Patent Application
20210191833
Benefit is claimed under 35 U.S.C. 119(a)-(d) to Foreign Application Serial No. 201941052880 filed in India entitled “IMPROVED COMPONENT DETECTION AND AWARENESS IN A COMPUTING ENVIRONMENT BY AUTOMATICALLY IDENTIFYING PHYSCIAL COMPONENTS HOUSING THE COMPONENT WITHIN THE COMPUTING ENVIRONMENT” on Dec. 19, 2019, by VMWARE, Inc., which is herein incorporated in its entirety by reference for all purposes.
In a machine learning environment, feature selection (sometimes referred to as “variable selection”, “attribute selection”, or similar) is a critical part of the machine learning process. Feature selection specifically refers to determining which features are important and, therefore, should be used in the creation and operation of a machine learning model. In the feature selection process, a subset of important and/or relevant features is selected from a larger set of features. The subset of important and/or relevant features are then deemed to be of importance to and are, therefore, used in the construction of the machine learning environment.
In various computing environments, including machine learning environments, it is necessary to provide component identification and awareness in a networking and security for the various components in the computing environment in order to protect against numerous cyber threats. One such security measure is provided by the NSX™ platform 804 of VMware, Inc developed by VMware, Inc. of Palo Alto, Calif. Typically, a system administrator (e.g., an Information Technology (IT) administrator, or the like) registers those machines or components of the computing environment, for which the IT administrator desires protection against cyber threats, with a security system such as the above-mentioned NSX™ platform 804 of VMware, Inc. Conventionally, the IT administrator registers the machines or components by manually defining or listing the components, including virtualized machines or components, within the computing environment that are to be registered with the security system being used. Once the various machines or components (virtual and/or physical) are registered with the identification system, the various machines or components are protected by the identification system. Conversely, machines or components which are not registered with the identification system are not protected by the identification system. It will be understood that due to the number of machines or components typically found in a computing environment (and due to the computational overhead required for the identification system to monitor the registered machines or components) it is only feasibly to register a subset of the machines or components with the computing environment.
VMs and hosts are not rack aware i.e., unless manually tagged in the vCenter, there is currently no information available about which data center rack a particular server is mounted on and hence that information cannot be used to determine whether two VMs with a heavy network communication flow between them are on the same rack (and hence on the same TOR switch) or not, which too affects the network latency for communication happening between the VMs. Furthermore, in conventional implementations as stated above, (in for example, vCenter) require a manual logging of host-to-VM relationships. Due to the dynamic nature of Data centers and the like, manual log records are not always accurate and rapidly become out of date. Further, in an NSX or ML environment, as VMs (or other components) are automatically provisioned and unprovisioned, manual logging is not feasible. With such automated computing resource (e.g., rack) awareness, the present invention is able to effectively route communications, and also reduce or eliminate Elephant hairpins and the like. The present invention will reduce network latency, reduce loads on communication networks, and increase communication and operation efficiency by integrating vRNI with intelligent rack management and also leveraging the blade/chassis information from converged infrastructures, the present invention automatically determines the rack in which a particular host is mounted and correlate the information with the VMs and use it in designing the network topology in the computing environment.
In an NSX or automated ML environment, this invention allows for intelligent communication between components. In one embodiment, kernel-to-kernel communication between co-located/co-hosted VMs, in contrast with conventional methods where communication is first routed through a distantly located network router and then ultimately back to a destination VM even when the source VM and the destination VM have closely located hosts (or even co-hostedby the same machine), allow communications to occur by reducing the number of physical devices the communication is routed through.
It should also be noted that most computing environments, including machine learning environments are not static. That is, various machines or components are constantly being added to, or removed from, the computer environment. As such changes are made to the computing environment, it is frequently necessary to amend or change which of the various machines or components (virtual and/or physical) are registered with the security system. Hence, in conventional approaches, and IT administrator (or similar) is required to at least periodically reassess which machines or components the IT administrator needs to register for protection with the security system. Hence, it is possible that newly added important and/or extremely relevant features of a machine learning environment are not be properly registered for appropriate protection by the security system. It is also possible that machines or components which once warranted protection by the security system, no longer require such security protection.
Thus, conventional approaches for providing network access and security to machines or components of a computing environment, including a machine learning environment, are highly dependent upon the skill and knowledge of a system administrator. Also, conventional approaches for providing security to machines or components of a computing environment, are not acceptable in complex and frequently revised computing environments.
Additionally, many conventional network and security systems require every machine or component within a computing environment be assigned to a particular scope and service group so that the intended states can be derived from the service type. As the size and complexity of computing environments increases, such a requirement may require a high-level system administrator to manually register as many as thousands (or many more) of the machines or components (such as, for example, virtual machines) with the security system. Thus, such conventionally mandated registration of the machines or components is not a trivial job. This burden of manual registration is made even more burdensome considering that the target users of many security systems are often experienced or very high-level personnel such as, for example, Chief Information Security Officers (CISOs) and their teams who already have heavy demands on their time.
Furthermore, even such high-level personnel may not have full knowledge of the network topology of the computing environment or understanding of the functionality of every machine or component within the computing environment. Hence, even when possible, the time and/or person-hours necessary to perform and complete such a conventionally required configuration for a security system can extend to days, weeks, months or even longer.
Moreover, even when such conventionally required manual registration of the various machines or components is completed, it is not uncommon that entities, including the aforementioned very high level personnel, have failed to properly assign the proper scopes and services to the various machines or components of the computing environment. Furthermore, in conventional security systems, it not uncommon to find such improper assignment of scopes and services to the various machines or components of the computing environment even after a conventional security system has been operational for years since its initial deployment. As a result, such improper assignment of the scopes and services to the various machines or components of the computing environment may have significantly and deleteriously impacted the security protection performance of conventional security systems even for a prolonged duration.
Furthermore, as stated above, most computing environments, including machine learning environments are not static. That is, various machines or components are constantly being added to, or removed from, the computing environment. As such changes are made to the computing environment, it is necessary to review the changed computing environment and once again assign the proper scopes and services to the various machines or components of the newly changed computing environment. Hence, the aforementioned overhead associated with the assignment of scopes and services to the various machines or components of the computing environment will not only occur at the initial phase when deploying a conventional security system, but such aforementioned overhead may also occur each time the computing environment is expanded, updated, or otherwise altered. This includes instances in which the computing environment is altered, for example, by is expanding, updating, or otherwise altering, for example, the roles of machine or components including, but not limited to, virtual machines of the computing environment.
The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments of the present technology and, together with the description, serve to explain the principles of the present technology.
The drawings referred to in this description should not be understood as being drawn to scale except if specifically noted.
Reference will now be made in detail to various embodiments of the present technology, examples of which are illustrated in the accompanying drawings. While the present technology will be described in conjunction with these embodiments, it will be understood that they are not intended to limit the present technology to these embodiments. On the contrary, the present technology is intended to cover alternatives, modifications and equivalents, which may be included within the spirit and scope of the present technology as defined by the appended claims. Furthermore, in the following description of the present technology, numerous specific details are set forth in order to provide a thorough understanding of the present technology. In other instances, well-known methods, procedures, components, and circuits have not been described in detail as not to unnecessarily obscure aspects of the present technology.
Some portions of the detailed descriptions which follow are presented in terms of procedures, logic blocks, processing and other symbolic representations of operations on data bits within a computer memory. These descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. In the present application, a procedure, logic block, process, or the like, is conceived to be one or more self-consistent procedures or instructions leading to a desired result. The procedures are those requiring physical manipulations of physical quantities. Usually, although not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated in an electronic device.
It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussions, it is appreciated that throughout the description of embodiments, discussions utilizing terms such as “displaying”, “identifying”, “generating”, “deriving”, “providing,” “utilizing”, “determining,” or the like, refer to the actions and processes of an electronic computing device or system such as: a host processor, a processor, a memory, a virtual storage area network (VSAN), a virtualization management server or a virtual machine (VM), among others, of a virtualization infrastructure or a computer system of a distributed computing system, or the like, or a combination thereof. The electronic device manipulates and transforms data, represented as physical (electronic and/or magnetic) quantities within the electronic device's registers and memories, into other data similarly represented as physical quantities within the electronic device's memories or registers or other such information storage, transmission, processing, or display components.
Embodiments described herein may be discussed in the general context of processor-executable instructions residing on some form of non-transitory processor-readable medium, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or distributed as desired in various embodiments.
In the Figures, a single block may be described as performing a function or functions; however, in actual practice, the function or functions performed by that block may be performed in a single component or across multiple components, and/or may be performed using hardware, using software, or using a combination of hardware and software. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure. Also, the example mobile electronic device described herein may include components other than those shown, including well-known components.
The techniques described herein may be implemented in hardware, software, firmware, or any combination thereof, unless specifically described as being implemented in a specific manner. Any features described as modules or components may also be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, the techniques may be realized at least in part by a non-transitory processor-readable storage medium comprising instructions that, when executed, perform one or more of the methods described herein. The non-transitory processor-readable data storage medium may form part of a computer program product, which may include packaging materials.
The non-transitory processor-readable storage medium may comprise random access memory (RAM) such as synchronous dynamic random access memory (SDRAM), read only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), FLASH memory, other known storage media, and the like. The techniques additionally, or alternatively, may be realized at least in part by a processor-readable communication medium that carries or communicates code in the form of instructions or data structures and that can be accessed, read, and/or executed by a computer or other processor.
The various illustrative logical blocks, modules, circuits and instructions described in connection with the embodiments disclosed herein may be executed by one or more processors, such as one or more motion processing units (MPUs), sensor processing units (SPUs), host processor(s) or core(s) thereof, digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), application specific instruction set processors (ASIPs), field programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. The term “processor,” as used herein may refer to any of the foregoing structures or any other structure suitable for implementation of the techniques described herein. In addition, in some embodiments, the functionality described herein may be provided within dedicated software modules or hardware modules configured as described herein. Also, the techniques could be fully implemented in one or more circuits or logic elements. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of an SPU/MPU and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with an SPU core, MPU core, or any other such configuration.
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System 100 also includes an I/O device 120 for coupling system 100 with external entities. For example, in one embodiment, I/O device 120 is a modem for enabling wired or wireless communications between system 100 and an external network such as, but not limited to, the Internet.
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First, a brief overview of an embodiment of the present Component Awareness and Proximity Detection provisioning invention, is provided below. Various embodiments of the present invention provide a method and system for automated feature selection within a machine learning environment.
More specifically, the various embodiments of the present invention provide a novel approach for automatically providing an identification of computer resources and their physical residence for provisioning to logical components for the various machines or components of a computing environment such as, for example, machine learning environment. In one embodiment, an IT administrator (or other entity such as, but not limited to, a user/company/organization etc.) registers multiple number of machines or components, such as, for example, virtual machines onto a virtual computer system platform, such as, for example, the NSX™ platform from VMware, Inc. of Palo Alto. In the present embodiment, the IT administrator is not required to manually label all the virtual machines with the corresponding service type or indicate the importance of the particular machine or component. Further, the IT administrator is not required to selectively list only those machines or components which the IT administrator feels warrant protection from the security system platform. Instead, and as will be described below in detail, in various embodiments, the present invention, will automatically determine which machines or component are to be protected by the security system.
As will also be described below, in various embodiments, the present invention is a computing module which integrated within a virtual computing system such as, for example, the NSX™ platform of VMware, Inc. of Palo Alto. In various embodiments, the present invention provides a near neighbor identification methodology utilizing an automated “rack identification and awareness” methodology to map components and their respective hosts and, importantly, the particular rack location of the host, will itself figure out the service type and corresponding importance of various machines or components after observing the activity by each of the machines or components for a period of time.
Importantly, for purposes and brevity and clarity, the following detailed description of the various embodiments of the present invention, will be described using an example in which the embodiments of the present Component Awareness and Proximity Detection (CA-PD) invention are integrated into security system, such as, but not limited to, NSX™ platform from VMware, Inc. of Palo Alto, Calif. Importantly, although the description and examples herein refer to embodiments of the present invention applied to the above security system with, for example, its corresponding set of functions, it should be understood that the embodiments of the present invention are well suited to use with various other types of computer systems. Furthermore, although, for purposes of brevity and clarity, the present description and examples herein refer to NSX™ platform, it should be understood that the NSX™ platform 804 from VMware, Inc. of Palo Alto, Calif., may also be defined to include various other components, such as, but not limited to, an appliance module (NSX™ Appliance), and an NSX™ MP (management plane) component.
Additionally, for purposes of brevity and clarity, the present application will refer to “machines or components” of a computing environment. It should be noted that for purposes of the present application, the terms “machines or components” is intended to encompass physical (e.g., hardware and software based) computing machines, physical components (such as, for example, physical modules or portions of physical computing machines) which comprise such physical computing machines, aggregations or combination of various physical computing machines, aggregations or combinations or various physical and logical components and the like. Further, it should be noted that for purposes of the present application, the terms “machines or components” is also intended to encompass virtualized (e.g., virtual and software based) computing machines, virtual components (such as, for example, virtual modules or portions of virtual computing machines) which comprise such virtual computing machines, aggregations or combination of various virtual computing machines, aggregations or combinations or various virtual components and the like.
Additionally, for purposes of brevity and clarity, the present application will refer to machines or components of a computing environment. It should be noted that for purposes of the present application, the term “computing environment” is intended to encompass any computing environment (e.g., a plurality of coupled computing machines or components including, but not limited to, a networked plurality of computing devices, a neural network, a machine learning environment, and the like). Further, in the present application, the computing environment may be comprised of only physical computing machines, only virtualized computing machines, or, more likely, some combination of physical and virtualized computing machines.
Furthermore, again for purposes and brevity and clarity, the following description of the various embodiments of the present invention, will be described as integrated within a networking and security system. Importantly, although the description and examples herein refer to embodiments of the present invention integrated within a security system with, for example, its corresponding set of functions, it should be understood that the embodiments of the present invention are well suited to not being integrated into a security system and operating separately from a security system. Specifically, embodiments of the present invention can be integrated into a system other than a networking and security system. Embodiments of the present invention can operate as a stand-alone module without requiring integration into another system. In such an embodiment, results from the present invention regarding feature selection and/or the importance of various machines or components of a computing environment can then be provided as desired to a separate system or to an end user such as, for example, an IT administrator.
Importantly, the embodiments of the present component awareness and Proximity Detection (CA-PD) invention significantly extend what was previously possible with respect to providing component awareness, provisioning and security for machines or components of a computing environment. Various embodiments of the present component awareness and Proximity Detection (CA-PD) invention enable the improved capabilities while reducing reliance upon, for example, an IT administrator, to selectively register various machines or components of a computing environment for security protection and monitoring. This contrasts with conventional approaches for providing networking and security to various machines or components of a computing environment which highly dependent upon the skill and knowledge of a system administrator. Thus, embodiments of present component awareness and Proximity Detection identification (CA-PD) invention provide a methodology which extends well beyond what was previously known.
Also, although certain components are depicted in, for example, embodiments of the Component Awareness and Proximity Detection (CA-PD) invention, it should be understood that, for purposes of clarity and brevity, each of the components may themselves be comprised of numerous modules or macros which are not shown.
Procedures of the present Component Awareness and Proximity Detection (CA-PD) invention are performed in conjunction with various computer software and/or hardware components. It is appreciated that in some embodiments, the procedures may be performed in a different order than described above, and that some of the described procedures may not be performed, and/or that one or more additional procedures to those described may be performed. Further some procedures, in various embodiments, are carried out by one or more processors under the control of computer-readable and computer-executable instructions that are stored on non-transitory computer-readable storage media. It is further appreciated that one or more procedures of the present may be implemented in hardware, or a combination of hardware with firmware and/or software.
Hence, the embodiments of the present Component Awareness and Proximity Detection (CA-PD) invention greatly extend beyond conventional methods for providing security to machines or components of a computing environment. Moreover, embodiments of the present invention amount to significantly more than merely using a computer to provide conventional security measures to machines or components of a computing environment. Instead, embodiments of the present invention specifically recite a novel process, necessarily rooted in computer technology, for Component Awareness and Proximity Detection (CA-PD) invention.
Furthermore, in various embodiments of the present invention, and as will be described in detail below, a networking or security system, such as, but not limited to, the NSX platform or NSX™ platform from VMware, Inc. of Palo Alto, Calif. will include novel networking and security solution for a computing environment (including, but not limited to a data center comprising a virtual environment). In embodiments of the present invention, unlike conventional security systems which “chases the threats”, the present security system will instead focus on monitoring the intended states of applications, machines or components of the computing environment, and the present security system will raise alarms if any anomaly behavior is detected.
Additionally, as will be described in detail below, embodiments of the present invention provide a security system including a novel search feature for machines or components (including, but not limited to, virtual machines) of the computing environment. The novel search feature of the present network and security system enables ends users to readily assign the proper and scopes and services the machines or components of the computing environment, Moreover, the novel search feature of the present security system enables end users to identify various machines or components (including, but not limited to, virtual machines) similar to given and/or previously identified machines or components (including, but not limited to, virtual machines) when such machines or component satisfy a particular given criteria. Hence, as will be described in detail below, in embodiments of the present networking and security system, the novel search feature functions by finding or identifying the “siblings” of various other machines or components (including, but not limited to, virtual machines) within the computing environment.
As stated above, feature selection which is also known as “variable selection”, “attribute selection” and the like, is an import process of machine learning. The process of feature selection helps to determine which features are most relevant or important to use to create a machine learning model (predictive model).
In embodiments of the present invention, a networking and security system such as, for example, the NSX™ platform from VMware, Inc. of Palo Alto, Calif. will utilize a Component Awareness and Proximity Detection (CA-PD) module to automatically perform the feature selection process. That is, as will be described in detail below, in embodiments of the present Component Awareness and Proximity Detection (CA-PD) invention, a computing module, such as, for example, CA-PD module 199 of
Several selection methodologies are currently utilized in the art of feature selection. The common selection algorithms include three classes: Filter Methods, Wrapper Methods and Embedded Methods. In Filter Methods, scores are assigned to each feature based on a statistical measurement. The features are then ranked by their scores and are either selected to be kept as relevant features or they are deemed to not be relevant features and are removed from or not included in dataset of those features defined as relevant features. One of the most popular algorithms of the Filter Methods classification is the Chi Squared Test. Algorithms in the Wrapper Methods classification consider the selection of a set of features as a search result from the best combinations. One such example from the Wrapper Methods classification is called the “recursive feature elimination” algorithm. Finally, algorithms in the Embedded Methods classification learn features while the machine learning model is being created, instead of prior to the building of the model. Examples of Embedded Method algorithms include the “LASSO” algorithm and the “Elastic Net” algorithm.
Embodiments of the present Component Awareness and Proximity Detection (CA-PD) invention utilize a statistic model to determine the importance of a particular feature within, for example, a machine learning environment.
In an NSX or automated ML environment, this invention allows for intelligent communication between components. In one embodiment, kernel-to-kernel communication between co-located/co-hosted VMs, in contrast with conventional methods where communication is first routed through a distantly located network router and then ultimately back to a destination VM even when the source VM and the destination VM have closely located hosts (or even co-hosted by the same machine), allow communications to occur by reducing the number of physical devices the communication is routed through.
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In one such embodiment, end user 430 will, for example, use the results from CA-PD module 199 to manually assign the appropriate network or security protection and monitoring (which is then applied, for example, by a network and security system such as, for example, system 420) corresponding to the importance of various machines or components of computing environment 410. In one embodiment, the operations of present Component Awareness and Proximity Detection (CA-PD) invention are performed, for example, by feature selection module 803 of
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Importantly, the embodiments of the present Component Awareness and Proximity Detection (CA-PD) invention significantly extend what was previously possible with respect to providing network security for machines or components of a computing environment. Various embodiments of the present Component Awareness and Proximity Detection (CA-PD) invention enable the improved capabilities while reducing reliance upon, for example, an IT administrator, to selectively register various machines or components of a computing environment for security protection and monitoring. This contrasts with conventional approaches for providing security to various machines or components of a computing environment which highly dependent upon the skill and knowledge of a system administrator.
Furthermore, embodiments of the present Component Awareness and Proximity Detection (CA-PD) invention utilize a novel feature selection methodology, including the resource identification and degrees of separation analysis, for feature selection and importance determination for features and corresponding machines or components of a computing environment. Even further, embodiments of the present Component Awareness and Proximity Detection (CA-PD) invention utilize the above-mentioned novel feature identification and selection methodology in an automated manner and then various embodiments also automatically (e.g., without requiring intervention of an IT administrator) apply, via a networking and security system, appropriate monitoring and protection to the various features (and corresponding machines or components) of the computer environment. Thus, embodiments of present Component Awareness and Proximity Detection (CA-PD) invention provide a methodology which greatly and non-obviously extends well beyond what was previously known.
Hence, the embodiments of the present Component Awareness and Proximity Detection (CA-PD) invention greatly extend beyond conventional methods for performing feature selection within a computing environment. Moreover, embodiments of the present invention amount to significantly more than merely using a computer to provide conventional security measures to machines or components of a computing environment. Instead, embodiments of the present invention specifically recite a novel process, necessarily rooted in computer technology, for automated Component Awareness and Proximity Detection (CA-PD).
Additionally, embodiments of the present Component Awareness and Proximity Detection (CA-PD) invention greatly extend beyond conventional methods for providing security to machines or components of a computing environment. That is, embodiments of the present invention amount to significantly more than merely using a computer to provide conventional networking and security measures to machines or components of a computing environment. Instead, embodiments of the present invention specifically recite a novel process, necessarily rooted in computer technology, for automated identification of components with their corresponding host and their degree of separation from each other, and then using the results to automatically assign appropriate provisioning/unprovisioning measures to the various machines or components of a computing environment.
In various embodiments, the present Component Awareness and Proximity Detection (CA-PD) invention automatically provides feature selection information. In so doing, the present embodiments enable improved security monitoring for the various machines or components of a computing environment. Thus, embodiments of the present invention teach novel approaches for using a computer to overcome a problem specifically arising in the computer-based realm of providing network access and security to various machines or components of a computing environment, such as, for example, a machine learning environment.
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In such an embodiment, the novel aspects of the present invention run independently from the main component of a network and security system because the novel component, such as the VM search module 802 (also referred to as a VM Search Service) uses Machine Learning (ML) techniques which heavily rely on data processing, data mining and advanced computations such as matrix operations.
Hence, the computational requirements of the VM search module 802 are quite different from the computational requirements of the overall security system such as, but not limited to, the NSX™ platform 804 of VMware, Inc developed by VMware, Inc. of Palo Alto, Calif. It should be noted that for purposes of brevity and clarity, the abbreviation VM is used herein to refer to the term “virtual machine”. It should be noted, however, that the various embodiments of the present invention are not limited solely to use with virtual machines, but, instead, the various embodiments of the present invention are well suited to use with various other machines or components (including, but not limited to, virtual machines) within a computing environment.
Additionally, in various embodiments of the present invention, by having the novel aspects of the present invention run independently from the main component of a network and security system, embodiments of the present invention enable engineers working on the novel VM search module 802 to have different skill sets than the skill sets of the traditional application developers who typically work on conventional security systems. As yet another advantage of embodiments of the present invention, in which the novel VM search module 802 runs separately from the network and security system, the separately operating novel VM search module 802 has reduced interference with the functions of the conventional network and security system.
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In various embodiments of the present invention, the CA-PD feature selection analysis is directly used to find VMs matching a given host. To find VMs for a given service, the present CA-PD feature selection analysis is extended.
In various embodiments of the present invention, after the above-described CA-PD feature selection and analysis, the novel VM search module 802 of the present embodiment computes the weight score for each feature accordingly to the CA-PD feature selection and analysis, and saves the results in the local machine learning (ML) database. Also, in some embodiments, the above-mentioned local machine learning (ML) database is comprised, for example, of ML non-relational database (DB) of
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At 915 of workflow 900, the present novel Component Awareness and Proximity Detection module 199 correlates the configuration information from vCenter about all network hosts and their respective physical network adapter in the server (pnics), The configuration information is presented to workflow 900. At 920 the Media Access Control (MAC) address of the pnic is compared against the MAC address of the network interface to confirm if the request is valid.
If the request is valid a list of hosts and their corresponding rack information is generated. If the request is invalid the present novel Component search module 802 returns to 910 to regenerate the rack/enclosure information, as well as the host/chassis information in the computing environment. as shown at 910, to, for example, a graphic user interface used by the user to submit the search request.
At 930 of workflow 900, the present novel VM search module 802 will utilize, for example, ML non-relational database (DB) to find all the classified machines or components (e.g., but not limited to, virtual machines (VMs)) in the computing environment and generates a list of all host servers and their associated rack information.
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At step 1015, the degree separation module 530 correlates the configuration data gathered in step 1010 to generate a graph of components with their corresponding rack information.
At step 1020, the configuration information with the associated communication flow information is used to identify the rack-host maps in the computing environment. With this mapping, the degree of separation between hosts is generated at step 1025 to allow the provisioning of components in the computing environment. In one embodiment, critical components are provisioned at step 1030 in such a way to ensure that they do not reside in the physical location. By ensuring that the degree of separation between hosts in the computing environment are mitigated to a reasonably low number, the present invention reduces network latency, reduce loads on communication networks, and increase communication and operation efficiency.
Hence, embodiments of the present invention greatly extend beyond conventional methods for providing security to machines or components of a computing environment. Moreover, embodiments of the present invention amount to significantly more than merely using a computer to provide conventional security measures to machines or components of a computing environment. Instead, embodiments of the present invention specifically recite a novel process, necessarily rooted in computer technology, for providing security to machines or components of a computing environment.
Furthermore, in various embodiments of the present invention, a security system, such as, but not limited to, the NSX™ platform 804 from VMware, Inc. of Palo Alto, Calif. will include a novel security solution for a computing environment (including, but not limited to a data center comprising a virtual environment). In embodiments of the present invention, unlike conventional security systems which “chases the threats”, the present security system focuses on monitoring the intended states of applications, machines or components of the computing environment, and the present security system will raise alarms if any anomaly behavior is detected.
Additionally, embodiments of the present invention provide a security system including a novel search feature for machines or components (including, but not limited to, virtual machines) of the computing environment. The novel search feature of the present security system enables ends users to readily assign the proper and scopes and services the machines or components of the computing environment, Moreover, the novel search feature of the present security system enables end users to identify various machines or components (including, but not limited to, virtual machines) similar to given and/or previously identified machines or components (including, but not limited to, virtual machines) when such machines or component satisfy a particular given criteria. Hence, in embodiments of the present network and security system, the novel search feature functions by finding or identifying the “siblings” of various other machines or components (including, but not limited to, virtual machines) within the computing environment.
The examples set forth herein were presented in order to best explain, to describe particular applications, and to thereby enable those skilled in the art to make and use embodiments of the described examples. However, those skilled in the art will recognize that the foregoing description and examples have been presented for the purposes of illustration and example only. The description as set forth is not intended to be exhaustive or to limit the embodiments to the precise form disclosed. Rather, the specific features and acts described above are disclosed as example forms of implementing the Claims.
Reference throughout this document to “one embodiment,” “certain embodiments,” “an embodiment,” “various embodiments,” “some embodiments,” “various embodiments”, or similar term, means that a particular feature, structure, or characteristic described in connection with that embodiment is included in at least one embodiment. Thus, the appearances of such phrases in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics of any embodiment may be combined in any suitable manner with one or more other features, structures, or characteristics of one or more other embodiments without limitation.
| Number | Date | Country | Kind |
|---|---|---|---|
| 201941052880 | Dec 2019 | IN | national |
