Patent Publication Number: US-2022224619-A1

Title: Api dependency error and latency injection

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is a continuation of and claims priority to and the benefit of U.S. Non-Provisional application Ser. No. 16/415,688, filed on May 17, 2019, titled “API DEPENDENCY ERROR AND LATENCY INJECTION”, the entire contents of which are herein incorporated by reference in its entirety. 
    
    
     FIELD OF THE DISCLOSURE 
     The present application generally relates to communications, including but not limited to systems and methods for network resource monitoring. 
     BACKGROUND 
     Network provided services, such as web applications, virtual machines, hosted resources, or other such services, may be adversely affected by errors and latency. However, in many instances, these errors may be intermittent, making it difficult to proactively identify and mitigate problems. 
     SUMMARY 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features, nor is it intended to limit the scope of the claims included herewith. 
     In some implementations, to proactively monitor and identify issues with resources, a system may be configured with the ability to introduce synthetic errors and latency, with “synthetic” referring to intentionally introduced errors as opposed to “organic” errors or errors that arise within the system and not from intentional introduction during testing. The configuration and enablement of these synthetic errors and latency may be managed within or for each service. For example, in some implementations, by passing all calls through a proxy capable of injecting errors and latency, any service in communication with the proxy can reap the benefits of error detection and mitigation, such as validating how a service behaves when a dependent service begins misbehaving. By providing a consistent and standard configuration for injecting errors and latency, all services can be tested with this method. 
     The proxy handling all East/West microservice traffic (e.g. network traffic communicated between microservices of the services such as within a data center or between data centers) may pull from a centralized configuration to determine error rates, latencies, and activities to be injected when criteria is met. This configuration may also include time frames or schedules for testing, as well as filtering for what calls and services are impacted. Responses to synthetic errors may be from a third party call, a static document, or a custom script language to respond with payloads based on the request. 
     An aspect provides a method for validating a microservice. The method includes (a) identifying, by a device intermediary to a plurality of microservices, a synthetic error and a first criteria for implementing the synthetic error to validate a first microservice of the plurality of microservices. The method also includes (b) determining, by the device, that the first criteria for implementing the synthetic error has been met. The method also includes (c) receiving, by the device, a request from the first microservice to access a second microservice of the plurality of microservices. The method also includes (d) communicating, by the device responsive to the determination, to the first microservice, a response on behalf of the second microservice, the response implementing the synthetic error. The method also includes (e) validating, by the device, that the first microservice one of handled or did not handle the synthetic error. 
     In some implementations, the first criteria comprises one of a time period, a duration or a frequency. In some implementations, the first criteria comprises one of a status or a condition of one or more of the plurality of microservices. In a further implementation, (b) further includes monitoring, by the device, one of the status or the condition of the one or more of the plurality of microservices to determine that the first criteria has been met. 
     In some implementations, the first criteria comprises identification of a type of request. In a further implementation, (c) further includes determining, by the device, that the request corresponds to the type of request of the first criteria. 
     In some implementations, (a) further includes identifying, by the device, a latency time and a second criteria for implementing the latency. In a further implementation, the method includes determining, by the device, that the second criteria has been met. In a still further implementation, the method includes receiving, by the device, a second request from the first microservice to access one of the plurality of microservices; delaying, by the device, a second response to the second request in accordance with the latency time; and validating, by the device, that the first microservice one of handled or did not handle the latency. 
     In some implementations, the method includes identifying, by the device, one of a plurality of synthetic errors or a plurality of latency times to implement responsive to a plurality of criteria to validate one or more of the plurality of microservices; and responding, by the device, to requests by implementing the one of the plurality of synthetic errors or one of the plurality of latency times. 
     In another aspect, the present disclosure is directed to a system for implementing a synthetic error to validate a microservice. The system includes a device comprising one or more processors, coupled to memory and intermediary to a plurality of microservices. The device is configured to: identify a synthetic error and a first criteria for implementing the synthetic error to validate a first microservice of the plurality of microservices; determine that the first criteria for implementing the synthetic error has been met; receive a request from the first microservice to access a second microservice of the plurality of microservices; communicate, responsive to the determination, to the first microservice, a response on behalf of the second microservice, the response implementing the synthetic error; and validate that the first microservice one of handled or did not handle the first synthetic error. 
     In some implementations, the first criteria comprises one of a time period, a duration, or a frequency. In some implementations, the first criteria comprises one of a status or a condition of one or more of the plurality of microservices. In a further implementation, the device is further configured to monitor one of the status or the condition of the one or more of the plurality of microservices to determine that the first criteria has been met. 
     In some implementations, the first criteria comprises identification of a type of request. In a further implementation, the device is further configured to determine the request corresponds to the type of request of the first criteria. 
     In some implementations, the device is further configured to identify a latency time and a second criteria for implementing the latency. In a further implementation, the device is further configured to determine that the second criteria has been met. In a still further implementation, the device is further configured to receive a second request from the first microservice to access one of the plurality of microservices; delay a second response to the second request in accordance with the latency time; and validate that the first microservice one of handled or did not handle the latency. 
     In some implementations, the device is further configured to identify, in order to validate one or more of the plurality of microservices, one of a plurality of synthetic errors or a plurality of latency times to implement responsive to a plurality of criteria; and respond to requests to implement the one of the plurality of synthetic errors or one of the plurality of latency times. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Objects, aspects, features, and advantages of embodiments disclosed herein will become more fully apparent from the following detailed description, the appended claims, and the accompanying drawing figures in which like reference numerals identify similar or identical elements. Reference numerals that are introduced in the specification in association with a drawing figure may be repeated in one or more subsequent figures without additional description in the specification in order to provide context for other features, and not every element may be labeled in every figure. The drawing figures are not necessarily to scale, emphasis instead being placed upon illustrating embodiments, principles and concepts. The drawings are not intended to limit the scope of the claims included herewith. 
         FIG. 1A  is a block diagram of a network computing system, in accordance with an illustrative embodiment; 
         FIG. 1B  is a block diagram of a network computing system for delivering a computing environment from a server to a client via an appliance, in accordance with an illustrative embodiment; 
         FIG. 1C  is a block diagram of a computing device, in accordance with an illustrative embodiment; 
         FIG. 2  is a block diagram of an appliance for processing communications between a client and a server, in accordance with an illustrative embodiment; 
         FIG. 3  is a block diagram of a virtualization environment, in accordance with an illustrative embodiment; 
         FIG. 4  is a block diagram of a cluster system, in accordance with an illustrative embodiment; 
         FIG. 5A  is a block diagram of a service graph based system, in accordance with an illustrative embodiment; 
         FIG. 5B  is a block diagram of a service graph, in accordance with an illustrative embodiment; 
         FIG. 5C  is a flow diagram of a method of using a service graph, in accordance with an illustrative embodiment; 
         FIG. 6A  is a block diagram of an implementation of a system for synthetic error injection and monitoring; and 
         FIG. 6B  is a flow diagram of a method for synthetic error injection and monitoring, according to some implementations. 
     
    
    
     DETAILED DESCRIPTION 
     For purposes of reading the description of the various embodiments below, the following descriptions of the sections of the specification and their respective contents may be helpful: 
     Section A describes a network environment and computing environment which may be useful for practicing embodiments described herein; 
     Section B describes embodiments of systems and methods for delivering a computing environment to a remote user; 
     Section C describes embodiments of systems and methods for virtualizing an application delivery controller; 
     Section D describes embodiments of systems and methods for providing a clustered appliance architecture environment; and 
     Section E describes embodiments of systems and methods for API dependency error and latency injection. 
     A. Network and Computing Environment 
     Referring to  FIG. 1A , an illustrative network environment  100  is depicted. Network environment  100  may include one or more clients  102 ( 1 )- 102 ( n ) (also generally referred to as local machine(s)  102  or client(s)  102 ) in communication with one or more servers  106 ( 1 )- 106 ( n ) (also generally referred to as remote machine(s)  106  or server(s)  106 ) via one or more networks  104 ( 1 )- 104   n  (generally referred to as network(s)  104 ). In some embodiments, a client  102  may communicate with a server  106  via one or more appliances  200 ( 1 )- 200   n  (generally referred to as appliance(s)  200  or gateway(s)  200 ). 
     Although the embodiment shown in  FIG. 1A  shows one or more networks  104  between clients  102  and servers  106 , in other embodiments, clients  102  and servers  106  may be on the same network  104 . The various networks  104  may be the same type of network or different types of networks. For example, in some embodiments, network  104 ( 1 ) may be a private network such as a local area network (LAN) or a company Intranet, while network  104 ( 2 ) and/or network  104 ( n ) may be a public network, such as a wide area network (WAN) or the Internet. In other embodiments, both network  104 ( 1 ) and network  104 ( n ) may be private networks. Networks  104  may employ one or more types of physical networks and/or network topologies, such as wired and/or wireless networks, and may employ one or more communication transport protocols, such as transmission control protocol (TCP), internet protocol (IP), user datagram protocol (UDP) or other similar protocols. 
     As shown in  FIG. 1A , one or more appliances  200  may be located at various points or in various communication paths of network environment  100 . For example, appliance  200  may be deployed between two networks  104 ( 1 ) and  104 ( 2 ), and appliances  200  may communicate with one another to work in conjunction to, for example, accelerate network traffic between clients  102  and servers  106 . In other embodiments, the appliance  200  may be located on a network  104 . For example, appliance  200  may be implemented as part of one of clients  102  and/or servers  106 . In an embodiment, appliance  200  may be implemented as a network device such as Citrix networking (formerly NetScaler®) products sold by Citrix Systems, Inc. of Fort Lauderdale, Fla. 
     As shown in  FIG. 1A , one or more servers  106  may operate as a server farm  38 . Servers  106  of server farm  38  may be logically grouped, and may either be geographically co-located (e.g., on premises) or geographically dispersed (e.g., cloud based) from clients  102  and/or other servers  106 . In an embodiment, server farm  38  executes one or more applications on behalf of one or more of clients  102  (e.g., as an application server), although other uses are possible, such as a file server, gateway server, proxy server, or other similar server uses. Clients  102  may seek access to hosted applications on servers  106 . 
     As shown in  FIG. 1A , in some embodiments, appliances  200  may include, be replaced by, or be in communication with, one or more additional appliances, such as WAN optimization appliances  205 ( 1 )- 205 ( n ), referred to generally as WAN optimization appliance(s)  205 . For example, WAN optimization appliance  205  may accelerate, cache, compress or otherwise optimize or improve performance, operation, flow control, or quality of service of network traffic, such as traffic to and/or from a WAN connection, such as optimizing Wide Area File Services (WAFS), accelerating Server Message Block (SMB) or Common Internet File System (CIFS). In some embodiments, appliance  205  may be a performance enhancing proxy or a WAN optimization controller. In one embodiment, appliance  205  may be implemented as Citrix SD-WAN products sold by Citrix Systems, Inc. of Fort Lauderdale, Fla. 
     Referring to  FIG. 1B , an example network environment,  100 ′, for delivering and/or operating a computing network environment on a client  102  is shown. As shown in  FIG. 1B , a server  106  may include an application delivery system  190  for delivering a computing environment, application, and/or data files to one or more clients  102 . Client  102  may include client agent  120  and computing environment  15 . Computing environment  15  may execute or operate an application,  16 , that accesses, processes or uses a data file  17 . Computing environment  15 , application  16  and/or data file  17  may be delivered via appliance  200  and/or the server  106 . 
     Appliance  200  may accelerate delivery of all or a portion of computing environment  15  to a client  102 , for example by the application delivery system  190 . For example, appliance  200  may accelerate delivery of a streaming application and data file processable by the application from a data center to a remote user location by accelerating transport layer traffic between a client  102  and a server  106 . Such acceleration may be provided by one or more techniques, such as: 1) transport layer connection pooling, 2) transport layer connection multiplexing, 3) transport control protocol buffering, 4) compression, 5) caching, or other techniques. Appliance  200  may also provide load balancing of servers  106  to process requests from clients  102 , act as a proxy or access server to provide access to the one or more servers  106 , provide security and/or act as a firewall between a client  102  and a server  106 , provide Domain Name Service (DNS) resolution, provide one or more virtual servers or virtual internet protocol servers, and/or provide a secure virtual private network (VPN) connection from a client  102  to a server  106 , such as a secure socket layer (SSL) VPN connection and/or provide encryption and decryption operations. 
     Application delivery management system  190  may deliver computing environment  15  to a user (e.g., client  102 ), remote or otherwise, based on authentication and authorization policies applied by policy engine  195 . A remote user may obtain a computing environment and access to server stored applications and data files from any network-connected device (e.g., client  102 ). For example, appliance  200  may request an application and data file from server  106 . In response to the request, application delivery system  190  and/or server  106  may deliver the application and data file to client  102 , for example via an application stream to operate in computing environment  15  on client  102 , or via a remote-display protocol or otherwise via remote-based or server-based computing. In an embodiment, application delivery system  190  may be implemented as any portion of the Citrix Workspace Suite™ by Citrix Systems, Inc., such as Citrix Virtual Apps and Desktops (formerly XenApp® and XenDesktop®). 
     Policy engine  195  may control and manage the access to, and execution and delivery of, applications. For example, policy engine  195  may determine the one or more applications a user or client  102  may access and/or how the application should be delivered to the user or client  102 , such as a server-based computing, streaming or delivering the application locally to the client  120  for local execution. 
     For example, in operation, a client  102  may request execution of an application (e.g., application  16 ′) and application delivery system  190  of server  106  determines how to execute application  16 ′, for example based upon credentials received from client  102  and a user policy applied by policy engine  195  associated with the credentials. For example, application delivery system  190  may enable client  102  to receive application-output data generated by execution of the application on a server  106 , may enable client  102  to execute the application locally after receiving the application from server  106 , or may stream the application via network  104  to client  102 . For example, in some embodiments, the application may be a server-based or a remote-based application executed on server  106  on behalf of client  102 . Server  106  may display output to client  102  using a thin-client or remote-display protocol, such as the Independent Computing Architecture (ICA) protocol by Citrix Systems, Inc. of Fort Lauderdale, Fla. The application may be any application related to real-time data communications, such as applications for streaming graphics, streaming video and/or audio or other data, delivery of remote desktops or workspaces or hosted services or applications, for example infrastructure as a service (IaaS), desktop as a service (DaaS), workspace as a service (WaaS), software as a service (SaaS) or platform as a service (PaaS). 
     One or more of servers  106  may include a performance monitoring service or agent  197 . In some embodiments, a dedicated one or more servers  106  may be employed to perform performance monitoring. Performance monitoring may be performed using data collection, aggregation, analysis, management and reporting, for example by software, hardware or a combination thereof. Performance monitoring may include one or more agents for performing monitoring, measurement and data collection activities on clients  102  (e.g., client agent  120 ), servers  106  (e.g., agent  197 ) or an appliance  200  and/or  205  (agent not shown). In general, monitoring agents (e.g.,  120  and/or  197 ) execute transparently (e.g., in the background) to any application and/or user of the device. In some embodiments, monitoring agent  197  includes any of the product embodiments referred to as Citrix Analytics or Citrix Application Delivery Management by Citrix Systems, Inc. of Fort Lauderdale, Fla. 
     The monitoring agents  120  and  197  may monitor, measure, collect, and/or analyze data on a predetermined frequency, based upon an occurrence of given event(s), or in real time during operation of network environment  100 . The monitoring agents may monitor resource consumption and/or performance of hardware, software, and/or communications resources of clients  102 , networks  104 , appliances  200  and/or  205 , and/or servers  106 . For example, network connections such as a transport layer connection, network latency, bandwidth utilization, end-user response times, application usage and performance, session connections to an application, cache usage, memory usage, processor usage, storage usage, database transactions, client and/or server utilization, active users, duration of user activity, application crashes, errors, or hangs, the time required to log-in to an application, a server, or the application delivery system, and/or other performance conditions and metrics may be monitored. 
     The monitoring agents  120  and  197  may provide application performance management for application delivery system  190 . For example, based upon one or more monitored performance conditions or metrics, application delivery system  190  may be dynamically adjusted, for example periodically or in real-time, to optimize application delivery by servers  106  to clients  102  based upon network environment performance and conditions. 
     In described embodiments, clients  102 , servers  106 , and appliances  200  and  205  may be deployed as and/or executed on any type and form of computing device, such as any desktop computer, laptop computer, or mobile device capable of communication over at least one network and performing the operations described herein. For example, clients  102 , servers  106  and/or appliances  200  and  205  may each correspond to one computer, a plurality of computers, or a network of distributed computers such as computer  101  shown in  FIG. 1C . 
     As shown in  FIG. 1C , computer  101  may include one or more processors  103 , volatile memory  122  (e.g., RAM), non-volatile memory  128  (e.g., one or more hard disk drives (HDDs) or other magnetic or optical storage media, one or more solid state drives (SSDs) such as a flash drive or other solid state storage media, one or more hybrid magnetic and solid state drives, and/or one or more virtual storage volumes, such as a cloud storage, or a combination of such physical storage volumes and virtual storage volumes or arrays thereof), user interface (UI)  123 , one or more communications interfaces  118 , and communication bus  150 . User interface  123  may include graphical user interface (GUI)  124  (e.g., a touchscreen, a display, etc.) and one or more input/output (I/O) devices  126  (e.g., a mouse, a keyboard, etc.). Non-volatile memory  128  stores operating system  115 , one or more applications  116 , and data  117  such that, for example, computer instructions of operating system  115  and/or applications  116  are executed by processor(s)  103  out of volatile memory  122 . Data may be entered using an input device of GUI  124  or received from I/O device(s)  126 . Various elements of computer  101  may communicate via communication bus  150 . Computer  101  as shown in  FIG. 1C  is shown merely as an example, as clients  102 , servers  106  and/or appliances  200  and  205  may be implemented by any computing or processing environment and with any type of machine or set of machines that may have suitable hardware and/or software capable of operating as described herein. 
     Processor(s)  103  may be implemented by one or more programmable processors executing one or more computer programs to perform the functions of the system. As used herein, the term “processor” describes an electronic circuit that performs a function, an operation, or a sequence of operations. The function, operation, or sequence of operations may be hard coded into the electronic circuit or soft coded by way of instructions held in a memory device. A “processor” may perform the function, operation, or sequence of operations using digital values or using analog signals. In some embodiments, the “processor” can be embodied in one or more application specific integrated circuits (ASICs), microprocessors, digital signal processors, microcontrollers, field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), multi-core processors, or general-purpose computers with associated memory. The “processor” may be analog, digital or mixed-signal. In some embodiments, the “processor” may be one or more physical processors or one or more “virtual” (e.g., remotely located or “cloud”) processors. 
     Communications interfaces  118  may include one or more interfaces to enable computer  101  to access a computer network such as a LAN, a WAN, or the Internet through a variety of wired and/or wireless or cellular connections. 
     In described embodiments, a first computing device  101  may execute an application on behalf of a user of a client computing device (e.g., a client  102 ), may execute a virtual machine, which provides an execution session within which applications execute on behalf of a user or a client computing device (e.g., a client  102 ), such as a hosted desktop session, may execute a terminal services session to provide a hosted desktop environment, or may provide access to a computing environment including one or more of: one or more applications, one or more desktop applications, and one or more desktop sessions in which one or more applications may execute. 
     B. Appliance Architecture 
       FIG. 2  shows an example embodiment of appliance  200 . As described herein, appliance  200  may be implemented as a server, gateway, router, switch, bridge or other type of computing or network device. As shown in  FIG. 2 , an embodiment of appliance  200  may include a hardware layer  206  and a software layer  205  divided into a user space  202  and a kernel space  204 . Hardware layer  206  provides the hardware elements upon which programs and services within kernel space  204  and user space  202  are executed and allow programs and services within kernel space  204  and user space  202  to communicate data both internally and externally with respect to appliance  200 . As shown in  FIG. 2 , hardware layer  206  may include one or more processing units  262  for executing software programs and services, memory  264  for storing software and data, network ports  266  for transmitting and receiving data over a network, and encryption processor  260  for encrypting and decrypting data such as in relation to Secure Socket Layer (SSL) or Transport Layer Security (TLS) processing of data transmitted and received over the network. 
     An operating system of appliance  200  allocates, manages, or otherwise segregates the available system memory into kernel space  204  and user space  202 . Kernel space  204  is reserved for running kernel  230 , including any device drivers, kernel extensions or other kernel related software. As known to those skilled in the art, kernel  230  is the core of the operating system, and provides access, control, and management of resources and hardware-related elements of application  104 . Kernel space  204  may also include a number of network services or processes working in conjunction with cache manager  232 . 
     Appliance  200  may include one or more network stacks  267 , such as a TCP/IP based stack, for communicating with client(s)  102 , server(s)  106 , network(s)  104 , and/or other appliances  200  or  205 . For example, appliance  200  may establish and/or terminate one or more transport layer connections between clients  102  and servers  106 . Each network stack  267  may include a buffer  243  for queuing one or more network packets for transmission by appliance  200 . 
     Kernel space  204  may include cache manager  232 , packet engine  240 , encryption engine  234 , policy engine  236  and compression engine  238 . In other words, one or more of processes  232 ,  240 ,  234 ,  236  and  238  run in the core address space of the operating system of appliance  200 , which may reduce the number of data transactions to and from the memory and/or context switches between kernel mode and user mode, for example since data obtained in kernel mode may not need to be passed or copied to a user process, thread or user level data structure. 
     Cache manager  232  may duplicate original data stored elsewhere or data previously computed, generated or transmitted to reducing the access time of the data. In some embodiments, the cache memory may be a data object in memory  264  of appliance  200 , or may be a physical memory having a faster access time than memory  264 . 
     Policy engine  236  may include a statistical engine or other configuration mechanism to allow a user to identify, specify, define or configure a caching policy and access, control and management of objects, data or content being cached by appliance  200 , and define or configure security, network traffic, network access, compression or other functions performed by appliance  200 . 
     Encryption engine  234  may process any security related protocol, such as SSL or TLS. For example, encryption engine  234  may encrypt and decrypt network packets, or any portion thereof, communicated via appliance  200 , may setup or establish SSL, TLS or other secure connections, for example between client  102 , server  106 , and/or other appliances  200  or  205 . In some embodiments, encryption engine  234  may use a tunneling protocol to provide a VPN between a client  102  and a server  106 . In some embodiments, encryption engine  234  is in communication with encryption processor  260 . Compression engine  238  compresses network packets bi-directionally between clients  102  and servers  106  and/or between one or more appliances  200 . 
     Packet engine  240  may manage kernel-level processing of packets received and transmitted by appliance  200  via network stacks  267  to send and receive network packets via network ports  266 . Packet engine  240  may operate in conjunction with encryption engine  234 , cache manager  232 , policy engine  236  and compression engine  238 , for example to perform encryption/decryption, traffic management such as request-level content switching and request-level cache redirection, and compression and decompression of data. 
     User space  202  is a memory area or portion of the operating system used by user mode applications or programs otherwise running in user mode. A user mode application may not access kernel space  204  directly and uses service calls in order to access kernel services. User space  202  may include graphical user interface (GUI)  210 , a command line interface (CLI)  212 , shell services  214 , health monitor  216 , and daemon services  218 . GUI  210  and CLI  212  enable a system administrator or other user to interact with and control the operation of appliance  200 , such as via the operating system of appliance  200 . Shell services  214  include the programs, services, tasks, processes or executable instructions to support interaction with appliance  200  by a user via the GUI  210  and/or CLI  212 . 
     Health monitor  216  monitors, checks, reports and ensures that network systems are functioning properly and that users are receiving requested content over a network, for example by monitoring activity of appliance  200 . In some embodiments, health monitor  216  intercepts and inspects any network traffic passed via appliance  200 . For example, health monitor  216  may interface with one or more of encryption engine  234 , cache manager  232 , policy engine  236 , compression engine  238 , packet engine  240 , daemon services  218 , and shell services  214  to determine a state, status, operating condition, or health of any portion of the appliance  200 . Further, health monitor  216  may determine if a program, process, service or task is active and currently running, check status, error or history logs provided by any program, process, service or task to determine any condition, status or error with any portion of appliance  200 . Additionally, health monitor  216  may measure and monitor the performance of any application, program, process, service, task or thread executing on appliance  200 . 
     Daemon services  218  are programs that run continuously or in the background and handle periodic service requests received by appliance  200 . In some embodiments, a daemon service may forward the requests to other programs or processes, such as another daemon service  218  as appropriate. 
     As described herein, appliance  200  may relieve servers  106  of much of the processing load caused by repeatedly opening and closing transport layer connections to clients  102  by opening one or more transport layer connections with each server  106  and maintaining these connections to allow repeated data accesses by clients via the Internet (e.g., “connection pooling”). To perform connection pooling, appliance  200  may translate or multiplex communications by modifying sequence numbers and acknowledgment numbers at the transport layer protocol level (e.g., “connection multiplexing”). Appliance  200  may also provide switching or load balancing for communications between the client  102  and server  106 . 
     As described herein, each client  102  may include client agent  120  for establishing and exchanging communications with appliance  200  and/or server  106  via a network  104 . Client  102  may have installed and/or execute one or more applications that are in communication with network  104 . Client agent  120  may intercept network communications from a network stack used by the one or more applications. For example, client agent  120  may intercept a network communication at any point in a network stack and redirect the network communication to a destination desired, managed or controlled by client agent  120 , for example to intercept and redirect a transport layer connection to an IP address and port controlled or managed by client agent  120 . Thus, client agent  120  may transparently intercept any protocol layer below the transport layer, such as the network layer, and any protocol layer above the transport layer, such as the session, presentation or application layers. Client agent  120  can interface with the transport layer to secure, optimize, accelerate, route or load-balance any communications provided via any protocol carried by the transport layer. 
     In some embodiments, client agent  120  is implemented as an Independent Computing Architecture (ICA) client developed by Citrix Systems, Inc. of Fort Lauderdale, Fla. Client agent  120  may perform acceleration, streaming, monitoring, and/or other operations. For example, client agent  120  may accelerate streaming an application from a server  106  to a client  102 . Client agent  120  may also perform end-point detection/scanning and collect end-point information about client  102  for appliance  200  and/or server  106 . Appliance  200  and/or server  106  may use the collected information to determine and provide access, authentication and authorization control of the client&#39;s connection to network  104 . For example, client agent  120  may identify and determine one or more client-side attributes, such as: the operating system and/or a version of an operating system, a service pack of the operating system, a running service, a running process, a file, presence or versions of various applications of the client, such as antivirus, firewall, security, and/or other software. 
     C. Systems and Methods for Providing Virtualized Application Delivery Controller 
     Referring now to  FIG. 3 , a block diagram of a virtualized environment  300  is shown. As shown, a computing device  302  in virtualized environment  300  includes a virtualization layer  303 , a hypervisor layer  304 , and a hardware layer  307 . Hypervisor layer  304  includes one or more hypervisors (or virtualization managers)  301  that allocates and manages access to a number of physical resources in hardware layer  307  (e.g., physical processor(s)  321  and physical disk(s)  328 ) by at least one virtual machine (VM) (e.g., one of VMs  306 ) executing in virtualization layer  303 . Each VM  306  may include allocated virtual resources such as virtual processors  332  and/or virtual disks  342 , as well as virtual resources such as virtual memory and virtual network interfaces. In some embodiments, at least one of VMs  306  may include a control operating system (e.g.,  305 ) in communication with hypervisor  301  and used to execute applications for managing and configuring other VMs (e.g., guest operating systems  310 ) on device  302 . 
     In general, hypervisor(s)  301  may provide virtual resources to an operating system of VMs  306  in any manner that simulates the operating system having access to a physical device. Thus, hypervisor(s)  301  may be used to emulate virtual hardware, partition physical hardware, virtualize physical hardware, and execute virtual machines that provide access to computing environments. In an illustrative embodiment, hypervisor(s)  301  may be implemented as a Citrix Hypervisor by Citrix Systems, Inc. of Fort Lauderdale, Fla. In an illustrative embodiment, device  302  executing a hypervisor that creates a virtual machine platform on which guest operating systems may execute is referred to as a host server.  302   
     Hypervisor  301  may create one or more VMs  306  in which an operating system (e.g., control operating system  305  and/or guest operating system  310 ) executes. For example, the hypervisor  301  loads a virtual machine image to create VMs  306  to execute an operating system. Hypervisor  301  may present VMs  306  with an abstraction of hardware layer  307 , and/or may control how physical capabilities of hardware layer  307  are presented to VMs  306 . For example, hypervisor(s)  301  may manage a pool of resources distributed across multiple physical computing devices. 
     In some embodiments, one of VMs  306  (e.g., the VM executing control operating system  305 ) may manage and configure other of VMs  306 , for example by managing the execution and/or termination of a VM and/or managing allocation of virtual resources to a VM. In various embodiments, VMs may communicate with hypervisor(s)  301  and/or other VMs via, for example, one or more Application Programming Interfaces (APIs), shared memory, and/or other techniques. 
     In general, VMs  306  may provide a user of device  302  with access to resources within virtualized computing environment  300 , for example, one or more programs, applications, documents, files, desktop and/or computing environments, or other resources. In some embodiments, VMs  306  may be implemented as fully virtualized VMs that are not aware that they are virtual machines (e.g., a Hardware Virtual Machine or HVM). In other embodiments, the VM may be aware that it is a virtual machine, and/or the VM may be implemented as a paravirtualized (PV) VM. 
     Although shown in  FIG. 3  as including a single virtualized device  302 , virtualized environment  300  may include a plurality of networked devices in a system in which at least one physical host executes a virtual machine. A device on which a VM executes may be referred to as a physical host and/or a host machine. For example, appliance  200  may be additionally or alternatively implemented in a virtualized environment  300  on any computing device, such as a client  102 , server  106  or appliance  200 . Virtual appliances may provide functionality for availability, performance, health monitoring, caching and compression, connection multiplexing and pooling and/or security processing (e.g., firewall, VPN, encryption/decryption, etc.), similarly as described in regard to appliance  200 . 
     In some embodiments, a server may execute multiple virtual machines  306 , for example on various cores of a multi-core processing system and/or various processors of a multiple processor device. For example, although generally shown herein as “processors” (e.g., in  FIGS. 1C, 2 and 3 ), one or more of the processors may be implemented as either single- or multi-core processors to provide a multi-threaded, parallel architecture and/or multi-core architecture. Each processor and/or core may have or use memory that is allocated or assigned for private or local use that is only accessible by that processor/core, and/or may have or use memory that is public or shared and accessible by multiple processors/cores. Such architectures may allow work, task, load or network traffic distribution across one or more processors and/or one or more cores (e.g., by functional parallelism, data parallelism, flow-based data parallelism, etc.). 
     Further, instead of (or in addition to) the functionality of the cores being implemented in the form of a physical processor/core, such functionality may be implemented in a virtualized environment (e.g.,  300 ) on a client  102 , server  106  or appliance  200 , such that the functionality may be implemented across multiple devices, such as a cluster of computing devices, a server farm or network of computing devices, etc. The various processors/cores may interface or communicate with each other using a variety of interface techniques, such as core to core messaging, shared memory, kernel APIs, etc. 
     In embodiments employing multiple processors and/or multiple processor cores, described embodiments may distribute data packets among cores or processors, for example to balance the flows across the cores. For example, packet distribution may be based upon determinations of functions performed by each core, source and destination addresses, and/or whether: a load on the associated core is above a predetermined threshold; the load on the associated core is below a predetermined threshold; the load on the associated core is less than the load on the other cores; or any other metric that can be used to determine where to forward data packets based in part on the amount of load on a processor. 
     For example, data packets may be distributed among cores or processes using receive-side scaling (RSS) in order to process packets using multiple processors/cores in a network. RSS generally allows packet processing to be balanced across multiple processors/cores while maintaining in-order delivery of the packets. In some embodiments, RSS may use a hashing scheme to determine a core or processor for processing a packet. 
     The RSS may generate hashes from any type and form of input, such as a sequence of values. This sequence of values can include any portion of the network packet, such as any header, field or payload of network packet, and include any tuples of information associated with a network packet or data flow, such as addresses and ports. The hash result or any portion thereof may be used to identify a processor, core, engine, etc., for distributing a network packet, for example via a hash table, indirection table, or other mapping technique. 
     D. Systems and Methods for Providing a Distributed Cluster Architecture 
     Although shown in  FIGS. 1A and 1B  as being single appliances, appliances  200  may be implemented as one or more distributed or clustered appliances. Individual computing devices or appliances may be referred to as nodes of the cluster. A centralized management system may perform load balancing, distribution, configuration, or other tasks to allow the nodes to operate in conjunction as a single computing system. Such a cluster may be viewed as a single virtual appliance or computing device.  FIG. 4  shows a block diagram of an illustrative computing device cluster or appliance cluster  400 . A plurality of appliances  200  or other computing devices (e.g., nodes) may be joined into a single cluster  400 . Cluster  400  may operate as an application server, network storage server, backup service, or any other type of computing device to perform many of the functions of appliances  200  and/or  205 . 
     In some embodiments, each appliance  200  of cluster  400  may be implemented as a multi-processor and/or multi-core appliance, as described herein. Such embodiments may employ a two-tier distribution system, with one appliance if the cluster distributing packets to nodes of the cluster, and each node distributing packets for processing to processors/cores of the node. In many embodiments, one or more of appliances  200  of cluster  400  may be physically grouped or geographically proximate to one another, such as a group of blade servers or rack mount devices in a given chassis, rack, and/or data center. In some embodiments, one or more of appliances  200  of cluster  400  may be geographically distributed, with appliances  200  not physically or geographically co-located. In such embodiments, geographically remote appliances may be joined by a dedicated network connection and/or VPN. In geographically distributed embodiments, load balancing may also account for communications latency between geographically remote appliances. 
     In some embodiments, cluster  400  may be considered a virtual appliance, grouped via common configuration, management, and purpose, rather than as a physical group. For example, an appliance cluster may comprise a plurality of virtual machines or processes executed by one or more servers. 
     As shown in  FIG. 4 , appliance cluster  400  may be coupled to a first network  104 ( 1 ) via client data plane  402 , for example to transfer data between clients  102  and appliance cluster  400 . Client data plane  402  may be implemented a switch, hub, router, or other similar network device internal or external to cluster  400  to distribute traffic across the nodes of cluster  400 . For example, traffic distribution may be performed based on equal-cost multi-path (ECMP) routing with next hops configured with appliances or nodes of the cluster, open-shortest path first (OSPF), stateless hash-based traffic distribution, link aggregation (LAG) protocols, or any other type and form of flow distribution, load balancing, and routing. 
     Appliance cluster  400  may be coupled to a second network  104 ( 2 ) via server data plane  404 . Similarly to client data plane  402 , server data plane  404  may be implemented as a switch, hub, router, or other network device that may be internal or external to cluster  400 . In some embodiments, client data plane  402  and server data plane  404  may be merged or combined into a single device. 
     In some embodiments, each appliance  200  of cluster  400  may be connected via an internal communication network or back plane  406 . Back plane  406  may enable inter-node or inter-appliance control and configuration messages, for inter-node forwarding of traffic, and/or for communicating configuration and control traffic from an administrator or user to cluster  400 . In some embodiments, back plane  406  may be a physical network, a VPN or tunnel, or a combination thereof. 
     E. Service Graph Based Platform and Technology 
     Referring now to  FIGS. 5A-5C , implementation of systems and methods for a service graph based platform and technology will be discussed. A service graph is a useful technology tool for visualizing a service by its topology of components and network elements. Services may be made up of microservices with each microservice handling a particular set of one or more functions of the service. Network traffic may traverse the service topology such as a client communicating with a server to access service (e.g., north-south traffic). Network traffic of a service may include network traffic communicated between microservices of the services such as within a data center or between data centers (e.g., east-west traffic). The service graph may be used to identify and provide metrics of such network traffic of the service as well as operation and performance of any network elements used to provide the service. Service graphs may be used for identifying and determining issues with the service and which part of the topology causing the issue. Services graphs may be used to provide for administering, managing and configuring of services to improve operational performance of such services. 
     Referring to  FIG. 5A , an implementation of a system for service graphs, such as those illustrated in  FIG. 5B , will be described. A device on a network, such as a network device  200 ,  205  or a server  206 , may include a service graph generator and configurator  512 , a service graph display  514  and service graph monitor  516 . The service graph generator and configurator  512  (generally referred to as service graph generator  512 ), may identify a topology  510  of elements in the network and metrics  518  related to the network and the elements, to generate and/or configure service graphs  505 A-N. The service graphs  505 A-N (generally referred to as service graphs  505 ) may be stored in one or more databases, with any of the metric  518 ′ and/or topology  510 ′. The service graphic generator  512  may generate data of the service graphs  505  to be displayed in a display or rendered form such as via a user interface, generated referred to as service graph display  514 . Service graph monitor  516  may monitor the network elements of the topology and service for metrics  518  to configure and generate a service graph  505  and/or to update dynamically or in real-time the elements and metrics  518  of or represented by a service graph display  514 . 
     The topology  510  may include data identifying, describing, specifying or otherwise representing any elements used, traversed in accessing any one or more services or otherwise included with or part of such one or more services, such as any of the services  275  described herein. The topology may include data identifying or describing any one or more networks and network elements traversed to access or use the services, including any network devices, routers, switches, gateways, proxies, appliances, network connections or links, Internet Service Providers (ISPs), etc. The topology may include data identifying or describing any one or more applications, software, programs, services, processes, tasks or functions that are used or traversed in accessing a service. In some implementations, a service may be made up or include multiple microservices, each providing one or more functions, functionality or operations of or for a service. The topology may include data identifying or describing any one or more components of a service, such as programs, functions, applications or microservices used to provide the service. The topology may include parameters, configuration data and/or metadata about any portion of the topology, such as any element of the topology. 
     A service graph  505  may include data representing the topology of a service  275 , such any elements making up such a service or used by the service, for example as illustrated in  FIG. 5B . The service graph may be in a node base form, such as graphical form of nodes and each node representing an element or function of the topology of the service. A service graph may represent the topology of a service using nodes connected among each other via various connectors or links, which may be referred to as arcs. The arc may identify a relationship between elements connected by the arc. Nodes and arcs may be arranged in a manner to identify or describe one or more services. Nodes and arcs may be arranged in a manner to identify or describe functions provided by the one or more services. For example, a function node may represent a function that is applied to the traffic, such as a transform (SSL termination, VPN gateway), filter (firewalls), or terminal (intrusion detection systems). A function within the service graph might use one or more parameters and have one or more connectors. 
     The service graph may include any combination of nodes and arcs to represent a service, topology or portions thereof. Nodes and arcs may be arranged in a manner to identify or describe the physical and/or logical deployment of the service and any elements used to access the service. Nodes and arcs may be arranged in a manner to identify or describe the flow of network traffic in accessing or using a service. Nodes and arcs may be arranged in a manner to identify or describe the components of a service, such as multiple microservices that communicate with each other to provide functionality of the service. The service graph may be stored in storage such as a database in a manner in order for the service graph generator to generate a service graph in memory and/or render the service graph in display form  514 . 
     The service graph generator  512  may include an application, program, library, script, service, process, task or any type and form of executable instructions for establishing, creating, generating, implementing, configuring or updating a service graph  505 . The service graph generator may read and/or write data representing the service graph to a database, file or other type of storage. The service graph generator may comprise logic, functions and operations to construct the arrangement of nodes and arcs to have an electronic representation of the service graph in memory. The service graph generator may read or access the data in the database and store data into data structures and memory elements to provide or implement a node based representation of the service graph that can be updated or modified. The service graph generator may use any information from the topology to generate a service graph. The service graph generator may make network calls or use discovery protocols to identify the topology or any portions thereof. The service graph generator may use any metrics, such as in memory or storage or from other devices, to generate a service graph. The service graph generator may comprise logic, functions and operations to construct the arrangement of nodes and arcs to provide a graphical or visual representation of the service graph, such as on a user interface of a display device. The service graph generator may comprise logic, functions and operations to configure any node or arc of the service graph to represent a configuration or parameter of the corresponding or underlying element represented by the node or arc. The service graph generator may comprise logic, functions and operations to include, identify or provide metrics in connection with or as part of the arrangement of nodes and arcs of the service graph display. The service graph generator may comprise an application programming interface (API) for programs, applications, services, tasks, processes or systems to create, modify or interact with a service graph. 
     The service graph display  514  may include any graphical or electronic representation of a service graph  505  for rendering or display on any type and form of display device. The service graph display may be rendered in visual form to have any type of color, shape, size or other graphical indicators of the nodes and arcs of the service graph to represent a state or status of the respective elements. The service graph display may be rendered in visual form to have any type of color, shape, size or other graphical indicators of the nodes and arcs of the service graph to represent a state or status of one or more metrics. The service graph display may comprise any type of user interface, such as a dashboard, that provides the visual form of the service graph. The service graph display may include any type and form of user interface elements to allow users to interact, interface or manipulate a service graph. Portion of the service graph display may be selectable to identify information, such as metrics or topology information about that portion of the service graph. Portions of the service graph display may provide user interface elements for users to take an action with respect to the service graph or portion thereof, such as to modify a configuration or parameter of the element. 
     The service graph monitor  518  may include an application, program, library, script, service, process, task or any type and form of executable instructions to receive, identify, process metrics  518  of the topology  510 . The service graph monitor  518  monitors via metrics  518  the configuration, performance and operation of elements of a service graph. The service graph monitor may obtain metrics from one or more devices on the network. The service graph monitor may identify or generate metrics from network traffic traversing the device(s) of the service graph monitor. The service graph monitor may receive reports of metrics from any of the elements of the topology, such as any elements represented by a node in the service graph. The service graph monitor may receive reports of metrics from the service. From the metrics, the service graph monitor may determine the state, status or condition of an element represented in or by the service graph, such as by a node of the service graph. From the metrics, the service graph monitor may determine the state, status or condition of network traffic or network connected represented in or by the service graph, such as by an arc of the service graph. The service graph generator and/or service graph monitor may update the service graph display, such as continuously or in predetermined frequencies or event based, with any metrics or any changed in the state, status or condition of a node or arc, element represented by the node or arc, the service, network or network traffic traversing the topology. 
     The metrics  518 ,  518 ′ (generally referred to as metrics  518 ) may be stored on network device in  FIG. 5B , such as in memory or storage. The metrics  518 ,  518 ′ may be stored in a database on the same device or over a network to another device, such as a server. Metrics may include any type and form of measurement of any element of the topology, service or network. Metrics may include metrics on volume, rate or timing of requests or responses received, transmitted or traversing the network element represented by the node or arc. A Metrics may include metrics on usage of a resource by the element represented by the node or arc, such as memory, bandwidth. Metrics may include metrics on performance and operation of a service, including any components or microservices of the service, such as rate of response, transaction responses and times. 
       FIG. 5B  illustrates an implementation of a service graph in connection with microservices of a service in view of east-west network traffic and north-south network traffic. In brief overview, clients  102  may access via one or more networks  104  a data center having servers  106 A- 106 N (generally referred to as servers  106 ) providing one or more services  275 A- 275 N (generally referred to as services  275 ). The services may be made up multiple microservices  575 A- 575 N (generally referred to as microservice or micro service  575 ). Service  275 A may include microservice  575 A and  575 N while service  275 B may include microservice  575 B and  575 N. The microservices may communicate among the microservices via application programming interface (APIs). A service graph  505  may represent a topology of the services and metrics on network traffic, such as east-west network traffic and north-south network traffic. 
     North-south network traffic generally describes and is related to network traffic between clients and servers, such as client via networks  104  to servers of data center and/or servers to clients via network  104  as shown in  FIG. 5B . East-west network traffic generally describes and is related to network traffic between elements in the data centers, such as data center to data center, server to server, service to service or microservice to microservice. 
     A service  275  may comprise microservices  575 . In some aspects, microservices is a form of service-oriented architecture style wherein applications are built as a collection of different smaller services rather than one whole or singular application (referred to sometimes as a monolithic application). Instead of a monolithic application, a service has several independent applications or services (e.g., microservices) that can run on their own and may be created using different coding or programming languages. As such, a larger server can be made up of simpler and independent programs or services that are executable by themselves. These smaller programs or services are grouped together to deliver the functionalities of the larger service. In some aspects, a microservices based service structures an application as a collection of services that may be loosely coupled. The benefit of decomposing a service into different smaller services is that it improves modularity. This makes the application or service easier to understand, develop, test, and be resilient to changes in architecture or deployment. 
     A microservice includes an implementation of one or more functions or functionality. A microservice may be a self-contained piece of business function(s) with clear or established interfaces, such as an application programming interface (API). In some implementations, a microservice may be deployed in a virtual machine or a container. A service may use one or more functions on one microservice and another one or more functions of a different microservice. In operating or executing a service, one microservice may make API calls to another microservice and the microservice may provide a response via an API call, event handler or other interface mechanism. In operating or executing a microservice, the microservice may make an API call to another microservice, which in its operation or execution, makes a call to another microservice, and so on. 
     The service graph  505  may include multiple nodes  570 A-N connected or linked via one or more or arcs  572 A- 572 N. The service graph may have different types of nodes. A node type may be used to represent a physical network element, such as a server, client, appliance or network device. A node type may be used to represent an end point, such as a client or server. A node type may be used to represent an end point group, such as group of clients or servers. A node type may be used to represent a logical network element, such as a type of technology, software or service or a grouping or sub-grouping of elements. A node type may be used to represent a functional element, such as functionality to be provided by an element of the topology or by the service. 
     The configuration and/or representation of any of the nodes  570  may identify a state, a status and/or metric(s) of the element represented by the node. Graphical features of the node may identify or specify an operational or performance characteristic of the element represented by the node. A size, color or shape of the node may identify an operational state of whether the element is operational or active. A size, color or shape of the node may identify an error condition or issue with an element. A size, color or shape of the node may identify a level of volume of network traffic, a volume of request or responses received, transmitted or traversing the network element represented by the node. A size, color or shape of the node may identify a level of usage of a resource by the element represented by the node, such as memory, bandwidth, CPU or storage. A size, color or shape of the node may identify relativeness with respect to a threshold for any metric associated with the node or the element represented by the node. 
     The configuration and/or representation of any of the arcs  572  may identify a state, status and/or metric(s) of the element represented by the arc. Graphical features of the arc may identify or specify an operational or performance characteristic of the element represented by the arc. A size, color or shape of the node may identify an operational state of whether the network connection represented by the arc is operational or active. A size, color or shape of the arc may identify an error condition or issue with a connection associated with the arc. A size, color or shape of the arc may identify an error condition or issue with network traffic associated with the arc. A size, color or shape of the arc may identify a level of volume of network traffic, a volume of request or responses received, transmitted or traversing the network connection or link represented by the arc. A size, color or shape of the arc may identify a level of usage of a resource by network connection or traffic represented by the arc, such as bandwidth. A size, color or shape of the node may identify relativeness with respect to a threshold for any metric associated with the arc. In some implementations, a metric for the arc may include any measurement of traffic volume per arc, latency per arc or error rate per arc. 
     Referring now to  FIG. 5C , an implementation of a method for generating and displaying a service graph will be described. In brief overview of method  580 , at step  582 , a topology is identified, such as for a configuration of one or more services. At step  584 , the metrics of elements of the topology, such as for a service are monitored. At step  586 , a service graph is generated and configured. At step  588 , a service graph is displayed. At step  590 , issues with configuration, operation and performance of a service or the topology may be identified or determined. 
     At step  582 , a device identifies a topology for one or more services. The device may obtain, access or receive the topology  510  from storage, such as a database. The device may be configured with a topology for a service, such as by a user. The device may discover the topology or portions therefore via one more discovery protocols communicated over the network. The device may obtain or receive the topology or portions thereof from one or more other devices via the network. The device may identify the network elements making up one or more services. The device may identify functions providing the one or more services. The device may identify other devices or network elements providing the functions. The device may identify the network elements for north-west traffic. The device may identify the network elements for east-west traffic. The device may identify the microservices providing a service. In some implementations, the service graph generator establishes or generates a service graph based on the topology. The service graph may be stored to memory or storage. 
     At step  584 , the metrics of elements of the topology, such as for a service are monitored. The device may receive metrics about the one or more network elements of the topology from other devices. The device may determine metrics from network traffic traversing the device. The device may receive metrics from network elements of the topology, such as via reports or events. The device may monitor the service to obtain or receive metrics about the service. The metrics may be stored in memory or storage, such as in association with a corresponding service graph. The device may associate one or more of the metrics with a corresponding node of a service graph. The device may associate one or more of the metrics with a corresponding arc of a service graph. The device may monitor and/or obtain and/or receive metrics on a scheduled or predetermined frequency. The device may monitor and/or obtain and/or receive metrics on a continuous basis, such as in real-time or dynamically when metrics change. 
     At step  586 , a service graph is generated and configured. A service graph generator may generate a service graph based at least on the topology. A service graph generator may generate a service graph based at least on a service. A service graph generator may generate a service graph based on multiple services. A service graph generator may generate a service graph based at least on the microservices making up a service. A service graph generator may generate a service graph based on a data center, servers of the data center and/or services of the data center. A service graph generator may generate a service graph based at least on east-west traffic and corresponding network elements. A service graph generator may generate a service graph based at least on north-south traffic and corresponding network elements. A service graph generator may configure the service graph with parameters, configuration data or meta-data about the elements represented by a node or arc of the service graph. The service graph may be generated automatically by the device. The service graph may be generated responsive to a request by a user, such as via a comment to or user interface of the device. 
     At step  588 , a service graph is displayed. The device, such as via service graph generator, may create a service graph display  514  to be displayed or rendered via a display device, such as presented on a user interface. The service graph display may include visual indicators or graphical characteristics (e.g., size, shape or color) of the nodes and arcs of the service graph to identify status, state or condition of elements associated with or corresponding to a node or arc. The service graph display may be displayed or presented via a dashboard or other user interface in which a user may monitor the status of the service and topology. The service graph display may be updated to show changes in metrics or the status, state and/or condition of the service, the topology or any elements thereof. Via the service graph display, a user may interface or interact with the service graph to discover information, data and details about any of the network elements, such as the metrics of a microservice of a service. 
     At step  590 , issues with configuration, operation and performance of a service or the topology may be identified or determined. The device may determine issues with the configuration, operation or performance of a service by comparing metrics of the service to thresholds. The device may determine issues with the configuration, operation or performance of a service by comparing metrics of the service to previous or historical values. The device may determine issues with the configuration, operation or performance of a service by identifying a change in a metric. The device may determine issues with the configuration, operation or performance of a service by identifying a change in a status, state or condition of a node or arc or elements represented by the node or arc. The device may change the configuration and/or parameters of the service graph. The device may change the configuration of the service. The device may change the configuration of the topology. The device may change the configuration of network elements making up the topology or the service. A user may determine issues with the configuration, operation or performance of a service by reviewing, exploring or interacting with the service graph display and any metrics. The user may change the configuration and/or parameters of the service graph. The user may change the configuration of the service. The user may change the configuration of the topology. The device may change the configuration of network elements making up the topology or the service. 
     F. API Dependency Error and Latency Injection 
     Network provided services, such as web applications, virtual machines, hosted resources, or other such services, may be adversely affected by errors and latency. However, in many instances, these errors may be intermittent, making it difficult to proactively identify and mitigate problems. For example, intermittent noise may cause wireless access points to experience latency or dropped connections, memory or disk errors may cause processing resources to occasionally hang, etc. In typical systems, administrators frequently must wait until the error occurs and hope to catch it “in the act” in order to diagnose the issue. While logging may help somewhat in reviewing past errors, particularly complex errors may need multiple occurrences to diagnose and repair. Furthermore, all of these processes are reactive, in that administrators must wait until an error has occurred, and cannot identify and prevent future errors that have not yet occurred. In very complex systems, in which errors may come as a result of interactions between services or microservices, a potential for an error may go unnoticed for months or even years before it occurs. 
     Accordingly, in some implementations, to proactively monitor and identify issues with resources, a system may be configured with the ability to introduce synthetic errors and latency, with “synthetic” referring to intentionally introduced errors as opposed to “organic” errors or errors that arise within the system and not from intentional introduction during testing. The configuration and enablement of these synthetic errors and latency may be managed within or for each service. For example, in some implementations, by passing all calls through a proxy capable of injecting errors and latency, any service in communication with the proxy can reap the benefits of error detection and mitigation, such as validating how a service behaves when a dependent service begins misbehaving. By providing a consistent and standard configuration for injecting errors and latency, all services can be tested with this method. 
     The proxy handling all East/West microservice traffic (e.g. network traffic communicated between microservices of the services such as within a data center or between data centers) may pull from a centralized configuration to determine error rates, latencies, and activities to be injected when criteria is met. This configuration may also include time frames or schedules for testing, as well as filtering for what calls and services are impacted. Responses to synthetic errors may be from a third party call, a static document, or a custom script language to respond with payloads based on the request. 
     The systems and methods discussed herein provide the ability to: 
     Inject latency for calls between microservices, such as East/West traffic traversing an intermediary device or proxy between microservices; 
     Inject errors for calls between microservices, including dropped packets, corrupted packets, packet fragmentation, jitter, reordering, or any other sort of error; 
     Configure intra-service error/latency rate and return payloads, such as sub-MTU payloads, payloads greater than an MTU and fragmented, etc. 
     Provide filters on when to apply errors/latency, such as by time of day, day of week, or in response to other triggers, such as increased fragmentation or packet loss, loss of communications to a monitoring server, etc.; 
     Provide actions to run a script to calculate payload for errors; 
     Provide static error codes and payloads, such as predetermined error codes for various detected issues; and 
     Identify calls impacted by synthetic errors, such as via http response headers. 
     Accordingly, various behaviors and synthetic errors may be injected responsive to various triggers. For example, in one implementation, a predetermined percentage of calls or communications to a first service Y from a second service X, such as 10%, may be responded to by a monitoring server or intermediary device with HTTP-500 errors, simulating internal server errors at service Y. In another implementation, a monitoring system or intermediary device may gradually increase latency of packets traversing the device to and/or from a service from Oms to 2000 ms and back down to Oms over the course of 10 minutes, allowing measurement of the service&#39;s ability to handle high and changing latency. In still another implementation, an intermediary device or monitoring server may delay communications to a selected geographical region or block of IP addresses by a predetermined amount, such as 30 seconds, and respond with a static HTTP-503 response, simulating packet loss or server dropouts. The services&#39; responses to these synthetic errors may be measured and the service validated or invalidated, responsive to whether it respectively handles or does not handle the errors (e.g. whether the server successfully resumes communications, recovers from errors, properly logs errors, etc.). 
       FIG. 6A  is a block diagram of an implementation of a system for synthetic error injection and monitoring. A network device  200 ,  205 , sometimes referred to as a proxy, intermediary device, monitoring device or server, or by other such terms, may be deployed intermediary to a plurality of microservices  575 A- 575 N. The network device  200 ,  205  may comprise an error generator  600 , a database of criteria or parameters, a service validator  604 , and/or a database or log of measurements or validation results  606 . 
     In some implementations, an error generator  600  may comprise an application, service, server, daemon, routine, or other executable logic for injecting synthetic errors and/or adding latency to communications traversing the network device. In some implementations, error generator  600  may comprise hardware, such as a hardware switch, buffer, or other device for delaying, reordering, or filtering communications traversing the device. In some implementations, error generator  600  may comprise hardware such as an FPGA or ASIC implementing packet filtering, buffering, injection, and/or modifications of packet headers and/or payloads. As discussed above, synthetic errors may comprise any sort of injected or intentionally created error, including packet dropouts, corruption, fragmentation, reordering, error codes, or other such errors. 
     In some implementations, error generator  600  may be configured to inject synthetic errors or latency to a communication flow based on one or more criteria stored in a database  602  or other data structure (e.g. flat file, XML data file, etc.). The criteria may comprise time periods, durations, or frequency for error injection or combinations of these (e.g. once per week, from 2-3 AM, for 1 minute at a time, every ten minutes). The criteria may comprise a status or condition of a microservice, such as when usage or CPU or memory load of a microservice is less than a threshold, when the microservice has fewer than a threshold number of concurrent users or connections, when the microservice has had uptime greater than a predetermined duration, etc. The criteria may comprise a type of packet or contents of a packet traversing the device, such as when a service has received a request to initiate a connection from a device in a particular region or IP range, or when the service has responded with an error message to a request, or any other type and form of packet. Multiple criteria may be applied to trigger synthetic error injection, such as a particular time of day and usage less than a threshold. Additionally, multiple criteria may be used separately as triggers for synthetic error injection to a particular microservice, such as if the service has had more than a threshold number of connections or if a time is between 2 and 3 AM. Combinations of the criteria may be configured via Boolean logic in some implementations (e.g. ([criteria 1]AND[criteria 2])OR[criteria 3], etc.) or may be applied individually, with an intermediary device iterating through potential criteria. 
     In some implementations, a service validator  604  may comprise an application, service, server, daemon, routine, or other executable logic for measuring a response or responses to a synthetic error and validating or invalidating a service or microservice. For example, service validator  604  may monitor the time it takes for a communications flow to recover and resume after intentionally dropping packets or inserting out of order packets or spurious requests in the flow. Service validator  604  may perform any type of measurement, including measuring packet sizes, sequence numbers, jitter, latency, processing delay, etc. Measurements need not be confined to packet or connection characteristics, and may also include status of microservices obtained via API or remote procedure calls to the microservices, such as processor or memory load, uptime, number of concurrent connections or users, or any other type and form of information. The service validator  604  may determine whether a microservice properly handles or does not handle a synthetic error, e.g. based on whether the microservice is able to resume normal operations within a predetermined time after the synthetic error, whether the microservice properly reports or logs an error (which may be retrieved via an API or remote procedure call), etc. A microservice has not handled an error when it is unable to recover, when it fails to log or report the error, when communications remain impaired or delayed, when users experience dropouts, delays, or errors, etc. 
     Measurements and/or validation results may be stored in a database  606  or other data structure maintained by service validator  604 . In some implementations, validation results and/or measurements may be displayed via a user interface, e.g. to an administrator of the system or a user or users of the microservices. For example, in some implementations, a user interface may be provided to users of the system with icons or other identifiers of one or more microservices, and with overlay icons or other indicators (e.g. colored dots, plus signs or minus signs, scores, or other such indicators) indicating whether a microservice has or has not been validated and/or is able to recover or has recovered from errors. 
     Although shown separately, one or more of components  600 - 606  may be provided together, e.g. as part of a monitoring application or service. Similarly, although shown on a single device  200 ,  205 , in many implementations, components  600 - 606  may be divided amongst a plurality of devices  200 ,  205  (e.g. an error generator  600  on a first device intermediary to microservices  575 , and a service validator  604  on a second device not intermediary to microservices  575  but in communication with them to retrieve status information). 
     As shown, network device  200 ,  205  may be deployed intermediary to a plurality of microservices  575  (as well as potentially between one or more microservices  575  and one or more client devices or other servers, not illustrated). Accordingly, in many such implementations, error generator  600  may be able to insert synthetic errors into communications or add latency to communications traversing the network device  200 ,  205  between microservices  575  (e.g. East/West communications) as well as between microservices and clients or other servers (e.g. North/South communications). 
       FIG. 6B  is a flow diagram of a method  640  for synthetic error injection and monitoring, according to some implementations. At step  620 , a monitoring device or intermediary device may identify one or more synthetic errors to apply to one or more microservices, and corresponding criteria or parameters for applying the errors. The criteria may comprise a status or condition of a corresponding microservice, a type of request, a time period, a duration, a frequency, or any other type and form of criteria, including average packet loss, block error rates, numbers of concurrent users or connections, type of service, etc. 
     At step  622 , the device may determine whether the criteria has been met. In some implementations, the device may monitor packets or communications traversing the device to and/or from a microservice, may monitor a clock or timer or network time source or other such time indicator, and/or may monitor status of one or more microservices. In some implementations, the device may periodically request a status from a microservice (e.g. via an API or remote procedure call) and may receive a status report indicating the status (e.g. CPU utilization, memory utilization, bandwidth utilization, error rates, number of concurrent connections, data transmitted or received, etc.). If the criteria has not been met, then steps  622  and/or  620 - 622  may be repeated periodically. 
     If the criteria has been met, the intermediary device may implement or apply the synthetic error. At step  624 , the device may receive an intra-service request (or, in some implementations, an inter-service request, such as a request from an external server). The request may comprise any type and form of a request, such as a data request, a synchronization request, an RPC or API call, or any other such request. In some implementations, the request may be a response to a prior request, and may be referred to as a ‘request’ because some further data exchange is implied (e.g. an acknowledgement or other data transfer). At step  626 , the device may apply the synthetic error using the received request. Applying the synthetic error may comprise buffering or holding the request for a period of time prior to transmitting or forwarding the request to the destination microservice, thereby adding latency, in some implementations. In other implementations, applying the synthetic error may comprise editing a header and/or payload of the request, such as by adding random data to the header or payload, truncating the header and/or payload, modifying data in the header or payload (e.g. incrementing or decrementing sequence numbers or editing options flags, etc.), fragmenting the request into multiple packets, or performing other error-causing modifications. As discussed above, in some implementations, criteria may comprise parameters for applying the synthetic error, such as a latency time to apply to a packet or request, a rate of change of latency, etc. The synthetic error may be applied in accordance with the criteria. At step  628 , the modified or buffered request may be forwarded to the recipient microservice. 
     In some implementations, applying the synthetic error may comprise dropping the request, and, in a further implementation, transmitting an error code or other status identifier (e.g. HTTP 503 code). In many implementations, multiple synthetic errors may be applied together (e.g. buffering the request for a predetermined time period, then dropping the request and sending an error code). 
     At step  630 , the device may determine whether the microservice has properly handled the synthetic error. Handling the error may comprise retransmitting the initial request; reporting an error to a monitoring server, administrator, or the device; transmitting additional data such as a request with a modified window size (e.g. to reduce bandwidth requirements or avoid congestion implied by the synthetic error, etc.); reconnecting a lost or dropped connection; throttling bandwidth on other connections; etc. If the microservice properly handled the synthetic error, then at step  632 , the device may validate the microservice. Validating the microservice may comprise storing an indicator in a log, updating a user interface on the device or a client device, or performing other such features. 
     Conversely, if the microservice did not properly handle the synthetic error, then at step  634 , the device may invalidate the microservice. Invalidating the microservice may comprise storing an indicator in a log, updating a user interface on the device or a client device, sending a notification or message to another device of an administrator, or performing other such features. 
     Accordingly, the systems and methods discussed herein enable an administrator to proactively monitor and identify issues with resources. The system may be configured with the ability to introduce synthetic errors and latency. Any service in communication with system may reap the benefits of error detection and mitigation, such as validating how a service behaves when a dependent service begins misbehaving. By providing a consistent and standard configuration for injecting errors and latency, all services can be tested with this method. 
     Various elements, which are described herein in the context of one or more embodiments, may be provided separately or in any suitable subcombination. For example, the processes described herein may be implemented in hardware, software, or a combination thereof. Further, the processes described herein are not limited to the specific embodiments described. For example, the processes described herein are not limited to the specific processing order described herein and, rather, process blocks may be re-ordered, combined, removed, or performed in parallel or in serial, as necessary, to achieve the results set forth herein. 
     It will be further understood that various changes in the details, materials, and arrangements of the parts that have been described and illustrated herein may be made by those skilled in the art without departing from the scope of the following claims.