Self-managing database system using machine learning

A self-managing database system includes a metrics collector to collect metrics data from one or more databases of a computing system and an anomaly detector to analyze the metrics data and detect one or more anomalies. The system includes a causal inference engine to mark one or more nodes in a knowledge representation corresponding to the metrics data for the one or more anomalies and to determine a root cause with a highest probability of causing the one or more anomalies using the knowledge representation. The system includes a self-healing engine, to take at least one remedial action for the one or more databases in response to determination of the root cause.

TECHNICAL FIELD

One or more implementations relate to management of database processing, and more specifically to self-managing databases in a distributed system of a cloud computing environment.

BACKGROUND

“Cloud computing” services provide shared resources, software, and information to computers and other devices upon request or on demand. Cloud computing typically involves the over-the-Internet provision of dynamically scalable and often virtualized resources by a cloud service provider (CSP). Technological details can be abstracted from end-users, who no longer have need for expertise in, or control over, the technology infrastructure “in the cloud” that supports them. In cloud computing environments, software applications can be accessible over the Internet rather than installed locally on personal or in-house computer systems. Some of the applications or on-demand services provided to end-users can include the ability for a user to create, view, modify, store and share documents and other files.

Cloud computing systems are becoming increasingly more complex and system availability has become one of the most important requirements for users. A system failure often no longer impacts a single user or a small set of users, but instead the impact may be widespread and global. Significant downtime directly impacts the trust of users and partners as well as the reputation of the CSP. The failure of a hardware or software component in a cloud computing environment is inevitable, leading to a service incident. Responding to a typical incident in a large-scale, worldwide cloud computing environment may require the efforts of many people, such as system administrators, database experts, software engineers, hardware engineers, and project managers to identify and rectify a root cause of the incident.

DETAILED DESCRIPTION

A CSP securely stores and manages customer/user data using a database infrastructure. Availability of the database infrastructure is considered by many CSPs to be key to business success. A typical database (DB) infrastructure includes many software and hardware components such as relational databases, database servers, storage area networks (SANs), internal networks, etc. In some cloud computing environments, there are hundreds of these DB infrastructure units (also called pods) running at sites worldwide. In some cases, there are thousands of metrics/key performance indicators (KPIs) which are continually gathered from DB infrastructure components. The metrics/KPIs indicate the health of the system. At this scale, it's impossible for a DB infrastructure engineer to process all these metrics in real time, identify the root cause of a problem and quickly resolve the problem. The inability to quickly respond to and fix problems affects the availability of the DB infrastructure.

In response, embodiments of the present invention apply an anomaly detection process along with a Bayesian network as a knowledge representation to enable automatic analysis of relationships between metrics across multiple layers, in real time to determine the root cause of a problem and apply self-healing remediation actions.

FIG. 1Aillustrates a block diagram of an example of a cloud computing environment10in which an on-demand database service can be used in accordance with some implementations. Environment10includes user systems12(e.g., customer's computing systems), a network14, a database system16(also referred to herein as a “cloud-based system” or a “cloud computing system”), a processing device17, an application platform18, a network interface20, a tenant database22for storing tenant data (such as data sets), a system database24for storing system data, program code26for implementing various functions of the database system16(including a visual data cleaning application), and process space28for executing database system processes and tenant-specific processes, such as running applications for customers as part of an application hosting service. In some other implementations, environment10may not have all these components or systems, or may have other components or systems instead of, or in addition to, those listed above. In some embodiments, tenant database22is a shared storage.

In some implementations, environment10is a computing environment in which an on-demand database service exists. An on-demand database service, such as that which can be implemented using database system16, is a service that is made available to users outside an enterprise (or enterprises) that owns, maintains, or provides access to database system16. As described above, such users generally do not need to be concerned with building or maintaining database system16. Instead, resources provided by database system16may be available for such users' use when the users need services provided by database system16; that is, on the demand of the users. Some on-demand database services can store information from one or more tenants into tables of a common database image to form a multi-tenant database system (MTS). The term “multi-tenant database system” can refer to those systems in which various elements of hardware and software of a database system may be shared by one or more customers or tenants. For example, a given application server may simultaneously process requests for a large number of customers, and a given database table may store rows of data for a potentially much larger number of customers. A database image can include one or more database objects. A relational database management system (RDBMS) or the equivalent can execute storage and retrieval of information against the database object(s).

Application platform18can be a framework that allows the applications of database system16to execute, such as the hardware or software infrastructure of database system16. In some implementations, application platform18enables the creation, management and execution of one or more applications developed by the provider of the on-demand database service, users accessing the on-demand database service via user systems12, or third-party application developers accessing the on-demand database service via user systems12.

In some implementations, database system16implements a web-based customer relationship management (CRM) system. For example, in some such implementations, database system16includes application servers configured to implement and execute CRM software applications as well as provide related data, code, forms, renderable web pages, and documents and other information to and from user systems12and to store to, and retrieve from, a database system related data, objects, and World Wide Web page content. In some MTS implementations, data for multiple tenants may be stored in the same physical database object in tenant database22. In some such implementations, tenant data is arranged in the storage medium(s) of tenant database22so that data of one tenant is kept logically separate from that of other tenants so that one tenant does not have access to another tenant's data, unless such data is expressly shared. Database system16also implements applications other than, or in addition to, a CRM application. For example, database system16can provide tenant access to multiple hosted (standard and custom) applications, including a CRM application. User (or third-party developer) applications, which may or may not include CRM, may be supported by application platform18. Application platform18manages the creation and storage of the applications into one or more database objects and the execution of the applications in one or more virtual machines in the process space of database system16.

According to some implementations, each database system16is configured to provide web pages, forms, applications, data, and media content to user (client) systems12to support the access by user systems12as tenants of database system16. As such, database system16provides security mechanisms to keep each tenant's data separate unless the data is shared. If more than one MTS is used, they may be located in close proximity to one another (for example, in a server farm located in a single building or campus), or they may be distributed at locations remote from one another (for example, one or more servers located in city A and one or more servers located in city B). As used herein, each MTS could include one or more logically or physically connected servers distributed locally or across one or more geographic locations. Additionally, the term “server” is meant to refer to a computing device or system, including processing hardware and process space(s), an associated storage medium such as a memory device or database, and, in some instances, a database application, such as an object-oriented database management system (OODBMS) or a relational database management system (RDBMS), as is well known in the art. It should also be understood that “server system”, “server”, “server node”, and “node” are often used interchangeably herein. Similarly, the database objects described herein can be implemented as part of a single database, a distributed database, a collection of distributed databases, a database with redundant online or offline backups or other redundancies, etc., and can include a distributed database or storage network and associated processing intelligence.

Network14can be or include any network or combination of networks of systems or devices that communicate with one another. For example, network14can be or include any one or any combination of a local area network (LAN), wide area network (WAN), telephone network, wireless network, cellular network, point-to-point network, star network, token ring network, hub network, or other appropriate configuration. Network14can include a Transfer Control Protocol and Internet Protocol (TCP/IP) network, such as the global internetwork of networks often referred to as the “Internet” (with a capital “I”). The Internet will be used in many of the examples herein. However, it should be understood that the networks that the disclosed implementations can use are not so limited, although TCP/IP is a frequently implemented protocol.

User systems12(e.g., operated by customers) can communicate with database system16using TCP/IP and, at a higher network level, other common Internet protocols to communicate, such as the Hyper Text Transfer Protocol (HTTP), Hyper Text Transfer Protocol Secure (HTTPS), File Transfer Protocol (FTP), Apple File Service (AFS), Wireless Application Protocol (WAP), etc. In an example where HTTP is used, each user system12can include an HTTP client commonly referred to as a “web browser” or simply a “browser” for sending and receiving HTTP signals to and from an HTTP server of the database system16. Such an HTTP server can be implemented as the sole network interface20between database system16and network14, but other techniques can be used in addition to or instead of these techniques. In some implementations, network interface20between database system16and network14includes load sharing functionality, such as round-robin HTTP request distributors to balance loads and distribute incoming HTTP requests evenly over a number of servers. In MTS implementations, each of the servers can have access to the MTS data; however, other alternative configurations may be used instead.

User systems12can be implemented as any computing device(s) or other data processing apparatus or systems usable by users to access database system16. For example, any of user systems12can be a desktop computer, a workstation, a laptop computer, a tablet computer, a handheld computing device, a mobile cellular phone (for example, a “smartphone”), or any other Wi-Fi-enabled device, WAP-enabled device, or other computing device capable of interfacing directly or indirectly to the Internet or other network. When discussed in the context of a user, the terms “user system,” “user device,” and “user computing device” are used interchangeably herein with one another and with the term “computer.” As described above, each user system12typically executes an HTTP client, for example, a web browsing (or simply “browsing”) program, such as a web browser based on the WebKit platform, Microsoft's Internet Explorer browser, Netscape's Navigator browser, Opera's browser, Mozilla's Firefox browser, Google's Chrome browser, or a WAP-enabled browser in the case of a cellular phone, personal digital assistant (PDA), or other wireless device, allowing a user (for example, a subscriber of on-demand services provided by database system16) of user system12to access, process, and view information, pages, and applications available to it from database system16over network14.

Each user system12also typically includes one or more user input devices, such as a keyboard, a mouse, a trackball, a touch pad, a touch screen, a pen or stylus, or the like, for interacting with a graphical user interface (GUI) provided by the browser on a display (for example, a monitor screen, liquid crystal display (LCD), light-emitting diode (LED) display, etc.) of user system12in conjunction with pages, forms, applications, and other information provided by database system16or other systems or servers. For example, the user interface device can be used to access data and applications hosted database system16, and to perform searches on stored data, or otherwise allow a user to interact with various GUI pages that may be presented to a user. As discussed above, implementations are suitable for use with the Internet, although other networks can be used instead of or in addition to the Internet, such as an intranet, an extranet, a virtual private network (VPN), a non-TCP/IP based network, any LAN or WAN or the like.

The users of user systems12may differ in their respective capacities, and the capacity of a particular user system12can be entirely determined by permissions (permission levels) for the current user of such user system. For example, where a salesperson is using a particular user system12to interact with database system16, that user system can have the capacities allotted to the salesperson. However, while an administrator is using that user system12to interact with database system16, that user system can have the capacities allotted to that administrator. Where a hierarchical role model is used, users at one permission level can have access to applications, data, and database information accessible by a lower permission level user, but may not have access to certain applications, database information, and data accessible by a user at a higher permission level. Thus, different users generally will have different capabilities with regard to accessing and modifying application and database information, depending on the users' respective security or permission levels (also referred to as “authorizations”).

According to some implementations, each user system12and some or all of its components are operator-configurable using applications, such as a browser, including computer code executed using a central processing unit (CPU), such as a Core® processor commercially available from Intel Corporation or the like. Similarly, database system16(and additional instances of an MTS, where more than one is present) and all of its components can be operator-configurable using application(s) including computer code to run using processing device17, which may be implemented to include a CPU, which may include an Intel Core® processor or the like, or multiple CPUs. Each CPU may have multiple processing cores.

Database system16includes non-transitory computer-readable storage media having instructions stored thereon that are executable by or used to program a server or other computing system (or collection of such servers or computing systems) to perform some of the implementation of processes described herein. For example, program code26can include instructions for operating and configuring database system16to intercommunicate and to process web pages, applications (including visual data cleaning applications), and other data and media content as described herein. In some implementations, program code26can be downloadable and stored on a hard disk, but the entire program code, or portions thereof, also can be stored in any other volatile or non-volatile memory medium or device as is well known, such as a read-only memory (ROM) or random-access memory (RAM), or provided on any media capable of storing program code, such as any type of rotating media including floppy disks, optical discs, digital video discs (DVDs), compact discs (CDs), micro-drives, magneto-optical discs, magnetic or optical cards, nanosystems (including molecular memory integrated circuits), or any other type of computer-readable medium or device suitable for storing instructions or data. Additionally, the entire program code, or portions thereof, may be transmitted and downloaded from a software source over a transmission medium, for example, over the Internet, or from another server, as is well known, or transmitted over any other existing network connection as is well known (for example, extranet, virtual private network (VPN), local area network (LAN), etc.) using any communication medium and protocols (for example, TCP/IP, HTTP, HTTPS, Ethernet, etc.) as are well known. It will also be appreciated that computer code for the disclosed implementations can be realized in any programming language that can be executed on a server or other computing system such as, for example, C, C++, HTML, any other markup language, Java™, JavaScript, ActiveX, any other scripting language, such as VBScript, and many other programming languages as are well known.

FIG. 1Billustrates a block diagram of example implementations of elements ofFIG. 1Aand example interconnections between these elements according to some implementations. That is,FIG. 1Balso illustrates environment10, but inFIG. 1B, various elements of database system16and various interconnections between such elements are shown with more specificity according to some more specific implementations. In some implementations, database system16may not have the same elements as those described herein or may have other elements instead of, or in addition to, those described herein.

InFIG. 1B, user system12includes a processor system12A, a memory system12B, an input system12C, and an output system12D. The processor system12A can include any suitable combination of one or more processors. The memory system12B can include any suitable combination of one or more memory devices. The input system12C can include any suitable combination of input devices, such as one or more touchscreen interfaces, keyboards, mice, trackballs, scanners, cameras, or interfaces to networks. The output system12D can include any suitable combination of output devices, such as one or more display devices, printers, or interfaces to networks.

InFIG. 1B, network interface20is implemented as a set of HTTP application servers1001-100N. Each application server100, also referred to herein as an “app server,” is configured to communicate with tenant database22and tenant data23stored therein, as well as system database24and system data25stored therein, to serve requests received from user systems12. Tenant data23can be divided into individual tenant storage spaces112, which can be physically or logically arranged or divided. Within each tenant storage space112, tenant data114and application metadata116can similarly be allocated for each user. For example, a copy of a user's most recently used (MRU) items can be stored in tenant data114. Similarly, a copy of MRU items for an entire organization that is a tenant can be stored to tenant space112.

Database system16ofFIG. 1Balso includes a user interface (UI)30and an application programming interface (API)32. Process space28includes system process space102, individual tenant process spaces104and a tenant management process space110. Application platform18includes an application setup mechanism38that supports application developers' creation and management of applications. Such applications and others can be saved as metadata into tenant database22by save routines36for execution by subscribers as one or more tenant process spaces104managed by tenant management process space110, for example. Invocations to such applications can be coded using procedural language for structured query language (PL/SQL)34, which provides a programming language style interface extension to the API32. A detailed description of some PL/SQL language implementations is discussed in commonly assigned U.S. Pat. No. 7,730,478, titled METHOD AND SYSTEM FOR ALLOWING ACCESS TO DEVELOPED APPLICATIONS VIA A MULTI-TENANT ON-DEMAND DATABASE SERVICE, issued on Jun. 1, 2010, and hereby incorporated by reference herein in its entirety and for all purposes. Invocations to applications can be detected by one or more system processes, which manage retrieving application metadata116for the subscriber making the invocation and executing the metadata as an application in a virtual machine.

Each application server100can be communicably coupled with tenant database22and system database24, for example, having access to tenant data23and system data25, respectively, via a different network connection. For example, one application server1001can be coupled via the network14(for example, the Internet), another application server1002can be coupled via a direct network link, and another application server100Ncan be coupled by yet a different network connection. Transfer Control Protocol and Internet Protocol (TCP/IP) are examples of typical protocols that can be used for communicating between application servers100and database system16. However, it will be apparent to one skilled in the art that other transport protocols can be used to optimize database system16depending on the network interconnections used.

In some implementations, each application server100is configured to handle requests for any user associated with any organization that is a tenant of database system16. Because it can be desirable to be able to add and remove application servers100from the server pool at any time and for various reasons, in some implementations there is no server affinity for a user or organization to a specific application server100. In some such implementations, an interface system implementing a load balancing function (for example, an F5 Big-IP load balancer) is communicably coupled between application servers100and user systems12to distribute requests to application servers100. In one implementation, the load balancer uses a least-connections algorithm to route user requests to application servers100. Other examples of load balancing algorithms, such as round robin and observed-response-time, also can be used. For example, in some instances, three consecutive requests from the same user could hit three different application servers100, and three requests from different users could hit the same application server100. In this manner, by way of example, database system16can be a multi-tenant system in which database system16handles storage of, and access to, different objects, data, and applications across disparate users and organizations.

In one example storage use case, one tenant can be a company that employs a sales force where each salesperson uses database system16to manage aspects of their sales. A user can maintain contact data, leads data, customer follow-up data, performance data, goals and progress data, etc., all applicable to that user's personal sales process (for example, in tenant database22). In an example of a MTS arrangement, because all of the data and the applications to access, view, modify, report, transmit, calculate, etc., can be maintained and accessed by a user system12having little more than network access, the user can manage his or her sales efforts and cycles from any of many different user systems. For example, when a salesperson is visiting a customer and the customer has Internet access in their lobby, the salesperson can obtain critical updates regarding that customer while waiting for the customer to arrive in the lobby.

While each user's data can be stored separately from other users' data regardless of the employers of each user, some data can be organization-wide data shared or accessible by several users or all of the users for a given organization that is a tenant. Thus, there can be some data structures managed database system16that are allocated at the tenant level while other data structures can be managed at the user level. Because an MTS can support multiple tenants including possible competitors, the MTS can have security protocols that keep data, applications, and application use separate. Also, because many tenants may opt for access to an MTS rather than maintain their own system, redundancy, up-time, and backup are additional functions that can be implemented in the MTS. In addition to user-specific data and tenant-specific data, database system16also can maintain system level data usable by multiple tenants or other data. Such system level data can include industry reports, news, postings, and the like that are sharable among tenants.

In some implementations, user systems12(which also can be client systems) communicate with application servers100to request and update system-level and tenant-level data from database system16. Such requests and updates can involve sending one or more queries to tenant database22or system database24. Database system16(for example, an application server100in database system16) can automatically generate one or more SQL statements (for example, one or more SQL queries) designed to access the desired information. System database24can generate query plans to access the requested data from the database. The term “query plan” generally refers to one or more operations used to access information in a database system.

Each database can generally be viewed as a collection of objects, such as a set of logical tables, containing data fitted into predefined or customizable categories. A “table” is one representation of a data object and may be used herein to simplify the conceptual description of objects and custom objects according to some implementations. It should be understood that “table” and “object” may be used interchangeably herein. Each table generally contains one or more data categories logically arranged as columns or fields in a viewable schema. Each row or element of a table can contain an instance of data for each category defined by the fields. For example, a CRM database can include a table that describes a customer with fields for basic contact information such as name, address, phone number, fax number, etc. Another table can describe a purchase order, including fields for information such as customer, product, sale price, date, etc. In some MTS implementations, standard entity tables can be provided for use by all tenants. For CRM database applications, such standard entities can include tables for case, account, contact, lead, and opportunity data objects, each containing pre-defined fields. As used herein, the term “entity” also may be used interchangeably with “object” and “table.”

In some MTS implementations, tenants are allowed to create and store custom objects, or may be allowed to customize standard entities or objects, for example by creating custom fields for standard objects, including custom index fields. Commonly assigned U.S. Pat. No. 7,779,039, titled CUSTOM ENTITIES AND FIELDS IN A MULTI-TENANT DATABASE SYSTEM, issued on Aug. 17, 2010, and hereby incorporated by reference herein in its entirety and for all purposes, teaches systems and methods for creating custom objects as well as customizing standard objects in a multi-tenant database system. In some implementations, for example, all custom entity data rows are stored in a single multi-tenant physical table, which may contain multiple logical tables per organization. It is transparent to customers that their multiple “tables” are in fact stored in one large table or that their data may be stored in the same table as the data of other customers.

FIG. 2Ashows a system diagram illustrating example architectural components of an on-demand database service environment200according to some implementations. A client machine communicably connected with the cloud204, generally referring to one or more networks in combination, as described herein, can communicate with the on-demand database service environment200via one or more edge routers208and212. A client machine can be any of the examples of user systems12described above. The edge routers can communicate with one or more core switches220and224through a firewall216. The core switches can communicate with a load balancer228, which can distribute server load over different pods, such as the pods240and244. Pods240and244, which can each include one or more servers or other computing resources, can perform data processing and other operations used to provide on-demand services. Communication with the pods can be conducted via pod switches232and236. Components of the on-demand database service environment can communicate with database storage256through a database firewall248and a database switch252.

As shown inFIGS. 2A and 2B, accessing an on-demand database service environment can involve communications transmitted among a variety of different hardware or software components. Further, the on-demand database service environment200is a simplified representation of an actual on-demand database service environment. For example, while only one or two devices of each type are shown inFIGS. 2A and 2B, some implementations of an on-demand database service environment can include anywhere from one to many devices of each type. Also, the on-demand database service environment need not include each device shown inFIGS. 2A and 2Bor can include additional devices not shown inFIGS. 2A and 2B.

Additionally, it should be appreciated that one or more of the devices in the on-demand database service environment200can be implemented on the same physical device or on different hardware. Some devices can be implemented using hardware or a combination of hardware and software. Thus, terms such as “data processing apparatus,” “machine,” “server,” “device,” and “processing device” as used herein are not limited to a single hardware device; rather, references to these terms can include any suitable combination of hardware and software configured to provide the described functionality.

Cloud204is intended to refer to a data network or multiple data networks, often including the Internet. Client machines communicably connected with cloud204can communicate with other components of the on-demand database service environment200to access services provided by the on-demand database service environment. For example, client machines can access the on-demand database service environment to retrieve, store, edit, or process information. In some implementations, edge routers208and212route packets between cloud204and other components of the on-demand database service environment200. For example, edge routers208and212can employ the Border Gateway Protocol (BGP). The BGP is the core routing protocol of the Internet. Edge routers208and212can maintain a table of Internet Protocol (IP) networks or ‘prefixes,’ which designate network reachability among autonomous systems on the Internet.

In some implementations, firewall216can protect the inner components of the on-demand database service environment200from Internet traffic. Firewall216can block, permit, or deny access to the inner components of on-demand database service environment200based upon a set of rules and other criteria. Firewall216can act as one or more of a packet filter, an application gateway, a stateful filter, a proxy server, or any other type of firewall.

In some implementations, core switches220and224are high-capacity switches that transfer packets within the on-demand database service environment200. Core switches220and224can be configured as network bridges that quickly route data between different components within the on-demand database service environment. In some implementations, the use of two or more core switches220and224can provide redundancy or reduced latency.

In some implementations, pods240and244perform the core data processing and service functions provided by the on-demand database service environment. Each pod can include various types of hardware or software computing resources. An example of the pod architecture is discussed in greater detail with reference toFIG. 2B. In some implementations, communication between pods240and244is conducted via pod switches232and236. Pod switches232and236can facilitate communication between pods240and244and client machines communicably connected with cloud204, for example, via core switches220and224. Also, pod switches232and236may facilitate communication between pods240and244and database storage256. In some implementations, load balancer228can distribute workload between pods240and244. Balancing the on-demand service requests between the pods can assist in improving the use of resources, increasing throughput, reducing response times, or reducing overhead. Load balancer228may include multilayer switches to analyze and forward traffic.

In some implementations, access to database storage256is guarded by a database firewall248. Database firewall248can act as a computer application firewall operating at the database application layer of a protocol stack. Database firewall248can protect database storage256from application attacks such as SQL injection, database rootkits, and unauthorized information disclosure. In some implementations, database firewall248includes a host using one or more forms of reverse proxy services to proxy traffic before passing it to a gateway router. Database firewall248can inspect the contents of database traffic and block certain content or database requests. Database firewall248can work on the SQL application level atop the TCP/IP stack, managing applications' connection to the database or SQL management interfaces as well as intercepting and enforcing packets traveling to or from a database network or application interface.

In some implementations, communication with database storage256is conducted via database switch252. Multi-tenant database storage256can include more than one hardware or software components for handling database queries. Accordingly, database switch252can direct database queries transmitted by other components of the on-demand database service environment (for example, pods240and244) to the correct components within database storage256. In some implementations, database storage256is an on-demand database system shared by many different organizations as described above with reference toFIGS. 1A and 1B.

FIG. 2Bshows a system diagram further illustrating example architectural components of an on-demand database service environment according to some implementations. Pod244can be used to render services to a user of on-demand database service environment200. In some implementations, each pod includes a variety of servers or other systems. Pod244includes one or more content batch servers264, content search servers268, query servers282, file servers286, access control system (ACS) servers280, batch servers284, and app servers288. Pod244also can include database instances290, quick file systems (QFS)292, and indexers294. In some implementations, some or all communication between the servers in pod244can be transmitted via pod switch236.

In some implementations, app servers288include a hardware or software framework dedicated to the execution of procedures (for example, programs, routines, scripts) for supporting the construction of applications provided by on-demand database service environment200via pod244. In some implementations, the hardware or software framework of an app server288is configured to execute operations of the services described herein, including performance of the blocks of various methods or processes described herein. In some alternative implementations, two or more app servers288can be included and cooperate to perform such methods, or one or more other servers described herein can be configured to perform the disclosed methods.

Content batch servers264can handle requests internal to the pod. Some such requests can be long-running or not tied to a particular customer. For example, content batch servers264can handle requests related to log mining, cleanup work, and maintenance tasks. Content search servers268can provide query and indexer functions. For example, the functions provided by content search servers268can allow users to search through content stored in the on-demand database service environment. File servers286can manage requests for information stored in file storage298. File storage298can store information such as documents, images, and binary large objects (BLOBs). In some embodiments, file storage298is a shared storage. By managing requests for information using file servers286, the image footprint on the database can be reduced. Query servers282can be used to retrieve information from one or more file systems. For example, query servers282can receive requests for information from app servers288and transmit information queries to network file systems (NFS)296located outside the pod.

Pod244can share a database instance290configured as a multi-tenant environment in which different organizations share access to the same database. Additionally, services rendered by pod244may call upon various hardware or software resources. In some implementations, ACS servers280control access to data, hardware resources, or software resources. In some implementations, batch servers284process batch jobs, which are used to run tasks at specified times. For example, batch servers284can transmit instructions to other servers, such as app servers288, to trigger the batch jobs.

In some implementations, QFS292is an open source file system available from Sun Microsystems, Inc. The QFS can serve as a rapid-access file system for storing and accessing information available within the pod244. QFS292can support some volume management capabilities, allowing many disks to be grouped together into a file system. File system metadata can be kept on a separate set of disks, which can be useful for streaming applications where long disk seeks cannot be tolerated. Thus, the QFS system can communicate with one or more content search servers268or indexers294to identify, retrieve, move, or update data stored in NFS296or other storage systems.

In some implementations, one or more query servers282communicate with the NFS296to retrieve or update information stored outside of the pod244. NFS296can allow servers located in pod244to access information to access files over a network in a manner similar to how local storage is accessed. In some implementations, queries from query servers282are transmitted to NFS296via load balancer228, which can distribute resource requests over various resources available in the on-demand database service environment. NFS296also can communicate with QFS292to update the information stored on NFS296or to provide information to QFS292for use by servers located within pod244.

In some implementations, the pod includes one or more database instances290. Database instance290can transmit information to QFS292. When information is transmitted to the QFS, it can be available for use by servers within pod244without using an additional database call. In some implementations, database information is transmitted to indexer294. Indexer294can provide an index of information available in database instance290or QFS292. The index information can be provided to file servers286or QFS292. In some embodiments, there may be a plurality of database instances stored and accessed throughout the system.

The exemplary computer system300includes a processing device (processor)302, a main memory304(e.g., ROM, flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), etc.), a static memory306(e.g., flash memory, static random access memory (SRAM), etc.), and a data storage device320, which communicate with each other via a bus310.

Computer system300may further include a network interface device308. Computer system300also may include a video display unit312(e.g., a liquid crystal display (LCD), a cathode ray tube (CRT), or a touch screen), an alphanumeric input device314(e.g., a keyboard), a cursor control device316(e.g., a mouse or touch screen), and a signal generation device322(e.g., a loudspeaker).

Power device318may monitor a power level of a battery used to power computer system300or one or more of its components. Power device318may provide one or more interfaces to provide an indication of a power level, a time window remaining prior to shutdown of computer system300or one or more of its components, a power consumption rate, an indicator of whether computer system is utilizing an external power source or battery power, and other power related information. In some implementations, indications related to power device318may be accessible remotely (e.g., accessible to a remote back-up management module via a network connection). In some implementations, a battery utilized by power device318may be an uninterruptable power supply (UPS) local to or remote from computer system300. In such implementations, power device318may provide information about a power level of the UPS.

Data storage device320may include a computer-readable storage medium324(e.g., a non-transitory computer-readable storage medium) on which is stored one or more sets of instructions326(e.g., software) embodying any one or more of the methodologies or functions described herein. Instructions326may also reside, completely or at least partially, within main memory304and/or within processor302during execution thereof by computer system300, main memory304, and processor302also constituting computer-readable storage media. Instructions326may further be transmitted or received over a network330(e.g., network14) via network interface device308.

In one implementation, instructions326include instructions for performing any of the implementations described herein. While computer-readable storage medium324is shown in an exemplary implementation to be a single medium, it is to be understood that computer-readable storage medium324may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions.

FIG. 4is a diagram of a self-managing database system400using machine learning according to some embodiments. According to some embodiments, self-managing database system400is executed by one or more of database system16ofFIG. 1, pod240or244ofFIG. 2A, batch servers284, database instance290, or app servers288ofFIG. 2B, and/or processor302ofFIG. 3. In an embodiment, self-managing database system400is invoked in the cloud computing environment once every five minutes. In other embodiments, other frequencies are used.

Metrics collector402collects DB and/or operating system (OS) metrics, error codes, and system/DB change information (collectively called metrics data herein) from any one or more of the components described inFIGS. 1, 2, and 3. Metrics data are collected at any frequency, at predetermined times, or upon occurrence of an error. It is anticipated that in embodiments of the present invention the quantity and size of metric data received from system components worldwide in real time will be very large (e.g., thousands, tens of thousands, or even hundreds of thousands of metric data values per unit time, and tens, hundreds, or even thousands of gigabytes of data per unit time). In one embodiment, the unit of time is a minute, but the unit of time is configurable. Metrics collector402accepts the metrics data and stores metrics data in metrics database404for subsequent use by self-managing database system400.

FIG. 5is a diagram of metrics data collection according to some embodiments. Dynamic configuration502comprises a data structure to define the metrics data that is to be collected and the frequency of collection. In an embodiment, dynamic configuration502is specified by a system administrator. In an embodiment, dynamic configuration502is a script written in a markup language. Metrics collector402reads dynamic configuration502to control operations of OS metrics collector504, DB metrics collector506, log scanner508, and change scanner510of metrics collector402. OS metrics collector504collects OS metrics data and stores the OS metrics data in OS data processing area512of memory304. DB metrics collector506collects DB metrics data and stores the DB metrics data in DB data processing area514of memory304. Log scanner508collects metrics data from log files and stores the log files metrics data in log data processing area516of memory304. Change scanner510collects metrics data from changes made to DBs or other software components and stores the changes metrics data in change scanner data processing area518of memory304.

Thus, metrics collector402stores metrics data in memory304for use in real time processing of recently obtained metrics data by self-managing database system400. In one embodiment, the time period for this metrics data is the past two hours and is updated every minute. Thus, memory304always stores the latest two hours of metrics data. In other embodiments, other time periods may be used for recent metrics data (e.g., 30 minutes, one hour, four hours, a day, etc.) and the update schedule may be changed (e.g., every two minutes, every five minutes, every ten minutes, and so on). In one embodiment, metrics data is segregated in memory304into separate portions of memory as described above. Thus, for example, OS metrics data is stored in OS data processing area512, DB metrics data is stored in DB data processing area514, log metrics data is stored in log data processing area516, and change scanner data is stored in change scanner data processing area518. In other embodiments, the different types of metrics data may be co-mingled and stored in the same area of memory304. In some embodiments, the different types of metrics data are tagged to identify the type of data. Data loader520stores metrics data in metrics database404. In an embodiment, metrics database404is a part of system database24ofFIG. 1and/or data storage device320ofFIG. 3. In an embodiment, metrics data includes historical metrics data and recent metrics data. In one embodiment, historical metrics data includes metrics data received longer than two hours ago, and recent metrics data includes metrics data received less than or equal to two hours ago. In other embodiments, other time thresholds may be used (e.g., 30 minutes, one hour, and so on). Thus, in some embodiments, the metrics collector402stores metrics data collected within a most recent selected period of time in memory304of the computing system (e.g., a random access memory (RAM) and metrics data collected earlier than the most recent selected period of time (e.g., historical metrics data) is stored in a long term storage device (e.g., hard drive, solid state drive, etc.) of the computing system by data loader520. In one embodiment, data loader520copies metrics data from memory304into metrics database404on a periodic basis, such as every minute. In other embodiments, other time periods are used. This ensures that if the system crashes, the metrics data in memory is not lost.

In embodiment of the present invention, production DB infrastructure components are heterogeneous and training a model which can fit well for all DB components of the cloud computing environment is tedious and time intensive for system engineers. In response, an anomaly detection approach is used in embodiments to detect abnormal patterns in metrics database404, then the results of anomaly detection analysis are passed to a Bayesian network (in one embodiment) for root cause analysis.

One known technique for identifying anomalies includes computation of an Extreme Student Deviate (ESD). In ESD, a “Z score” for an observed value is computed using the following formula:

ESDzscore=xk-mean⁡(X)σ
where xkis an observed metric data value, X is a distribution, and δ is the standard deviation. Since the ESD formula is based on mean ( ) and standard deviation, this ESD formula is highly sensitive to outlier values and is not suitable for managing databases in the cloud computing environment.

In embodiments of the present invention, a new anomaly detection formula for a Z score is used:
zscore(ofxr)=(xr−median(Xh))÷(median(|Xh(i))−median(Xh)|i=1 . . . n))
where xris a metrics data sample value from recent metrics data, Xhare metrics data sample values of “h” hours (e.g., historical metrics data set values), and Xh(i)where i=1 . . . nare metrics data sample values in Xh.

FIGS. 6A and 6Bare flow diagrams600,620of anomaly detector406processing according to some embodiments. In an embodiment, anomaly detector406is executed repeatedly while a cloud computing environment is operational at a frequency that is selectable. At block602, anomaly detector406gets a previously computed metric summary and a last metrics computation time of the metric summary from metrics database404in data storage device320and loads this data into memory304. In an embodiment, the metric summary includes the median, median absolute deviation (MAD) and critical threshold value for a metric as performed below at block610. At block604, in one embodiment, anomaly detector406determines if the last metrics computation time is more than 24 hours old (e.g., as compared to the current time). In other embodiments, other time periods may be used (e.g., two days, three days, 12 hours, 6 hours, and so on). In one embodiment, if 24 hours has not passed since the last metrics computation, then processing continues at block6B onFIG. 6B. In one embodiment, if at least 24 hours has passed, then processing continues with block606. At block606, if anomaly detector406determines that the current time is not off-peak, then processing continues at block6B ofFIG. 6B. In one embodiment, off-peak times are outside of normal business hours for a geographic location, such as between 5 pm and 9 am. In other embodiments, other times for off-peak may be used. If the current time is off-peak, then at block608anomaly detector406gets historical metrics data for one or more metrics from metrics database404. In an embodiment, the historical metrics data includes two months of metrics data from metrics database404for each metric. In other embodiment, other amounts of historical metrics data are obtained.

At block610, anomaly detector406computes a metrics summary of a median, a MAD, and a critical threshold value based on a predetermined significance level (e.g., alpha=0.02) for each metric based at least in part on the historical metrics data. In an embodiment, these computations are performed during an off-peak time for the cloud computing environment, since these computations are typically computationally intensive. At block612, anomaly detector406stores the metric summary and current metric computation time in metrics database404.

Processing continues with block622ofFIG. 6B, where anomaly detector406gets recent metrics data from memory304. In an embodiment, the recent metrics data includes the last five minutes of metrics data for one or more metrics. In other embodiments, other time thresholds can be used. In an embodiment, one or more metrics are included in the recent metrics data and anomaly detector processing includes performing blocks624through634for each metric. In an embodiment, five data samples are taken for each metric represented in the recent metrics data. In other embodiment, other numbers of samples per metric may be used. At block624, for a current metric the anomaly detector computes a Z score for each sample of the current metric in real time using the metric summary (read from metrics database404) and the anomaly detection formula of embodiments of the present invention. The Z score represents an anomaly value for the sample.

At block626, the Z score of each sample is compared against the current metric's critical threshold value (Z critical) to check if the current sample is an outlier or not.
Test(|zscore(ofxr)|>|Zcritical(@a)|)

At block626, if the absolute value of the Z score for a sample is greater than the critical threshold then anomaly detector406at block630marks the sample as anomalous and saves this information. Otherwise, anomaly detector406at block628marks the sample as normal and saves this information. In one embodiment, anomaly detector may pre-set all samples of a metric as normal and only change the annotation of the sample if an anomaly is detected. At block632, if the number of anomalous samples in the current metric is greater than a predetermined threshold, then the current metric is marked as anomalous at block634and this information is saved in an anomalous metric data structure in memory304(e.g., part of metrics data and anomalies408). In an embodiment, the threshold may be three when the sample size is five (e.g., when three out of five samples are anomalous, then the current metric is considered to be anomalous). Other sample sizes and thresholds may be used in various embodiments. Processing continues at block636, where anomaly detector determines if all metrics have been processed for the second set. If so, anomaly detector processing is done at block638when all samples of all metrics of the recent metrics data have been processed. Otherwise, anomaly detector processing continues with the next metric of the recent metrics data at block624.

FIG. 7is an example graph of a Z score distribution according to some embodiments. In this example, a Z score greater than or equal to 2 is considered to be anomalous702, and a Z score less than or equal to −2 is considered to be anomalous704. A Z score between −2 and 2 is considered to be non-anomalous706.

Returning toFIG. 4, metrics and anomalies408includes metrics data from metrics database404and anomalies identified by anomaly detector406. Metrics and anomalies408information is input along with knowledge representation412to causal inference engine410. In an embodiment, knowledge representation412is a Bayesian network. In other embodiments, other structures for knowledge representation may be used. Causal inference engine410determines a root cause of an issue highlighted by one or more anomalies detected by anomaly detector406. Metrics and their relationships are modeled in knowledge representation412. In an embodiment, the Bayesian network is populated a priori using subject matter expertise (e.g., from DB engineers, for example) and historical data insights. A conditional probability distribution table is generated for each node and evaluated using historical data insights.

FIG. 8is an example diagram of a knowledge representation412according to some embodiments. In the example, nodes in the Bayesian network800are built and populated for analyzing a database infrastructure (such as database system16). In other examples, other Bayesian networks may be built and populated based on subject matter knowledge for a specific scenario. Each node of the Bayesian network includes a conditional probability distribution table as shown below. Each node represents an event such as a possible error condition in database system16and relationships with other nodes in the Bayesian network. By analyzing the Bayesian network, causal inference engine410determines a most likely root cause for an issue indicated by anomalies.

FIG. 9is a flow diagram900of causal inference engine410processing according to some embodiments. At block902, causal inference engine410gets anomalous metrics (as determined by anomaly detector406inFIGS. 6A and 6B) from metrics and anomalies408. At block904, causal inference engine410processes the anomalous metrics for the anomalous metrics data structure created at block634and marks nodes corresponding to anomalous metrics in knowledge representation412(e.g., a node such as app lock802of Bayesian network800). In an embodiment, a node with an anomalous metric is marked as an “evidence node” in Bayesian network800. At block906, for each marked node (e.g., each evidence node), causal inference engine410traverses from the marked node to a leaf node (e.g., a possible root cause node) and from the marked node to a root (impact) node of Bayesian network800. The combination of the two traversals is a potential root cause path. The potential root cause path is saved. At block908, causal inference engine410, for each potential root cause path, computes a probability of the leaf node for the potential root cause path being the root cause of the issue indicated by the anomalous metrics data. In an embodiment, the probability is computed using Bayes Theorem. At block910, causal inference engine410determines the path that has the highest probability for the leaf node and marks the path as the root cause path. At block912, the leaf node with the highest probability is marked as the root cause of the issue. The root cause is output as inference conclusions414.

In one illustrative example, assume anomaly detector406detects anomalies for log file sync836, log file parallel write834, and DB sequential read828events ofFIG. 8. Based on example Bayesian network800and the tables shown above, the probability of an I/O issue is 92.22% and the probability of a DB bug is 20.88%. Since the probability of an I/O issue is higher than the probability of a DB bug, causal inference engine410determines that the root cause of the issue is an I/O issue. This can be seen by a cause and effect chain of nodes I/O failure824->storage latency826->log file parallel write834->log file sync836->commit838->DB events808->DB health810.

In another illustrative example, assume anomaly detector406detects anomalies for log file sync836, however log file parallel write834and DB file sequential read828events are reported as normal. Based on example Bayesian network800and the tables shown above, the probability of an I/O issue is 19.32% and the probability of a DB bug is 66.67%. Since the probability of a DB bug is higher than the probability of an I/O issue, causal inference engine410determines that the root cause of the issue is a DB bug. This can be seen by a cause and effect chain of nodes DB bug832->log file sync836->commit838->DB events808->DB health810.

Turning back toFIG. 4, inference conclusions414are input to self-healing engine416, along with healing rules418. Self-healing engine416takes specified pre-defined remedial actions in response to inference conclusions414(including one or more root causes as determined by causal inference engine410) to fix one or more errors in database systems16and/or other software and/or hardware components of the computing system indicated by the anomalies. Examples of remedial actions include killing one or more database sessions which is causing application level locks, killing the database session using high temporary table space usages or other resources, restarting services which are hung up, and so on. Self-healing engine416gets the identified root cause and reads a self-healing action name and script from healing rules418for the identified root cause in inference conclusions414. Self-healing engine416executes one or more healing actions420using the script to fix the issue identified by the root cause. In this way, self-healing engine416automatically resolves the issue when possible. If the issue cannot be automatically resolved, in an embodiment a case is created in a case management system (not shown) with details of anomalies detected and identified root cause. The case will then be handled using a manual approach by system administrators and/or systems engineers.

Finally, visualization and reporting422is executed to display and report the anomalies and/or root causes to system administrators and/or DB engineers. In an embodiment, the visualization and reporting are done using a web-based user interface. In an embodiment, the user interface comprises an analytic dashboard. In another embodiment, the visualization and reporting are done using reports (such as hypertext markup language (HTML) reports) attached to the case in the case management system for manual review of the issue.

Embodiments of the present invention provide at least several advantages. Because of the complexity involved, training and developing a model to capture all possible scenarios is laborious. In embodiments, the model doesn't need extensive training because the anomaly detection approach is used to detect abnormalities in the cloud computing environments having large numbers of databases. In embodiments, the model captures expert/engineer knowledge with accuracy and enables the system to make accurate predictions, performs impact analysis, and makes a self-healing decision. The combination of anomaly detection and use of a Bayesian network for the knowledge representation of experts/engineers ensures effectiveness in identifying and fixing errors. The accuracy in capturing an anomaly and performing causal inference with self-healing actions is high within this model because the cloud computing infrastructure component's metrics are heavily used to perform root cause analysis and make remediation decisions. Embodiments also help system administrators and/or systems engineers to detect new and complex unknown situations in the computing system which were not previously humanly possible to detect, thereby improving the resiliency of the cloud computing infrastructure. Metrics can be correlated on the fly and the expert knowledge representation helps to find multiple cause and effect relations given the confidence level and given the evidence.

Examples of systems, apparatuses, computer-readable storage media, and methods according to the disclosed implementations are described in this section. These examples are being provided solely to add context and aid in the understanding of the disclosed implementations. It will thus be apparent to one skilled in the art that the disclosed implementations may be practiced without some or all of the specific details provided. In other instances, certain process or method operations, also referred to herein as “blocks,” have not been described in detail in order to avoid unnecessarily obscuring the disclosed implementations. Other implementations and applications also are possible, and as such, the following examples should not be taken as definitive or limiting either in scope or setting.

In the detailed description, references are made to the accompanying drawings, which form a part of the description and in which are shown, by way of illustration, specific implementations. Although these disclosed implementations are described in sufficient detail to enable one skilled in the art to practice the implementations, it is to be understood that these examples are not limiting, such that other implementations may be used and changes may be made to the disclosed implementations without departing from their spirit and scope. For example, the blocks of the methods shown and described herein are not necessarily performed in the order indicated in some other implementations. Additionally, in some other implementations, the disclosed methods may include more or fewer blocks than are described. As another example, some blocks described herein as separate blocks may be combined in some other implementations. Conversely, what may be described herein as a single block may be implemented in multiple blocks in some other implementations. Additionally, the conjunction “or” is intended herein in the inclusive sense where appropriate unless otherwise indicated; that is, the phrase “A, B, or C” is intended to include the possibilities of “A,” “B,” “C,” “A and B,” “B and C,” “A and C,” and “A, B, and C.”

The words “example” or “exemplary” are used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “example” or “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the words “example” or “exemplary” is intended to present concepts in a concrete fashion.

In addition, the articles “a” and “an” as used herein and in the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. Reference throughout this specification to “an implementation,” “one implementation,” “some implementations,” or “certain implementations” indicates that a particular feature, structure, or characteristic described in connection with the implementation is included in at least one implementation. Thus, the appearances of the phrase “an implementation,” “one implementation,” “some implementations,” or “certain implementations” in various locations throughout this specification are not necessarily all referring to the same implementation.

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 discussion, it is appreciated that throughout the description, discussions utilizing terms such as “receiving,” “retrieving,” “transmitting,” “computing,” “generating,” “adding,” “subtracting,” “multiplying,” “dividing,” “optimizing,” “calibrating,” “detecting,” “performing,” “analyzing,” “determining,” “enabling,” “identifying,” “modifying,” “transforming,” “applying,” “aggregating,” “extracting,” “registering,” “querying,” “populating,” “hydrating,” “updating,” or the like, refer to the actions and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (e.g., electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission, or display devices.

The specific details of the specific aspects of implementations disclosed herein may be combined in any suitable manner without departing from the spirit and scope of the disclosed implementations. However, other implementations may be directed to specific implementations relating to each individual aspect, or specific combinations of these individual aspects. Additionally, while the disclosed examples are often described herein with reference to an implementation in which a computing environment is implemented in a system having an application server providing a front end for an on-demand database service capable of supporting multiple tenants, the present implementations are not limited to multi-tenant databases or deployment on application servers. Implementations may be practiced using other database architectures, i.e., ORACLE®, DB2® by IBM, and the like without departing from the scope of the implementations claimed. Moreover, the implementations are applicable to other systems and environments including, but not limited to, client-server models, mobile technology and devices, wearable devices, and on-demand services.

It should also be understood that some of the disclosed implementations can be embodied in the form of various types of hardware, software, firmware, or combinations thereof, including in the form of control logic, and using such hardware or software in a modular or integrated manner. Other ways or methods are possible using hardware and a combination of hardware and software. Any of the software components or functions described in this application can be implemented as software code to be executed by one or more processors using any suitable computer language such as, for example, C, C++, Java™ (a trademark of Sun Microsystems, Inc.), or Perl using, for example, existing or object-oriented techniques. The software code can be stored as non-transitory instructions on any type of tangible computer-readable storage medium (referred to herein as a “non-transitory computer-readable storage medium”). Examples of suitable media include random access memory (RAM), read-only memory (ROM), magnetic media such as a hard-drive or a floppy disk, or an optical medium such as a compact disc (CD) or digital versatile disc (DVD), flash memory, and the like, or any combination of such storage or transmission devices. Computer-readable media encoded with the software/program code may be packaged with a compatible device or provided separately from other devices (for example, via Internet download). Any such computer-readable medium may reside on or within a single computing device or an entire computer system and may be among other computer-readable media within a system or network. A computer system, or other computing device, may include a monitor, printer, or other suitable display for providing any of the results mentioned herein to a user.

The disclosure also relates to apparatuses, devices, and system adapted/configured to perform the operations herein. The apparatuses, devices, and systems may be specially constructed for their required purposes, may be selectively activated or reconfigured by a computer program, or some combination thereof.

In the foregoing description, numerous details are set forth. It will be apparent, however, to one of ordinary skill in the art having the benefit of this disclosure, that the present disclosure may be practiced without these specific details. While specific implementations have been described herein, it should be understood that they have been presented by way of example only, and not limitation. The breadth and scope of the present application should not be limited by any of the implementations described herein but should be defined only in accordance with the following and later-submitted claims and their equivalents. Indeed, other various implementations of and modifications to the present disclosure, in addition to those described herein, will be apparent to those of ordinary skill in the art from the foregoing description and accompanying drawings. Thus, such other implementations and modifications are intended to fall within the scope of the present disclosure.

Furthermore, although the present disclosure has been described herein in the context of a particular implementation in a particular environment for a particular purpose, those of ordinary skill in the art will recognize that its usefulness is not limited thereto and that the present disclosure may be beneficially implemented in any number of environments for any number of purposes. Accordingly, the claims set forth below should be construed in view of the full breadth and spirit of the present disclosure as described herein, along with the full scope of equivalents to which such claims are entitled.