Patent Application
20230056637
The present invention relates generally to a system and method for managing hardware and software configuration solutions for service providers and in particular to a method and associated system for improving hardware and software technology associated with ingesting and analyzing data associated with hardware and software service components; and configuring additional managed hardware and software service components with respect to operationally functionality.
A first aspect of the invention provides a hardware device comprising a processor coupled to a computer-readable memory unit, the memory unit comprising instructions that when executed by the processor implements a configuration solution service management method comprising: ingesting, by the processor from managed hardware and software service components associated with a first entity, data and associated metadata associated with establishing baseline analysis code; analyzing, by the processor, the data and associated metadata via execution of code associated with a recurrent neural network (RNN) and long short term memory (LSTM) configured to determine if a current managed hardware or software solution service design is associated with client requirements and industry trends; first generating, by the processor based on results of the analyzing, recommendations, statistical analysis, and predictions associated with implemented managed solution services with respect to the managed hardware and software service components; second generating, by the processor, a proposed new client profile configured to predict future managed solution service offerings associated with additional managed hardware and software service components for the first entity, a sizing of the additional managed hardware and software service components, and current trends within an industry and geography associated with the first entity; and configuring, by the processor based on the proposed new client profile, the additional managed hardware and software service components with respect to operationally functionality for the first entity.
A second aspect of the invention provides a configuration solution service management method comprising: ingesting, by a processor of a hardware device from managed hardware and software service components associated with a first entity, data and associated metadata associated with establishing baseline analysis code; analyzing, by the processor, the data and associated metadata via execution of code associated with a recurrent neural network (RNN) and long short term memory (LSTM) configured to determine if a current managed hardware or software solution service design is associated with client requirements and industry trends; first generating, by the processor based on results of the analyzing, recommendations, statistical analysis, and predictions associated with implemented managed solution services with respect to the managed hardware and software service components; second generating, by the processor, a proposed new client profile configured to predict future managed solution service offerings associated with additional managed hardware and software service components for the first entity, a sizing of the additional managed hardware and software service components, and current trends within an industry and geography associated with the first entity; and configuring, by the processor based on the proposed new client profile, the additional managed hardware and software service components with respect to operationally functionality for the first entity.
A third aspect of the invention provides A computer program product, comprising a computer readable hardware storage device storing a computer readable program code, the computer readable program code comprising an algorithm that when executed by a processor of a hardware device implements a configuration solution service management method, the method comprising: ingesting, by the processor from managed hardware and software service components associated with a first entity, data and associated metadata associated with establishing baseline analysis code; analyzing, by the processor, the data and associated metadata via execution of code associated with a recurrent neural network (RNN) and long short term memory (LSTM) configured to determine if a current managed hardware or software solution service design is associated with client requirements and industry trends; first generating, by the processor based on results of the analyzing, recommendations, statistical analysis, and predictions associated with implemented managed solution services with respect to the managed hardware and software service components; second generating, by the processor, a proposed new client profile configured to predict future managed solution service offerings associated with additional managed hardware and software service components for the first entity, a sizing of the additional managed hardware and software service components, and current trends within an industry and geography associated with the first entity; and configuring, by the processor based on the proposed new client profile, the additional managed hardware and software service components with respect to operationally functionality for the first entity.
The present invention advantageously provides a simple method and associated system capable of managing hardware and software configuration solution services.
System 100 enables a process for collecting, mining, analyzing, obtaining trending data analysis, and providing data models and associated code by industry for creating accurate solution service predictions and associated hardware and software sizing. Likewise, system 100 enables a global telecommunication system (GTS) centralized managed service database for retrieving technical data, client geography data, industry data, environmental sizing data, growth trend data (with respect to a datacenter and/or emerging distributed data platforms), infrastructure data, middleware data, and application data fully managed as a solution service. Data gathering software tools may be enabled to gather metadata from managed solution service (hardware and software) components and/or individual entities to analyze metrics and analyze data for predicting accurate service architectural solutioning for single or bundled solution services, such as storage as a service (STaaS), backup as a service (BaaS), and disaster recovery as a service (DRaaS).
Data sources used to collect metadata may include a client configuration management database (CMDB) system such as a change control system for gathering historical, trend. and change rates with respect to specific hardware/software environments. Subsequently, managed service DB data is used to obtain metrics to validate and accurately predict client specific solution hardware and software architectures. Therefore, data analysis may be implemented during an architecting phase or to correctly size solutions accurately.
System 100 is configured to improve overengineering and/or under engineering technical solutions with respect to associated hardware and software. Under engineered solutions may result in retrofitting running environments which may cause system outages, loss of data, and service interruptions. Therefore, system 100 enables accurate and precise solutions associated with correct client growth trends for running environments optimally.
System 100 of
System 100 enables the following baseline analysis functionality with respect to configuring managed hardware and software solution services associated with operational functionality:
Relevant data and metadata associated with establishing a hardware and software service component baseline is ingested such that (during an initial deal sales contract) data and metadata associated with a client, a cost case, and deal information is ingested. For example, the data and metadata may be associated with a company, an industry, a geography, a legacy or greenfield, tower device counts (storage footprints, computing footprints, server counts, client device counts, network device counts, etc.), corporate data, yearly sales, level of integration (verticals), analysts assigned storage manager (SM), subject matter expert (SME), computer science and engineering (CSE), etc.). Likewise, the data and metadata may be associated with parameters associated with selection of relevant data for analysis with respect to existing practice parameters via execution of natural language processing (NLP) text analysis to extract tagged data with respect to relevant ingestion attributes. The tagged data is weighted and scored based on an SME evaluation with respect to quality status. Additionally, previously retrieved metadata current sizing tool parameters, and contract information is analyzed via learning module code and an initial hardware/software operation deal is generated. The initial hardware/software operation deal is compared with respect to insights gathered such that multi-client hardware/software environments are compared with respect to industry, infrastructure technologies, software applications, historical trending, and future forecasting for implementing industry leading solutions. When the initial hardware/software operation deal is executed, a solution architect runs executes gathering tools or custom scripts with respect to a client environment to gather data via sensors, agents, or data retrieval scripts (e.g., log extraction, SQL queries, system info, application commands, etc.). The scripts may be configured to run on a backup/restore server system for collecting data such as, inter alia, reporting info, internal system database (DB) queries with respect to data, operating system data, backup hardware/software product data, internal DB data, logs, etc. Additionally, when the initial hardware/software operation deal is executed: a system change rate is modified, a current hardware/software sizing is modified, a number and attributes of servers are modified, a growth rate is modified, changes over time are modified. Furthermore, the following functionality is enabled:
System 100 enables the following system profile analysis and operational functionality with respect to a hardware/software system sizing:
The system profile analysis is initialized when a solution architect retrieves gathered data and feeds it into a managed service meta DB analytics system. Subsequently, a massive repository of meta data analysis results is configured to associate a client with industry and geography trending analysis data. Likewise, analysis systems (comprising a single point reference) are configured to track environmental variables for multiple clients with respect to, inter alia, a 6 month history, growth data, solution issues, a baseline for all client’s managed services, a baseline for comparable services (e.g., size, industry, geography)
System 100 enables the following system analysis and output functionality with respect to a hardware/software system operational functionality:
Long short term memory (RNN-LSTM) ingested data is analyzed and processed via execution of a data science recurrent neural network (RNN). The RNN analyzes time with respect to execution of a LSTM long term memory neural network algorithm. The RNN is configured to model a sequence of data such that each data sample may be dependent on previous data samples. Likewise, the LSTM is configured to classify, process, predict, and train a data model with respect to key metrics, a success growth/rate, and a capacity load resulting in a generated output comprising recommendations, a resize for accuracy growth, and a metadata and deal analysis, a statistical analysis, and predictions based on clients within a same industry, geography, and custom configuration. Subsequently, the RNN-LSTM calculates trends with respect to operation of an external cloud DB and a configuration management database and a new client profile is generated.
The following implementation example describes a key usage process associated retrieval of best practice security standards at backup/restore sever level.
The process is initiated when feedback data is provided to sellers for selecting hardware/software products and an associated portfolio. For example, feedback data may include trends, industry specific technology adoption rates and standards such as financial industry utilizing data in flight encryption, etc. Subsequently, hardware/software device reliability factors are retrieved (from associated meta data) and analyzed. For example, device reliability factors associated with tape libraries and meta data error codes may be retrieved from multiple libraries and analyzed for predicting imminent component failure-based code patterns. Associated hardware error code patterns and related meta data may be used to predict error code patterns leading up to failures and provide auto self-healing fault recovery using machine learning processes to perform development enhancements for hardware solutions. Likewise, statistics associated with failures, crash causes, and outage events may be analyzed for building reliable hardware trending data for use in understanding a competitive landscape. Software and service products may be compared with respect to industry and corporate sizes to determine reliability, performance, and industry trends. The aforementioned process results in retrieval of best practice security standards at multiple levels for managed service component/devices.
In step 202, the data and associated metadata is analyzed via execution of code associated with a recurrent neural network (RNN) and long short term memory (LSTM) configured to determine if a current managed hardware or software solution service design is associated with client requirements and industry trends. Associated RNN model sequences of the data and associated metadata are executed such that each sample of the data and associated metadata is determined to be dependent from previous samples associated with the data and associated metadata. The LSTM may be associated with previously generated data within a memory structure. Likewise, the LSTM may be configured to be executed to classify, process, predict, and train a model generated as a result of the analysis of step 202.
In step 204, recommendations, statistical analysis, and predictions are generated based on results of the analysis of step 202. The recommendations, statistical analysis, and predictions are associated with implemented managed solution services with respect to the managed hardware and software service components. Generating the recommendations, statistical analysis, and predictions may be executed based on: client requirements of the entity with respect to client requirements of additional entities within a same industry of the entity, geographical and custom configurations hardware and software service components of the additional entities, and industry trends associated with the additional entities and determined via execution of the code associated with the RNN and LSTM.
In step 208, a proposed new client profile is generated. The proposed new client profile is configured to predict future managed solution service offerings associated with additional managed hardware and software service components for the entity, a sizing of the additional managed hardware and software service components, and current trends within an industry and geography associated with the entity. In step 210, the additional managed hardware and software service components are configured with respect to operationally functionality for the entity. In step 212, the additional managed hardware and software service components are deployed with respect to a facility of the entity based on results of step 210.
Aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, microcode, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module,” or “system.”
The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.
The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage. The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user’s computer, partly on the user’s computer, as a stand-alone software package, partly on the user’s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user’s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer readable program instructions may be provided to a processor of a computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be accomplished as one step, executed concurrently, substantially concurrently, in a partially or wholly temporally overlapping manner, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.
The computer system 90 illustrated in
In some embodiments, rather than being stored and accessed from a hard drive, optical disc or other writeable, rewriteable, or removable hardware memory device 95, stored computer program code 84 (e.g., including algorithms) may be stored on a static, nonremovable, read-only storage medium such as a Read-Only Memory (ROM) device 85, or may be accessed by processor 91 directly from such a static, nonremovable, read-only medium. Similarly, in some embodiments, stored computer program code 97 may be stored as computer-readable firmware 85, or may be accessed by processor 91 directly from such firmware 85, rather than from a more dynamic or removable hardware data-storage device 95, such as a hard drive or optical disc.
Still yet, any of the components of the present invention could be created, integrated, hosted, maintained, deployed, managed, serviced, etc. by a service supplier who offers to improve hardware and software technology associated with ingesting and analyzing data associated with hardware and software service components; and configuring additional managed hardware and software service components with respect to operationally functionality. Thus, the present invention discloses a process for deploying, creating, integrating, hosting, maintaining, and/or integrating computing infrastructure, including integrating computer-readable code into the computer system 90, wherein the code in combination with the computer system 90 is capable of performing a method for enabling a process for improving hardware and software technology associated with ingesting and analyzing data associated with hardware and software service components; and configuring additional managed hardware and software service components with respect to operationally functionality. In another embodiment, the invention provides a business method that performs the process steps of the invention on a subscription, advertising, and/or fee basis. That is, a service supplier, such as a Solution Integrator, could offer to enable a process for improving hardware and software technology associated with ingesting and analyzing data associated with hardware and software service components; and configuring additional managed hardware and software service components with respect to operationally functionality. In this case, the service supplier can create, maintain, support, etc. a computer infrastructure that performs the process steps of the invention for one or more customers. In return, the service supplier can receive payment from the customer(s) under a subscription and/or fee agreement and/or the service supplier can receive payment from the sale of advertising content to one or more third parties.
While
It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed.
Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.
Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service’s provider.
Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).
Resource pooling: the provider’s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).
Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the consumer is to use the provider’s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.
Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes.
Referring now to
Referring now to
Hardware and software layer 60 includes hardware and software components. Examples of hardware components include: mainframes 61; RISC (Reduced Instruction Set Computer) architecture based servers 62; servers 63; blade servers 64; storage devices 65; and networks and networking components 66. In some embodiments, software components include network application server software 67 and database software 68.
Virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers 71; virtual storage 72; virtual networks 73, including virtual private networks; virtual applications and operating systems 74; and virtual clients 75.
In one example, management layer 80 may provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 87 provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 88 provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.
Workloads layer 101 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation 102; software development and lifecycle management 103; virtual classroom education delivery 133; data analytics processing 134; transaction processing 106; and for improving video and software technology associated with extracting from a video stream and categorizing, skeleton points associated with a video representation of a user executing user movement actions; generating initial visual windows surrounding a group of skeleton points; extracting and linking feature vectors with point coordinates; and improving hardware and software technology associated with ingesting and analyzing data associated with hardware and software service components; and configuring additional managed hardware and software service components with respect to operationally functionality 107.
While embodiments of the present invention have been described herein for purposes of illustration, many modifications and changes will become apparent to those skilled in the art. Accordingly, the appended claims are intended to encompass all such modifications and changes as fall within the true spirit and scope of this invention.
