Patent Publication Number: US-11379443-B2

Title: Detecting outliers in server transaction time as a form of time series data

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a continuation of and claims priority to and the benefit of U.S. patent application Ser. No. 15/496,871, titled “DETECTING OUTLIERS IN SERVER TRANSACTION TIME AS A FORM OF TIME SERIES DATA,” and filed on Apr. 25, 2017, the contents of all of which are hereby incorporated herein by reference in its entirety for all purposes. 
    
    
     BACKGROUND 
     Complex systems benefit from performance monitoring, by which deviation from a norm may be detected. In some contexts, it may be difficult to establish a norm. For example, systems handling workloads that vary based on outside factors may exhibit changes in performance that are related to the outside factors and should still be considered within a range of normal. This variation in normality makes it difficult for an automated performance monitoring system to detect deviation from the norm. 
     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 at least one aspect, described is a method for detecting outliers on a series of data. The method includes receiving, by a device, a plurality of data points and adding, by the device, a received data point to a first window of data comprising at least a predetermined number of received data points from the plurality of data points, responsive to detecting that the received data point is not an outlier from the first window of data. The method includes detecting, by the device, that one or more next data points of the received plurality of data points are outliers from the first window of data and determining, by the device, that a count of the one or more next data points that are outliers exceeds a predetermined threshold. The method includes establishing, by the device, responsive to determining that the count exceeds the predetermined threshold, a second window of data comprising at least one of the one or more next data points 
     In at least one aspect, described is a system for outlier detection on a series of data. The system includes a processor coupled to memory and configured to execute instructions to receive a plurality of data points; add a received data point to a first window of data comprising at least a predetermined number of received data points from the plurality of data points, responsive to detecting that the received data point is not an outlier from the first window of data; and detect that one or more next data points of the received plurality of data points are outliers from the first window of data. The processor is configured to execute instructions to determine that a count of the one or more next data points that are outliers exceeds a predetermined threshold and to establish, responsive to determining that the count exceeds the predetermined threshold, a second window of data comprising at least one of the one or more next data points. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWING FIGURES 
       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. 5  is a flowchart for an example method of detecting outliers on a series of data; and 
         FIG. 6  is a flowchart for an example method of detecting outliers in data read from one or more log files. 
     
    
    
     DETAILED DESCRIPTION 
     One aspect of performance monitoring is to accumulate values for one or more performance-related metrics and identify any sudden or unexpected changes. When there are such changes, a performance monitoring system might, for example, generate alerts and/or trigger corrective actions. For example, in a distributed computing context or in a data center context, a monitoring system may need to detect when a server has potentially failed. The monitoring system might react to the potential failure by, for example, restarting the server, signaling an administrator to evaluate the server, or by reconfiguring a load balancer to redirect work to other servers. 
     To determine that there has been a sudden or unexpected change, the monitor first establishes a baseline for normal values of the one or more performance-related metrics. In some instances, the normal range might be configured by an administrator. However, in some contexts, the normal range may be responsive to dynamic conditions and environmental factors such as time-of-day, variations in traffic profiles, network topology, and so forth. One technique is to accumulate a set of measurements and compare future measurements to the accumulated set. If the new measurement is a statistical outlier from the set, it may indicate a problem. 
     However, in some instances, the values gathered for the one or more performance-related metrics might not accurately indicate a normal state. This may happen, for example, where contextual changes result in a new normal state. Accordingly, monitoring systems face a technical problem distinguishing between problematic unexpected values for these metrics as compared to a shift in normal expected values for the metrics. 
     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 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 CloudBridge® 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 XenApp® or 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), 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 appliances  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 EdgeSight by Citrix Systems, Inc. of Fort Lauderdale, Fla. 
     The monitoring agents 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 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. 
     Additional details of the implementation and operation of network environment  100 , clients  102 , servers  106 , and appliances  200  and  205  may be as described in U.S. Pat. No. 9,538,345, issued Jan. 3, 2017 to Citrix Systems, Inc. of Fort Lauderdale, Fla., the teachings of which are hereby incorporated herein by reference. 
       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 layers 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. 
     Additional details of the implementation and operation of appliance  200  may be as described in U.S. Pat. No. 9,538,345, issued Jan. 3, 2017 to Citrix Systems, Inc. of Fort Lauderdale, Fla., the teachings of which are hereby incorporated herein by reference. 
     Referring now to  FIG. 3 , a block diagram of a virtualized environment  400  is shown. As shown, a computing device  402  in virtualized environment  400  includes a virtualization layer  403 , a hypervisor layer  404 , and a hardware layer  407 . Hypervisor layer  404  includes one or more hypervisors (or virtualization managers)  401  that allocates and manages access to a number of physical resources in hardware layer  407  (e.g., physical processor(s)  421  and physical disk(s)  428 ) by at least one virtual machine (VM) (e.g., one of VMs  406 ) executing in virtualization layer  403 . Each VM  406  may include allocated virtual resources such as virtual processors  432  and/or virtual disks  442 , as well as virtual resources such as virtual memory and virtual network interfaces. In some embodiments, at least one of VMs  406  may include a control operating system (e.g.,  405 ) in communication with hypervisor  401  and used to execute applications for managing and configuring other VMs (e.g., guest operating systems  410 ) on device  402 . 
     In general, hypervisor(s)  401  may provide virtual resources to an operating system of VMs  406  in any manner that simulates the operating system having access to a physical device. Thus, hypervisor(s)  401  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)  401  may be implemented as a XEN hypervisor, for example as provided by the open source Xen.org community. In an illustrative embodiment, device  402  executing a hypervisor that creates a virtual machine platform on which guest operating systems may execute is referred to as a host server. In such an embodiment, device  402  may be implemented as a XEN server as provided by Citrix Systems, Inc., of Fort Lauderdale, Fla. 
     Hypervisor  401  may create one or more VMs  406  in which an operating system (e.g., control operating system  405  and/or guest operating system  410 ) executes. For example, the hypervisor  401  loads a virtual machine image to create VMs  406  to execute an operating system. Hypervisor  401  may present VMs  406  with an abstraction of hardware layer  407 , and/or may control how physical capabilities of hardware layer  407  are presented to VMs  406 . For example, hypervisor(s)  401  may manage a pool of resources distributed across multiple physical computing devices. 
     In some embodiments, one of VMs  406  (e.g., the VM executing control operating system  405 ) may manage and configure other of VMs  406 , 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)  401  and/or other VMs via, for example, one or more Application Programming Interfaces (APIs), shared memory, and/or other techniques. 
     In general, VMs  406  may provide a user of device  402  with access to resources within virtualized computing environment  400 , for example, one or more programs, applications, documents, files, desktop and/or computing environments, or other resources. In some embodiments, VMs  406  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  402 , virtualized environment  400  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  400  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 . 
     Additional details of the implementation and operation of virtualized computing environment  400  may be as described in U.S. Pat. No. 9,538,345, issued Jan. 3, 2017 to Citrix Systems, Inc. of Fort Lauderdale, Fla., the teachings of which are hereby incorporated herein by reference. 
     In some embodiments, a server may execute multiple virtual machines  406 , 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.,  400 ) 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. 
     Additional details of the implementation and operation of a multi-processor and/or multi-core system may be as described in U.S. Pat. No. 9,538,345, issued Jan. 3, 2017 to Citrix Systems, Inc. of Fort Lauderdale, Fla., the teachings of which are hereby incorporated herein by reference. 
     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  600 . A plurality of appliances  200  or other computing devices (e.g., nodes) may be joined into a single cluster  600 . Cluster  600  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  600  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  600  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  600  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  600  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  600  may be coupled to a first network  104  via client data plane  602 , for example to transfer data between clients  102  and appliance cluster  600 . Client data plane  602  may be implemented a switch, hub, router, or other similar network device internal or external to cluster  600  to distribute traffic across the nodes of cluster  600 . 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  600  may be coupled to a second network  104 ′ via server data plane  604 . Similarly to client data plane  602 , server data plane  604  may be implemented as a switch, hub, router, or other network device that may be internal or external to cluster  600 . In some embodiments, client data plane  602  and server data plane  604  may be merged or combined into a single device. 
     In some embodiments, each appliance  200  of cluster  600  may be connected via an internal communication network or back plane  606 . Back plane  606  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  600 . In some embodiments, back plane  606  may be a physical network, a VPN or tunnel, or a combination thereof. 
     In some embodiments, a router may be connected to an external network  104 , and connected to a network interface of each appliance  200 . In some embodiments, this router or switch may be referred to as an interface manager or interface master  608 , and may further be configured to distribute traffic evenly across the nodes in the appliance cluster  600 . In some embodiments, the interface master  608  may comprise a flow distributor external to appliance cluster  600 . In other embodiments, the interface master  608  may comprise one of the appliances  200  in the appliance cluster  600 . For example, a first appliance  200 ( 1 ) may serve as the interface master  608 , receiving incoming traffic for the appliance cluster  600  and distributing the traffic across each of appliances  200 ( 2 )- 200 ( n ). In some embodiments, return traffic may similarly flow from each of appliances  200 ( 2 )- 200 ( n ) via the first appliance  200 ( a ) serving as the interface master  608 . In other embodiments, return traffic from each of appliances  200 ( 2 )- 200 ( n ) may be transmitted directly to a network  104 ,  104 ′, or via an external router, switch, or other device. In some embodiments, appliances  200  of the appliance cluster not serving as an interface master may be referred to as interface slaves  610 ( a )- 610 ( n ). 
     The interface master  608  may perform load balancing or traffic flow distribution in any of a variety of ways. For example, in some embodiments, the interface master  608  may comprise a router performing equal-cost multi-path (ECMP) routing with next hops configured with appliances or nodes of the cluster. The interface master may use an open-shortest path first (OSPF). In some embodiments, the interface master  608  may use a stateless hash-based mechanism for traffic distribution, such as hashes based on IP address or other packet information tuples, as discussed above. Hash keys and/or salt may be selected for even distribution across the nodes. In other embodiments, the interface master  608  may perform flow distribution via link aggregation (LAG) protocols, or any other type and form of flow distribution, load balancing, and routing. 
     Additional details of cluster  600  may be as described in U.S. Pat. No. 9,538,345, issued Jan. 3, 2017 to Citrix Systems, Inc. of Fort Lauderdale, Fla., the teachings of which are hereby incorporated herein by reference. 
     Some embodiments include a monitor node such as a client  102 , a server  106 , a client agent  120 , an appliance  200 , a virtual appliance in a virtualized environment  400 , an appliance cluster  600 , a master node for an appliance cluster  600  (such as the interface master  608 ), or any other device capable of performing the monitoring functions described. As introduced above, one aspect of performance monitoring is to accumulate values for one or more performance-related metrics and identify any sudden or unexpected changes. The monitor node accumulates values for performance metrics corresponding to the performance of one or more monitored devices, such as a client  102 , a server  106 , an appliance  200 , a virtual appliance in a virtualized environment  400 , an appliance cluster  600 , a participant node for an appliance cluster  600  (such as the interface slave  610 ), or any other device that might be monitored as described. In the following description a monitor is described as accumulating data for performance of servers. However, this should not be read as limited to servers; any device may be monitored in the manner described. Likewise, in some embodiments described below, the monitor persists data in a database. However, any data storage context or device may be used to persist data; the term database should not be read to restrict or limit to any particular data storage or organizing implementation. 
     The monitor node determines whether a data point is an outlier based on available data corresponding to what the monitor node should expect for the data point. For example, in some embodiments, the monitor node establishes a set of boundaries marking an expected range for the data point, beyond which it would be considered an outlier. In some such embodiments, one end of the range may be fixed at a lower boundary (e.g., zero) and the other end of the range may be set to an upper boundary (an upper threshold). Where data point values are always positive, they monitor node need only compare the values to the upper threshold. In some embodiments, the monitor node adjusts the boundaries (or upper boundary) based on previously received data points. For example, in some embodiments, a monitor node uses a forecasting approach for detecting outliers by comparing new measurements to an expected value based on a history of past measurements. In some such embodiments, the monitor node calculates the expected value from a sliding window of a predetermined number of previous measurements leading up to the new measurement under consideration. In some embodiments, the monitor node pads the expected value with a range of acceptable deviation, that is, the monitor node identifies a range of values that the monitor node will accept as sufficiently close to the expected value to be considered normal. For example, in a Mean model approach, an extra band, in form of factors of standard-deviation, is added above and below the calculated expected value. In another similar example, using Holt-Winters, the expected value is increased (for an upper bound) and decreased (for a lower bound) by a percentage point. This can, for example, account for noise in the measurement data. If the new measurement is outside the range of acceptable values, the monitor node will deem the new measurement an outlier. Generally, embodiments may incorporate any method of outlier detection. 
     In some embodiments, the monitor node omits outliers from the previous measurements included in the sliding window. Including outliers in the sliding window can skew the expected value calculations and produce inaccurate predictions. However, if the behavior being measured legitimately changes (e.g., due to a configuration change, a contextual change, etc.), this can lead to incorrectly identifying measurements as outliers. Accordingly, the monitor node maintains statistics to identify when an excessive number of outliers have been omitted and, when the statistics satisfy a reconfiguration criteria, the monitor node reconfigures to accommodate the change in behavior. For example, in some embodiments, the monitor node replaces the sliding window when a number of consecutively identified outliers exceed a threshold. In some embodiments, the sliding window is a set of training data for generating predictions, and resetting or replacing the sliding window effectively retrains the monitor node. 
       FIG. 5  is a flowchart for an example method  500  of detecting outliers on a series of data. In broad overview of the method  500 , at stage  510  a monitor node receives (or begins to receive) a plurality of data points. At stage  520 , the monitor node adds, to a window of data, at least a predetermined number of the received data points. At stage  530 , the monitor node determines whether a next received data point is an outlier from the window of data. If it isn&#39;t, then at stage  540 , the monitor node adds the received data point to the window of data and the method  500  returns to stage  530  to handle another next received data point. However, if at stage  530 , the monitor node determines that a next received data point is an outlier from the window of data, then at stage  550 , the monitor node updates outlier statistics data. For example, in some embodiments, the monitor node maintains a count of outliers received. At stage  560 , the monitor node determines whether the count of the received data points determined to be outliers exceeds a predetermined threshold. If not, then the method  500  returns to stage  530  to handle another next received data point. However, if at stage  560 , the monitor node determines that the count of the received data points determined to be outliers exceeds the predetermined threshold, then at stage  570 , the monitor node establishes a new window of data. In some embodiments, the last identified outlier is added to the reset window of data. The method  500  then returns to stage  520  and the monitor node adds received data points to the reset window until it again has at least the predetermined number received data points. 
     Referring to  FIG. 5  in more detail, at stage  510  the monitor node receives (or begins to receive) a plurality of data points. In some embodiments, the monitor node receives the data points from one or more data sources, e.g., sensors, instruments, log files, etc. In some embodiments, the monitor node receives the data points in a data stream. In some embodiments, the monitor node receives the data points in a deterministic order. In some embodiments, the data points are sequenced, e.g., as a time sequence of data. 
     In some embodiments, each data point represents a measurement, e.g., from a sensor. In some embodiments, each data point is a server transaction time. In some embodiments, each data point is a value in a time series. In some embodiments, each data point corresponds to a set of values, e.g., an identifier (such as a identifier for a transaction), an event time (such as a timestamp marking completion of the identified transaction), and a value associated with the event (such as time to completion of the transaction or a timestamp corresponding to the beginning of the transaction). 
     At stage  520 , the monitor node adds, to a window of data, at least a predetermined number of the received data points. In some embodiments, the window of data is a sliding window representing a view of a set of the last received data points. In some embodiments, the monitor node implements the window of data as a first-in first-out (FIFO) queue. In some embodiments, the monitor node implements the window of data using a circular buffer. In some embodiments, if the window of data includes at least a predetermined number of received data points, then the monitor node removes a data point (e.g., the least-recently received data point) whenever it adds a received data point. This keeps the size of the window of data constant once it has accumulated the predetermined number of received data points. In some embodiments, at stage  520 , the monitor node populates the window of data with the predetermined number of received data points without considering whether the data points are outliers. 
     At stage  530 , the monitor node determines whether a next received data point is an outlier from the window of data. If it isn&#39;t, then at stage  540 , the monitor node adds the received data point to the window of data and the method  500  returns to stage  530  to handle another next received data point. In some embodiments, the monitor node determines that a next received data point is an outlier from the window of data by establishing boundaries for the window of data, e.g., a maximum (and/or minimum) value, and then determining whether the next received data point is within the established boundaries. In some embodiments, the monitor node determines that a next received data point is an outlier from the window of data by comparing the next received data point to a mean average of the data represented in the window of data and determining whether the next received data point is within a particular range of the average (e.g., equal to the mean average plus or minus a range such as 1% of the average, within a number of standard deviations from the average, etc.). In some embodiments, the monitor node determines that a next received data point is an outlier from the window of data by comparing the next received data point to an upper (or lower) boundary for the data represented in the window of data. For example, the boundary may be the largest (or smallest) value in the window of data plus (or minus) a buffer (e.g., a percentage of the range from the smallest to largest values). In some embodiments, the boundary is an upper (or lower) quartile of the data represented in the window of data. In some embodiments, the monitor node uses one or more of the following known methods for outlier detection: Tukey&#39;s Test, Peirce&#39;s Criterion, a mean and standard deviation test such as Chauvenet&#39;s criterion or Grubb&#39;s test, or any other method for outlier detection. 
     If at stage  530 , the monitor node determines that a next received data point is an outlier from the window of data, then at stage  550 , the monitor node updates outlier statistics data. For example, in some embodiments, the monitor node maintains a count of outliers received. In some embodiments, the count of outliers received is a count of consecutive outliers received and the monitor node resets the count of outliers to zero when a non-outlier is received. In some embodiments, the monitor node reduces the count of outliers when a non-outlier is received, e.g., reducing it by one or more, halving it, reducing it by some other fraction, etc. In some embodiments, the monitor node resets the count of outliers to zero when a minimum number of consecutive non-outliers are received (e.g., after two consecutive non-outliers are received). In some embodiments, the monitor node maintains outlier statistics to keep a ratio of outliers detected as compared to non-outliers received. 
     At stage  560 , the monitor node determines whether the count of the received data points determined to be outliers exceeds a predetermined threshold. In some embodiments, the threshold is a configurable number. In some embodiments, the threshold is a number of consecutive received outlier data points that would indicate a behavioral shift in a measured system. 
     If, at stage  560 , the monitor node determines that the threshold has not been met or exceeded, then the method  500  returns to stage  530  to handle another next received data point. In some embodiments, the outlier is not added to the window of data. In some embodiments, prior to (or concurrently with) returning to stage  530 , the monitor node signals an administrator or otherwise reports the presence of an outlier. 
     At stage  570 , if the monitor node determines at stage  560  that the count of the received data points determined to be outliers exceeds the predetermined threshold, then the monitor node establishes a new window of data. In some embodiments, the monitor node adds the last identified outlier to the new window of data. The new window replaces the previous window. In some embodiments, at stage  560 , the monitor node resets the window of data, e.g., clearing data represented in the window or marking it as stale. In some embodiments, the monitor node uses a counter to track the number of data points added to the window of data at stage  520  and, at stage  560 , the monitor node resets the counter such that new data points will be added at stage  520  (replacing older data points) to bring the number of data points represented in the window back up to the predetermined number of data points. 
     The method  500  returns, after stage  570 , to stage  520  where the monitor node adds received data points to the reset window until it again has at least the predetermined number received data points. 
     In some embodiments, the monitor node analyzes measurements that come from multiple sources, e.g., multiple log files. If trend data is not shared during analysis of the multiple sources, there is more opportunity for invalid data to pollute the baseline, leading to less accurate predictions. For example, in some contexts, measured systems record data to log files (e.g., transaction logs indicating the length of various server transactions) and the monitor node processes the log files, e.g., to validate performance. In some embodiments, the monitor node maintains context data between log files, facilitating seamless analysis across a larger pool of data. This, in turn, can yield improved analysis and better predictions. 
     In some embodiments, the monitor node calculates a Moving Average. For example, using pre-configured values for a window size N and maximum threshold number of outliers M, the monitor node executes the following routine upon the arrival of a new data point: 
     
       
         
           
               
               
             
               
                   
               
             
            
               
                  1. 
                 If a data point number (in a series) is less than N: 
               
            
           
           
               
               
            
               
                  2. 
                 Save the data point; 
               
               
                  3. 
                 Continue with next received data point; 
               
            
           
           
               
               
            
               
                  4. 
                 Otherwise: 
               
            
           
           
               
               
            
               
                  5. 
                 Calculate an expected range from a window of N last saved data points; 
               
               
                  6. 
                 If the data point is within the expected range: 
               
            
           
           
               
               
            
               
                  7. 
                 Save the data point (shifting it into the window); 
               
            
           
           
               
               
            
               
                  8. 
                 If the data point is outside the expected range (i.e., if it&#39;s an outlier): 
               
            
           
           
               
               
            
               
                  9. 
                 If a count of consecutively received outliers &gt;= M: 
               
            
           
           
               
               
            
               
                 10. 
                 The data point is now first, and 
               
               
                 11. 
                 return to saving N data points; 
               
            
           
           
               
               
            
               
                 12. 
                 Otherwise, report the outlier without saving it. 
               
               
                   
               
            
           
         
       
     
       FIG. 6  is a flowchart for an example method  601  of detecting outliers in data read from one or more log files. The method  601  has some similarities to the method  500  described above in reference to  FIG. 5 . In broad overview, in the method  601 , the monitor node populates a sliding window with data extracted from log files; the monitor node accumulates data in a sliding window up to a predetermined number of data points (similar to stage  520 ) and then compares additional data points to boundaries based on the accumulated data in the sliding window (similar to stage  530 ). The method  601  starts by identifying a first (or next) file. At stage  605 , a monitor node receives data from the next file and, at stage  612 , reads a data point from the file. At stage  620 , the monitor node determines whether it has reached the end of the file. If so, at stage  622 , the monitor node saves state information in a database and returns to stage  605  to begin another file. Otherwise, at stage  630 , the monitor node determines if there is enough data in a sliding window of data. If not, then at stage  640 , the monitor node adds the data point to the sliding window of data. The monitor node accumulates data for the window from these added data points combined with previous state stored in the database, read by the monitor node at stage  624 . At stage  645 , the monitor node updates window boundaries based on the accumulated data for the window. If, at stage  630 , there is enough data in the sliding window for analysis, then at stage  650 , the monitor node uses the updated boundaries (from stage  645 ) to determine whether a received data point is within the bounds. At stage  660 , if the data point was within bounds at stage  650 , then the monitor node adds the data point to the window and shifts a least-recently added data point out of the window. The method  601  then returns to state  645  and updates the window boundaries. At stage  670 , if the data point was not within bounds at stage  650 , then the monitor node determines whether a consecutive outlier count exceeds a threshold. If not, the monitor node reports the outlier at stage  675  and continues reading data points from the file at stage  612 . Otherwise, if the count of consecutive outliers exceeds the threshold, then at stage  680  the monitor node resets the window of data. In some embodiments, the monitor node includes one or more of the last received outlier data points in a new window of data. The method continues to read data points from the file at stage  612 . 
     Referring to  FIG. 6  in more detail, the method  601  starts by identifying a first (or next) file. In some embodiments, a monitor node is configured to analysis log files in a particular location (e.g., a file directory, a uniform resource identifier (“URI”) such as a uniform resource locator (“URL”), a database, or any other specific location). In some embodiments, a monitor node is configured to analysis a specific set of log files. The method  601  is described in terms of an endless loop. Each iteration returns to stage  601  to identify a next (initially a first) log file the method  601  iterates through all log files until it runs out of files or is interrupted. 
     At stage  605 , a monitor node receives data from the first (or next) file. In some embodiments, the monitor node reads data directly from the file. In some embodiments, the monitor node reads data for the file from an intermediary source such as a database or repository. In some embodiments, the file is encrypted and the monitor node decrypts the file. 
     At stage  612 , the monitor node reads (or tries to read) a data point from the file. In some embodiments, the file contains structured data, e.g., in a comma-separated format, eXtensible Markup Language (“XML”) format, or some other structured format. In some embodiments, the file contains plaintext log entries. In such embodiments, the monitor node parses the log entries and identifies one or more data points from the plaintext log entries. For example, in some embodiments, the plaintext log entries include a structure-formatted portion and an unstructured portion (e.g., an introductory header in a custom format preceding each unstructured portion). In some such embodiments, the monitor node parses the log entries and identifies one or more data points from the structured-formatted portions, e.g., identifying an event identifier and a timestamp from the structured portion. 
     In some embodiments, each data point represents a value for a metric, e.g., for a performance metric. In some embodiments, each data point represents a measurement, e.g., from a sensor. In some embodiments, each data point is a server transaction time. In some embodiments, each data point is a value in a time series. In some embodiments, each data point corresponds to a set of values, e.g., an identifier (such as an identifier for a transaction), an event time (such as a timestamp marking completion of the identified transaction), and a value associated with the event (such as time to completion of the transaction or a timestamp corresponding to the beginning of the transaction). 
     At stage  620 , the monitor node determines whether it has reached the end of the file. In some embodiments, a read failure at stage  612  indicates end of file. In some embodiments, the monitor node ascertains the number of records in a file and determines that the last record read corresponds to the last record in the file. In some embodiments, the monitor node reads an end of file marker at stage  612  and determines, from the end of file marker, that it has reached the end of file. 
     At stage  622 , if the monitor node determines that it has reached the end of the file at stage  620 , then the monitor node saves state information in a database (or other storage system) and returns to stage  605  to begin another file. The state information can then be used to resume the analysis from another file using the same context, e.g., at stage  624 . In some embodiments, the monitor node reads data points from the file into a sliding window of most recently read data points and, at stage  622 , the monitor node saves or state information for the sliding window, e.g., a copy of the data represented in the sliding window, boundary information for the data represented in the sliding window, a mean average value for the data represented in the sliding window, an expected next value based on the data represented in the sliding window, and/or any other such data representative of the data represented in the sliding window. In some embodiments, the monitor node saves statistics about the log files in the database. For example, in some embodiments, the monitor node keeps statistics about how many outlier data points have been read, how many consecutive outlier data points have been read, etc. Data recovered from the database at stage  624  may be persisted in the database by the monitor node at stage  622 . Data persisted in the database at stage  622  may be recovered from the database by the monitor node at stage  622 . 
     At stage  630 , if the monitor node determines that it has not reached the end of the file at stage  620 , then the monitor node determines if there is enough data in the sliding window of data for analysis. In some embodiments, there is enough data in the sliding window if there is at least a predetermined number of data points represented in the sliding window. In some embodiments, the monitor node keeps track of how many data points are represented in the sliding window, and determines whether there is enough data by comparing the number of data points to a threshold. If, at stage  630 , there is enough data in the sliding window for analysis, then the method  601  proceeds to stage  650  to determine whether a received data point is within identified boundaries. 
     At stage  640 , when the monitor node determines that there are not enough data points at stage  630 , then the monitor node accumulates more data for the window. The monitor node adds the data point read at stage  612  to the sliding window. In some embodiments, the sliding window is initially empty. In some embodiments, monitor node initializes the sliding window using the previous state data stored in the database, as read by the monitor node at stage  624 . 
     At stage  645 , the monitor node updates window boundaries based on the accumulated data for the window. In some embodiments, the monitor node determines, at stage  650 , that a next received data point is an outlier from the window of data by establishing boundaries for the window of data, e.g., a maximum (and/or minimum) value, and then determining whether the next received data point is within the established boundaries. In some embodiments, the lower boundary is fixed at zero and the monitor node updates the maximum boundary at stage  645 . In some embodiments, the monitor node identifies a mean average of the data represented in the window of data and establishes the boundaries at values above (and, in some embodiments, below) the mean average, e.g., at the mean average plus a buffer amount such as 1% of the average, an amount based on a number of standard deviations from the average, etc. In some embodiments, the monitor node sets the upper boundary at an upper quartile of the data represented in the window of data. 
     If, at stage  630 , there is enough data in the sliding window for analysis, then at stage  650 , the monitor node uses the updated boundaries (from stage  645 ) to determine whether a received data point is within the bounds. In some embodiments, the monitor node uses one or more of the following known methods for outlier detection: Tukey&#39;s Test, Peirce&#39;s Criterion, a mean and standard deviation test such as Chauvenet&#39;s criterion or Grubb&#39;s test, or any other method for outlier detection. 
     At stage  660 , if the data point was within bounds at stage  650 , then the monitor node adds the data point to the window and, when there is already at least a predetermined number of data points in the window, shifts a least-recently added data point out of the window. In some embodiments, the monitor node uses a circular buffer to represent the sliding window of data, where the circular buffer has capacity for exactly the predetermined number of data points; in such embodiments, adding a new data point overwrites a least-recently added data point. In some embodiments, the monitor node uses a first-in first-out (“FIFO”) queue to represent the window of data. Shifting a new value into the queue also shifts a least-recently added value out of the queue. The method  601  then returns to state  645  and updates the window boundaries. 
     At stage  670 , if the data point was not within bounds at stage  650 , then the monitor node determines whether a consecutive outlier count exceeds a threshold. The monitor node maintains a count of consecutive outliers detected at stage  650 . In some embodiments, the monitor node includes this count in the state information saved at stage  622  and read at stage  624 . In some embodiments, the monitor node resets the count whenever it adds a data point to the window at stage  660 . In some embodiments, the threshold is a configurable value. In some embodiments, the threshold is a percentage of the number of data points represented in the sliding window. In some embodiments, the threshold is set based on a length of time represented by the data points. For example, in some embodiments, the threshold is set such that if outliers are detected consistently over a length of time, then the consecutive outlier count exceeds the threshold. 
     At stage  675 , when the consecutive outlier count does not exceed the threshold, the monitor node reports the outlier at stage  675  and continues reading data points from the file at stage  612 . In some embodiments, the reported outlier is not added to the window of data. In some embodiments, the monitor node reports the outlier by generating a message to an administrator, e.g., by sending an email, sending an SMS text message, generating an automated telephone call, setting an error flag, adding a record to an error log, generating an interrupt, or any other manner of reporting. In some embodiments, the monitor node does use a different reporting mechanism based on the count of consecutive outliers. For example, in some embodiments, the monitor node records a first outlier in an error log file without alerting an administrator, but then alerts an administrator for a second outlier consecutive to the first outlier. 
     At stage  680 , when the consecutive outlier count does exceed the threshold at stage  670 , the monitor node resets, refreshes, or replaces the window of data. In some embodiments, the monitor node reports the outlier, e.g., in the same manner described for stage  675 . In some embodiments, the monitor node reports the reset event to the administrator. In some embodiments, the monitor node includes one or more of the last received outlier data points in a new window of data. In some embodiments, the monitor node adds the last identified outlier to the new window of data. The new window replaces the previous window. In some embodiments, the monitor node resets the window of data, e.g., clearing data represented in the window or marking it as stale. 
     The method continues to read data points from the file at stage  612 . 
     In some embodiments, the monitor node may be used by a system to control or regulate the system. For example, a load balancer may distribute workload across a plurality of servers based on expected server transaction times. The monitor node may generate an expected server transaction time from the sliding window of data. For example, the expected server transaction time may be the mean average of measured transaction times represented in the sliding window. If the expected server transaction time for a server in the plurality is above a benchmark, the load balancer may redistribute workload away from the server to improve overall throughput. For example, the benchmark may be some percentage above the average server transaction time for all servers in the plurality. 
     The systems and methods described may be used in a variety of embodiments. For example, and without limitation: 
     In at least one aspect, the above describes a method for detecting outliers on a series of data. The method includes receiving, by a device, a plurality of data points and adding, by the device, a received data point to a first window of data comprising at least a predetermined number of received data points from the plurality of data points, responsive to detecting that the received data point is not an outlier from the first window of data. The method includes detecting, by the device, that one or more next data points of the received plurality of data points are outliers from the first window of data and determining, by the device, that a count of the one or more next data points that are outliers exceeds a predetermined threshold. The method includes establishing, by the device, responsive to determining that the count exceeds the predetermined threshold, a second window of data comprising at least one of the one or more next data points. 
     Some embodiments of the method include adding, to the first window of data, by the device, consecutively received data points from the plurality of data points, up to at least the predetermined number. In some embodiments of the method, the count of the one or more next data points that are outliers is a number of consecutively received data points determined to be outliers. Some embodiments of the method include replacing the first window of data with the second window of data. In some embodiments of the method, the windows of data are represented, by the device, using a first-in-first-out (FIFO) queue, and the method includes shifting the queue to add the received data point while keeping a size of the queue equal to the predetermined number. Some embodiments of the method include detecting whether a given data point is an outlier from the first window of data using a moving average of data in the first window. 
     In some embodiments of the method, the plurality of data points are values corresponding to performance of a server in a plurality of servers. Some such embodiments of the method include adding, by the device to the second window of data, received data points from the plurality of data points, up to at least the predetermined number; determining an average of data points in the second window; and modifying a load distribution across the plurality of servers responsive to determining that the average is outside a predetermined range. 
     In some embodiments of the method, the plurality of data points are values corresponding to performance of one or more servers in a plurality of servers, and the method includes modifying a load distribution across the plurality of servers based on values of the performance metric represented in the second window of data. In some embodiments of the method, the plurality of data points are measurements of transaction times at one or more servers in a plurality of servers. In some embodiments of the method, each of the data points are respectively each associated with a corresponding event time and the plurality of data points are sequenced by the corresponding event times. 
     Some embodiments of the method include receiving, by the device, the plurality of data points from a set of files; adding, by the device, at least one data point from a first file in the set of files to the first window of data; and adding, by the device, at least one data point from a second file in the set of files to the first window of data. Some such embodiments include recording, in storage, data representative of the first window of data and the count of the one or more next data points that are outliers respective to the first file. Some embodiments of the method include receiving, by the device, the plurality of data points from a set of files; adding at least one data point from a first file in the set of files to the first window of data; recording, in storage, data representative of the first window of data and the count of the one or more next data points that are outliers respective to the first file; including, by the device, in the second window of data, the recorded data representative of the first window of data; and adding at least one data point from a second file in the set of files to the second window of data. In some such embodiments, the device uses the recorded count as a starting point for a count associated with the second file. 
     In at least one aspect, these methods may be encoded as computer-readable instructions for execution by one or more processors. The computer-readable instructions can be encoded on non-transitory computer-readable media. 
     In at least one aspect, the above describes a system for outlier detection on a series of data. The system includes a processor coupled to memory and configured to execute instructions to receive a plurality of data points; add a received data point to a first window of data comprising at least a predetermined number of received data points from the plurality of data points, responsive to detecting that the received data point is not an outlier from the first window of data; and detect that one or more next data points of the received plurality of data points are outliers from the first window of data. The processor is configured to execute instructions to determine that a count of the one or more next data points that are outliers exceeds a predetermined threshold and to establish, responsive to determining that the count exceeds the predetermined threshold, a second window of data comprising at least one of the one or more next data points. 
     In some embodiments of the system, the processor is configured to execute instructions to add, to the first window of data, consecutively received data points from the plurality of data points, up to at least the predetermined number. In some embodiments, the count of the one or more next data points that are outliers is a number of consecutively received data points determined to be outliers. In some embodiments of the system, the processor is configured to execute instructions to replace the first window of data with the second window of data. In some embodiments, the windows of data are represented, by the system, using a first-in-first-out (FIFO) queue, and the processor is configured to execute instructions to shift the queue to add the received data point while keeping a size of the queue equal to the predetermined number. In some embodiments of the system, the processor is configured to execute instructions to detect whether a given data point is an outlier from the first window of data using a moving average of data in the first window. 
     In some embodiments of the system, the plurality of data points are values corresponding to performance of a server in a plurality of servers. In some embodiments of the system, the processor is configured to execute instructions to add, to the second window of data, received data points from the plurality of data points, up to at least the predetermined number; determine an average of data points in the second window; and modify a load distribution across the plurality of servers responsive to determining that the average is outside a predetermined range. 
     In some embodiments of the system, the plurality of data points are values corresponding to performance of one or more servers in a plurality of servers, and the processor is configured to modify a load distribution across the plurality of servers based on values of the performance metric represented in the second window of data. In some embodiments, the plurality of data points are measurements of transaction times at one or more servers in a plurality of servers. In some embodiments, each of the data points are respectively each associated with a corresponding event time and the plurality of data points are sequenced by the corresponding event times. 
     In some embodiments of the system, the processor is configured to execute instructions to receive the plurality of data points from a set of files; add at least one data point from a first file in the set of files to the first window of data; and add at least one data point from a second file in the set of files to the first window of data. In some such embodiments, the processor is configure to record, in storage, data representative of the first window of data and the count of the one or more next data points that are outliers respective to the first file. In some embodiments, the processor receives the plurality of data points by reading a file. In some embodiments, the processor receives the plurality of data points by streaming the file, e.g., over a network connection. In some embodiments of the system, the processor is configured to execute instructions to receive the plurality of data points from a set of files; add at least one data point from a first file in the set of files to the first window of data; record, in storage, data representative of the first window of data and the count of the one or more next data points that are outliers respective to the first file; include, in the second window of data, the recorded data representative of the first window of data; and add at least one data point from a second file in the set of files to the second window of data. In some such embodiments, the system uses the recorded count as a starting point for a count associated with the second file. 
     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.