Claims:We Claim:
1. A method of dynamic deployment and vertical scaling of applications in a cloud environment, the method comprising:
receiving, by an application deployment system (101), information of one or more system parameters required by one or more applications of a target system (103);
performing, by the application deployment system (101), one or more processing operations on the information to identify storage access details for a predefined time period of the target system (103), wherein the storage access details comprises information on previously accessed and cached storage blocks;
estimating, by the application deployment system (101), an ideal configuration for the target system (103) based on current requirement of one or more system parameters and the identified storage access details, by using at least one of a rule-based model, a neural network model, and statistical model; and
performing, by the application deployment system (101), dynamic deployment of the one or more applications based on the ideal configuration.

2. The method as claimed in claim 1, wherein the one or more system parameters comprises Central Processing Unit (CPU) utilization, memory, cache memory, and network utilisation of the target system and information about one or more Application Programming Interface (API) access, CPU, memory, cache memory, and network utilization by the one or more applications.

3. The method as claimed in claim 1, wherein the one or more processing operations comprises error detection and correction, selection of a data window, normalization, segregation, data labelling, and data enrichment on the one or more system parameters.

4. The method as claimed in claim 3, wherein the data enrichment comprises:

selecting, by the application deployment system (101), a first-time interval and a second time interval, wherein the first time internal and the second time interval is associated with historic data of the one or more applications for a predetermined duration;
merging, by the application deployment system (101), a plurality of storage access dataset with a plurality of Application Programming Interface (API) access dataset associated with the first-time interval based on a predefined threshold time; and
determining, by the application deployment system (101), the storage access details based on the merged dataset.

5. The method as claimed in claim 4, wherein the plurality of storage access dataset is merged with the plurality of API access dataset when a difference of time interval between the storage access dataset and the API access dataset is within a predefined threshold time.

6. The method as claimed in claim 1, wherein estimating the configuration for the target system (103) using the rule-based model comprises:

checking, by the application deployment system (101), usage of the one or more system parameters based on a predefined threshold value of one or more corresponding system parameters;
initiating, by the application deployment system (101), the deployment of the one or more applications based on the checking at a predefined time, wherein the one or more applications are deployed when the usage reaches the predefined threshold value; and
determining, by the application deployment system (101), an ideal configuration for the target system (103) required for deploying the one or more applications for the predefined time.

7. The method as claimed in claim 1, wherein the neural network model is trained using plurality of historic application data.

8. The method as claimed in claim 1 further comprising identifying a lean time period for other applications associated with a fixed capacity system to utilise available system resources, by:
selecting a time-interval within which the lean time period is to be calculated along with a minimal lean time;
dividing the time-interval into predefined equal intervals;
determining the lean time period for a predefined one or more system parameters;
calculating total requirement of the one or more system parameters required by the other applications at a plurality of intervals, wherein the plurality of intervals is stored, if total interval of the plurality of intervals is within a preset threshold of time interval;
selecting a minimal time interval among the plurality of intervals which spans for maximum durations; and
identifying the lean time period within the selected minimal time interval.

9. The method as claimed in claim 1 further comprising caching the storage blocks based on memory utilisation by:
obtaining a plurality of storage access dataset and a plurality of Application Programming Interface (API) access dataset from a database;
examining total number of unique blocks from the plurality of storage access dataset and the plurality of Application Programming Interface (API) access dataset and performing:
(a) adding the unique blocks to a prefetch set, if available memory is more than a predefined percentage and perform (d); or
(b) adding a predefined number of repeated blocks in the prefetch set, if the available memory is less than requirement;
(c) rechecking the available memory and selecting a predefined used blocks from the plurality of storage access dataset and the plurality of Application Programming Interface (API) access dataset, in case the memory becomes available; and
(d) identifying and transmitting the storage blocks based on the selected predefined used blocks for prefetching.

10. The method as claimed in claim 1 further comprising, before deployment of the one or more applications, orchestrating the target system (103) based on the ideal configuration and continuously monitoring performance of the target system based on the ideal configuration, wherein the orchestration of the target system is performed after performing a prefetch operation for storage blocks and Application Programming Interface (API) in order to activate cached storage blocks based on a predefined threshold time.

11. The method as claimed in claim 10 further comprising:
reconfiguring the target system (103) to previous configuration on identifying performance issues based on the monitoring; or
decommissioning previous configuration of the one or more applications based on the monitoring.

12. An application deployment system (101) of dynamic deployment and vertical scaling of applications in a cloud environment, comprising:
a processor (121); and
a memory (119) communicatively coupled to the processor (121), wherein the memory (119) stores processor instructions, which, on execution, causes the processor (121) to:
receive information of one or more system parameters required by one or more applications of a target system (103);
perform one or more processing operation on the received information to identify storage access details for a predefined time period of the target system (103), wherein the storage access details comprise information on previously accessed and cached storage blocks;
estimate an ideal configuration for the target system (103) based on current requirement of one or more system parameters and the identified storage access details by using at least one of a rule-based model, a neural network model and statistical model; and
perform dynamic deployment of the one or more applications based on the estimated ideal configuration.

Dated this 10th day of October, 2019

Madhusudan S T
Of K&S Partners
Agent for the Applicant
IN/PA-1297
, Description:TECHNICAL FIELD
The present subject matter is related in general to cloud computing and cloud computing applications, more particularly, but not exclusively to a method and system for dynamic deployment and vertical scaling of applications in a cloud environment.