstdcxx-ng 8.2.0 hdf63c60_1 anaconda
mkl 2019.3 199 anaconda
mkl_fft 1.0.10 py37ha843d7b_0 anaconda
mkl_random 1.0.2 py37hd81dba3_0 anaconda
ncurses 6.1 he6710b0_1 anaconda
numpy 1.16.2 py37h7e9f1db_0 anaconda
numpy-base 1.16.2 py37hde5b4d6_0 anaconda
openssl 1.1.1 h7b6447c_0 anaconda
pandas 0.24.2 py37he6710b0_0 anaconda
patsy 0.5.1 py37_0 anaconda
pip 19.0.3 py37_0 anaconda
python 3.7.3 h0371630_0 anaconda
python-dateutil 2.8.0 py37_0 anaconda
pytz 2018.9 py37_0 anaconda
readline 7.0 h7b6447c_5 anaconda
scipy 1.2.1 py37h7c811a0_0 anaconda
setuptools 40.8.0 py37_0 anaconda
six 1.12.0 py37_0 anaconda
sqlite 3.27.2 h7b6447c_0 anaconda
statsmodels 0.9.0 py37h035aef0_0 anaconda
tk 8.6.8 hbc83047_0 anaconda
wheel 0.33.1 py37_0 anaconda
xz 5.2.4 h14c3975_4 anaconda
zlib 1.2.11 h7b6447c_3 anaconda
This repository includes the information about the simulated workloads.
Copy all repository in a directory. Change instalation diretory on bin/run_compare.sh BASE variable for your instalation directory before run simulation.
BASE=/full_instalation_directory
A XWindows Server is needed to output simulation graphs. Be sure a display is avalable running xeyes or xclock commands. We ran it using putty with X11forwarding option active and Xming Server on Windows client side.
For basic execution run bin/run_compare.sh with options. The first parameter is the algorithm used and the second one is the workload type.
The help is shown by running bin/run_compare.sh without options:
./run_compare.sh Option Load
option:
C - Conventional
B - Bioinspired
P - Prediction
Load:
1 - Synthetic load
2 - FIFA load
3 - NASA loadFor example, executing bin/run_compare.sh B 1 will run a bio-inspired algorithm with synthetic load.
Multiple file are added in this version to run all the tests. The configuration file for a simulation can use the following parameters in the corresponding sections:
[Cell] Section. This section contain information about the cell behaviour.
- deadprobability - Dead probability for algorithm limits.
- dead_cpu_use - Limit for dead resource use.
- moveprobability - Not used
- vscaprobability - Vertical scaling probability
- duplprobability - Creation area probability
- dupl_cpu_use - Limit for X (creation area)
- init_process_capacity - Initial node capacity
- variation_process_capacity - Step variation for vertical scaling
- max_history - Not used
- X_total_containers - Total containers created for X actions
[Simulation] Section for simulation
- minimun_cells_running - Mininum running cells
[Container] Container part for simulation
- cicles_req - Not operative
- slot_time - Not operative
- init_container_cicles_capacity - Not operative
- max_scale_limit - Maximum running vertical scaling capacity
- min_scale_limit - Minimum running vertical scaling capacity
- load_balancing_algorithm - Load balancing algorithm
- icm_simulation - Is an ICM algoritm simulation
- icm_size - ICM size for request data
- icm_neighbourhood_part - ICM part for neighbouhood of NOX funciton
- icm_local_part - ICM part for local part of NOX function
Notice the following example:
[Cell]
deadprobability = 80
dead_cpu_use = 30
moveprobability = 10
vscaprobability = 80
duplprobability = 80
dupl_cpu_use = 90
init_process_capacity=1000
variation_process_capacity=25
max_history=30
X_total_containers=1
[Simulation]
minimun_cells_running = 5
prediction=0
[Container]
cicles_req=1000000
slot_time=5000
init_container_cicles_capacity=200000
max_scale_limit=1000000000000
min_scale_limit=500
load_balancing_algoritm=2
icm_simulation=0
icm_size=20
icm_neibourhood_part=90
icm_local_part=10
- J Herrera - Initial work - jherrera@upv.es
- G Moltó - Supervisor - gmolto@dsic.upv.es
This project is licensed under the Apache 2.0 License - see the LICENSEE file for details.