KR102483422B1 - Cloud service convergence system - Google Patents

Abstract

The present invention relates to a cloud service convergence system that virtualizes a resource in a cloud environment configured with a plurality of server clusters, and integrates and manages the virtualized resource. The cloud service convergence system comprises: a network management part; a resource management part; and a backup management part. Therefore, the present invention can integrally manage the virtualized resource.

Description

Cloud service convergence system {Cloud service convergence system}

The present invention relates to a cloud service convergence system, and more particularly, to a cloud service convergence system that virtualizes resources in a cloud environment composed of a plurality of server clusters and integrates and manages the virtualized resources.

Cloud computing is a technology that integrates and provides resources of computers existing in different physical locations through virtualization technology, and corresponds to a user-centered computer environment.

Cloud computing technologies can be largely classified into Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). SaaS refers to application services operated in a cloud environment. IaaS refers to a service that allows data center resources such as servers and storage to be borrowed and used over the Internet. PaaS is a service that provides a platform necessary for developing software services.

In particular, the IaaS cloud service is a service that provides IT infrastructure, that is, computing resources, network resources, storage resources, etc., to users through the Internet, and provides services based on virtualization of the infrastructure.

If you look at the IaaS cloud service from a data center point of view, each element is defined and used in terms of software, so computers can be divided into SDC (software defined compute), networks SDN (software defined network), and storage SDS (software defined storage). there is.

On the other hand, the above-mentioned background art is technical information that the inventor possessed for derivation of the present invention or acquired in the process of derivation of the present invention, and cannot necessarily be said to be known art disclosed to the general public prior to filing the present invention. .

Korean Patent Registration No. 10-2076626

One aspect of the present invention provides a cloud service convergence system that virtualizes resources in a cloud environment composed of a plurality of server clusters and integrates and manages the virtualized resources.

The technical problem of the present invention is not limited to the technical problem mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description.

A cloud service convergence system according to an embodiment of the present invention virtualizes resources in a cloud environment composed of a plurality of server clusters and manages the virtualized resources in an integrated manner.

The cloud service convergence system,

A network management unit that creates a cluster by connecting a plurality of physical servers;

a resource management unit that virtualizes and manages computing resources, storage resources, network resources, and memory resources; and

A backup management unit that extracts backup target data requiring backup from data stored in the platform built through the cloud service convergence system and selectively backs up only the extracted backup target data;

The backup management unit,

A backup target determining unit that classifies data that is determined to require backup and data that is not determined to be backed up among data changes occurring between predetermined backup cycles using an artificial neural network; and

and a backup performer that periodically backs up only data set as a backup target by the backup target determiner.

The cloud service convergence system includes an abnormal data derivation unit based on big data analysis that determines whether abnormal data is included in data,

The abnormal data derivation unit,

a collection management module that sets collection criteria for at least one of a data source module to be collected, a keyword to be collected, and a period to be collected;

a data collection module that collects big data from the corresponding data source module according to the data collection criteria set by the collection management module and stores it in a database module;

A morpheme analysis module that analyzes the morphemes of the big data stored in the database module, classifies them by morpheme, and generates morpheme analysis data; and

A data analysis module that processes morpheme analysis data with a statistical analysis algorithm based on distributed parallel processing to calculate statistical values and outputs malicious code according to the statistical values.

According to one aspect of the present invention described above, it is possible to provide a platform service capable of virtualizing resources in a cloud environment composed of a plurality of server clusters and managing the virtualized resources in an integrated manner.

1 and 2 are diagrams showing a schematic configuration of a cloud service convergence system according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The detailed description of the present invention which follows refers to the accompanying drawings which illustrate, by way of illustration, specific embodiments in which the present invention may be practiced. These embodiments are described in sufficient detail to enable one skilled in the art to practice the present invention. It should be understood that the various embodiments of the present invention are different from each other but are not necessarily mutually exclusive. For example, specific shapes, structures, and characteristics described herein may be implemented in another embodiment without departing from the spirit and scope of the invention in connection with one embodiment. Additionally, it should be understood that the location or arrangement of individual components within each disclosed embodiment may be changed without departing from the spirit and scope of the invention. Accordingly, the detailed description set forth below is not to be taken in a limiting sense, and the scope of the present invention, if properly described, is limited only by the appended claims, along with all equivalents as claimed by those claims. Like reference numbers in the drawings indicate the same or similar function throughout the various aspects.

Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the drawings.

1 and 2 are diagrams showing a schematic configuration of a cloud service convergence system according to an embodiment of the present invention.

The cloud service convergence system according to the present invention virtualizes resources in a cloud environment composed of a plurality of server clusters and manages the virtualized resources in an integrated manner.

Specifically, the cloud service convergence system 1 according to the present invention includes a network management unit 100 , a resource management unit 200 and a backup management unit 300 .

The network management unit creates a cluster by connecting a plurality of physical servers.

Kubernetes is an open source platform for deploying, scaling and managing containerized applications. It provides an open and efficient model for application packaging, deployment, service discovery, scaling and rolling upgrades.

A cluster is a connection between one master and a plurality of nodes, and the master and nodes are physical servers. The master manages the entire cluster, and the nodes are agents that communicate with the master, providing compute, networking, and storage resources to applications, and running additional components for logging, monitoring, service discovery, and optional add-ons.

The resource management unit virtualizes and manages computing resources, storage resources, network resources, and memory resources.

The most widely used and well-known software-defined technology in data centers is server virtualization. Server virtualization technology enables multiple virtual machines to be created on a single physical machine with guaranteed computing resources. A virtual machine within a physical machine is used to build various services based on Service Oriented Architecture (SOA) that can provide specific functions as subservices of a broader service. That is, multiple cooperating virtual machines must provide a single service to each virtual machine acting as a subservice, and all virtual machines must work together to ensure that the service runs properly.

In a data center, all virtual machines related to a single service can initially be deployed on a single physical machine. However, there are cases in which the service needs to be expanded when the number of customers increases or when additional functions are needed to increase the efficiency of the service. When extending a service, a virtual machine is created on the physical machine if the amount of available resources of the previously used physical machine is not sufficient for the virtual machine being added. Some virtual machines of the service have a high dependency on each other, but the degree of dependency between some virtual machines may be small.

In addition, even among virtual machines belonging to the same service, traffic dependencies between pairs of virtual machines are greatly different. Distributing virtual machines with high dependencies across multiple physical machines can increase data traffic volume in the data center network, which can negatively affect service performance.

Software defined data center (SDDC) is a new paradigm for data centers that enables dynamic management and configuration of data center resources such as compute, networking and storage. With SDDC, customers can use the newly created infrastructure they request for service development and operation within minutes. SDDC can also control all underlying resources regardless of vendor dependencies. Thus, service providers do not have to worry about complex infrastructure deployment and management.

In general, SDDC is defined as the integration of various software-defined technologies such as Software-Defined Networking (SDN), Software-Defined Computing (SDC) and Software Defined Standard (SDS).

SDN is a new paradigm of networking that moves the control plane from network devices to a single server. All insignificance of network devices is developed directly by centralized controller software running on servers.

SDC is the first step in developing an SDDC. SDC is based on using server virtualization to efficiently utilize system resources and make critical decisions quickly.

SDS is used to manage large amounts of data in storage systems by isolating the data control layer from the data storage layer.

The resource management unit may provide a platform for integratively managing virtualized resources (SDC, SDS, and SDN) using the SDDC technology, organizing hardware infrastructure resources and software platform resources, and providing internal services.

The backup management unit may periodically back up data stored in the platform built through the cloud service convergence system.

In particular, the backup management unit according to the present invention is characterized in that it extracts backup target data requiring backup from data stored in the user terminal using a pre-learned artificial neural network, and periodically backs up only the extracted backup target data.

To this end, the backup management unit includes a backup target determination unit and a backup execution unit.

The backup target determination unit extracts data that needs to be backed up from among all data stored in the cloud using a pre-learned artificial neural network.

Specifically, the backup target determining unit determines whether to back up the changed data using a pre-learned artificial neural network when a data change is detected during a backup time period. That is, the backup target determination unit classifies data that is determined to require backup among data changes that occur between backup cycles and data that is determined not to be backed up using an artificial neural network, and performs the next backup according to the classified result. It is characterized by selectively backing up only the data that needs to be backed up.

To this end, the backup target determination unit searches for path information (eg, a folder) including data (files) stored in the cloud, and assigns a first weight according to the type of the searched path information. The backup target determiner sets a first weight value to be applied for each data item by referring to a pre-stored lookup table. Here, the lookup table may have a data structure in which, when a user sets an importance for each storage path, different weight values are set according to the set importance.

In addition, the backup target determination unit assigns a second weight to each data according to a result of comparing data stored in the cloud with each other.

Specifically, the backup target determination unit extracts text from all data stored in the cloud, and divides the extracted text into words having minimum meaning. The backup target determination unit may calculate the second weight to be applied to specific data using the following equation. The following equation is an equation for calculating the second weight to be applied to the first data.

[Equation 1]

Second weight to be applied to the first data = (number of duplicated words/(total number of words-words constituting the first data))*A

For example, the backup target determination unit searches for the number of words excluding words extracted from the first data among all words extracted from the data stored in the cloud, sets them as the first variable value, and sets the number of words extracted from the data stored in the cloud to the number of words extracted from the data stored in the cloud. 1 Set the number of overlapping words among words excluding words extracted from data and words extracted from the first data as the value of the second variable, and set the ratio of the value of the first variable to the value of the second variable as a constant value It is possible to calculate the second weight by calculating .

Here, A is a constant value and is variable according to the number of words extracted from all data stored in the cloud.

In one embodiment, this constant value is characterized in that it is set in proportion to the number of words extracted from all data stored in the cloud. For example, the constant value A may be set to 100 when the total number of words extracted from all data stored in the cloud is 1000, and set to 1000 when the total number of words extracted from all data stored in the cloud is 10000.

The backup target determination unit may apply the first weight and the second weight set in this way for each data (file).

If the calculation value of the first weight and the second weight is less than a preset reference value, the backup target determination unit determines that the corresponding data is unimportant data and excludes it from the backup target. On the other hand, if the calculation value of the first weight and the second weight is greater than or equal to a predetermined reference value, the backup target determining unit determines that the corresponding data is important data and includes it in the backup target.

The backup performer backs up data at every preset backup time period. For example, by periodically performing backup on a weekly basis, the backup performer may selectively back up only data set as a backup target by the backup target determiner among data stored in the cloud every week.

As such, the backup management unit according to the present invention can provide an efficient backup service of data stored in the cloud by selectively backing up only important data.

In some other embodiments, the cloud service convergence system includes a collection management module for setting at least one collection criterion of a collection target data source module, a collection target keyword, and a collection target period; a data collection module that collects big data from the corresponding data source module according to the data collection criteria set by the collection management module and stores it in a database module; A morpheme analysis module that analyzes the morphemes of the big data stored in the database module, classifies them by morpheme, and generates morpheme analysis data; and a data analysis module that processes the morphological analysis data with a distributed parallel processing-based statistical analysis algorithm to calculate statistical values and outputs malicious code according to the statistical values; a big data analysis-based abnormal data derivation system (for convenience of description) It is possible to automatically determine whether malicious code is included in the data stored in the cloud convergence system according to the present invention by using (not shown in the drawing).

Here, the data collection unit generates morpheme analysis data by classifying the received evaluation data for each morpheme having meaning, and a distributed file system for storing the morpheme analysis data; a data processing unit providing a virtual database interface to process morphological analysis data existing in the distributed file system based on structured query language (SQL); and executing a statistical analysis algorithm through a virtualization database interface provided by the data processing unit, and from the morpheme analysis data, the number of instance frequencies by year, the number of simultaneous occurrences between words by year, the number of occurrence frequencies by year, and the number of occurrences by year It may include a statistical analysis unit that calculates at least one or more of processed values from the number of co-occurrences between words.

The statistical analysis unit may calculate an increase rate of the number of occurrences for each word based on the number of instance frequencies and the number of simultaneous occurrences between words for each year.

In addition, the statistical analysis unit divides the number of instance frequencies per year by the number of documents per year to standardize the number of occurrence frequencies per year of the words per document to calculate an increase rate of the standardized number of occurrence frequencies for each word. can

In addition, the statistical analysis unit calculates an increase rate of centrality of the degree of connection for each word using the matrix of the number of co-occurrences between words, and the standardized rate of increase in the number of appearances for each word and the increase rate of centrality of the degree of connection for each word are a word having an upper certain portion or more; Words that are less than or equal to a lower certain portion can be derived as malicious code.

Meanwhile, the data collection module may collect big data including at least one unstructured data of image, video, voice, sensor, GPS, GIS, and M2M data.

Accordingly, the cloud service convergence system can improve the security of backup data by filtering data containing malicious code in advance through big data analysis through the abnormal data derivation system including the above configuration.

Such a technique for providing a data backup system with improved security according to the present invention is implemented as an application or implemented in the form of program instructions that can be executed through various computer components and recorded on a computer-readable recording medium. . The computer readable recording medium may include program instructions, data files, data structures, etc. alone or in combination.

Program instructions recorded on the computer-readable recording medium may be those specially designed and configured for the present invention, or those known and usable to those skilled in the art of computer software.

Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROMs and DVDs, and magneto-optical media such as floptical disks. media), and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like.

Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter or the like as well as machine language codes such as those produced by a compiler. The hardware device may be configured to act as one or more software modules to perform processing according to the present invention and vice versa.

Although the above has been described with reference to embodiments, those skilled in the art will understand that the present invention can be variously modified and changed without departing from the spirit and space of the present invention described in the claims below. You will be able to.

100: network management unit
200: resource management unit
300: backup management unit

Claims (3)

In a cloud service convergence system that virtualizes resources in a cloud environment composed of a plurality of server clusters and integrates and manages virtualized resources,
The cloud service convergence system,
A network management unit that creates a cluster by connecting a plurality of physical servers;
a resource management unit that virtualizes and manages computing resources, storage resources, network resources, and memory resources; and
A backup management unit that extracts backup target data requiring backup from data stored in the platform built through the cloud service convergence system and selectively backs up only the extracted backup target data;
The backup management unit,
A backup target determining unit that classifies data that is determined to require backup and data that is not determined to be backed up among data changes occurring between predetermined backup cycles using an artificial neural network; and
A backup performer for periodically backing up only the data set as backup targets by the backup target determination unit;
The backup target determination unit,
Referring to a pre-stored lookup table and assigning a first weight to each data according to path information of the data;
A second weight is given for each data based on the following equation,
Excluding data in which the calculated value of the first weight and the second weight is less than a preset reference value is excluded from the backup target, and includes data in which the calculated value of the first weight and the second weight is greater than or equal to the reference value is included in the backup target. , cloud service convergence system.

math formula
The second weight to be applied to the nth data = (value of the first variable/value of the second variable)*A
Here, the value of the first variable is the number of overlapping words among the words extracted from the n-th data, and the value of the second variable is the word extracted from the n-th data out of the total number of words extracted from the data stored in the cloud service convergence system. It is a value excluding the number, and A is a constant value set in proportion to the number of words extracted from data stored in the cloud service convergence system.

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