MACHINE LEARNING ANOMALY DETECTION MECHANISM

Abstract

Techniques and structures to facilitate anomaly detection within a networking system, including retrieving performance metric messages generated at a server, transmitting a compute request including the performance metric messages to a database system to perform an anomaly detection computation, retrieving data resulting from the anomaly detection computation and publishing the data resulting from the anomaly detection computation, wherein the anomaly detection computation comprises executing a machine learning model to process the performance metric messages to determine whether anomalous usage of the system has been detected.

Description

CROSS REFERENCE TO RELATED APPLICATIONS

The present patent application is a continuation in part application claiming priority from U.S. application Ser. No. 16/261,753, filed Jan. 30, 2019, which is currently pending.

TECHNICAL FIELD

One or more implementations relate to monitoring customer relationship management (CRM) systems.

BACKGROUND

Customer Relationship Management (CRM) systems are implemented to manage a company's relationships and interactions with customers and potential customers by compiling data from a range of different communication channels, including a company's website, telephone, email, live chat, marketing materials and social media. Due to the high volume of requests, it is often necessary to monitor CRM systems to determine whether there are anomalies occurring that may affect system performance.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following drawings like reference numbers are used to refer to like elements. Although the following figures depict various examples, one or more implementations are not limited to the examples depicted in the figures.

FIG. 1 illustrates one embodiment of a system;

FIG. 2 illustrates one embodiment of a host organization;

FIG. 3 illustrates one embodiment of a server;

FIG. 4 is a flow diagram illustrating one embodiment of a process performed by a client;

FIG. 5 illustrates one embodiment of a database system;

FIG. 6 illustrates one embodiment of a prediction generation engine;

FIG. 7 is a flow diagram illustrating one embodiment of an anomaly detection process;

FIG. 8 illustrates a computer system according to one embodiment;

FIG. 9 illustrates an environment wherein an on-demand database service might be used according to one embodiment; and

FIG. 10 illustrates elements of environment of FIG. 9 and various possible interconnections between these elements according to one embodiment.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth. However, embodiments of the invention may be practiced without these specific details. In other instances, well-known structures and techniques have not been shown in detail in order not to obscure the understanding of this description.

Methods and systems are provided to facilitate anomaly detection in a CRM. In embodiments, a client within an application server retrieves performance metric messages generated at the server, transmits a compute request including the performance metric messages to a database system to perform an anomaly detection computation, retrieves data resulting from the anomaly detection computation from the database system and publishes the data resulting from the anomaly detection computation.

It is contemplated that embodiments and their implementations are not merely limited to multi-tenant database system (“MTDBS”) and can be used in other environments, such as a client-server system, a mobile device, a personal computer (“PC”), a web services environment, etc. However, for the sake of brevity and clarity, throughout this document, embodiments are described with respect to a multi-tenant database system, such as Salesforce.com®, which is to be regarded as an example of an on-demand services environment. Other on-demand services environments include Salesforce® Exact Target Marketing Cloud™.

As used herein, a term multi-tenant database system refers to those systems in which various elements of hardware and software of the database system may be shared by one or more customers. For example, a given application server may simultaneously process requests for a great number of customers, and a given database table may store rows for a potentially much greater number of customers. As used herein, the term query plan refers to a set of steps used to access information in a database system.

Embodiments are described with reference to an embodiment in which techniques for facilitating management of data in an on-demand services environment are implemented in a system having an application server providing a front end for an on-demand database service capable of supporting multiple tenants, embodiments are not limited to multi-tenant databases nor deployment on application servers. Embodiments may be practiced using other database architectures, i.e., ORACLE®, DB2® by IBM and the like without departing from the scope of the embodiments claimed.

FIG. 1 illustrates one embodiment of a system 100 having a computing device. In one embodiment, computing device 120 includes a host server computer serving a host machine. The term “user” may refer to a system user, such as (without limitation) a software/application developer, a system administrator, a database administrator, an information technology professional, a program manager, product manager, etc. The term “user” may further refer to an end-user, such as (without limitation) one or more of customer organizations 121A-N and/or their representatives (e.g., individuals or groups working on behalf of one or more of customer organizations 121A-N), such as a salesperson, a sales manager, a product manager, an accountant, a director, an owner, a president, a system administrator, a computer programmer, an information technology (“IT”) representative, etc.

In one embodiment, computing device 120 may serve as a service provider core (e.g., Salesforce.com® core) in communication with one or more database(s) 140, one or more client computers 130A-N, over one or more network(s) 135, and any number and type of dedicated nodes. Computing device 120 may include (without limitation) server computers (e.g., cloud server computers, etc.), desktop computers, cluster-based computers, set-top boxes (e.g., Internet-based cable television set-top boxes, etc.), etc. Computing device 120 includes an operating system (“OS”) 106 serving as an interface between one or more hardware/physical resources of computing device 120 and one or more client devices 130A-130N, etc. Computing device 120 further includes processor(s) 102, memory 104, input/output (“I/O”) sources 108, such as touchscreens, touch panels, touch pads, virtual or regular keyboards, virtual or regular mice, etc.

In one embodiment, host organization 101 may further employ a production environment that is communicably interfaced with client devices 130A-N through host organization 101. Client devices 130A-N may include (without limitation) customer organization-based server computers, desktop computers, laptop computers, mobile computing devices, such as smartphones, tablet computers, personal digital assistants, e-readers, media Internet devices, smart televisions, television platforms, wearable devices (e.g., glasses, watches, bracelets, smartcards, jewelry, clothing items, etc.), media players, global positioning system -based navigation systems, cable setup boxes, etc.

In one embodiment, the illustrated multi-tenant database system 150 includes database(s) 140 to store (without limitation) information, relational tables, datasets, and underlying database records having tenant and user data therein on behalf of customer organizations 121A-N (e.g., tenants of multi-tenant database system 150 or their affiliated users). In alternative embodiments, a client-server computing architecture may be utilized in place of multi-tenant database system 150, or alternatively, a computing grid, or a pool of work servers, or some combination of hosted computing architectures may be utilized to carry out the computational workload and processing that is expected of host organization 101.

The illustrated multi-tenant database system 150 is shown to include one or more of underlying hardware, software, and logic elements 145 that implement, for example, database functionality and a code execution environment within host organization 101. In accordance with one embodiment, multi-tenant database system 150 further implements databases 140 to service database queries and other data interactions with the databases 140. In one embodiment, hardware, software, and logic elements 145 of multi-tenant database system 150 and its other elements, such as a distributed file store, a query interface, etc., may be separate and distinct from customer organizations (121A-121N) which utilize the services provided by host organization 101 by communicably interfacing with host organization 101 via network(s) 135 (e.g., cloud network, the Internet, etc.). In such a way, host organization 101 may implement on-demand services, on-demand database services, cloud computing services, etc., to subscribing customer organizations 121A-121N.

In some embodiments, host organization 101 receives input and other requests from a plurality of customer organizations 121A-N over one or more networks 135; for example, incoming search queries, database queries, application programming interface (“API”) requests, interactions with displayed graphical user interfaces and displays at client devices 130A-N, or other inputs may be received from customer organizations 121A-N to be processed against multi-tenant database system 150 as queries via a query interface and stored at a distributed file store, pursuant to which results are then returned to an originator or requestor, such as a user of client devices 130A-N at any of customer organizations 121A-N.

As aforementioned, in one embodiment, each customer organization 121A-N is an entity selected from a group consisting of a separate and distinct remote organization, an organizational group within host organization 101, a business partner of host organization 101, a customer organization 121A-N that subscribes to cloud computing services provided by host organization 101, etc.

In one embodiment, requests are received at, or submitted to, a web server within host organization 101. Host organization 101 may receive a variety of requests for processing by host organization 101 and its multi-tenant database system 150. For example, incoming requests received at the web server may specify which services from host organization 101 are to be provided, such as query requests, search request, status requests, database transactions, graphical user interface requests and interactions, processing requests to retrieve, update, or store data on behalf of one of customer organizations 121A-N, code execution requests, and so forth. Further, the web-server at host organization 101 may be responsible for receiving requests from various customer organizations 121A-N via network(s) 135 on behalf of the query interface and for providing a web-based interface or other graphical displays to one or more end-user client devices 130A-N or machines originating such data requests.

Further, host organization 101 may implement a request interface via the web server or as a stand-alone interface to receive requests packets or other requests from the client devices 130A-N. The request interface may further support the return of response packets or other replies and responses in an outgoing direction from host organization 101 to one or more client devices 130A-N.

It is to be noted that any references to software codes, data and/or metadata (e.g., Customer Relationship Model (“CRM”) data and/or metadata, etc.), tables (e.g., custom object table, unified index tables, description tables, etc.), computing devices (e.g., server computers, desktop computers, mobile computers, such as tablet computers, smartphones, etc.), software development languages, applications, and/or development tools or kits (e.g., Force.com®, Force.com Apex™ code, JavaScript™, jQuery™, Developerforce™, Visualforce™, Service Cloud Console Integration Toolkit (“Integration Toolkit” or “Toolkit”), Platform on a Service™ (“PaaS”), Chatter® Groups, Sprint Planner®, MS Project®, etc.), domains (e.g., Google®, Facebook®, LinkedIn®, Skype®, etc.), etc., discussed in this document are merely used as examples for brevity, clarity, and ease of understanding and that embodiments are not limited to any particular number or type of data, metadata, tables, computing devices, techniques, programming languages, software applications, software development tools/kits, etc.

It is to be noted that terms like “node”, “computing node”, “server”, “server device”, “cloud computer”, “cloud server”, “cloud server computer”, “machine”, “host machine”, “device”, “computing device”, “computer”, “computing system”, “multi-tenant on-demand data system”, “multi-tenant database system” and the like, may be used interchangeably throughout this document. It is to be further noted that terms like “code”, “software code”, “application”, “software application”, “program”, “software program”, “package”, “software code”, “code”, and “software package” may be used interchangeably throughout this document. Moreover, terms like “job”, “input”, “request”, and “message” may be used interchangeably throughout this document.

FIG. 2 illustrates a more detailed embodiment of a host organization 101. As shown in FIG. 2, host organization 101 includes one or more application servers 220 coupled to a web server 250. According to one embodiment, web server web server 250 receives performance metric messages (or metrics data) generated at a client within each application server 220, where the performance metrics include non-customer-specific information indicating the performance of components within host organization 101. Each application server 220 represents a server in a Point of Development (POD) (or instance machine). An instance machine (or instance) comprises a cluster of infrastructure (e.g., servers, software, networking equipment, etc.) that hosts one or more customer organizations (or tenants) within host organization 101.

According to one embodiment, web server 250 also includes an anomaly detection mechanism that is implemented to detect and/or predict anomalous usage of host organization 101 resources based upon streaming metrics data transmitted from servers 220. In a further embodiment, each server 220 is configured to retrieve the data resulting from anomaly detection computations (or anomaly detection results data) performed at web server 250 and publish the data to a monitor 260 and an incident database 270 to persist the information for review at server.

In one embodiment, the metrics data comprises data related to four types of performance metrics, including: request latency, resource utilization percentage, number of active connections in the database. Moreover, the performance metrics may further include data related to trust, application feature and database performance metrics. However other embodiments may be implemented using a different quantity and/or different types of metrics to perform anomaly detection. In a further embodiment, the anomaly detection results data includes an anomaly score, timestamp, probability of incident and contributing features towards an incident, as discussed in more detail below.

Monitor 260 provides real-time, and historical insight into CRM data. In a further embodiment, visualization is provided by monitor 260 via a dashboard user interface (UI) 267. The dashboard UI 267 enables the manipulation, querying and transforming of metrics data. In still a further embodiment, monitor 260 provides for alert generation upon receiving one or more incidents indicating a detection of anomalies within the metrics data. In such an embodiment, monitor 260 generates an alert and displays the one or more incidents dashboard UI 267. In embodiments, monitor 260 is implemented via an Argus open source application.

FIG. 3 illustrates one embodiment of an application server 220. As shown in FIG. 3, application server 220 includes a client application (or client) 310 implemented to facilitate communication with web server 250. Client 310 includes interfaces 320, calculation engine 330, tracker 340 and publisher 350. In such an embodiment, client 310 transmits metric data to, as well as receives resulting anomaly detection results data from, web server 250.

Interfaces 320 comprise application program interfaces (APIs) that facilitate communication within client 310. For instance, a core application API is provided to retrieve the metrics data for transmission to web server 250 as input for anomaly detection, and to publish the anomaly detection results data. Additionally, a Representational state transfer (REST) API may be implemented to publish the anomaly detection results data to incident database 270.

Tracker 340 facilitates anomaly detection computations, as well as the return of the anomaly detection results data. In one embodiment, tracker 340 transmits a compute request (or command) to web server 250 including metrics data as input for the computation. In such an embodiment, the metrics data is retrieved from for each instance machine associated with the application server 220. In a further embodiment, tracker 340 transmits a fetch command to web server 250 to retrieve the anomaly detection results data.

Tracker 340 also generates periodic tasks to initiate anomaly detection computations. For example, tracker 340 may invoke tasks to initiate a compute command at predetermined intervals (e.g., 45, 60 or 90 seconds, etc.) to retrieve metrics data input via an interface 320 and transmit the metrics data to web server 250 for a compute command. In one embodiment, tracker 340 attempts to transmit compute requests for all database nodes until a request is successful.

Additionally, tracker 340 periodically invokes tasks to initiate the fetch commands to retrieve the anomaly detection results upon a successful compute command. In this embodiment, tracker 340 invokes tasks to initiate fetch commands at the predetermined intervals to fetch the results for all database nodes from the web server 250 until the results have been successfully received. In a further embodiment, tracker 340 logs the anomaly detection results data. Publisher 350 publishes the anomaly detection results data to monitor 260 and database 270.

FIG. 4 is a flow diagram of a method 400 illustrating one embodiment of a process performed by an application server client. Method 400 may be performed by processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, etc.), software (such as instructions run on a processing device), or a combination thereof. The processes of method 400 are illustrated in linear sequences for brevity and clarity in presentation; however, it is contemplated that any number of them can be performed in parallel, asynchronously, or in different orders. Further, for brevity, clarity, and ease of understanding, many of the components and processes described with respect to FIGS. 1-3 may not be repeated or discussed hereafter.

Method 400 begins at processing block 410, where a task is invoked at a client (e.g., client 310) to initiate anomaly detection computations. As discussed above, tasks are invoked at predetermined intervals (e.g., 60 seconds). At processing block 420, the client, in response to the invocation, retrieves metrics data to be used as input for the anomaly detection computations. At processing block 430, the client transmits a compute request to the web server 250. As discussed above, the client periodically attempts to send compute requests for all database nodes until a request is successful.

At processing block 440, the client retrieves the anomaly detection results data from the web server 250. As discussed above, the client periodically transmits a fetch request to the web server 250 to retrieve the results for all database nodes until the results are successfully received. At processing block 450, the anomaly detection results data are published. As discussed above, the results are published to monitor 260 and database 270.

FIG. 5 illustrates one embodiment of a web server 250, including an anomaly detection mechanism 520. As mentioned above, web server 510 communicates with a client 310 at an application server 220. In one embodiment, web server 250 and client 310 communicate via a Hypertext Transfer Protocol (HTTP). Anomaly detection mechanism 520 is implemented to detect anomalous usage of host organization 101 resources based upon the streaming metrics data received at web server 250 from a client 310.

According to one embodiment, anomaly detection mechanism 510 includes queuing system 524, data transformation logic 526 and a prediction generation engine 528. Queueing system 524 receives the metrics input data for anomaly detection processing. In one embodiment, queueing system 524 generates a data set for each instance machine. Additionally, a data set is a data stream of anomaly detection data associated with each instance, which includes a series of metrics that includes a data point corresponding to each minute over a seven day interval (e.g., 1,440 data points per day and 10,080 data points per interval). However other embodiments may implement different data set implementations. In one embodiment, queueing system 524 may be implemented via an Apache Kafka system.

Once queued, the data is stored in an in-memory database 529 for quick access during anomaly computation at prediction generation engine 528 since the same data may often be accessed over multiple iterations. According to one embodiment, each instance data stream is bound using an ordered set data structure. As a result, the data sets are sorted to align the data since the queued data may be out of order.

Data transformation logic 526 transforms a data set prior to anomaly prediction. In one embodiment, data transformation logic 526 computes statistics (e.g., standard deviation) to prepare for anomaly prediction. In one embodiment, data transformation comprises normalization of the performance metrics, adjustment of database performance metrics based on application feature metrics, and sigmoid and moving average calculations. In a further embodiment, the data transformation also includes performing moving average and moving standard deviation calculations. Specifically, the formulas for a target metric are as follows:

• • df[target+‘_ma_5m’].fillna(df[target].mean( ),inplace=True)
  • df[target+‘_std_60m’].fillna(df[target+‘_std_60m’].mean( )inplace=True)
  • For “CONN_POOL_AVAILABLE” and “CONN_POOL_WAITING” normalization related calculation:
  • df [‘conn_pool_num_available_normed’]=df[‘conn_pool_num_available’].apply(lambda x:(1−1/(1+np.exp(−0.85(x−17.5)))) if x>=0 else 1)
  • df [‘conn_pool_num_waiting normed’]=df[‘conn_pool_num_waiting’].apply(lambda x:0 if x<=0 else (1/(1+np.exp(−4.0(x−0.1)))))
  • For “CPU_UTIL_PCT” and “AAS” standard dev. regulation related:
  • df[‘cpu_util_pct_std_60 m’]=df.apply(lambda x:(min(1.5,x[‘cpu_util_pct_std_60 m’]) if x[‘cpu_util_pct’]<63 else
  • x[‘cpu_util_pct_std_60 m’]),axis=1)
  • df[‘aas_std_60 m’]=df.apply(lambda x:(min(5.0,x[‘aas_std_60 m’]) if x[‘aas’]<137 else x[‘aas_std_60 m’]),axis=1)

Prediction generation engine 528 performs anomaly detection processing on the transformed data. According to one embodiment, prediction generation engine 528 comprises a plurality of processing engines to enable simultaneous processing of up to N data streams instances in parallel from a plurality of clients 310. For example, FIG. 6 illustrates one embodiment of prediction generation engine 528. As shown in FIG. 6, prediction generation engine 528 load generation logic 526 includes a plurality of processing engines 630 (e.g., 630(A)-630(N)) to process metrics data received from clients 310 (e.g., 310(A)-310(N)).

According to one embodiment, each processing engine 630 implements a machine learning model to perform the anomaly detection on a data stream to predict anomalies. In such an embodiment, each processing engine 630 reads a data set associated with an instance and performs a statistical machine learning algorithm on the data. In a further embodiment, the machine learning algorithm examines metrics for each data point in the data set (e.g., a metric associated with minute in the seven day interval) to determine whether one or more of the data points is anomalous.

In yet a further embodiment, a processing engine 630 performs a scoring process and generates an incident alert in response to a detection of one or more anomalous data points. In embodiments, the scoring process may be performed via one of a variety of functions (e.g., sigmoid, linear, power, quadratic, step, etc.). In one embodiment, a data set of metrics, as well as the scoring data and incident alerts (if applicable), are stored at in-memory database 529 as anomaly detection results data upon completion of the anomaly detection process. In such an embodiment, the anomaly detection results data are stored in a results container. As defined herein, a container is a standard unit of software .hat packages the anomaly detection results data and its dependencies (e.g., libraries and configuration files). As discussed above, client 310 at a server 220 issues a fetch command to retrieve the anomaly detection results data from the appropriate container.

FIG. 7 is a flow diagram of a method 700 illustrating one embodiment of a process performed by an anomaly detection mechanism. Method 700 may be performed by processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, etc.), software (such as instructions run on a processing device), or a combination thereof. The processes of method 700 are illustrated in linear sequences for brevity and clarity in presentation; however, it is contemplated that any number of them can be performed in parallel, asynchronously, or in different orders. Further, for brevity, clarity, and ease of understanding, many of the components and processes described with respect to FIGS. 1-6 may not be repeated or discussed hereafter.

Method 700 begins at processing block 710, where a stream of metrics data is received (e.g., at a database system). At processing block 720, the anomaly detection data is queued as instance data sets and stored in an in-memory database. At processing block 730, processing requests associated with each instance are generated. At processing block 740, the instance data sets are transmitted and processed for anomaly detection in parallel via machine learning. At processing block 750, the results of the anomaly detection process (e.g., scoring and incidents) are stored in a results container.

FIG. 8 illustrates a diagrammatic representation of a machine 900 in the exemplary form of a computer system, in accordance with one embodiment, within which a set of instructions, for causing the machine 900 to perform any one or more of the methodologies discussed herein, may be executed. Machine 900 is the same as or similar to computing devices 120, 130A-N of FIG. 1. In alternative embodiments, the machine may be connected (e.g., networked) to other machines in a network (such as host machine 120 connected with client machines 130A-N over network(s) 135 of FIG. 1), such as a cloud-based network, Internet of Things (IoT) or Cloud of Things (CoT), a Local Area Network (LAN), a Wide Area Network (WAN), a Metropolitan Area Network (MAN), a Personal Area Network (PAN), an intranet, an extranet, or the Internet. The machine may operate in the capacity of a server or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment or as a server or series of servers within an on-demand service environment, including an on-demand environment providing multi-tenant database storage services. Certain embodiments of the machine may be in the form of a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a server, a network router, switch or bridge, computing system, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines (e.g., computers) that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

The exemplary computer system 900 includes a processor 902, a main memory 504 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), etc., static memory such as flash memory, static random access memory (SRAM), volatile but high-data rate RAM, etc.), and a secondary memory 918 (e.g., a persistent storage device including hard disk drives and persistent multi-tenant data base implementations), which communicate with each other via a bus 930. Main memory 904 includes emitted execution data 924 (e.g., data emitted by a logging framework) and one or more trace preferences 923 which operate in conjunction with processing logic 926 and processor 902 to perform the methodologies discussed herein.

Processor 902 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processor 902 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, processor implementing other instruction sets, or processors implementing a combination of instruction sets. Processor 902 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. Processor 902 is configured to execute the processing logic 926 for performing the operations as described with reference to FIG. 1 and other Figures discussed herein.

The computer system 900 may further include a network interface card 908. The computer system 900 also may include a user interface 910 (such as a video display unit, a liquid crystal display (LCD), or a cathode ray tube (CRT)), an alphanumeric input device 912 (e.g., a keyboard), a cursor control device 914 (e.g., a mouse), and a signal generation device 916 (e.g., an integrated speaker). The computer system 900 may further include peripheral device 936 (e.g., wireless or wired communication devices, memory devices, storage devices, audio processing devices, video processing devices, etc. The computer system 900 may further include a Hardware based API logging framework 934 capable of executing incoming requests for services and emitting execution data responsive to the fulfillment of such incoming requests.

The secondary memory 918 may include a machine-readable storage medium (or more specifically a machine-accessible storage medium) 931 on which is stored one or more sets of instructions (e.g., software 922) embodying any one or more of the methodologies as described with reference to FIG. 1, respectively, and other figures discussed herein. The software 522 may also reside, completely or at least partially, within the main memory 904 and/or within the processor 902 during execution thereof by the computer system 900, the main memory 904 and the processor 902 also constituting machine-readable storage media. The software 922 may further be transmitted or received over a network 920 via the network interface card 908. The machine-readable storage medium 931 may include transitory or non-transitory machine-readable storage media.

Portions of various embodiments may be provided as a computer program product, which may include a computer-readable medium having stored thereon computer program instructions, which may be used to program a computer (or other electronic devices) to perform a process according to the embodiments. The machine-readable medium may include, but is not limited to, floppy diskettes, optical disks, compact disk read-only memory (CD-ROM), and magneto-optical disks, ROM, RAM, erasable programmable read-only memory (EPROM), electrically EPROM (EEPROM), magnet or optical cards, flash memory, or other type of media/machine-readable medium suitable for storing electronic instructions.

The techniques shown in the figures can be implemented using code and data stored and executed on one or more electronic devices (e.g., an end station, a network element). Such electronic devices store and communicate (internally and/or with other electronic devices over a network) code and data using computer-readable media, such as non-transitory computer -readable storage media (e.g., magnetic disks; optical disks; random access memory; read only memory; flash memory devices; phase-change memory) and transitory computer-readable transmission media (e.g., electrical, optical, acoustical or other form of propagated signals such as carrier waves, infrared signals, digital signals). In addition, such electronic devices typically include a set of one or more processors coupled to one or more other components, such as one or more storage devices (non-transitory machine-readable storage media), user input/output devices (e.g., a keyboard, a touchscreen, and/or a display), and network connections. The coupling of the set of processors and other components is typically through one or more busses and bridges (also termed as bus controllers). Thus, the storage device of a given electronic device typically stores code and/or data for execution on the set of one or more processors of that electronic device. Of course, one or more parts of an embodiment may be implemented using different combinations of software, firmware, and/or hardware.

FIG. 9 illustrates a block diagram of an environment 1010 wherein an on-demand database service might be used. Environment 1010 may include user systems 1012, network 1014, system 1016, processor system 1017, application platform 618, network interface 1020, tenant data storage 1022, system data storage 1024, program code 1026, and process space 1028. In other embodiments, environment 1010 may not have all of the components listed and/or may have other elements instead of, or in addition to, those listed above.

Environment 1010 is an environment in which an on-demand database service exists. User system 1012 may be any machine or system that is used by a user to access a database user system. For example, any of user systems 1012 can be a handheld computing device, a mobile phone, a laptop computer, a workstation, and/or a network of computing devices. As illustrated in herein FIG. 9 (and in more detail in FIG. 10) user systems 1012 might interact via a network 1014 with an on-demand database service, which is system 1016.

An on-demand database service, such as system 1016, is a database system that is made available to outside users that do not need to necessarily be concerned with building and/or maintaining the database system, but instead may be available for their use when the users need the database system (e.g., on the demand of the users). Some on-demand database services may store information from one or more tenants stored into tables of a common database image to form a multi-tenant database system (MTS). Accordingly, “on-demand database service 1016” and “system 1016” will be used interchangeably herein. A database image may include one or more database objects. A relational database management system (RDMS) or the equivalent may execute storage and retrieval of information against the database object(s). Application platform 1018 may be a framework that allows the applications of system 1016 to run, such as the hardware and/or software, e.g., the operating system. In an embodiment, on-demand database service 1016 may include an application platform 1018 that enables creation, managing and executing one or more applications developed by the provider of the on-demand database service, users accessing the on-demand database service via user systems 1012, or third party application developers accessing the on-demand database service via user systems 1012.

The users of user systems 1012 may differ in their respective capacities, and the capacity of a particular user system 1012 might be entirely determined by permissions (permission levels) for the current user. For example, where a salesperson is using a particular user system 1012 to interact with system 1016, that user system has the capacities allotted to that salesperson. However, while an administrator is using that user system to interact with system 1016, that user system has the capacities allotted to that administrator. In systems with a hierarchical role model, users at one permission level may 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 will have different capabilities with regard to accessing and modifying application and database information, depending on a user's security or permission level.

Network 1014 is any network or combination of networks of devices that communicate with one another. For example, network 1014 can be any one or any combination of a LAN (local area network), WAN (wide area network), telephone network, wireless network, point-to-point network, star network, token ring network, hub network, or other appropriate configuration. As the most common type of computer network in current use is a TCP/IP (Transfer Control Protocol and Internet Protocol) network, such as the global internetwork of networks often referred to as the “Internet” with a capital “I,” that network will be used in many of the examples herein. However, it should be understood that the networks that one or more implementations might use are not so limited, although TCP/IP is a frequently implemented protocol.

User systems 1012 might communicate with system 1016 using TCP/IP and, at a higher network level, use other common Internet protocols to communicate, such as HTTP, FTP, AFS, WAP, etc. In an example where HTTP is used, user system 1012 might include an HTTP client commonly referred to as a “browser” for sending and receiving HTTP messages to and from an HTTP server at system 1016. Such an HTTP server might be implemented as the sole network interface between system 1016 and network 1014, but other techniques might be used as well or instead. In some implementations, the interface between system 1016 and network 1014 includes load-sharing functionality, such as round-robin HTTP request distributors to balance loads and distribute incoming HTTP requests evenly over a plurality of servers. At least as for the users that are accessing that server, each of the plurality of servers has access to the MTS' data; however, other alternative configurations may be used instead.

In one embodiment, system 1016, shown in FIG. 9, implements a web-based customer relationship management (CRM) system. For example, in one embodiment, system 616 includes application servers configured to implement and execute CRM software applications as well as provide related data, code, forms, webpages and other information to and from user systems 1012 and to store to, and retrieve from, a database system related data, objects, and Webpage content. With a multi-tenant system, data for multiple tenants may be stored in the same physical database object, however, tenant data typically is arranged so 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. In certain embodiments, system 1016 implements applications other than, or in addition to, a CRM application. For example, system 1016 may 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 the application platform 618, which manages creation, storage of the applications into one or more database objects and executing of the applications in a virtual machine in the process space of the system 1016.

One arrangement for elements of system 1016 is shown in FIG. 9, including a network interface 1020, application platform 1018, tenant data storage 1022 for tenant data 1023, system data storage 1024 for system data 1025 accessible to system 1016 and possibly multiple tenants, program code 1026 for implementing various functions of system 1016, and a process space 1028 for executing MTS system processes and tenant-specific processes, such as running applications as part of an application hosting service. Additional processes that may execute on system 1016 include database-indexing processes.

Several elements in the system shown in FIG. 9 include conventional, well-known elements that are explained only briefly here. For example, each user system 1012 could include a desktop personal computer, workstation, laptop, PDA, cell phone, or any wireless access protocol (WAP) enabled device or any other computing device capable of interfacing directly or indirectly to the Internet or other network connection. User system 1012 typically runs an HTTP client, e.g., a browsing program, such as Microsoft's Internet Explorer browser, Netscape's Navigator browser, Opera's browser, or a WAP-enabled browser in the case of a cell phone, PDA or other wireless device, or the like, allowing a user (e.g., subscriber of the multi-tenant database system) of user system 1012 to access, process and view information, pages and applications available to it from system 1016 over network 1014. User system 1012 further includes Mobile OS (e.g., iOS® by Apple®, Android®, WebOS® by Palm®, etc.). Each user system 1012 also typically includes one or more user interface devices, such as a keyboard, a mouse, trackball, touch pad, touch screen, pen or the like, for interacting with a graphical user interface (GUI) provided by the browser on a display (e.g., a monitor screen, LCD display, etc.) in conjunction with pages, forms, applications and other information provided by system 1016 or other systems or servers. For example, the user interface device can be used to access data and applications hosted by system 1016, and to perform searches on stored data, and otherwise allow a user to interact with various GUI pages that may be presented to a user. As discussed above, embodiments are suitable for use with the Internet, which refers to a specific global internetwork of networks. However, it should be understood that other networks can be used instead of 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.

According to one embodiment, each user system 1012 and all of its components are operator configurable using applications, such as a browser, including computer code run using a central processing unit such as an Intel Core® processor or the like. Similarly, system 1016 (and additional instances of an MTS, where more than one is present) and all of their components might be operator configurable using application(s) including computer code to run using a central processing unit such as processor system 1017, which may include an Intel Pentium® processor or the like, and/or multiple processor units. A computer program product embodiment includes a machine-readable storage medium (media) having instructions stored thereon/in which can be used to program a computer to perform any of the processes of the embodiments described herein. Computer code for operating and configuring system 1016 to intercommunicate and to process webpages, applications and other data and media content as described herein are preferably downloaded and stored on a hard disk, but the entire program code, or portions thereof, may also be stored in any other volatile or non-volatile memory medium or device as is well known, such as a ROM or RAM, or provided on any media capable of storing program code, such as any type of rotating media including floppy disks, optical discs, digital versatile disk (DVD), compact disk (CD), microdrive, and magneto-optical disks, and magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data. Additionally, the entire program code, or portions thereof, may be transmitted and downloaded from a software source over a transmission medium, e.g., over the Internet, or from another server, as is well known, or transmitted over any other conventional network connection as is well known (e.g., extranet, VPN, LAN, etc.) using any communication medium and protocols (e.g., TCP/IP, HTTP, HTTPS, Ethernet, etc.) as are well known. It will also be appreciated that computer code for implementing embodiments can be implemented in any programming language that can be executed on a client system and/or server or server 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 may be used. (Java™ is a trademark of Sun Microsystems, Inc.).

According to one embodiment, each system 1016 is configured to provide webpages, forms, applications, data and media content to user (client) systems 1012 to support the access by user systems 1012 as tenants of system 1016. As such, system 1016 provides 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 (e.g., in a server farm located in a single building or campus), or they may be distributed at locations remote from one another (e.g., 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 and/or physically connected servers distributed locally or across one or more geographic locations. Additionally, the term “server” is meant to include a computer system, including processing hardware and process space(s), and an associated storage system and database application (e.g., OODBMS or RDBMS) as is well known in the art. It should also be understood that “server system” and “server” are often used interchangeably herein. Similarly, the database object described herein can be implemented as single databases, a distributed database, a collection of distributed databases, a database with redundant online or offline backups or other redundancies, etc., and might include a distributed database or storage network and associated processing intelligence.

FIG. 10 also illustrates environment 1010. However, in FIG. 10 elements of system 1016 and various interconnections in an embodiment are further illustrated. FIG. 10 shows that user system 1012 may include processor system 1012A, memory system 1012B, input system 1012C, and output system 1012D. FIG. 10 shows network 1014 and system 1016. FIG. 10 also shows that system 1016 may include tenant data storage 1022, tenant data 1023, system data storage 1024, system data 1025, User Interface (UI) 1130, Application Program Interface (API) 1132, PL/SOQL 1134, save routines 1136, application setup mechanism 1138, applications servers 11001-1100_N, system process space 1102, tenant process spaces 1104, tenant management process space 1110, tenant storage area 1112, user storage 1114, and application metadata 1116. In other embodiments, environment 1010 may not have the same elements as those listed above and/or may have other elements instead of, or in addition to, those listed above.

User system 1012, network 1014, system 1016, tenant data storage 1022, and system data storage 1024 were discussed above in FIG. 9. Regarding user system 1012, processor system 1012A may be any combination of one or more processors. Memory system 1012B may be any combination of one or more memory devices, short term, and/or long term memory. Input system 1012C may be any combination of input devices, such as one or more keyboards, mice, trackballs, scanners, cameras, and/or interfaces to networks. Output system 1012D may be any combination of output devices, such as one or more monitors, printers, and/or interfaces to networks. As shown by FIG. 10, system 1016 may include a network interface 1020 (of FIG. 9) implemented as a set of HTTP application servers 1100, an application platform 1018, tenant data storage 1022, and system data storage 1024. Also shown is system process space 1102, including individual tenant process spaces 1104 and a tenant management process space 1110. Each application server 1100 may be configured to tenant data storage 1022 and the tenant data 1023 therein, and system data storage 1024 and the system data 1025 therein to serve requests of user systems 1012. The tenant data 1023 might be divided into individual tenant storage areas 1112, which can be either a physical arrangement and/or a logical arrangement of data. Within each tenant storage area 1112, user storage 1114 and application metadata 1116 might be similarly allocated for each user. For example, a copy of a user's most recently used (MRU) items might be stored to user storage 1114. Similarly, a copy of MRU items for an entire organization that is a tenant might be stored to tenant storage area 1112. A UI 1130 provides a user interface and an API 71132 provides an application programmer interface to system 1016 resident processes to users and/or developers at user systems 1012. The tenant data and the system data may be stored in various databases, such as one or more Oracle™ databases.

Application platform 1018 includes an application setup mechanism 1138 that supports application developers' creation and management of applications, which may be saved as metadata into tenant data storage 1022 by save routines 1136 for execution by subscribers as one or more tenant process spaces 1104 managed by tenant management process 1110 for example. Invocations to such applications may be coded using PL/SOQL 1134 that provides a programming language style interface extension to API 1132. A detailed description of some PL/SOQL language embodiments is discussed in commonly owned U.S. Pat. No. 7,730,478 entitled, “Method and System for Allowing Access to Developed Applicants via a Multi-Tenant Database On-Demand Database Service”, issued Jun. 1, 2010 to Craig Weissman, which is incorporated in its entirety herein for all purposes. Invocations to applications may be detected by one or more system processes, which manage retrieving application metadata 1116 for the subscriber making the invocation and executing the metadata as an application in a virtual machine.

Each application server 1100 may be communicably coupled to database systems, e.g., having access to system data 1025 and tenant data 1023, via a different network connection. For example, one application server 11001 might be coupled via the network 1014 (e.g., the Internet), another application server 1100_N-1 might be coupled via a direct network link, and another application server 1100_N might be coupled by yet a different network connection. Transfer Control Protocol and Internet Protocol (TCP/IP) are typical protocols for communicating between application servers 1100 and the database system. However, it will be apparent to one skilled in the art that other transport protocols may be used to optimize the system depending on the network interconnect used.

In certain embodiments, each application server 1100 is configured to handle requests for any user associated with any organization that is a tenant. Because it is desirable to be able to add and remove application servers from the server pool at any time for any reason, there is preferably no server affinity for a user and/or organization to a specific application server 1100. In one embodiment, therefore, an interface system implementing a load balancing function (e.g., an F5 Big-IP load balancer) is communicably coupled between the application servers 1100 and the user systems 1012 to distribute requests to the application servers 1100. In one embodiment, the load balancer uses a least connections algorithm to route user requests to the application servers 1100. Other examples of load balancing algorithms, such as round robin and observed response time, also can be used. For example, in certain embodiments, three consecutive requests from the same user could hit three different application servers 1100, and three requests from different users could hit the same application server 1100. In this manner, system 1016 is multi-tenant, wherein system 1016 handles storage of, and access to, different objects, data and applications across disparate users and organizations.

As an example of storage, one tenant might be a company that employs a sales force where each salesperson uses system 1016 to manage their sales process. Thus, a user might 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 (e.g., in tenant data storage 1022). In an example of a MTS arrangement, since all of the data and the applications to access, view, modify, report, transmit, calculate, etc., can be maintained and accessed by a user system having nothing more than network access, the user can manage his or her sales efforts and cycles from any of many different user systems. For example, if a salesperson is visiting a customer and the customer has Internet access in their lobby, the salesperson can obtain critical updates as to that customer while waiting for the customer to arrive in the lobby.

While each user's data might be separate from other users' data regardless of the employers of each user, some data might be organization-wide data shared or accessible by a plurality of users or all of the users for a given organization that is a tenant. Thus, there might be some data structures managed by system 1016 that are allocated at the tenant level while other data structures might be managed at the user level. Because an MTS might support multiple tenants including possible competitors, the MTS should 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 may be implemented in the MTS. In addition to user-specific data and tenant specific data, system 1016 might also maintain system level data usable by multiple tenants or other data. Such system level data might include industry reports, news, postings, and the like that are sharable among tenants.

In certain embodiments, user systems 1012 (which may be client systems) communicate with application servers 1100 to request and update system-level and tenant-level data from system 1016 that may require sending one or more queries to tenant data storage 1022 and/or system data storage 1024. System 1016 (e.g., an application server 1100 in system 1016) automatically generates one or more SQL statements (e.g., one or more SQL queries) that are designed to access the desired information. System data storage 1024 may generate query plans to access the requested data from the database.

Each database can generally be viewed as a collection of objects, such as a set of logical tables, containing data fitted into predefined 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. 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 record of a table contains an instance of data for each category defined by the fields. For example, a CRM database may include a table that describes a customer with fields for basic contact information such as name, address, phone number, fax number, etc. Another table might describe a purchase order, including fields for information such as customer, product, sale price, date, etc. In some multi-tenant database systems, standard entity tables might be provided for use by all tenants. For CRM database applications, such standard entities might include tables for Account, Contact, Lead, and Opportunity data, each containing pre-defined fields. It should be understood that the word “entity” may also be used interchangeably herein with “object” and “table”.

In some multi-tenant database systems, tenants may be allowed to create and store custom objects, or they may be allowed to customize standard entities or objects, for example by creating custom fields for standard objects, including custom index fields. U.S. patent application Ser. No. 10/817,161, filed Apr. 2, 2004, entitled “Custom Entities and Fields in a Multi-Tenant Database System”, and which is hereby incorporated herein by reference, teaches systems and methods for creating custom objects as well as customizing standard objects in a multi-tenant database system. In certain embodiments, 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.

Any of the above embodiments may be used alone or together with one another in any combination. Embodiments encompassed within this specification may also include embodiments that are only partially mentioned or alluded to or are not mentioned or alluded to at all in this brief summary or in the abstract. Although various embodiments may have been motivated by various deficiencies with the prior art, which may be discussed or alluded to in one or more places in the specification, the embodiments do not necessarily address any of these deficiencies. In other words, different embodiments may address different deficiencies that may be discussed in the specification. Some embodiments may only partially address some deficiencies or just one deficiency that may be discussed in the specification, and some embodiments may not address any of these deficiencies.

While one or more implementations have been described by way of example and in terms of the specific embodiments, it is to be understood that one or more implementations are not limited to the disclosed embodiments. To the contrary, it is intended to cover various modifications and similar arrangements as would be apparent to those skilled in the art. Therefore, the scope of the appended claims should be accorded the broadest interpretation so as to encompass all such modifications and similar arrangements. It is to be understood that the above description is intended to be illustrative, and not restrictive.

Claims

1. A method to facilitate anomaly detection at a system, comprising:

retrieving performance metric messages generated at a server;

transmitting a compute request including the performance metric messages to perform an anomaly detection computation, wherein the anomaly detection computation comprises executing a machine learning model to process the performance metric messages to determine whether anomalous usage of the system has been detected;

retrieving data resulting from the anomaly detection computation; and

publishing the data resulting from the anomaly detection computation.

2. The method of claim 1, further comprising invoking a first task to retrieve the performance metric messages and transmit the compute request to the database system.

3. The method of claim 2, wherein the performance metric messages are retrieved for each instance machine operation on the server.

4. The method of claim 2, wherein the first task is invoked at a predetermined interval.

5. The method of claim 4, further comprising invoking a second task to retrieve the data resulting from the anomaly detection computation.

6. The method of claim 5, wherein the second task is invoked at a predetermined interval.

7. The method of claim 1, wherein publishing the data comprises:

publishing the data to a dashboard user interface; and

publishing the data to an incident database.

8. The method of claim 7, further comprising further comprising displaying the data at the dashboard user interface.

9. The method of claim 1, wherein the data comprises an anomaly score.

10. A computing device comprising:

one or more processors to retrieve performance metric messages generated at a server, transmit a compute request including the performance metric messages to perform an anomaly detection computation, retrieve data resulting from the anomaly detection computation and publish the data resulting from the anomaly detection computation, wherein the anomaly detection computation comprises executing a machine learning model to process the performance metric messages to determine whether anomalous usage of the system has been detected.

11. The computing device of claim 10, wherein the one or more processors further invoke a first task to retrieve the performance metric messages and transmit the compute request to the database system.

12. The computing device of claim 10, wherein the performance metric messages are retrieved for each instance machine operation on the server.

13. The computing device of claim 11, wherein the first task is invoked at a predetermined interval.

14. The computing device of claim 13, wherein the one or more processors further invoke a second task to retrieve the data resulting from the anomaly detection computation.

15. The computing device of claim 10, wherein publishing the data comprises publishing the data to a dashboard user interface and publishing the data to an incident database.

16. A non-transitory computer-readable medium having stored thereon instructions that, when executed by one or more processors, are configurable to cause the one or more processors to:

retrieve performance metric messages generated at a server;

transmit a compute request including the performance metric messages to perform an anomaly detection computation, wherein the anomaly detection computation comprises executing a machine learning model to process the performance metric messages to determine whether anomalous usage of the system has been detected;

retrieve data resulting from the anomaly detection computation; and

publish the data resulting from the anomaly detection computation.

17. The non-transitory computer-readable medium of claim 16, having stored thereon instructions that, when executed by one or more processors, are configurable to further cause the one or more processors to invoke a first task to retrieve the performance metric messages and transmit the compute request to the database system.

18. The non-transitory computer-readable medium of claim 17, wherein the first task is invoked at a predetermined interval.

19. The non-transitory computer-readable medium of claim 18, having stored thereon instructions that, when executed by one or more processors, are configurable to further cause the one or more processors to invoke a second task to retrieve the data resulting from the anomaly detection computation.

20. The non-transitory computer-readable medium of claim 16, having stored thereon instructions that, when executed by one or more processors, are configurable to further cause the one or more processors to:

publish the data to a dashboard user interface; and

publish the data to an incident database.