Patent AU-2021101322-A4 (AU)
Title: AWS-Cloud Data (EC2) (Amazon Web Services) Performance improvement using Machine and Deep Learning Programming
Inventor: MUMMADI UPENDRA KUMAR
IPC: G06F3/06
Filed: 2021-03-14
Granted: 2021-05-06
Abstract: Our invention AWS-Cloud Data (EC2) (Amazon Web Services) Performance 
improvement using Machine and Deep Learning Programming is a distributed data 
management method for an Amazon Web Services (AWS) , EC2 platform and belongs 
to the field of computer engineering and information technology processing. The 
invention is also a quick and cheap data distributing method for the AWS platform 
and also Elastic Compute Cloud (EC2) service and the Elastic Block Storage (EBS) 
service provided on the basis of the AWS are explicit, centralized data partitioning 
and distributing, serial as per required auto shift to parallel matching mounting, 
storing and processing service separating and other means are adopted, the batched 
and quick data input preparation is carried out in a large-scale cluster. The invention 
is also the computing resources of the cluster are recycled the low-cost data storage 
and reusage are achieved and therefore the preparation time of cluster data is 
shortened to be minimal and the expense cost of the cluster data is reduced to be 
minimal. The invented technology is also some methods for providing machine
learning services and a machine-learning service executing on a mobile platform can 
receive data related to a plurality of features and also the received data can include 
data related to features received from an application and data related to features 
received from the mobile platform. The invented technology also includes the 
machine-learning service can determine at least one feature based on the received 
data and the machine-learning service can generate an output by performing a 
machine-learning operation on the at least one feature. The Invention is a machine
learning operation can be selected from among an operation of ranking the at least 
one feature, an operation of classifying the at least one feature, an operation of 
predicting the at least one feature, and an operation of clustering the at least one 
feature. The machine-learning service can send the output. Building binary packages 
for software products, especially large software products, is an intensive process that 
requires a high degree of computation. Thus, it may be desirable to distribute the 
workload across multiple computing nodes to complete the deployment process 
within an optimal period of time. A work environment that provides computing 
resources that can be used in an easily available, dynamically scalable distributed 
building process is a resilient computing cloud. In such an environment, a virtual 
machine may be instantiated and destroyed if a resource request for the build process 
is indicated. This has the advantage that dedicated hardware is not needed and the 
excess capacity on the hardware used when the build process is idle can be used for 
other computing tasks. The present invention provides a system, method, and 
computer storage medium for distributing readily available and scalable deployment 
services suitable for use in a resilient or other distributed environment.  
24
Receiving data related to a pluralty of features by a machineleamning service executing on a mobile platform 
[|Deterrminin at least one feature in the plurality of features hbased 
an 
the data uigthe machine-leamning service -
Generatin an outptb the mach nmn service promng 
plurality of featres, where the machine-learning operation Is 
selected from mog an oeainof ranking the at lest one 
feature, an orainof clsiyn the at least one featune, an 
operation of peitng the at latone feature, and an operation of 
cl usterIng teat least one feature 
r-1 40 
(Sending the output fom the machine-learning service 
AWdeNO 
Spot~ ~ ~ie Rait b;vour 
Usi AWSCLI 
CtAI GPU 55 
Vokane 
Me 
%done 
tMiningt d* 
FIG. 1 A is a flow chart of a method, in accordance with an example embodiment.