Orange-BOX delivers array container infrastructure as a service, offering organizations high-performance, yet simple, public cloud and private cloud software solutions for today’s demanding real-time web and mobile applications.

OBX architecture is on a mission to reinvent infrastructure monitoring and troubleshooting for today’s distributed, massively scaled world. It is dedicated to solving the biggest challenges faced by operations teams managing modern IT infrastructures. It should be intuitive. It should be modern and elegant. It should make you more productive. More powerful. And it should be, dare we say, fun to use.

obx visualizations have been created to illustrate different ways to observe device utilization on large scale environments.

OBX is an intuitive self-service interface so you can easily provision, configure, and manage storage and compute capacity. You can have your own private, dedicated cloud environment up and running in minutes. 


Hexagonal binning Architecture 

Scatter-plots are a straightforward way to visualize the storage performance and capacity data distribution in a XY plane, especially when we are looking for trends or clusters. But when you have a data-set with a large number of points, many of these data points can overlap. This overlapping effect can make difficult to see any trends or clusters. for this reason OBX uses a  Binning technique for data aggregation.

There are many reasons for using hexagons instead of squares or other shapes for binning a 2D surface as a plane. The most evident is that hexagons are more similar to circle than square. This translates in more efficient data aggregation around the bin center. 


obx adds heat map to storage arrays

Quantized Heat Map: to identify single or multiple-group devices at 100% utilization, minimum and maximum device utilization, and device utilization balance, all over time.

To visualize the distribution of utilization over time, a heat map can be created with time in hours on the x-axis and time in days on the y-axis. The heat map is a color-shaded matrix of pixels, where each pixel represents a unit or a group of units ( in this picture 1 volume/square). The amount of utilization that occurs in that time is shown by the color shade of the pixel: darker colors for max utilization, lighter colors for less. Apart from showing the utilization distribution, the heat map also conveys details on time patterns utilization by looking  where the darkest colors are grouped.
from utilization heat maps you can see patterns of utilization and where you are wasting resources 
design by Lucio Campanelli

lucio@inpulse.org

resources, references and sketches 

Some Resources:
http://www.meccanismocomplesso.org/en/hexagonal-binning/
http://www.purestorage.com/company/
http://doc.dataiku.com/dss/latest/visualization/std_charts.html#hexagonal-binning
http://www.theregister.co.uk/Print/2013/03/12/newvem_aws_heat_map/

https://sysdigcloud.com/
https://itzikr.wordpress.com/2012/09/02/the-vnx-storage-analytics-suite/

Other resources:
Video about the importance of Information Visualization for large data architecture: 
http://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization#t-1068913

Heat map video reference:
https://www.youtube.com/watch?v=oVKfoA_cujY

Cotrail storage solution manager from Juniper network 
https://techwiki.juniper.net/Documentation/Contrail/Contrail_Controller_Getting_Started_Guide/30_Installation/50_Contrail_Storage_Support

Datapipe service monitoring infrastructure 
http://www.datapipe.com/optimized_it_services/managed_services/monitoring_services/

Newven heatmap
http://www.theregister.co.uk/Print/2013/03/12/newvem_aws_heat_map/

Calendar heatmap 
http://blog.revolutionanalytics.com/2010/12/how-orbitz-uses-hadoop-and-r-to-optimize-hotel-search.html

code for calendar heatmap:
http://stackoverflow.com/questions/2076370/most-underused-data-visualization

code 2 for calendar heatmap
http://blog.revolutionanalytics.com/2010/12/how-orbitz-uses-hadoop-and-r-to-optimize-hotel-search.html

hexagonal binning heatmap 
http://stackoverflow.com/questions/7714677/r-scatterplot-with-too-many-points

hexagon binning from mapbox:
https://www.mapbox.com/blog/binning-alternative-point-maps/

Article to build hexagonal heatmaps :
http://www.meccanismocomplesso.org/en/hexagonal-binning/

Why using information visualization and D-trace: 
http://dtrace.org/blogs/brendan/2011/12/18/visualizing-device-utilization/

Docker and Hans Roseling 
http://www.r-bloggers.com/running-rstudio-via-docker-in-the-cloud/

heatmap vmware
https://itzikr.wordpress.com/2012/09/02/the-vnx-storage-analytics-suite/


storage vendors reference:
 http://www.gabesvirtualworld.com/tintri-vmstore-vm-only-storage-appliance/

OVH:
https://api.ovh.com/
http://www.ovh.com/us/dedicated-cloud/hardware/infrastructure/hosts/




 © LucioCampanelli. 2020,  All rights​ reserved. 


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