KR101881637B1 - Job process method and system for genome data analysis - Google Patents

Abstract

The present invention relates to a work processing method and system for processing a dielectric analysis work using computing resources required for analyzing dielectric data. A system for controlling a computing resource by controlling computing resources according to an embodiment of the present invention includes a reception processing module for receiving a genome analysis job from a user; An image selection module for selecting a pipeline image necessary for the dielectric analysis operation from a plurality of pipeline images; A resource management module that identifies required resources required for performing the genome analysis and selects one or more servers capable of supporting the required resources among a plurality of servers forming computer resources as a job analysis server; And an analysis processing module for loading the selected pipeline image on one or more servers selected by the job analysis server and controlling the genetic analysis job to be processed through the selected one or more servers.

Description

[0001] The present invention relates to a method and system for processing genome data,

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a work processing technique, and more particularly, to a work processing method and system for processing a dielectric analysis work using computing resources required for analyzing dielectric data.

In recent years, studies have been actively conducted to identify genes for human diseases. Among them, a genome map having a human genetic information is decoded to analyze a gene sequence, and a project for predicting the occurrence of a human disease is actively being carried out.

The project compares genome data of a specific user with reference genome data to identify genetic information (i.e., base sequence) of a specific user who has developed a mutation, and derives a disease correlation for the specific user based on the genetic information thus confirmed.

On the other hand, a computing system having a large-scale computer resource receives a genome data including a user's nucleotide sequence data from a client terminal, analyzes the genome data, and provides the analysis result to the client terminal. That is, a service has been disclosed in which a computing system performs an analysis of genome data and provides the analysis result to a user. The following patent documents disclose a genetic information management system and method.

Next Generation Sequencing (NGS), which has been recently developed, is a method of generating genetic sequence digital information (ie, genome analysis raw data) that has been fragmented through sequencing in DNA (deoxyribonucleic acid) Afterwards, it executes a genome analysis pipeline (hereinafter referred to as "pipeline") that extracts actual gene sequence information and mutation information through various analysis procedures by inputting a large amount of computing resources.

The pipeline work is to identify the final gene sequence through a sort of large-scale puzzle-matching operation in which genome analysis raw data is compared with a known genome standard sequence (ie, reference data) It consumes a lot of computing resources, and optimizing and accelerating it becomes a core technology that lowers the cost of gene analysis from an information processing point of view.

In the platforms and infrastructure that handle these pipeline tasks, the pipeline requires various procedures and computing resources depending on the analysis target and analysis purpose. For example, in the case of targeted sequencing that only inspects a region of a certain gene, a file analysis in the form of FASTQ of a capacity of several hundred megabytes (MB) is required. To do this, Sized CPU core and a certain amount of memory are required.

A genome analysis of the entire useful region of a human gene is called Whole Exome Sequencing (WES), which requires analysis of FASTQ files with a capacity of tens of gigabytes (GB), which requires more core and memory than target area sequencing . In addition, in the case of cancer analysis, a different procedure and corresponding computing resources (memory, core, disk, etc.) are needed because the analysis of both normal DNA and cancer DNA data is performed simultaneously.

Accordingly, conventionally, in order to perform a large-scale analysis, groups of data for each type of analysis are grouped, and a pipeline procedure necessary for each of them is distributed to each server, and collective execution is repeated to complete processing of the entire data. However, such a conventional technique has a problem that the infrastructure is directly set and managed for each situation as the pipeline procedures for the different types of analysis are different and complex, which leads to an increase in cost and inefficiency of operation.

Therefore, there is a method of pre-setting the infrastructure of each type in advance (that is, by automating), but this method has a problem of increasing the total idle rate of the system when specific type analysis is carried out. In addition, a small number of analysis tasks that occur frequently can be performed simultaneously on a single server. If the process is automated, even if the analysis size is small, the computational resources consumed by each procedure are irregular, This interferes with each other and affects the resources and hinders the stability of parallel processing. Therefore, the conventional pipeline processing technique secures the idle rate to a stable level to some degree even if some computation ability is left for each server for the stability of the parallel processing. That is, in the conventional pipeline processing technique, the idle rate of the server is controlled so as not to remain below the threshold value.

However, the conventional pipeline processing technique does not utilize the resources of the remaining servers, thus degrading the efficiency of the entire system.

Korean Patent No. 10-1188886

Disclosure of Invention Technical Problem [8] Accordingly, the present invention has been made in order to solve the conventional problems, and it is an object of the present invention to provide a method and system for processing data for analyzing dielectric data that minimizes the idle rate of the entire system and automatically processes various types of pipelines. There is a purpose.

Other objects and advantages of the present invention will become apparent from the following description, and it will be understood by those skilled in the art that the present invention is not limited thereto. It will also be readily apparent that the objects and advantages of the invention may be realized and attained by means of the instrumentalities and combinations particularly pointed out in the appended claims.

According to a first aspect of the present invention, there is provided a system for controlling a computing resource by controlling computing resources, the system comprising: a reception processing module for receiving a genome analysis job from a user; An image selection module for selecting a pipeline image necessary for the dielectric analysis operation from a plurality of pipeline images; A resource management module that identifies required resources required for performing the genome analysis and selects one or more servers capable of supporting the required resources among a plurality of servers forming computer resources as a job analysis server; And an analysis processing module for loading the selected pipeline image into one or more servers selected by the job analysis server and controlling the genome analysis job to be processed through the selected one or more servers.

According to a second aspect of the present invention, there is provided a method for controlling a computing resource in a work processing system, the method comprising: receiving a request for a genome analysis job from a user; Selecting a pipeline image necessary for the dielectric analysis operation from a plurality of pipeline images; Identifying a necessary resource required to perform the genome analysis operation; Selecting one or more servers capable of supporting the required resources among a plurality of servers forming computer resources as a job analysis server; And loading the selected pipe image into one or more servers selected by the job analysis server, and controlling the dielectric analysis job to be processed through the selected one or more servers.

According to the present invention, a pipeline image is loaded on a server according to a job type, and a job is processed through the pipeline image. Thus, not only can a dielectric analysis job be processed quickly, but also a merit .

In addition, the present invention has an advantage of minimizing the idle rate of the entire computing system by checking the resource status of each server at the time of job processing and selecting one or more servers as analysis target servers according to the resource status and the job type. In addition, the present invention has an advantage of further improving the resource efficiency of the entire system by changing the policy for selecting the analysis target server according to whether or not the computing resources are set to auto scaling.

In addition, the present invention provides an independent processing environment for each job so that the analysis server can isolate the dielectric analysis job, thereby improving the stability of job processing.

In addition, the present invention has an advantage of minimizing the occurrence of resource fragmentation by allocating resources so that n is a multiple of 2 (n is a natural number) in a predetermined unit standard when assigning server resources for job processing .

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments of the invention and, together with the description, serve to explain the principles of the invention. And shall not be construed as limited to such matters.
1 is a diagram showing a configuration of a job processing system according to an embodiment of the present invention.
FIG. 2 is a diagram illustrating an image table, FIG. 3 is a diagram illustrating an allocation policy table, and FIG. 4 is a diagram illustrating a resource state table.
5 is a flowchart illustrating a method for allocating computing resources in a work processing system and performing a dielectric analysis by loading pipeline images on computing resources, according to an embodiment of the present invention.

The foregoing and other objects, features and advantages of the present invention will become more apparent from the following detailed description of the present invention when taken in conjunction with the accompanying drawings, in which: There will be. In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail. Hereinafter, a preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram showing a configuration of a job processing system according to an embodiment of the present invention.

1, a work processing system 300 according to an exemplary embodiment of the present invention includes a plurality of servers 310-N, a repository 320, a reception processing module 330, a resource management module 340, The job processing system 300 communicates with the user terminal 100 via the network 200, including an image selection module 350, The network 200 includes an intranet, an Internet network, and a mobile communication network.

The modules included in the work processing system 300 may be implemented in hardware or software, or a combination of hardware and software. In addition, the task processing system 300 may include a memory and one or more processors, and the acceptance processing module 330, the resource management module 340, the image selecting module 350, Stored in a memory, and implemented in the form of a program executed by the one or more processors.

The user terminal 100 is a communication device such as a server, a computer, a computer system, etc. owned by a user or a corporation, and transmits job request information including raw data of the dielectric and job analysis type information to the job processing system 300 ), And receives the analysis result of the job completed in the job processing system (300). The primed data is fragmented gene sequence digital information generated by sequencing the DNA extracted in body fluids. The user terminal 100 may transmit job request information including FASTQ, BAM, VCF, and the like to the job processing system 300 as the raw data. Also, the user terminal 100 may record cancer analysis, rare disease analysis, obesity analysis, and the like in the job request information as the job analysis type information.

The plurality of servers 310-N is a means for forming computing resources, and a physical server or a logical server can be built in the work processing system 300. [ In addition, the server 310-N holds resources including a memory, a disk, a CPU core, and the like. The server 310-N loads the pipeline image under the control of the analysis processing module 360, performs the analysis work according to the pipeline, and stores the analysis result in the repository 320. The server 310-N may simultaneously process different types of jobs, and the plurality of servers 310-N may distribute one job.

The pipeline refers to a series of analysis tasks for confirming actual gene sequence information and mutation information through an analysis procedure of genomic raw data, and for deriving disease correlation based on the confirmed information. Further, the pipeline image described below is a program package in which one or more application programs necessary for the genetic analysis (i. E., Required for pipeline processing) and execution procedures and options are defined.

On the other hand, when each of the servers 310-N processes the dielectric analysis work by the pipeline, the server 310-N uses the resource (i.e., core, memory and disk) . The quarantine execution means that a memory, a CPU core, a disk (that is, a file system) or the like, when a job is analyzed in the same server and the same OS (Operating System), has a separate environment and processes necessary processes. This isolation is supported at the kernel level for operating systems such as Linux.

In addition, the computing resources formed by the resources of the respective servers 310-N may be set to an auto scaling environment. In the auto-scaling environment, some of the plurality of servers 310-N are activated to process jobs, but some servers 310-N are inactive and in a standby state, Some of the deactivated servers are activated and the computing resources are dynamically expanded. The server 310-N according to the present invention may be set to an auto-scaling environment to dynamically expand computing resources, or may not be set to an auto-scaling environment, thereby fixing the entire computing resources.

The repository 320 is a storage device such as a storage device, a database, or the like, stores a plurality of pipeline images, and in particular, stores pipeline images of the same type by version. The repository 320 stores job request information including raw data and job analysis type information and stores the results of the analysis job. The storage 320 may be a network attached storage device that is accessed through a communication network such as an intranet, a local area network (LAN), a wide area network (WLAN), a storage area network (SAN)

In particular, the repository 320 stores image tables, allocation policy tables, and resource state tables.

FIG. 2 is a diagram illustrating an image table, FIG. 3 is a diagram illustrating an allocation policy table, and FIG. 4 is a diagram illustrating a resource state table.

Referring to FIG. 2 to FIG. 4, the image table stores analysis type identification information, pipeline image identification information, and a version of the image mapped. That is, the identification information of the pipeline image used according to the analysis type and the version of this pipeline are recorded in the image table.

In the allocation policy table, required resources (that is, the number of cores, the memory capacity, and the disk capacity) are mapped and recorded according to the file type of the raw data and the size of the raw data. That is, necessary resources are classified according to the file type of the original data and the file capacity, and they are recorded in the allocation policy table. In the allocation policy table, the number of CPU cores and the memory capacity may be recorded so that n is a multiple of 2 (n is a natural number) based on a preset reference unit in order to minimize resource fragmentation. For example, the required number of cores may be recorded in the allocation policy table so that n is a multiple of 2 (i.e., 2, 4, 8, ...) based on one reference unit. In addition, the required memory capacity can be recorded in the allocation policy table such that it is set to n times (2 GB, 4 GB, 8 GB, 16 GB, ...) of 2 based on 1 GB as the reference unit.

The resource status table records server identification information, the total number of CPU cores in the server, the total memory capacity, the total disk capacity, the number of extra cores, the extra memory capacity, the extra disk capacity, and the number of execution tasks. The number of extra cores, the spare memory capacity, and the spare disk capacity mean resources available in the server, and the number of execution tasks means the number of genetic analysis tasks executed in the server. The spare core, the number, the extra memory capacity, the extra disk capacity, and the number of execution tasks are updated in real time by the resource management module 340.

Referring back to FIG. 1, the reception processing module 330 receives work request information from the user terminal 100 and provides the analysis result to the user terminal 100. That is, the reception processing module 330 receives job request information including raw data and job analysis type information from the user terminal 100 and stores the received job request information in the storage 320. When the result of the analysis operation is stored in the storage 320, the reception processing module 330 notifies the user terminal 100 that the analysis operation has been completed, and the result of the analysis operation is downloaded or viewed from the user terminal 100 .

The image selection module 350 selects the pipeline image required for the requested operation. Specifically, when the job request information is stored in the storage 320, the image selecting module 350 checks the job analysis type information included in the job request information, and determines whether the job analysis type information corresponds to the corresponding pipeline image and version It is checked and selected in the image table of the storage 320.

The resource management module 340 identifies the necessary resources required for the requested task and also selects the server 310-N that analyzes the task. The resource management module 340 monitors resources used by the server 310-N and updates the resource state table of the storage 320. [

The resource management module 340 checks the file type and the size of the raw data included in the job request information, and extracts necessary resources corresponding to the file type and size of the raw data (that is, the number of required cores, Capacity) in the allocation policy table. Also, the resource management module 340 selects at least one server capable of supporting the necessary resources as the job analysis server. At this time, the resource management module 340 checks whether or not the computing resources are set to auto-scaling, and selects a minimum number of servers as an analysis target server as described later with reference to FIG. Or a server having a high load factor can be selected as the analysis target server. In addition, when one or more job analysis servers are selected, the resource management module 340 determines resources used for the analysis work in the selected one or more servers 310-N.

The analysis processing module 360 loads the pipeline image into the selected one or more servers 310-N and processes each pipeline to be isolated from one or more servers 310-N. That is, the analysis processing module 360 loads the pipeline image into the selected server 310-N, and the resource required for the job processing in the server 310-N is dedicated for the analysis work through the pipeline To be executed. In addition, the analysis processing module 360 unloads the pipeline image mounted on the server 310-N when the genome analysis processing job is completed in the server 310-N.

5 is a flow diagram illustrating a method for allocating computing resources in a work processing system and performing a dielectric analysis by loading pipeline images on computing resources, according to one embodiment of the present invention.

5, the reception processing module 330 receives job request information including raw data and job analysis type information from the user terminal 100, and transmits the job request information to the storage 320 (S501). As the raw data, files of the FASTQ, BAM, VCF, and the like types can be recorded. In addition, as the job analysis type information, types of diseases to be analyzed, such as cancer analysis, rare disease analysis, and obesity analysis, may be recorded.

When the job request information is stored in the storage 320, the image selecting module 350 confirms the job analysis type information included in the job request information (S503), and the job analysis type information corresponds to the corresponding pipeline image and the version In the image table of the storage 320, the pipeline image and the version used for the requested genome analysis are selected (S505).

Next, the resource management module 340 checks the file type and size of the raw data included in the job request information, and extracts necessary resources corresponding to the file type and size of the raw data (i.e., In the allocation policy table of the storage 320 (S507). (N is a natural number) of 2 in the reference unit, and the resource management module 340 writes the necessary resources (that is, the number of cores and the memory capacity) The resources corresponding to n multiples of 2 can be identified as the computing resources required for the genome analysis. For example, in the allocation policy table, the number of required cores may be recorded so as to be n (for example, 2, 4, 8, ...) of 2 based on one reference unit, (I.e., 2GB, 4GB, 8GB, 16GB, ...) based on 1GB of 1GB, and thus the resource management module 340 can write 1x2n (n is a natural number) The required number of cores and the required memory capacity of 1GB × 2n (where n is a natural number) can be confirmed as computing resources required for dielectric analysis.

Next, the resource management module 340 determines whether the computing resources included in the plurality of servers 310-N are set in the auto-scaling environment (S509).

The resource management module 340 checks the redundant resources (i.e., spare core, spare memory capacity, and spare disk capacity) in the resource status table of the storage 320 when the computing resources are set to the auto-scaling environment, (I.e., the required number of cores, the required memory capacity, and the required disk capacity) (S511). Further, in the case of a dynamic auto-scaling environment in which the automatically deactivated server is activated and the total capacity is expanded when the entire computing resources are insufficient, the resource management module 340 may set the minimum number of servers The job analysis server is selected. That is, the resource management module 340 can support all of the necessary resources in one specific server, and selects only the specific server as the job analysis server, while the resource management module 340 selects one of the required resources The number of servers can be sequentially increased to continuously determine whether or not the required resources can be supported, and as a result, the number of servers capable of supporting the necessary resources is minimized. When the minimum number of servers capable of supporting the required resources is selected as the job analysis server, the fragmentation of the computing resources is minimized and the unnecessary expansion of computer resources is minimized in the auto scaling environment, thereby minimizing the idle rate of the entire system.

On the other hand, if the computing resource is not set to the auto-scaling environment, the resource management module 340 checks the extra resources (i.e., extra core, spare memory capacity, and extra disk capacity) in the resource status table of the storage 320 , The server having the highest idle rate is selected as the job analysis server (S513). In this case, if the server having the highest idle rate can not support all of the necessary resources, the resource management server 310-N may wait until the next idle rate can be supported, (310-N) is additionally selected. In addition, if the computing resource is a static environment in which the size of the computing resources is fixed, the resource management module 340 may allocate one or more servers capable of supporting the required resources to the job analysis server . The resource management module 340 applies a weight to each of the ratio of whether the idle rate of the server 310-N is a core ratio, a ratio of an extra memory, or a ratio of a disk capacity, and determines whether the weight is applied, The ratio of the memory and the spare disk capacity ratio can be added up to calculate the idle rate of the server. In an environment where no auto-scaling is set up, if a server with a high idle rate is selected as a job analysis server, the processing speed of the entire system is improved and the idle rate of the entire system is minimized.

Next, when one or more job analysis servers are selected, the resource management module 340 determines resources to be used for the analysis work in the selected one or more servers 310-N, The resource status table is updated by reflecting the extra resources (that is, extra core, extra memory, and extra disk capacity) and the number of execution jobs in the resource status table.

When a pipeline image is selected in the image selection module 350 and a server is selected in the resource management module 340 and resources used in each server 310-N are determined, the analysis processing module 360 determines The pipeline image is loaded onto one or more servers 310-N, and each pipeline is controlled to be isolated from one or more servers 310-N (S515, S517). That is, the analysis processing module 360 loads the pipeline image into the selected server 310-N, and the resource required for the job processing in the server 310-N is dedicated for the analysis work through the pipeline To be executed. Then, the server 310-N isolates and executes analysis work according to the pipeline using the allocated resources. On the other hand, when the pipeline image is first mounted, the server 310-N acquires the pipeline image stored in the storage 320, mounts the pipeline image, and quarantines the analysis job. On the other hand, if the pipeline image is an image that has already been mounted in the past, the server 310-N may mount the cached pipeline image, without acquiring the pipeline image from the repository 320, .

The one or more servers 310-N that isolate the pipeline-based analysis job stores the results of the analysis job in the repository 320 (S519). Then, the reception processing module 330 notifies the user terminal 100 of the completion of the analysis work, and the subscriber can connect to the work processing system 300 to download or browse the analysis results.

In addition, when the analysis operation is completed, the resource management module 340 releases the pipeline image when the operation according to the pipeline is completed in the server 310-N, and allocates the work for the operation according to the pipeline The necessary resources are returned (S521). The resource management module 340 updates the resource status table of the storage 320 so that the returned required resources are recorded in the spare resources of the corresponding server.

As described above, the job processing system 300 according to the present invention can load a pipeline image on one or more servers 310-N according to the job type, and allow the job to be processed through the pipeline image, Work can be done quickly. In addition, the job processing system 300 according to the present invention checks the resource status of each server 310-N at the time of job processing, and manages one or more servers 310-N according to the resource status and the job type, Thereby minimizing the idle rate of the entire computing system. In particular, the task processing system 300 according to the present invention improves the resource efficiency of the overall system by changing the policy for selecting the server 310-N according to whether or not the computing resources are set to auto-scaling. In addition, the work processing system 300 according to the present invention improves work process stability by providing an independent processing environment for each job so that the server 310-N can isolate the dielectric analysis job.

While the specification contains many features, such features should not be construed as limiting the scope of the invention or the scope of the claims. In addition, the features described in the individual embodiments herein may be combined and implemented in a single embodiment. Conversely, various features described in the singular < Desc / Clms Page number 5 > embodiments herein may be implemented in various embodiments individually or in combination as appropriate.

Although the operations have been described in a particular order in the figures, it should be understood that such operations are performed in a particular order as shown, or that all described operations are performed to obtain a sequence of sequential orders, or a desired result . In certain circumstances, multitasking and parallel processing may be advantageous. It should also be understood that the division of various system components in the above embodiments does not require such distinction in all embodiments. The above-described program components and systems can generally be implemented as a single software product or as a package in multiple software products.

The method of the present invention as described above can be implemented by a program and stored in a computer-readable recording medium (CD-ROM, RAM, ROM, floppy disk, hard disk, magneto optical disk, etc.). Such a process can be easily carried out by those skilled in the art and will not be described in detail.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention. The present invention is not limited to the drawings.

100: user terminal 200: network
300: job processing system 310: server
320: storage 330: reception processing module
340: resource management module 350: image selection module
360: Analysis processing module

Claims (14)

A system for controlling a computing resource to process a dielectric analysis job,
A reception processing module for receiving a genome analysis job from a user;
An image selection module for selecting a pipeline image necessary for the dielectric analysis operation from a plurality of pipeline images;
A resource management module that identifies required resources required for performing the genome analysis and selects one or more servers capable of supporting the required resources among a plurality of servers forming computer resources as a job analysis server; And
And an analysis processing module for loading the selected pipeline image on one or more servers selected by the job analysis server and controlling the genetic analysis job to be processed through the selected one or more servers,
Wherein the resource management module comprises:
A plurality of servers for establishing a computing resource are selected as the minimum number of servers capable of supporting the required resources among the plurality of servers, And selects at least one server as the job analysis server based on the order of the highest idle rate among a plurality of servers when the auto-scaling environment is not set. delete delete The method according to claim 1,
Wherein the resource management module comprises:
Checking the file type and capacity of the raw data received from the user and confirming the necessary resources corresponding to the file type and the capacity of the raw data and checking the necessary resources required to perform the genome analysis work Work processing system. The method according to claim 1,
Wherein the image selection module comprises:
Identifying a type of the genome analysis job, identifying the pipeline image identification information and version corresponding to the type, and selecting a pipeline image corresponding to the pipeline image identification information and version from among the plurality of pipeline images Wherein the work processing system comprises: The method according to claim 1,
Wherein the resource management module determines a resource to be allocated for the genetic analysis job among spare resources of each server selected by the job analysis server,
Wherein each of the servers selected by the job analyzing server isolates and processes the dielectric analysis job using the allocated resources. The method according to claim 6,
Wherein the resource management module comprises:
Wherein the number of CPU cores is assigned to the server such that the number of CPU cores is n times 2 (n is a natural number) based on a predetermined number of units. The method according to claim 6,
Wherein the resource management module comprises:
And the memory capacity is allocated to the server such that the capacity corresponds to n times 2 (n is a natural number) based on a unit capacity set in advance. The method according to claim 1,
Wherein the analysis processing module comprises:
And when the genome analysis operation is completed in the selected one or more servers, dismisses the pipeline image mounted on the selected one or more servers. A method for controlling a computing resource in a work processing system to process a dielectric analysis job,
Receiving a request for a genome analysis job from a user;
Selecting a pipeline image necessary for the dielectric analysis operation from a plurality of pipeline images;
Identifying a necessary resource required to perform the genome analysis operation;
Selecting one or more servers capable of supporting the required resources among a plurality of servers forming computer resources as a job analysis server; And
And loading the selected pipe image to one or more servers selected by the job analyzing server to control the genetic analysis job to be processed through the selected one or more servers,
Wherein the job analysis server comprises:
A plurality of servers for establishing a computing resource are selected as the minimum number of servers capable of supporting the required resources among the plurality of servers, Wherein the job analysis server selects one or more servers based on the order of the highest idle rate among a plurality of servers when the auto-scaling environment is not set. delete delete 11. The method of claim 10,
The method of claim 1,
Checking the file type and capacity of the raw data received from the user; And
And checking the required resources corresponding to the file type and the capacity of the raw data and confirming the necessary resources required to perform the genome analysis operation. 11. The method of claim 10,
Wherein the controlling comprises:
And releasing the pipeline image mounted on the selected one or more servers when the genome analysis operation is completed in the selected one or more servers.

Images