Patent Description:
<CIT> describes a testing system that includes capture-phase components for capturing and recording an application workload submitted to a production web application system and includes replay-phase components for replaying the captured application workload against a test web application system in a transactionally consistent manner. The document also describes a database capture component that records database commands and their sequence numbers. During replay a database replay component ensures that replay-phase database commands are executed by the test database server system in a manner that is transactionally consistent with the way those commands were captured during the capture phase.

<CIT> describes a method for replaying workload from a production database onto a target database. The transactions on the production database are monitored using one of a network capture method that monitors transactions to the database or using a Kernel capture driver that monitors transactions to the database from the operating system.

<CIT> describes a method for securely recreating and testing a production environment. A file system is mirrored from a captured state of a server in a live system into a test system. Workloads in the form of packets are monitored in the production system over a period of time and the packet data is anonymised. The anonymised captured workloads are replayed to the test server.

<CIT> describes improving communications between an application executing in an emulated environment in an operating system and a network stack in the operating system to allow the application access to additional information. The application may be able to access a network traffic log of the operating system, including contents of packets transmitted and received for the application. The network traffic log may be transmitted to the application by a non-emulated interface executing in the operating system. The application may merge the contents of the network traffic log with an internal application log based on matching similar events between the two logs.

The embodiments herein involve recording, by a simulation compiler and/or related devices, samples of actual, live network traffic transmitted to a software application in a production environment. This recording may be non-intrusive so that the performance of the software application is not adversely impacted. The recording may last anywhere from several seconds to several days (or more), and may be compressed and encoded into representations of transactions. Then, at a later point in time, a load generator may decode the representations and generate network traffic that is used to test a version of the software application in a non-production (testing) environment.

In this way, the software application is tested with a realistic collection of real-world transactions that provides a meaningfully representative load against the system under test. Furthermore, any behavioral anomalies (such as performance degradations, functionality failures, or crashes), which occurred during the recording phase can be reproduced in the non-production environment. As a result, the software application can be more thoroughly tested than it otherwise would be from conventional techniques. Furthermore, subtle defects that would normally only present themselves in the production environment can be reproduced as needed, debugged, and corrected.

Accordingly, a first aspect of the present invention provides a computing system according to claim <NUM>.

According to a second aspect of the present invention there is provided a method according to claim <NUM>.

These as well as other embodiments, aspects, advantages, and alternatives will become apparent to those of ordinary skill in the art by reading the following detailed description, with reference where appropriate to the accompanying drawings. Further, this summary and other descriptions and figures provided herein are intended to illustrate embodiments by way of example only and, as such, that numerous variations are possible. For instance, structural elements and process steps can be rearranged, combined, distributed, eliminated, or otherwise changed, while remaining within the scope of the embodiments as claimed.

Example methods, devices, and systems are described herein. It should be understood that the words "example" and "exemplary" are used herein to mean "serving as an example, instance, or illustration. " Any embodiment or feature described herein as being an "example" or "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments or features unless stated as such. Thus, other embodiments can be utilized and other changes can be made without departing from the scope of the subject matter presented herein.

Accordingly, the example embodiments described herein are not meant to be limiting. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations. For example, the separation of features into "client" and "server" components may occur in a number of ways.

A large enterprise is a complex entity with many interrelated operations. Some of these are found across the enterprise, such as human resources (HR), supply chain, information technology (IT), and finance. However, each enterprise also has its own unique operations that provide essential capabilities and/or create competitive advantages.

To support widely-implemented operations, enterprises typically use off-the-shelf software applications, such as customer relationship management (CRM) and human capital management (HCM) packages. However, they may also need custom software applications to meet their own unique requirements. A large enterprise often has dozens or hundreds of these custom software applications. Nonetheless, the advantages provided by the embodiments herein are not limited to large enterprises and may be applicable to an enterprise, or any other type of organization, of any size.

Many such software applications are developed by individual departments within the enterprise. These range from simple spreadsheets to custom-built software tools and databases. But the proliferation of siloed custom software applications has numerous disadvantages. It negatively impacts an enterprise's ability to run and grow its operations, innovate, and meet regulatory requirements. The enterprise may find it difficult to integrate, streamline and enhance its operations due to lack of a single system that unifies its subsystems and data.

To efficiently create custom applications, enterprises would benefit from a remotely-hosted application platform that eliminates unnecessary development complexity. The goal of such a platform would be to reduce time-consuming, repetitive application development tasks so that software engineers and individuals in other roles can focus on developing unique, high-value features.

In order to achieve this goal, the concept of Application Platform as a Service (aPaaS) is introduced, to intelligently automate workflows throughout the enterprise. An aPaaS system is hosted remotely from the enterprise, but may access data, applications, and services within the enterprise by way of secure connections. Such an aPaaS system may have a number of advantageous capabilities and characteristics. These advantages and characteristics may be able to improve the enterprise's operations and workflow for IT, HR, CRM, customer service, application development, and security.

The aPaaS system may support development and execution of model-view-controller (MVC) applications. MVC applications divide their functionality into three interconnected parts (model, view, and controller) in order to isolate representations of information from the manner in which the information is presented to the user, thereby allowing for efficient code reuse and parallel development. These applications may be web-based, and offer create, read, update, delete (CRUD) capabilities. This allows new applications to be built on a common application infrastructure.

The aPaaS system may support standardized application components, such as a standardized set of widgets for graphical user interface (GUI) development. In this way, applications built using the aPaaS system have a common look and feel. Other software components and modules may be standardized as well. In some cases, this look and feel can be branded or skinned with an enterprise's custom logos and/or color schemes.

The aPaaS system may support the ability to configure the behavior of applications using metadata. This allows application behaviors to be rapidly adapted to meet specific needs. Such an approach reduces development time and increases flexibility. Further, the aPaaS system may support GUI tools that facilitate metadata creation and management, thus reducing errors in the metadata.

The aPaaS system may support clearly-defined interfaces between applications, so that software developers can avoid unwanted inter-application dependencies. Thus, the aPaaS system may implement a service layer in which persistent state information and other data is stored.

The aPaaS system may support a rich set of integration features so that the applications thereon can interact with legacy applications and third-party applications. For instance, the aPaaS system may support a custom employee-onboarding system that integrates with legacy HR, IT, and accounting systems.

The aPaaS system may support enterprise-grade security. Furthermore, since the aPaaS system may be remotely hosted, it should also utilize security procedures when it interacts with systems in the enterprise or third-party networks and services hosted outside of the enterprise. For example, the aPaaS system may be configured to share data amongst the enterprise and other parties to detect and identify common security threats.

Other features, functionality, and advantages of an aPaaS system may exist. This description is for purpose of example and is not intended to be limiting.

As an example of the aPaaS development process, a software developer may be tasked to create a new application using the aPaaS system. First, the developer may define the data model, which specifies the types of data that the application uses and the relationships therebetween. Then, via a GUI of the aPaaS system, the developer enters (e.g., uploads) the data model. The aPaaS system automatically creates all of the corresponding database tables, fields, and relationships, which can then be accessed via an object-oriented services layer.

In addition, the aPaaS system can also build a fully-functional MVC application with client-side interfaces and server-side CRUD logic. This generated application may serve as the basis of further development for the user. Advantageously, the developer does not have to spend a large amount of time on basic application functionality. Further, since the application may be web-based, it can be accessed from any Internet-enabled client device. Alternatively or additionally, a local copy of the application may be able to be accessed, for instance, when Internet service is not available.

The aPaaS system may also support a rich set of pre-defined functionality that can be added to applications. These features include support for searching, email, templating, workflow design, reporting, analytics, social media, scripting, mobile-friendly output, and customized GUIs.

The following embodiments describe architectural and functional aspects of example aPaaS systems, as well as the features and advantages thereof.

<FIG> is a simplified block diagram exemplifying a computing device <NUM>, illustrating some of the components that could be included in a computing device arranged to operate in accordance with the embodiments herein. Computing device <NUM> could be a client device (e.g., a device actively operated by a user), a server device (e.g., a device that provides computational services to client devices), or some other type of computational platform. Some server devices may operate as client devices from time to time in order to perform particular operations, and some client devices may incorporate server features.

In this example, computing device <NUM> includes processor <NUM>, memory <NUM>, network interface <NUM>, and an input / output unit <NUM>, all of which may be coupled by a system bus <NUM> or a similar mechanism. In some embodiments, computing device <NUM> may include other components and/or peripheral devices (e.g., detachable storage, printers, and so on).

Processor <NUM> may be one or more of any type of computer processing element, such as a central processing unit (CPU), a co-processor (e.g., a mathematics, graphics, or encryption co-processor), a digital signal processor (DSP), a network processor, and/or a form of integrated circuit or controller that performs processor operations. In some cases, processor <NUM> may be one or more single-core processors. In other cases, processor <NUM> may be one or more multi-core processors with multiple independent processing units. Processor <NUM> may also include register memory for temporarily storing instructions being executed and related data, as well as cache memory for temporarily storing recently-used instructions and data.

Memory <NUM> may be any form of computer-usable memory, including but not limited to random access memory (RAM), read-only memory (ROM), and non-volatile memory (e.g., flash memory, hard disk drives, solid state drives, compact discs (CDs), digital video discs (DVDs), and/or tape storage). Thus, memory <NUM> represents both main memory units, as well as long-term storage. Other types of memory may include biological memory.

Memory <NUM> may store program instructions and/or data on which program instructions may operate. By way of example, memory <NUM> may store these program instructions on a non-transitory, computer-readable medium, such that the instructions are executable by processor <NUM> to carry out any of the methods, processes, or operations disclosed in this specification or the accompanying drawings.

As shown in <FIG>, memory <NUM> may include firmware 104A, kernel 104B, and/or applications 104C. Firmware 104A may be program code used to boot or otherwise initiate some or all of computing device <NUM>. Kernel 104B may be an operating system, including modules for memory management, scheduling and management of processes, input / output, and communication. Kernel 104B may also include device drivers that allow the operating system to communicate with the hardware modules (e.g., memory units, networking interfaces, ports, and busses), of computing device <NUM>. Applications 104C may be one or more user-space software programs, such as web browsers or email clients, as well as any software libraries used by these programs. Memory <NUM> may also store data used by these and other programs and applications.

Network interface <NUM> may take the form of one or more wireline interfaces, such as Ethernet (e.g., Fast Ethernet, Gigabit Ethernet, and so on). Network interface <NUM> may also support communication over one or more non-Ethernet media, such as coaxial cables or power lines, or over wide-area media, such as Synchronous Optical Networking (SONET) or digital subscriber line (DSL) technologies. Network interface <NUM> may additionally take the form of one or more wireless interfaces, such as IEEE <NUM> (Wifi), BLUETOOTH®, global positioning system (GPS), or a wide-area wireless interface. However, other forms of physical layer interfaces and other types of standard or proprietary communication protocols may be used over network interface <NUM>. Furthermore, network interface <NUM> may comprise multiple physical interfaces. For instance, some embodiments of computing device <NUM> may include Ethernet, BLUETOOTH®, and Wifi interfaces.

Input / output unit <NUM> may facilitate user and peripheral device interaction with example computing device <NUM>. Input / output unit <NUM> may include one or more types of input devices, such as a keyboard, a mouse, a touch screen, and so on. Similarly, input / output unit <NUM> may include one or more types of output devices, such as a screen, monitor, printer, and/or one or more light emitting diodes (LEDs). Additionally or alternatively, computing device <NUM> may communicate with other devices using a universal serial bus (USB) or high-definition multimedia interface (HDMI) port interface, for example.

In some embodiments, one or more instances of computing device <NUM> may be deployed to support an aPaaS architecture. The exact physical location, connectivity, and configuration of these computing devices may be unknown and/or unimportant to client devices. Accordingly, the computing devices may be referred to as "cloud-based" devices that may be housed at various remote data center locations.

<FIG> depicts a cloud-based server cluster <NUM> in accordance with example embodiments. In <FIG>, operations of a computing device (e.g., computing device <NUM>) may be distributed between server devices <NUM>, data storage <NUM>, and routers <NUM>, all of which may be connected by local cluster network <NUM>. The number of server devices <NUM>, data storages <NUM>, and routers <NUM> in server cluster <NUM> may depend on the computing task(s) and/or applications assigned to server cluster <NUM>.

For example, server devices <NUM> can be configured to perform various computing tasks of computing device <NUM>. Thus, computing tasks can be distributed among one or more of server devices <NUM>. To the extent that these computing tasks can be performed in parallel, such a distribution of tasks may reduce the total time to complete these tasks and return a result. For purpose of simplicity, both server cluster <NUM> and individual server devices <NUM> may be referred to as a "server device. " This nomenclature should be understood to imply that one or more distinct server devices, data storage devices, and cluster routers may be involved in server device operations.

Data storage <NUM> may be data storage arrays that include drive array controllers configured to manage read and write access to groups of hard disk drives and/or solid state drives. The drive array controllers, alone or in conjunction with server devices <NUM>, may also be configured to manage backup or redundant copies of the data stored in data storage <NUM> to protect against drive failures or other types of failures that prevent one or more of server devices <NUM> from accessing units of cluster data storage <NUM>. Other types of memory aside from drives may be used.

Routers <NUM> may include networking equipment configured to provide internal and external communications for server cluster <NUM>. For example, routers <NUM> may include one or more packet-switching and/or routing devices (including switches and/or gateways) configured to provide (i) network communications between server devices <NUM> and data storage <NUM> via cluster network <NUM>, and/or (ii) network communications between the server cluster <NUM> and other devices via communication link <NUM> to network <NUM>.

Additionally, the configuration of cluster routers <NUM> can be based at least in part on the data communication requirements of server devices <NUM> and data storage <NUM>, the latency and throughput of the local cluster network <NUM>, the latency, throughput, and cost of communication link <NUM>, and/or other factors that may contribute to the cost, speed, fault-tolerance, resiliency, efficiency and/or other design goals of the system architecture.

As a possible example, data storage <NUM> may include any form of database, such as a structured query language (SQL) database. Various types of data structures may store the information in such a database, including but not limited to tables, arrays, lists, trees, and tuples. Furthermore, any databases in data storage <NUM> may be monolithic or distributed across multiple physical devices.

Server devices <NUM> may be configured to transmit data to and receive data from cluster data storage <NUM>. This transmission and retrieval may take the form of SQL queries or other types of database queries, and the output of such queries, respectively. Additional text, images, video, and/or audio may be included as well. Furthermore, server devices <NUM> may organize the received data into web page representations. Such a representation may take the form of a markup language, such as the hypertext markup language (HTML), the extensible markup language (XML), or some other standardized or proprietary format. Moreover, server devices <NUM> may have the capability of executing various types of computerized scripting languages, such as but not limited to Perl, Python, PHP Hypertext Preprocessor (PHP), Active Server Pages (ASP), JavaScript, and so on. Computer program code written in these languages may facilitate the providing of web pages to client devices, as well as client device interaction with the web pages.

<FIG> depicts a remote network management architecture, in accordance with example embodiments. This architecture includes three main components, managed network <NUM>, remote network management platform <NUM>, and third-party networks <NUM>, all connected by way of Internet <NUM>.

Managed network <NUM> may be, for example, an enterprise network used by an entity for computing and communications tasks, as well as storage of data. Thus, managed network <NUM> may include various client devices <NUM>, server devices <NUM>, routers <NUM>, virtual machines <NUM>, firewall <NUM>, and/or proxy servers <NUM>. Client devices <NUM> may be embodied by computing device <NUM>, server devices <NUM> may be embodied by computing device <NUM> or server cluster <NUM>, and routers <NUM> may be any type of router, switch, or gateway.

Virtual machines <NUM> may be embodied by one or more of computing device <NUM> or server cluster <NUM>. In general, a virtual machine is an emulation of a computing system, and mimics the functionality (e.g., processor, memory, and communication resources) of a physical computer. One physical computing system, such as server cluster <NUM>, may support up to thousands of individual virtual machines. In some embodiments, virtual machines <NUM> may be managed by a centralized server device or application that facilitates allocation of physical computing resources to individual virtual machines, as well as performance and error reporting. Enterprises often employ virtual machines in order to allocate computing resources in an efficient, as needed fashion. Providers of virtualized computing systems include VMWARE® and MICROSOFT®.

Firewall <NUM> may be one or more specialized routers or server devices that protect managed network <NUM> from unauthorized attempts to access the devices, applications, and services therein, while allowing authorized communication that is initiated from managed network <NUM>. Firewall <NUM> may also provide intrusion detection, web filtering, virus scanning, application-layer gateways, and other applications or services. In some embodiments not shown in <FIG>, managed network <NUM> may include one or more virtual private network (VPN) gateways with which it communicates with remote network management platform <NUM> (see below).

Managed network <NUM> may also include one or more proxy servers <NUM>. An embodiment of proxy servers <NUM> may be a server device that facilitates communication and movement of data between managed network <NUM>, remote network management platform <NUM>, and third-party networks <NUM>. In particular, proxy servers <NUM> may be able to establish and maintain secure communication sessions with one or more computational instances of remote network management platform <NUM>. By way of such a session, remote network management platform <NUM> may be able to discover and manage aspects of the architecture and configuration of managed network <NUM> and its components. Possibly with the assistance of proxy servers <NUM>, remote network management platform <NUM> may also be able to discover and manage aspects of third-party networks <NUM> that are used by managed network <NUM>.

Firewalls, such as firewall <NUM>, typically deny all communication sessions that are incoming by way of Internet <NUM>, unless such a session was ultimately initiated from behind the firewall (i.e., from a device on managed network <NUM>) or the firewall has been explicitly configured to support the session. By placing proxy servers <NUM> behind firewall <NUM> (e.g., within managed network <NUM> and protected by firewall <NUM>), proxy servers <NUM> may be able to initiate these communication sessions through firewall <NUM>. Thus, firewall <NUM> might not have to be specifically configured to support incoming sessions from remote network management platform <NUM>, thereby avoiding potential security risks to managed network <NUM>.

In some cases, managed network <NUM> may consist of a few devices and a small number of networks. In other deployments, managed network <NUM> may span multiple physical locations and include hundreds of networks and hundreds of thousands of devices. Thus, the architecture depicted in <FIG> is capable of scaling up or down by orders of magnitude.

Furthermore, depending on the size, architecture, and connectivity of managed network <NUM>, a varying number of proxy servers <NUM> may be deployed therein. For example, each one of proxy servers <NUM> may be responsible for communicating with remote network management platform <NUM> regarding a portion of managed network <NUM>. Alternatively or additionally, sets of two or more proxy servers may be assigned to such a portion of managed network <NUM> for purposes of load balancing, redundancy, and/or high availability.

Remote network management platform <NUM> is a hosted environment that provides aPaaS services to users, particularly to the operators of managed network <NUM>. These services may take the form of web-based portals, for instance. Thus, a user can securely access remote network management platform <NUM> from, for instance, client devices <NUM>, or potentially from a client device outside of managed network <NUM>. By way of the web-based portals, users may design, test, and deploy applications, generate reports, view analytics, and perform other tasks.

As shown in <FIG>, remote network management platform <NUM> includes four computational instances <NUM>, <NUM>, <NUM>, and <NUM>. Each of these instances may represent a set of web portals, services, and applications (e.g., a wholly-functioning aPaaS system) available to a particular customer. In some cases, a single customer may use multiple computational instances. For example, managed network <NUM> may be an enterprise customer of remote network management platform <NUM>, and may use computational instances <NUM>, <NUM>, and <NUM>. The reason for providing multiple instances to one customer is that the customer may wish to independently develop, test, and deploy its applications and services. Thus, computational instance <NUM> may be dedicated to application development related to managed network <NUM>, computational instance <NUM> may be dedicated to testing these applications, and computational instance <NUM> may be dedicated to the live operation of tested applications and services. A computational instance may also be referred to as a hosted instance, a remote instance, a customer instance, or by some other designation.

The multi-instance architecture of remote network management platform <NUM> is in contrast to conventional multi-tenant architectures, over which multi-instance architectures have several advantages. In multi-tenant architectures, data from different customers (e.g., enterprises) are comingled in a single database. While these customers' data are separate from one another, the separation is enforced by the software that operates the single database. As a consequence, a security breach in this system may impact all customers' data, creating additional risk, especially for entities subject to governmental, healthcare, and/or financial regulation. Furthermore, any database operations that impact one customer will likely impact all customers sharing that database. Thus, if there is an outage due to hardware or software errors, this outage affects all such customers. Likewise, if the database is to be upgraded to meet the needs of one customer, it will be unavailable to all customers during the upgrade process. Often, such maintenance windows will be long, due to the size of the shared database.

In contrast, the multi-instance architecture provides each customer with its own database in a dedicated computing instance. This prevents coming ling of customer data, and allows each instance to be independently managed. For example, when one customer's instance experiences an outage due to errors or an upgrade, other computational instances are not impacted. Maintenance down time is limited because the database only contains one customer's data. Further, the simpler design of the multi-instance architecture allows redundant copies of each customer database and instance to be deployed in a geographically diverse fashion. This facilitates high availability, where the live version of the customer's instance can be moved when faults are detected or maintenance is being performed.

In order to support multiple computational instances in an efficient fashion, remote network management platform <NUM> may implement a plurality of these instances on a single hardware platform. For example, when the aPaaS system is implemented on a server cluster such as server cluster <NUM>, it may operate a virtual machine that dedicates varying amounts of computational, storage, and communication resources to instances. But full virtualization of server cluster <NUM> might not be necessary, and other mechanisms may be used to separate instances. In some examples, each instance may have a dedicated account and one or more dedicated databases on server cluster <NUM>. Alternatively, computational instance <NUM> may span multiple physical devices.

In some cases, a single server cluster of remote network management platform <NUM> may support multiple independent enterprises. Furthermore, as described below, remote network management platform <NUM> may include multiple server clusters deployed in geographically diverse data centers in order to facilitate load balancing, redundancy, and/or high availability.

Third-party networks <NUM> may be remote server devices (e.g., a plurality of server clusters such as server cluster <NUM>) that can be used for outsourced computational, data storage, communication, and service hosting operations. These servers may be virtualized (i.e., the servers may be virtual machines). Examples of third-party networks <NUM> may include AMAZON WEB SERVICES® and MICROSOFT® Azure. Like remote network management platform <NUM>, multiple server clusters supporting third-party networks <NUM> may be deployed at geographically diverse locations for purposes of load balancing, redundancy, and/or high availability.

Managed network <NUM> may use one or more of third-party networks <NUM> to deploy applications and services to its clients and customers. For instance, if managed network <NUM> provides online music streaming services, third-party networks <NUM> may store the music files and provide web interface and streaming capabilities. In this way, the enterprise of managed network <NUM> does not have to build and maintain its own servers for these operations.

Remote network management platform <NUM> may include modules that integrate with third-party networks <NUM> to expose virtual machines and managed services therein to managed network <NUM>. The modules may allow users to request virtual resources and provide flexible reporting for third-party networks <NUM>. In order to establish this functionality, a user from managed network <NUM> might first establish an account with third-party networks <NUM>, and request a set of associated resources. Then, the user may enter the account information into the appropriate modules of remote network management platform <NUM>. These modules may then automatically discover the manageable resources in the account, and also provide reports related to usage, performance, and billing.

Internet <NUM> may represent a portion of the global Internet. However, Internet <NUM> may alternatively represent a different type of network, such as a private wide-area or local-area packet-switched network.

<FIG> further illustrates the communication environment between managed network <NUM> and computational instance <NUM>, and introduces additional features and alternative embodiments. In <FIG>, computational instance <NUM> is replicated across data centers 400A and 400B. These data centers may be geographically distant from one another, perhaps in different cities or different countries. Each data center includes support equipment that facilitates communication with managed network <NUM>, as well as remote users.

In data center 400A, network traffic to and from external devices flows either through VPN gateway 402A or firewall 404A. VPN gateway 402A may be peered with VPN gateway <NUM> of managed network <NUM> by way of a security protocol such as Internet Protocol Security (IPSEC) or Transport Layer Security (TLS). Firewall 404A may be configured to allow access from authorized users, such as user <NUM> and remote user <NUM>, and to deny access to unauthorized users. By way of firewall 404A, these users may access computational instance <NUM>, and possibly other computational instances. Load balancer 406A may be used to distribute traffic amongst one or more physical or virtual server devices that host computational instance <NUM>. Load balancer 406A may simplify user access by hiding the internal configuration of data center 400A, (e.g., computational instance <NUM>) from client devices. For instance, if computational instance <NUM> includes multiple physical or virtual computing devices that share access to multiple databases, load balancer 406A may distribute network traffic and processing tasks across these computing devices and databases so that no one computing device or database is significantly busier than the others. In some embodiments, computational instance <NUM> may include VPN gateway 402A, firewall 404A, and load balancer 406A.

Data center 400B may include its own versions of the components in data center 400A. Thus, VPN gateway 402B, firewall 404B, and load balancer 406B may perform the same or similar operations as VPN gateway 402A, firewall 404A, and load balancer 406A, respectively. Further, by way of real-time or near-real-time database replication and/or other operations, computational instance <NUM> may exist simultaneously in data centers 400A and 400B.

Data centers 400A and 400B as shown in <FIG> may facilitate redundancy and high availability. In the configuration of <FIG>, data center 400A is active and data center 400B is passive. Thus, data center 400A is serving all traffic to and from managed network <NUM>, while the version of computational instance <NUM> in data center 400B is being updated in near-real-time. Other configurations, such as one in which both data centers are active, may be supported.

Should data center 400A fail in some fashion or otherwise become unavailable to users, data center 400B can take over as the active data center. For example, domain name system (DNS) servers that associate a domain name of computational instance <NUM> with one or more Internet Protocol (IP) addresses of data center 400A may re-associate the domain name with one or more IP addresses of data center 400B. After this re-association completes (which may take less than one second or several seconds), users may access computational instance <NUM> by way of data center 400B.

<FIG> also illustrates a possible configuration of managed network <NUM>. As noted above, proxy servers <NUM> and user <NUM> may access computational instance <NUM> through firewall <NUM>. Proxy servers <NUM> may also access configuration items <NUM>. In <FIG>, configuration items <NUM> may refer to any or all of client devices <NUM>, server devices <NUM>, routers <NUM>, and virtual machines <NUM>, any applications or services executing thereon, as well as relationships between devices, applications, and services. Thus, the term "configuration items" may be shorthand for any physical or virtual device, or any application or service remotely discoverable or managed by computational instance <NUM>, or relationships between discovered devices, applications, and services. Configuration items may be represented in a configuration management database (CMDB) of computational instance <NUM>.

As noted above, VPN gateway <NUM> may provide a dedicated VPN to VPN gateway 402A. Such a VPN may be helpful when there is a significant amount of traffic between managed network <NUM> and computational instance <NUM>, or security policies otherwise suggest or require use of a VPN between these sites. In some embodiments, any device in managed network <NUM> and/or computational instance <NUM> that directly communicates via the VPN is assigned a public IP address. Other devices in managed network <NUM> and/or computational instance <NUM> may be assigned private IP addresses (e.g., IP addresses selected from the <NUM>. <NUM> - <NUM>. <NUM> or <NUM>. <NUM> - <NUM>. <NUM> ranges, represented in shorthand as subnets <NUM>. <NUM>/<NUM> and <NUM>. <NUM>/<NUM>, respectively).

In order for remote network management platform <NUM> to administer the devices, applications, and services of managed network <NUM>, remote network management platform <NUM> may first determine what devices are present in managed network <NUM>, the configurations and operational statuses of these devices, and the applications and services provided by the devices, and well as the relationships between discovered devices, applications, and services. As noted above, each device, application, service, and relationship may be referred to as a configuration item. The process of defining configuration items within managed network <NUM> is referred to as discovery, and may be facilitated at least in part by proxy servers <NUM>.

For purpose of the embodiments herein, an "application" may refer to one or more processes, threads, programs, client modules, server modules, or any other software that executes on a device or group of devices. A "service" may refer to a high-level capability provided by multiple applications executing on one or more devices working in conjunction with one another. For example, a high-level web service may involve multiple web application server threads executing on one device and accessing information from a database application that executes on another device.

<FIG> provides a logical depiction of how configuration items can be discovered, as well as how information related to discovered configuration items can be stored. For sake of simplicity, remote network management platform <NUM>, third-party networks <NUM>, and Internet <NUM> are not shown.

In <FIG>, CMDB <NUM> and task list <NUM> are stored within computational instance <NUM>. Computational instance <NUM> may transmit discovery commands to proxy servers <NUM>. In response, proxy servers <NUM> may transmit probes to various devices, applications, and services in managed network <NUM>. These devices, applications, and services may transmit responses to proxy servers <NUM>, and proxy servers <NUM> may then provide information regarding discovered configuration items to CMDB <NUM> for storage therein. Configuration items stored in CMDB <NUM> represent the environment of managed network <NUM>.

Task list <NUM> represents a list of activities that proxy servers <NUM> are to perform on behalf of computational instance <NUM>. As discovery takes place, task list <NUM> is populated. Proxy servers <NUM> repeatedly query task list <NUM>, obtain the next task therein, and perform this task until task list <NUM> is empty or another stopping condition has been reached.

To facilitate discovery, proxy servers <NUM> may be configured with information regarding one or more subnets in managed network <NUM> that are reachable by way of proxy servers <NUM>. For instance, proxy servers <NUM> may be given the IP address range <NUM>. <NUM>/<NUM> as a subnet. Then, computational instance <NUM> may store this information in CMDB <NUM> and place tasks in task list <NUM> for discovery of devices at each of these addresses.

<FIG> also depicts devices, applications, and services in managed network <NUM> as configuration items <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>. As noted above, these configuration items represent a set of physical and/or virtual devices (e.g., client devices, server devices, routers, or virtual machines), applications executing thereon (e.g., web servers, email servers, databases, or storage arrays), relationships therebetween, as well as services that involve multiple individual configuration items.

Placing the tasks in task list <NUM> may trigger or otherwise cause proxy servers <NUM> to begin discovery. Alternatively or additionally, discovery may be manually triggered or automatically triggered based on triggering events (e.g., discovery may automatically begin once per day at a particular time).

In general, discovery may proceed in four logical phases: scanning, classification, identification, and exploration. Each phase of discovery involves various types of probe messages being transmitted by proxy servers <NUM> to one or more devices in managed network <NUM>. The responses to these probes may be received and processed by proxy servers <NUM>, and representations thereof may be transmitted to CMDB <NUM>. Thus, each phase can result in more configuration items being discovered and stored in CMDB <NUM>.

In the scanning phase, proxy servers <NUM> may probe each IP address in the specified range of IP addresses for open Transmission Control Protocol (TCP) and/or User Datagram Protocol (UDP) ports to determine the general type of device. The presence of such open ports at an IP address may indicate that a particular application is operating on the device that is assigned the IP address, which in turn may identify the operating system used by the device. For example, if TCP port <NUM> is open, then the device is likely executing a WINDOWS® operating system. Similarly, if TCP port <NUM> is open, then the device is likely executing a UNIX® operating system, such as LINUX®. If UDP port <NUM> is open, then the device may be able to be further identified through the Simple Network Management Protocol (SNMP). Other possibilities exist. Once the presence of a device at a particular IP address and its open ports have been discovered, these configuration items are saved in CMDB <NUM>.

In the classification phase, proxy servers <NUM> may further probe each discovered device to determine the version of its operating system. The probes used for a particular device are based on information gathered about the devices during the scanning phase. For example, if a device is found with TCP port <NUM> open, a set of UNIX®-specific probes may be used. Likewise, if a device is found with TCP port <NUM> open, a set of WINDOWS®-specific probes may be used. For either case, an appropriate set of tasks may be placed in task list <NUM> for proxy servers <NUM> to carry out. These tasks may result in proxy servers <NUM> logging on, or otherwise accessing information from the particular device. For instance, if TCP port <NUM> is open, proxy servers <NUM> may be instructed to initiate a Secure Shell (SSH) connection to the particular device and obtain information about the operating system thereon from particular locations in the file system. Based on this information, the operating system may be determined. As an example, a UNIX® device with TCP port <NUM> open may be classified as AIX®, HPUX, LINUX®, MACOS®, or SOLARIS®. This classification information may be stored as one or more configuration items in CMDB <NUM>.

In the identification phase, proxy servers <NUM> may determine specific details about a classified device. The probes used during this phase may be based on information gathered about the particular devices during the classification phase. For example, if a device was classified as LINUX®, a set of LINUXO-specific probes may be used. Likewise if a device was classified as WINDOWS® <NUM>, as a set of WINDOWS®-<NUM>-specific probes may be used. As was the case for the classification phase, an appropriate set of tasks may be placed in task list <NUM> for proxy servers <NUM> to carry out. These tasks may result in proxy servers <NUM> reading information from the particular device, such as basic input / output system (BIOS) information, serial numbers, network interface information, media access control address(es) assigned to these network interface(s), IP address(es) used by the particular device and so on. This identification information may be stored as one or more configuration items in CMDB <NUM>.

In the exploration phase, proxy servers <NUM> may determine further details about the operational state of a classified device. The probes used during this phase may be based on information gathered about the particular devices during the classification phase and/or the identification phase. Again, an appropriate set of tasks may be placed in task list <NUM> for proxy servers <NUM> to carry out. These tasks may result in proxy servers <NUM> reading additional information from the particular device, such as processor information, memory information, lists of running processes (applications), and so on. Once more, the discovered information may be stored as one or more configuration items in CMDB <NUM>.

Running discovery on a network device, such as a router, may utilize SNMP. Instead of or in addition to determining a list of running processes or other application-related information, discovery may determine additional subnets known to the router and the operational state of the router's network interfaces (e.g., active, inactive, queue length, number of packets dropped, etc.). The IP addresses of the additional subnets may be candidates for further discovery procedures. Thus, discovery may progress iteratively or recursively.

Once discovery completes, a snapshot representation of each discovered device, application, and service is available in CMDB <NUM>. For example, after discovery, operating system version, hardware configuration and network configuration details for client devices, server devices, and routers in managed network <NUM>, as well as applications executing thereon, may be stored. This collected information may be presented to a user in various ways to allow the user to view the hardware composition and operational status of devices, as well as the characteristics of services that span multiple devices and applications.

Furthermore, CMDB <NUM> may include entries regarding dependencies and relationships between configuration items. More specifically, an application that is executing on a particular server device, as well as the services that rely on this application, may be represented as such in CMDB <NUM>. For instance, suppose that a database application is executing on a server device, and that this database application is used by a new employee onboarding service as well as a payroll service. Thus, if the server device is taken out of operation for maintenance, it is clear that the employee onboarding service and payroll service will be impacted. Likewise, the dependencies and relationships between configuration items may be able to represent the services impacted when a particular router fails.

In general, dependencies and relationships between configuration items be displayed on a web-based interface and represented in a hierarchical fashion. Thus, adding, changing, or removing such dependencies and relationships may be accomplished by way of this interface.

Furthermore, users from managed network <NUM> may develop workflows that allow certain coordinated activities to take place across multiple discovered devices. For instance, an IT workflow might allow the user to change the common administrator password to all discovered LINUX® devices in single operation.

In order for discovery to take place in the manner described above, proxy servers <NUM>, CMDB <NUM>, and/or one or more credential stores may be configured with credentials for one or more of the devices to be discovered. Credentials may include any type of information needed in order to access the devices. These may include userid / password pairs, certificates, and so on. In some embodiments, these credentials may be stored in encrypted fields of CMDB <NUM>. Proxy servers <NUM> may contain the decryption key for the credentials so that proxy servers <NUM> can use these credentials to log on to or otherwise access devices being discovered.

The discovery process is depicted as a flow chart in <FIG>. At block <NUM>, the task list in the computational instance is populated, for instance, with a range of IP addresses. At block <NUM>, the scanning phase takes place. Thus, the proxy servers probe the IP addresses for devices using these IP addresses, and attempt to determine the operating systems that are executing on these devices. At block <NUM>, the classification phase takes place. The proxy servers attempt to determine the operating system version of the discovered devices. At block <NUM>, the identification phase takes place. The proxy servers attempt to determine the hardware and/or software configuration of the discovered devices. At block <NUM>, the exploration phase takes place. The proxy servers attempt to determine the operational state and applications executing on the discovered devices. At block <NUM>, further editing of the configuration items representing the discovered devices and applications may take place. This editing may be automated and/or manual in nature.

The blocks represented in <FIG> are for purpose of example. Discovery may be a highly configurable procedure that can have more or fewer phases, and the operations of each phase may vary. In some cases, one or more phases may be customized, or may otherwise deviate from the exemplary descriptions above.

<FIG> depicts a logical arrangement of devices for recording network transactions in a production instance of a remote network management platform. Herein, the term "production instance" refers to a computational instance that is being used in a production (live) environment to serve user requests from customers of the remote network management platform. In contrast, a "testing instance" may be used for testing of the software application(s) that make up the remote network management platform. The testing instance may be a non-production environment.

Remote network management platform <NUM> may include production instances dedicated to one or more particular enterprises, as embodied by server devices <NUM> and databases <NUM>. Thus, server devices <NUM> and databases <NUM> may be divided into multiple computational instances, each dedicated to a managed network. Any particular computational instance may include one or more dedicated server devices of server devices <NUM> and one or more dedicated databases of databases <NUM>.

Operationally, load balancer <NUM> may receive encrypted network traffic, in the form of packets, from network <NUM>. Load balancer <NUM> may serve as the endpoint for this encryption. For instance, the encrypted network traffic may include Secure Hypertext Transfer Protocol (HTTPS) traffic between a web client and a web service operated by a computational instance operating on one or more of server devices <NUM>. Load balancer <NUM> may be assigned an IP address associated with this web service (e.g., a virtual IP address). But rather than providing the web service, load balancer <NUM> may instead decrypt the HTTPS traffic and transmit this decrypted traffic to server devices <NUM>. In doing so, load balancer <NUM> may select one of server devices <NUM> of the target computational instance based on load reported by these server devices, based on a round-robin algorithm, randomly, or according to some other mechanism. Regardless of the exact selection technique that is employed, load balancer <NUM> may attempt to distribute incoming traffic across server devices <NUM> so that no one of these server devices is significantly more loaded than any other.

Once one of server devices <NUM> receives the decrypted traffic, this server device may carry out processing of the traffic. For instance, the server device may determine that the traffic requests delivery of a particular web page. In order to generate this web page, the server device may make one or more queries to databases <NUM>. Particularly, the server device selects a database from those available to the computational instance, and transmits the queries to this database.

Though not shown in <FIG> (because the architecture depicted is focused on incoming traffic), the server device may collate the responses from these queries into a representation of a web page, and transmit this representation as network traffic (e.g., packets) to the load balancer <NUM>. Load balancer <NUM> may encrypt this network traffic in accordance with HTTPS, and transmit the encrypted network traffic to the sender.

Another view of a production instance is shown in <FIG>. Therein, load balancer <NUM> distributes incoming traffic, by way of virtual local area network (VLAN) <NUM>, to m server devices <NUM>-<NUM>, <NUM>-<NUM>,. , <NUM>-m of the production instance. These server devices access, by way of VLAN <NUM>, one or more of n databases <NUM>-<NUM>, <NUM>-<NUM>,. Traffic filter device <NUM> may be communicatively coupled to load balancer <NUM>, either by way of VLAN <NUM> (solid line) or directly (dotted line). Storage device <NUM> and simulation compiler <NUM> may also communicate by way of VLAN <NUM>. Other arrangements of LANS, VLANs, and/or direct connections are possible.

In this context, a VLAN is partitioned and isolated from other VLANs at the data link layer level. Thus, for example, a single physical Ethernet LAN may be partitioned into multiple logical VLANs that are separate from one another. Each VLAN may be associated with a tag that is attached (e.g., prepended) to each packet thereon so that the VLAN to which each packet belongs can be unambiguously determined.

Turning back to <FIG>, traffic filter device <NUM> may be coupled to a port of load balancer <NUM> in order to record the incoming network traffic. Load balancer <NUM> may be arranged to transmit a copy of (e.g., mirror) the decrypted traffic from this port as well. Alternatively, traffic filter device <NUM> may be arranged to tap into a communication link between load balancer <NUM> and server devices <NUM>. In any event, a copy of the decrypted incoming traffic is made available to traffic filter device <NUM>.

Traffic filter device <NUM> is a network appliance or other device that can receive and capture a stream of packets, and apply a filter to these packets either during or after reception. For instance, traffic filter device <NUM> may receive the packets and store them in PCAP format. This particular format for storing captured packets is illustrated in <FIG>. Nonetheless, other packet storage formats may be used.

Traffic filter device <NUM> may also filter the incoming packets so that only packets that are likely to be of interest are stored. Filters can be applied to the packets based on their source IP address, destination IP address, source port number, destination port number, or any other information in the headers or payload of a packet. In order to capture incoming packets that are bound for a particular computational instance, a filter expression including a logical "or" operation over all IP addresses of those of server device <NUM> that are within the computational instance may be applied. For example, if a particular computational instance includes server devices assigned IP addresses of <NUM>. <NUM>, <NUM>. <NUM>, and <NUM>. <NUM>, a logical "or" expression "<NUM>. <NUM> OR <NUM>. <NUM> OR <NUM>. <NUM>" may be used to capture packets being transmitted only to the particular computational instance.

Storage device <NUM> may be used to store the packets filtered by traffic filter device <NUM>. Alternatively, the operations of traffic filter device <NUM> and storage device <NUM> may be combined into a single physical device or further divided amongst more than two physical devices. Regardless, storage device <NUM> may store the packets in non-volatile memory, such as on a solid-state drive, hard disk drive, or analogous storage medium. In order to conserve storage, traffic filter device <NUM> and/or storage device <NUM> may compress the packets prior to storage in storage device <NUM>.

Simulation compiler <NUM> gathers or is provided with information from storage device <NUM> and server devices <NUM>. As noted, simulation compiler <NUM> may receive copies of captured packets. Server devices <NUM> may store records of transactions in file system logs, and provide copies of these file system logs, periodically or on demand, to simulation compiler <NUM>.

While the network traffic collected by simulation compiler <NUM> is in the form of packets, the file system logs represent entire transactions at a higher level. For instance, an HTTP POST command received by server devices <NUM> may be represented in one or more packets. These packets are ultimately stored in storage device <NUM> and then are provided to simulation compiler <NUM> as a file containing these packets. In contrast, server devices <NUM> record a representation of the command as an entry in a log file. This entry may contain information stating that a transaction involved an HTTP POST command, as well as the parameters and body of the command. This representation is provided to simulation compiler <NUM>.

The recorded packet representations may include information that is omitted from the file system logs. For example, the file system logs may record only a single HTTP command per transaction, thus omitting any additional HTTP commands transmitted according to AJAX or a similar protocol. On the other hand, the file system logs capture information related to transactions that are not observable to traffic filter device <NUM>. For instance, transactions between various applications executing on one of server devices <NUM> may utilize a loopback mechanism so that the packets from these transactions never leave that server device - instead, the packets are internally routed from application to application by the networking stack.

Thus, recording both high-fidelity data (packets) and low-fidelity data (file system logs) results in none or few transactions being missed or failing to be recorded. Since there may be overlap between these data (e.g., some transactions will be represented in both the high-fidelity data and low-fidelity data), simulation compiler <NUM> de-duplicates the data by transaction type (e.g., HTTP POST, HTTP GET, HTTP HEAD, etc.) and timestamp, for example, so that each trace contains only one copy of each transaction. Recordings of high-fidelity data and/or low-fidelity data may be referred to herein as traces. More detail on the format of the high-fidelity data and low-fidelity data is provided below.

Although not shown in <FIG>, databases <NUM> may store images (snapshots) of the tables, fields, and other arrangements therein, as well as transaction logs that record changes to these tables, fields, and other arrangements. Databases <NUM> may copies of the images and transaction logs, periodically or on demand, to simulation compiler <NUM> or other components.

<FIG> depicts a logical arrangement of devices for playing back recorded transactions in a testing instance of a remote network management platform. While a single unit representing each of simulation controller <NUM>, load generator <NUM>, server devices <NUM>, and databases <NUM> is shown in this figure, in practice multiple physical or logical units or devices may be used for any of these components. Furthermore, simulation controller <NUM> may be the same device that collects traces from a production instance (e.g., simulation compiler <NUM>), or may be a different device.

A goal of this arrangement is to be able to accurately recreate, in a testing instance, a copy of a production instance as it was at a particular point in time. For example, the testing instance may be designed with the same number and layout of server devices <NUM> and databases <NUM>. The databases in the testing instance may be loaded with a stored image of the production instance's database, and this image may be played forward with stored database transaction logs to a particular point in time.

As noted above, simulation compiler <NUM> is provided with one or more of the compressed traffic files and/or file system logs starting at the particular point in time. From these, simulation compiler <NUM> creates a series of playback instructions used to generate transactions that replicate those that were recorded by traffic filter device <NUM> and server devices <NUM>.

Doing so provides a much more accurate way of testing remote network management platform <NUM>. Particularly, the use of real network traffic and the associated transactions against a replica of a production instance may allow for isolation of performance problems and other software defects that cannot be found through conventional regression and load testing.

As an example, the arrangement of <FIG> may be used to collect a week's worth of traffic and transactions from a production instance. Suppose that, during this week, the production instance suffered from poor performance during a particular hour. The configuration and environment of the production instance may be replicated in a testing instance. The database of the testing instance may be restored from an image of the database of the production instance, and played forward to around the beginning of when the poor performance began. Then, the compressed traffic files and file system logs from this point and time may be used to create a series of transactions that can further serve as the basis for traffic to be transmitted to the testing instance. The operation of the testing instance can be monitored in real time and it may be possible to determine the cause of the poor performance.

Thus, simulation controller <NUM> has access to traces representing transactions. As noted above, these traces were collected from network traffic (packets) transmitted to server devices of a production instance, as well as file system logs of the production instance.

These traces are provided to load generator <NUM> in the form of playback instructions. Each such instruction causes load generator <NUM> to generate one or more packets with a command, parameters, and/or payload specified by the trace.

Load generator <NUM>, in turn, transmits these packets as network traffic to server devices <NUM>. As part of carrying out these resulting transactions, server devices <NUM> may access databases <NUM>.

Another view of a testing instance is shown in <FIG>. Therein, VLAN <NUM>, m server devices <NUM>-<NUM>, <NUM>-<NUM>,. , <NUM>-m, VLAN <NUM>, and n databases <NUM>-<NUM>, <NUM>-<NUM>,. , <NUM>-n replicate VLAN <NUM>, m server devices <NUM>-<NUM>, <NUM>-<NUM>,. , <NUM>-m, VLAN <NUM>, and n databases <NUM>-<NUM>, <NUM>-<NUM>,. , <NUM>-n of the production instance. Load generators <NUM>-<NUM> and <NUM>-<NUM> also connect to VLAN <NUM> may be used to produce and send packets to server devices <NUM>-<NUM>, <NUM>-<NUM>,. Simulation controller <NUM> may be communicatively coupled to load generators <NUM>-<NUM> and <NUM>-<NUM> by way of VLAN <NUM>. Simulation controller <NUM> may provide playback instructions to load generators <NUM>-<NUM> and <NUM>-<NUM>.

Testing dashboard <NUM> may be communicatively coupled to simulation controller <NUM> by way of VLAN <NUM>. Testing dashboard <NUM> may provide a graphical user interface through which a user can manipulate simulation controller <NUM>. For example, the user may be able to view a list of available traces, select a trace for playback, begin playback of the trace, end playback of a selected trace, delete a trace, and so on. Testing dashboard <NUM> may also provide feedback regarding progress of any ongoing trace playback, and/or results of a played back trace.

The arrangement of <FIG> may be designed so that it produces little or no outbound traffic. In this way, the testing instance is unlikely to disrupt nearby production or testing instances.

As discussed above, simulation compiler <NUM> receives input from storage device <NUM> (in the form of one or more files of compressed network traffic) and server devices <NUM> (in the form of file system logs). Simulation compiler <NUM> correlates and cross-references the transactions represented by this input, and encode a combined representation of the total transactions experienced by server devices <NUM>. This combined representation may be a series of instructions that can be used to simulate the traffic from network <NUM> that was presented to server devices <NUM>.

The protocol of primary interest in a remote network management platform described herein is HTTP, as most client/server exchanges facilitated by the remote network management platform are web-based. HTTP transactions may consist of one or more requests and responses. HTTP traffic that is incoming to the remote network management platform may primarily or exclusively be requests.

HTTP requests have three main parameters: command, headers, and body. In general, a command (also referred to as a method) is defined at the beginning of the request, followed by one or more headers, followed by the body. A sample HTTP request <NUM> is shown in <FIG>.

HTTP commands describe the type of request and may include, for example, GET (requests data from a specified resource), POST (provides data to be processed to a resource), HEAD (similar to GET but returns only HTTP headers and no HTTP body), PUT (provides a representation of the specified resource), DELETE (deletes the specified resource), OPTIONS (requests a list of HTTP commands that the server device supports), and CONNECT (converts the request connection to a TCP/IP tunnel). Each of the "resources" described above may be an application or software module associated with a particular uniform resource locator (URL). In some embodiments, AJAX transactions by way of representational state transfer (REST) or simple object access protocol (SOAP) may use different or arbitrary HTTP commands.

In sample HTTP request <NUM>, the command type is POST. This command is encoded in the first line of the HTTP request, which is also referred to as the command line.

HTTP headers allow further information to be provided with a request. The format of these headers is a header name, followed by a colon, followed by a value associated with the header name. For instance, in sample HTTP request <NUM>, the "Host" header has a value of "www. org" and the "Content-Length" header has a value of "<NUM>".

An HTTP body (which may or may not be present in any particular HTTP request) includes data bytes encoding the details of the request. As examples, an HTTP body may include text, XML-encoded data, JSON-encoded data, or URL-encoded data. The body of sample HTTP request <NUM> includes XML-encoded data. This encoding is specified by the first line of the body (<?xml version=<NUM> encoding=UTF-<NUM>?>). The body continues until the end of the HTTP request.

When the original data to be transmitted includes non-printable and/or non-ASCII characters, URL-encoding may be used to replace each such character with an escape code representing that character. For example, the URL-encoding of the text string "Hello Wörld" may be "Hello+W%C3%B6rld".

Notably, the full URL of the request can be derived from the value of the "Host" header as well as the string immediately following the command declaration in the command line. Thus, in sample HTTP request <NUM>, the URL is "www. org/pub/WWW/".

Incoming packets to traffic filter device <NUM> can be stored in several possible formats as traces. One such format is the PCAP (packet capture) format, illustrated in <FIG>. File <NUM> represents a series of N+<NUM> captured packets in the PCAP format, stored in order of the time they were captured. PCAP header <NUM> is a data structure defined in <FIG>. Each of the N+<NUM> captured packets may be preceded by a per-packet header. An example per-packet header <NUM> is shown in <FIG>. File <NUM> may be a binary file that can be stored within short-term storage (e.g., main memory) or long-term storage (e.g., a disk drive) of traffic filter device <NUM>.

As noted above, <FIG> illustrates the contents of PCAP header <NUM>. There may be one instance of PCAP header <NUM> disposed at the beginning file <NUM>.

Magic number <NUM> may be a pre-defined marker of the beginning of a file with PCAP header <NUM>, and serves to indicate the byte-ordering of the computing device that performed the capture. For instance, magic number <NUM> may be defined to always have the hexadecimal value of 0xalb2c3d4 in the native byte ordering of the capturing device. If the device that reads file <NUM> finds magic number <NUM> to have this value, then the byte-ordering of this device and the capturing device is the same. If the device that reads file <NUM> finds magic number <NUM> to have a value of 0xd4c3b2a1, then this device may have to swap the byte-ordering of the fields that follow magic number <NUM>.

Major version <NUM> and minor version <NUM> may define the version of the PCAP format used in file <NUM>. In most instances, major version <NUM> is <NUM> and minor version <NUM> is <NUM>, which indicates that the version number is <NUM>.

Time zone offset <NUM> may specify the difference, in seconds, between the local time zone of the capturing device and Coordinated Universal Time (UTC). In some cases, the capturing device will set this field to <NUM> regardless of its local time zone.

Timestamp accuracy <NUM> may specify the accuracy of any time stamps in file <NUM>. In practice, this field is often set to <NUM>.

Capture length <NUM> may specify the maximum packet size, in bytes, that can be captured. In some embodiments, this value is set to <NUM>, but can be set to be smaller if the user is not interested in large-payload packets, for instance. If a packet larger than what is specified in this field is captured, it may be truncated to conform to the maximum packet size.

Datalink protocol <NUM> may specify the type of datalink interface on which the capture took place. For instance, this field may have a value of <NUM> for Ethernet, <NUM> for Wifi, and so on.

<FIG> illustrates the contents of per-packet header <NUM>. As shown in <FIG>, there may be one instance of per-packet header <NUM> for each packet represented in file <NUM>. Each instance of per-packet header <NUM> may precede its associated packet.

Timestamp seconds <NUM> and timestamp microseconds <NUM> may represent the time at which the associated packet was captured. As noted above, this may be the local time of the capturing device or UTC time.

Captured packet length <NUM> may specify the number of bytes of packet data actually captured and saved in file <NUM>. Original packet length <NUM> may specify the number of bytes in the packet as the packet appeared on the network on which it was captured.

In general, captured packet length <NUM> is expected to be less than or equal to original packet length <NUM>. For example, if capture length <NUM> is <NUM> bytes and a packet is <NUM> bytes, then captured packet length <NUM> and original packet length <NUM> may both be <NUM>. However, if the packet is <NUM> bytes, then captured packet length <NUM> may be <NUM> while original packet length <NUM> may be <NUM>.

Regardless of exact arrangement, the PCAP format (or any other comparable packet capture format) can be used to encode a sequence of one or more packets in a file. Such a file can be provided to simulation controller <NUM> for playback to a testing instance.

From the encoded packets in a high-fidelity trace, individual transactions are detected. These transactions are encoded into playback commands.

For example, the start of a TCP transaction may be detected by a TCP packet with the SYN flag set, and the end of a TCP transaction may be detect by a TCP packet with the FIN flag set. All TCP packets that are part of this transaction will have a timestamp between that of the TCP packet with the SYN flag set and the TCP packet with the FIN flag set, and will also have the same source IP addresses, destination IP addresses, source TCP port numbers, and destination TCP port numbers. Thus, all packets from a particular transaction and their ordering in time can be unambiguously identified. The payloads of these packets may be combined according to their ordering to determine the entire data transmitted from a client device to server devices <NUM>.

As noted above, the main protocol of interest in the remote network management platform described herein is HTTP. Each HTTP transaction may be represented as a <NUM>-entry vector of HTTP-related fields: (timestamp, session, username, HTTP command, URL, HTTP headers, HTTP body). Nonetheless, HTTP transactions may be encoded differently (such as with more or fewer fields), as may non-HTTP transactions.

The timestamp field of a transaction may be the time at which the first packet of the transaction was received by traffic filter device <NUM> or storage device <NUM>. This timestamp may be derived from fields <NUM> and/or <NUM> of the packet's per-packet PCAP header (see <FIG>).

The session field of a transaction may be a marker (e.g., a number, alphanumeric, or binary code) that uniquely identifies the transaction in space and time. For instance, a client device may use a particular marker or combination of markers in the payload of packets to identify the transaction.

The username field of a transaction may refer to a user for whom the transaction is carried out. For instance, the user may be logged into server devices <NUM>, and the user's name or identifier may appear in the payload of the transaction.

The HTTP command field for the transaction may identify any one of the HTTP commands described above (e.g., GET, POST, HEAD, PUT, DELETE, OPTIONS, or CONNECT), or any other HTTP command.

The URL field for the transaction may be the URL to which the HTTP request was sent. As noted above, this URL can be derived from the text immediately following the HTTP command type and the value of the "Host" header.

The HTTP header field for the transaction may include all HTTP headers and their associated values as appearing in the HTTP request. Similarly, the HTTP body field for the transaction may include the HTTP body appearing in the HTTP request.

Given these definitions, the <NUM>-entry vector for sample HTTP request <NUM> may take the form shown in the table below:.

Note that it is assumed that the timestamp that appears in the HTTP body is identical to the timestamp that appears in the appropriate per-packet PCAP header(s). In practice, this might not always be the case. Also, the HTTP body is abbreviated for convenience, as indicated by the ellipsis. In practice, the entire HTTP body may be present in the vector.

Once one or more transactions are encoded in this fashion, they may be stored (e.g., in a file) for later playback in a simulation environment.

As noted above, server devices <NUM> record representations of transactions in log files. For example, applications (e.g., web server applications) processing the transactions have dedicated log files to which these applications write the representations of each transaction. Alternatively or additionally, the applications processing the transactions may use a common log file format, such as syslog format. While these log files, also referred to a file system logs, may take on various formats and these formats may be user-customized, two possibilities are shown in <FIG> for sample HTTP request <NUM> of <FIG>.

Format <NUM> of <FIG> shows an example log format. This format includes the client IP address (e.g., the IP address of the device initiating the transaction), the client name, if applicable (e.g., found through a reverse DNS lookup of the client IP address), a user name associated with the transaction, a timestamp of the transaction, and a payload of the transaction.

Example log entry <NUM> shows how a log entry for sample HTTP request <NUM> might appear according to this format. The client IP address is <NUM>. <NUM> (read from the source IP address of incoming packets of the transaction), the client name is undefined (e.g., the reverse DNS lookup of <NUM>. <NUM> did not return an name), the user name is bobsmith@example. com (e.g., provided from the context of the transaction), the timestamp is <NUM>-<NUM>-<NUM><NUM>:<NUM>:<NUM> (e.g., the time at which a packet of the transaction arrived at server devices <NUM>), and the payload is "POST /pub/WWW/ HTTP/<NUM>. </request>". The payload may be URL-encoded.

Format <NUM> of <FIG> shows another example log format. This format includes a timestamp of the transaction, a log level (e.g., indicating a severity of the log entry), a user name associated with the transaction, the client IP address, and a payload of the transaction.

Example log entry <NUM> shows how a log entry for sample HTTP request <NUM> might appear according to this format. The timestamp is <NUM>-<NUM>-<NUM><NUM>:<NUM>:<NUM>, the log level of info (e.g., indicating an informational level of severity), the user name is bobsmith@example. com, the client IP address is <NUM>. <NUM>, and the payload is "POST /pub/WWW/ HTTP/<NUM>. </request>". The payload may be URL-encoded.

As described above, the main protocol of interest in the remote network management platform described herein is HTTP. For low-fidelity traces, each HTTP transaction may be represented as a <NUM>-entry vector of HTTP-related fields: (timestamp, session identifier, user name, payload). Nonetheless, HTTP transactions may be encoded differently (such as with more or fewer fields), as may non-HTTP transactions.

These entries may be read directly from the log files, to the extent that they exist in the log files. For instance, example log entry <NUM> provides a timestamp, user name, and payload, but not a session identifier, while example log entry <NUM> includes all of these values. Once one or more transactions are encoded in this fashion, they may be stored (e.g., in a file) for later playback in a simulation environment.

As described above, simulation compiler <NUM> receives both compressed traffic files from storage device <NUM> containing high-fidelity traces, as well as file system logs from server devices <NUM> containing low-fidelity traces. In some cases, these traces may contain duplicate transactions. Thus, it would be beneficial to remove these duplicate transactions from the playback commands.

In order to identify duplicate transactions, simulation compiler <NUM> may consider the timestamp, session identifier, and user name associated with each transaction. If any two or more transactions have the same values for all three of these parameters, then these transactions are almost certainly duplicates of one another, because the parameters uniquely identify a transaction in space and time. When a duplicate transaction is found, the high-fidelity version may be preferred over the low-fidelity version. Thus, for example, simulation compiler <NUM> may delete the <NUM>-entry vector of a transaction when a <NUM>-entry vector of that transaction already exists.

In some cases, fuzzy logic may be used with respect to the timestamps. This is because the timestamps associated with a transaction encoded in a high-fidelity trace may differ slightly from those of the same transaction encoded in a low-fidelity trace. Thus, two transactions may be considered to be duplicates if they have the same session identifier, user name, and their timestamps are within, e.g., <NUM> milliseconds, <NUM> milliseconds, or <NUM> milliseconds of one another.

Also, in some embodiments, other parameters may be used to identify duplicate transactions. For instance, the user name may be replaced with the client IP address. Alternatively, the user name might not be considered at all, and only the timestamp and session identifier may be considered for these purposes.

As noted above, the arrangement of <FIG> may be used to collect some extent (e.g., a week's worth) of traffic and transactions from a production instance. Suppose that, during this week, the production instance suffered from poor performance during a particular hour. The configuration and environment of the production instance may be created in a testing instance. The database of the testing instance may be restored from an image of the database of the production instance, and played forward to around the beginning of when the poor performance began. Then, the compressed traffic files and file system logs from this point and time are used to create a series of transactions that can further serve as the basis for traffic to be transmitted to the testing instance. Then, the operation of the testing instance can be monitored in real time and it may be possible to determine the cause of the poor performance.

To that end, simulation controller <NUM> transmits playback instructions to load generator <NUM> in order to cause load generator <NUM> to simulate the transactions represented by these playback instructions. As part of this process, simulation controller <NUM> may also exchange test coordination information with server devices <NUM> and database <NUM> to verify that these latter devices are ready for the simulation.

Particularly, load generator <NUM> may receive a playback instruction in the <NUM>-entry format, and generate one or more packets based on these entries. The generated packets may be transmitted to server devices <NUM>. Load generator <NUM> may also utilize a captured userid and password (and/or other credentials) to log in to server devices <NUM>.

As a more detailed example, consider the encoding of Table <NUM>. Load generator <NUM> may read the value of the HTTP host header (www. org) to determine the destination address. The simulation environment may be configured to map www. org to server devices <NUM>. Then, the HTTP command, URL, HTTP headers, and HTTP body are used to reconstruct the original HTTP payload.

Load generator <NUM> initiates a transaction with one of server devices <NUM> (e.g., by opening a TCP connection to this server device), and transmits the reconstructed payload to the server device as part of this transaction. Thus, one or more packets may be transmitted to the server device. Once the reconstructed payload has been transmitted, load generator <NUM> may terminate the transaction (e.g., closing the TCP connection to the server device). In some embodiments, load generator <NUM> may use the HtmlUnit library to create the transaction.

<FIG> and <FIG> are flow charts illustrating example embodiments. The processes illustrated by <FIG> and <FIG> may be carried out by a computing device, such as computing device <NUM>, and/or a cluster of computing devices, such as server cluster <NUM> which embodies simulation compiler <NUM> and/or simulation controller <NUM>. However, the processes can be carried out by other types of devices or device subsystems. For example, the processes could be carried out by a portable computer, such as a laptop or a tablet device.

The embodiments of <FIG> and <FIG> may be combined with features, aspects, and/or implementations of any of the previous figures or otherwise described herein.

Generally speaking, <FIG> depicts a process for capturing transactions that occur in a production instance, and encoding these transactions in the form of playback instructions. <FIG> depicts a process for decoding stored playback instructions in a testing instance in order to simulate transactions. Nonetheless, aspects of these two embodiments can be combined.

Block <NUM> of <FIG> involves receiving, by a simulation compiler device, a sequence of packets. The sequence of packets may have been transmitted to one or more computing devices and may represent one or more captured transactions that took place involving the one or more computing devices. The one or more computing devices may be part of a computational instance that also contains one or more databases. The simulation compiler device may be communicatively coupled to the one or more computing devices.

Block <NUM> involves identifying, by the simulation compiler device, a captured transaction within the sequence of packets.

Block <NUM> involves encoding, by the simulation compiler device, the captured transaction as a playback instruction. The playback instruction can be used to generate a further sequence of packets that, when transmitted to a computational instance used for testing, simulates the captured transaction.

In some embodiments, the captured transaction used TCP, and identifying the captured transaction within the sequence of packets involves: (i) scanning the sequence of packets for an initial packet of the captured transaction, where the initial packet of the captured transaction includes a TCP SYN flag that is set, (ii) determining transaction-identifying parameters from headers of the initial packet of the captured transaction, where the transaction-identifying parameters include a source IP address, a destination IP address, a source TCP port, and a destination TCP port, (iii) scanning the sequence of packets for a final packet of the captured transaction, where the final packet of the captured transaction includes a TCP FIN flag that is set and identical transaction-identifying parameters as those of the initial packet of the captured transaction, and (iv) identifying the captured transaction to include the initial packet of the captured transaction, the final packet of the captured transaction, and any packets within the sequence of packets that: (a) are temporally between the initial packet of the captured transaction and the final packet of the captured transaction, and (b) include identical transaction-identifying parameters as those of the initial packet of the captured transaction and the final packet of the captured transaction.

In some embodiments, the captured transaction is an HTTP request, and encoding the HTTP request as the playback instruction involves: (i) determining an HTTP command line from the HTTP request, (ii) determining HTTP header content from the HTTP request, where the HTTP header content follows the HTTP command line in the HTTP request, (iii) determining HTTP body content from the HTTP request, where the HTTP body content follows the HTTP headers in the HTTP request, and (iv) storing, as the playback instruction, the HTTP command line, the HTTP header content, and the HTTP body content.

In some embodiments, encoding the captured transaction as the playback instruction further involves: (i) deriving a URL from the HTTP command line and the HTTP header content, where the URL refers to a resource to which the HTTP request is addressed, and (ii) additionally storing, as part of the playback instruction, the URL. In these or other embodiments, encoding the captured transaction as the playback instruction may further involve: (i) determining a timestamp from the sequence of packets, where the timestamp represents a time during which the captured transaction took place, (ii) determining a session identifier from the sequence of packets, where the session identifier uniquely differentiates the captured transaction from other captured transactions within the sequence of packets, (iii) determining a user name from the sequence of packets, where the user name identifies an account associated with the particular computational instance, and (iv) additionally storing, as part of the playback instruction, the timestamp, the session identifier, and the user name.

In embodiments, the computational instance is configured to store logs of captured transactions processed by the computational instance, and the simulation compiler device is further configured to: (i) receive the logs from the computational instance, (ii) remove any duplicate captured transactions from the logs and the sequence of packets, and (iii) encode the captured transactions from the logs that are not duplicate captured transactions as playback instructions. In these or other embodiments, the captured transactions from the logs and the sequence of packets may be associated with respective timestamps, session identifiers, and user names, and removing duplicate captured transactions from the logs and the sequence of packets may involve identifying a particular captured transaction from the logs as a duplicate captured transaction when the particular captured transaction is associated with a timestamp, a session identifier, and a user name that are all associated with those from any one captured transaction from the sequence of packets. Alternatively or additionally, the captured transactions from the logs are associated with respective timestamps, session identifiers, user names, and payloads, and encoding the captured transaction from the logs that is not a duplicate captured transaction involves: (i) identifying a timestamp, a session identifier, a user name, and a payload from the particular captured transaction as logged, and (ii) encoding, as an additional playback instruction, the timestamp, the session identifier, the user name, and the payload.

Turning to <FIG>, block <NUM> involves receiving, by a load generator of a computing system arranged to simulate a computational instance in a production environment, a playback instruction that represents a captured transaction that occurred in the computational instance.

Block <NUM> involves , possibly in response to receiving the playback instruction, (i) decoding, by the load generator, the playback instruction into a sequence of packets, and (ii) transmitting, by the load generator, the sequence of packets.

Block <NUM> involves receiving, by a computing device of the computing system, the sequence of packets.

Block <NUM> may involve, possibly based on processing the sequence of packets, simulating, by the computing device, a transaction that simulates the captured transaction corresponding to the playback instruction, where the computing device, during the simulated transaction, requests and receives data from a database device of the computing system, and where the database device is configured to replicate a configuration and stored content of a database from the computational instance.

In some embodiments, the playback instruction includes: a timestamp representing a time during which the captured transaction took place, a session identifier that uniquely differentiates the captured transaction from other captured transactions that took place in the computational instance, a user name that identifies an account associated with the computational instance, and a payload representing data transmitted to the computational instance as part of the captured transaction, where the database device is configured to include the account.

In these or other embodiments, decoding the playback instruction into the sequence of packets may involve reading a destination address from the payload, and distributing the payload across the sequence of packets, where the one or more loadgenerator devices are further configured to transmit the sequence of packets to the destination address, and where the destination address is associated with the computing device. The captured transaction may have been captured in file system logs by the computational instance.

In these or other embodiments, the playback instruction may represent an HTTP request, and the payload may be further encoded to include: an HTTP command, a URL, HTTP header content, and an HTTP body all related to the HTTP request. Decoding the playback instruction into the sequence of packets may involve reading a destination address from the URL, and distributing at least some parts of the payload across the sequence of packets, where the one or more load generator devices are further configured to transmit the sequence of packets to the destination address, and where the destination address is associated with the computing device. The captured transaction may have been triggered by one or more packets captured in the computational instance. In some cases, the one or more packets were captured while incoming to the computational instance and were transmitted a source external to the computational instance.

In some embodiments, replicating the configuration and stored content of the database from the computational instance involves: (i) identifying a particular point in time to simulate, (ii) obtaining, from the computational instance, an image of the configuration and stored content of the database prior to the point in time, (iii) obtaining database logs representing database transactions that occurred between the time that the image was captured and the point in time, and (iv) installing the image on the one or more database devices and executing the database transactions from the database logs. The captured transaction may have occurred approximately at the point in time. For example, the captured transaction may have occurred within <NUM>, <NUM>, or <NUM> milliseconds of the point in time.

With respect to any or all of the message flow diagrams, scenarios, and flow charts in the figures and as discussed herein, each step, block, and/or communication can represent a processing of information and/or a transmission of information in accordance with example embodiments. Alternative embodiments are included within the scope of these example embodiments. In these alternative embodiments, for example, operations described as steps, blocks, transmissions, communications, requests, responses, and/or messages can be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved. Further, more or fewer blocks and/or operations can be used with any of the message flow diagrams, scenarios, and flow charts discussed herein, and these message flow diagrams, scenarios, and flow charts can be combined with one another, in part or in whole.

A step or block that represents a processing of information can correspond to circuitry that can be configured to perform the specific logical functions of aherein-described method or technique. Alternatively or additionally, a step or block that represents a processing of information can correspond to a module, a segment, or a portion of program code (including related data). The program code can include one or more instructions executable by a processor for implementing specific logical operations or actions in the method or technique. The program code and/or related data can be stored on any type of computer readable medium such as a storage device including RAM, a disk drive, a solid state drive, or another storage medium.

The computer readable medium can also include non-transitory computer readable media such as computer readable media that store data for short periods of time like register memory and processor cache. The computer readable media can further include non-transitory computer readable media that store program code and/or data for longer periods of time. Thus, the computer readable media may include secondary or persistent long term storage, like ROM, optical or magnetic disks, solid state drives, compact-disc read only memory (CD-ROM), for example. The computer readable media can also be any other volatile or non-volatile storage systems. A computer readable medium can be considered a computer readable storage medium, for example, or a tangible storage device.

Moreover, a step or block that represents one or more information transmissions can correspond to information transmissions between software and/or hardware modules in the same physical device. However, other information transmissions can be between software modules and/or hardware modules in different physical devices.

Claim 1:
A computing system (<NUM>) comprising:
a simulation controller device (<NUM>) configured to store playback instructions, wherein the playback instructions encode corresponding transactions that were captured in a computational instance deployed in a production environment and wherein the playback instructions include high-fidelity data corresponding to packets detected by a traffic filter device (<NUM>) that records incoming network traffic and low-fidelity data corresponding to file system logs provided by one or more server devices (<NUM>) in the production environment, the file system logs including information relating to transactions of the transactions that were captured in the computational instance that occurred between applications on the one or more server devices that utilize a loopback mechanism such that packets do not leave that server device and are internally routed from application to application, wherein the simulation controller device (<NUM>) is configured to correlate and cross-reference the high-fidelity data and the low-fidelity data and encode in the playback instructions a combined representation of total transactions experienced by the one or more server devices (<NUM>) in the production environment;
one or more load generator devices (<NUM>), coupled to the simulation controller device (<NUM>), configured to: (i) receive a playback instruction from the simulation controller device (<NUM>), and (ii) decode the playback instruction into a sequence of packets, wherein the playback instruction is from the stored playback instructions;
one or more computing devices (<NUM>), coupled to the one or more load generator devices (<NUM>), configured to: (i) receive the sequence of packets from the one or more load generator devices (<NUM>), and (ii) based on processing the sequence of packets, simulate a transaction corresponding to the playback instruction; and
one or more database devices (<NUM>), coupled to the one or more computing devices (<NUM>), configured to: (i) replicate a configuration and stored content of a database (<NUM>) from the computational instance deployed in the production environment, and (ii) provide parts of the stored content requested by the one or more computing devices (<NUM>) during the simulated transaction.