Patent Publication Number: US-2023138877-A1

Title: Cloud application scaler

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This is a continuation of U.S. patent application Ser. No. 16/587,875, filed Sep. 30, 2019, the entire contents of which are specifically incorporated by reference herein. 
    
    
     BACKGROUND 
     In a cloud environment, resources are typically provisioned to meet processing, memory, and other demands of applications, which can change over time. Provisioning more resources than are needed can reduce the ability to invoke additional applications. Under-provisioning of resources can be problematic where applications demand more processing or memory resources than have been allocated. Cloud-based management tools can attempt to automatically adjust resource allocation to match demand if applications are structured to meet the interface and formatting requirements of the tools. However, legacy applications may not be able to interface correctly with the tools unless the underlying code is modified to comply with the requirements of the tools. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The subject matter which is regarded as the invention is particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The features and advantages of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which: 
         FIG.  1    depicts a block diagram of a system according to some embodiments of the present invention; 
         FIG.  2    depicts a block diagram of a system according to some embodiments of the present invention; 
         FIG.  3    depicts a block diagram of interactions between a disposable application and an auto-scaler according to some embodiments of the present invention; 
         FIG.  4    depicts a block diagram of interactions between a non-disposable application and an artificial intelligence scaler according to some embodiments of the present invention; 
         FIG.  5    depicts a block diagram of types of data that can be captured in usage history and/or log files according to some embodiments of the present invention; 
         FIG.  6    depicts a simplified example of a dashboard according to some embodiments of the present invention; 
         FIG.  7    depicts a training and prediction process according to some embodiments of the present invention; 
         FIG.  8    depicts a process flow according to some embodiments of the present 
       invention; 
         FIG.  9    depicts a process flow according to some embodiments of the present invention; and 
         FIG.  10    depicts a process flow according to some embodiments of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     According to an embodiment, a system for an application cloud scaler is provided. The system can enable legacy applications that have not been configured as scalable or disposable applications with respect to a cloud environment to be scaled responsively and/or predictively. A scalable application in a cloud environment may be managed through a runtime platform and infrastructure automation to scale the amount of infrastructure resources needed to add instances (e.g., processes) of the application or reduce instances of the application. In order for the runtime platform to correctly understand the demands of applications, the applications are typically required to comply with multiple factors, e.g., twelve factors, such as a codebase, dependencies, configuration, backing services, delivery, stateless processes, port binding, concurrency, startup/shutdown, environment, log production, administrative processes, and the like. Applications deployed in the cloud environment that do not comply with the factors may be referred to as non-disposable applications, as an auto-scaler of the cloud environment may be unable to fully interface with such an application. Embodiments can include an artificial intelligence (AI) scaler that enables scaling of non-disposable applications within the cloud environment. Such an AI scaler can enable more effective management of computing resources within the cloud environment and thus provides technical benefits. 
     The AI scaler can dynamically scale up and scale down non-disposable applications in a cloud environment to reduce the risk of disposing of an application instance that is still processing or running an active session. The AI scaler enables non-disposable applications to execute similar to disposable applications, even though the non-disposable applications may not comply with twelve-factor application standards. The AI scaler can monitor resource utilization of a non-disposable application, such as processing and memory resource utilization, and monitor processes of the application so that processes finish prior to the reduction of resources for a scale down. As one example, the AI scaler can access utilization data of the non-disposable application periodically while the non-disposable application executes in the cloud environment. Utilization data can be processed to determine various metrics, such as normalized/idle processor usage of the application. Utilization data can be stored in log files or in a database for subsequent analysis by the AI scaler. As utilization data is collected over time for many application instances of the same or different non-disposable applications, patterns can be learned. The AI scaler can detect utilization spikes or idle periods and scale instances of the application up or down accordingly. The AI scaler can perform predictive scaling based on historical data. If patterns of high or low resource utilization are identified, the AI scaler can start more instances of a non-disposable application at specific times of day before the anticipated demand is realized. As startup times for some applications can take a substantial period of time, e.g., multiple minutes, having the applications started before the demand is realized can maintain efficient resource allocation. The AI scaler can learn to start the instances up ahead of time to anticipate an increased demand and have capacity ready. 
     Turning now to  FIG.  1   , a system  100  is depicted upon which application cloud scaling may be implemented. The system  100  includes a system management server  102  coupled to a network  104 . The system management server  102  can access a cloud environment  105  through the network  104 . The cloud environment  105  can execute a plurality of cloud applications  108 , such as one or more instances of disposable applications  110  and one or more instances of non-disposable applications  112 . A plurality of user systems  106  can interact with the cloud applications  108  and change the resulting demand in resources  114  needed to support execution of the cloud applications  108 . For example, the resources  114  can include processor resources  116 , memory resources  118 , disk resources  120 , and virtual machine resources  122 . The processor resources  116  can be defined based on a fractional portion of central processing unit (CPU) capacity needed by an instance of the cloud applications  108 . In some embodiments, depending on security constraints and/or other factors, the processor resources  116  can be allocated on a processing core basis, a device basis, or a virtual machine basis. The memory resources  118  can include short-term storage to support execution of a current instance of the cloud applications  108 , such as volatile memory. The disk resources  120  can include persistent storage needs to retain values through power cycles, such as files and databases in non-volatile storage. The virtual machine resources  122  can be used to establish an operating environment for the cloud applications  108 . The resources  114  of the cloud environment  105  are provisioned for use by the cloud applications  108  through a runtime platform  124  and infrastructure automation  126 . The runtime platform  124  can provide support services for the cloud applications  108  to access the resources  114 . The infrastructure automation  126  can determine which resources  114  should be allocated to the cloud applications  108  in response to requests through the runtime platform  124 . The cloud environment  105  can also include an auto-scaler  128  operable to enable scaling of the disposable applications  110  to increase or decrease a number of instances of the disposable applications  110  and/or make adjustments to the provisioning of the resources  114  for the disposable applications  110  without the disposable applications  110  explicitly requesting the scaling. Further, the cloud environment  105  can include one or more application programming interfaces (APIs)  130  that provide access to services and usage information associated with the cloud applications  108 , runtime platform  124 , infrastructure automation  126 , and/or resources  114 . 
     The system management server  102  can include multiple applications and data external to the cloud environment  105 . In the example of  FIG.  1   , the system management server  102  includes an AI scaler  132  that is configured to monitor performance of the non-disposable applications  112 . Performance data collected from the cloud environment  105  can be written back to the system management server  102  or stored elsewhere, such as a database server  134 , in log files  136  or as utilization data  138  in a database  140  managed by a database manager  142 . As one example, the AI scaler  132  or another monitoring service (not depicted) can query the cloud environment  105  through the APIs  130  to determine a current allocation of the resources  114  by the non-disposable applications  112 . Resulting logs associated with the non-disposable applications  112  can be written as log files  136  and further processed as usage history  138 , which may include other values beyond those directly output through the APIs  130 . As an example, the log files  136  may capture current utilization values, and the usage history  138  may include additional timing, system configuration data, and/or normalized values, depending upon the metrics of interest for the AI scaler  132  to further analyze. The database manager  142  can provide an interface for the AI scaler  132  to query and/or modify the usage history  138  in the database  140 . 
     In embodiments, the AI scaler  132  can access the log files  136  and/or usage history  138  to determine profiles and patterns  144 . The profiles and patterns  144  can summarize information, such as time-of-day, utilization of types of the resources  114  with the non-disposable applications  112 , demands from user systems  106 , demand correlations between multiple non-disposable applications  112 , demand correlations with disposable applications  110 , and other such associations. Constraints and rules  146  can be used and/or output as part of a training process of the AI scaler  132 . Results of applying the constraints and rules  146  with the profiles and patterns  144  by the AI scaler  132  can result in demand predictions  148 . The demand predictions  148  can predict when demand for the non-disposable applications  112  will likely increase or decrease. The AI scaler  132  can use the demand predictions  148  to trigger responses to scale up or scale down instances of the non-disposable applications  112  prior to occurrence of the change in demand. For example, if a time-of-day pattern is identified for one or more of the non-disposable applications  112 , and a scale-up time in known, the AI scaler  132  can initiate a scale-up operation such that the scale-up time aligns with the time of a predicted need for adding instances of the non-disposable applications  112 . As a further example, if an association pattern is identified between multiple cloud applications  108 , a change in demand for a cloud application  108  can trigger a predictive scaling of an associated cloud application  108  prior to an explicit request for scaling. When down-scaling is predicted, the AI scaler  132  can send a transaction blocking request through the APIs  130  to block new transactions from being initiated through a non-disposable application  112 , such that any open transactions in progress can complete before scaling down and re-enabling transactions to the non-disposable application  112 . 
     In some embodiments, the log files  136  may be received in different formats, depending upon an application type of the non-disposable applications  112 . To normalize formatting differences, the database  140  may be a schema-less database that stores records converted from application-specific data objects to a common format in the usage history  138 , such as a JavaScript Object Notation (JSON) format. In other embodiments, the database  140  can comprise a relational database or other architecture known in the art. 
     A system management server  102  can access the database  140  through the database manager  142  to analyze records of usage history  138  stored therein. The system management server  102  can display a summary of the usage history  138  through a dashboard  150 . The dashboard  150  may include a combination of trends, data values, gauges, allocation splits, and the like. Further, the AI scaler  132  executing on the system management server  102  can monitor resource allocation, trigger alerts, and determine resource allocation adjustment requests for the non-disposable applications  112  based on various thresholds that can also be monitored through the dashboard  150 . The system management server  102  and the database server  134  can comprise separate servers coupled to the network  104 . Further, the system management server  102  and the database server  134  can be combined with each other or with one or more other servers (not depicted). 
     In the example of  FIG.  1   , each of the system management server  102 , user systems  106 , and database server  134  can include a processor (e.g., a processing device, such as one or more microprocessors, one or more microcontrollers, one or more digital signal processors) that receives instructions (e.g., from memory or like device), executes those instructions, and performs one or more processes defined by those instructions. Instructions may be embodied, for example, in one or more computer programs and/or one or more scripts. In one example, the system  100  executes computer instructions for implementing the exemplary processes described herein. Instructions that implement various process steps can be executed by different elements of the system  100 , such as elements of the system management server  102 , processor resources  116 , and/or database server  134 . 
     The user systems  106  may each be implemented using a computer executing one or more computer programs for carrying out processes described herein. In one embodiment, the user systems  106  may each be a personal computer (e.g., a laptop, desktop, etc.), a network server-attached terminal (e.g., a thin client operating within a network), or a portable device (e.g., a tablet computer, personal digital assistant, smart phone, etc.). In an embodiment, the user systems  106  can be operated by users of cloud applications  108  and/or administrators. 
     Each of the system management server  102 , user systems  106 , and database server  134  can include a local data storage device, such as a memory device. A memory device, also referred to herein as “computer-readable memory” (e.g., non-transitory memory devices as opposed to transmission devices or media), may generally store program instructions, code, and/or modules that, when executed by a processing device, cause a particular machine to function in accordance with one or more embodiments described herein. 
     The network  104  can include any type of computer communication technology within the system  100  and can extend beyond the system  100  as depicted. Examples include a wide area network (WAN), a local area network (LAN), a global network (e.g., Internet), a virtual private network (VPN), and an intranet. Communication within the network  104  may be implemented using a wired network, an optical network, a wireless network, and/or any kind of physical network implementation known in the art. 
       FIG.  2    depicts a block diagram of a system  200  according to an embodiment. The system  200  is depicted embodied in a computer  201  in  FIG.  2   . The system  200  is an example of one of the system management server  102 , user systems  106 , or database server  134  of  FIG.  1   . 
     In an exemplary embodiment, in terms of hardware architecture, as shown in  FIG.  2   , the computer  201  includes a processing device  205  and a memory device  210  coupled to a memory controller  215  and an input/output controller  235 . The processing device  205  can also be referred to as a processing system  205  and may include multiple processors (e.g., one or more multi-core processors). The memory device  210  can also be referred to as a memory system  210  and may include multiple types of memory in various configurations, such as a combination memory cards and memory chips with volatile and/or non-volatile storage capacity. The input/output controller  235  may comprise, for example, one or more buses or other wired or wireless connections, as is known in the art. The input/output controller  235  may have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, to enable communications. Further, the computer  201  may include address, control, and/or data connections to enable appropriate communications among the aforementioned components. 
     In an exemplary embodiment, a keyboard  250  and mouse  255  or similar devices can be coupled to the input/output controller  235 . Alternatively, input may be received via a touch-sensitive or motion sensitive interface (not depicted). The computer  201  can further include a display controller  225  coupled to a display  230 . 
     The processing device  205  comprises a hardware device for executing software, particularly software stored in secondary storage  220  or memory device  210 . The processing device  205  may comprise any custom-made or commercially available computer processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the computer  201 , a semiconductor-based microprocessor (in the form of a microchip or chip set), a macro-processor, or generally any device for executing instructions. 
     The memory device  210  can include any one or combination of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)) and nonvolatile memory elements (e.g., ROM, erasable programmable read only memory (EPROM), electronically erasable programmable read only memory (EEPROM), flash memory, programmable read only memory (PROM), tape, compact disk read only memory (CD-ROM), flash drive, disk, hard disk drive, diskette, cartridge, cassette or the like, etc.). Moreover, the memory device  210  may incorporate electronic, magnetic, optical, and/or other types of storage media. Accordingly, the memory device  210  is an example of a tangible computer readable storage medium  240  upon which instructions executable by the processing device  205  may be embodied as a computer program product. The memory device  210  can have a distributed architecture, where various components are situated remotely from one another, but can be accessed by one or more instances of the processing device  205 . 
     The instructions in memory device  210  may include one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions. In the example of  FIG.  2   , the instructions in the memory device  210  include a suitable operating system (OS)  211  and program instructions  216 . The operating system  211  essentially controls the execution of other computer programs and provides scheduling, input-output control, file and data management, memory management, and communication control and related services. When the computer  201  is in operation, the processing device  205  is configured to execute instructions stored within the memory device  210 , to communicate data to and from the memory device  210 , and to generally control operations of the computer  201  pursuant to the instructions. Examples of program instructions  216  can include instructions to implement the cloud applications  108 , AI scaler  132 , database manager  142 , and/or dashboard  150  of  FIG.  1   . 
     The computer  201  of  FIG.  2    also includes a network interface  260  that can establish communication channels with one or more other computer systems via one or more network links of the network  104  of  FIG.  1   . The network interface  260  can support wired and/or wireless communication protocols known in the art. For example, when embodied in the system management server  102  of  FIG.  1   , the network interface  260  can establish communication channels with at least one of the cloud environment  105  of  FIG.  1    and the database server  134  of  FIG.  1    via the network  104  of  FIG.  1   . 
       FIG.  3    depicts an example of a block diagram  300  of interactions between the auto-scaler  128  and a disposable application  110  according to an embodiment and is described in reference to  FIGS.  1 - 3   . Within the cloud environment  105 , the auto-scaler  128  can comprise part of the runtime platform  124  of  FIG.  1    to scale up  302  the disposable application  110  by creating one or more additional instances  304  of the disposable application  110  to meet increased demands. The auto-scaler  128  can monitor and react to demands on the disposable application  110  based on multi-factor compliance of the disposable application  110 . When the demands on the disposable application  110  drop, such as during an idle period or after a peak demand is reduced, the auto-scaler  128  can scale down  306  the disposable application  110  with reduced instances  308  to align with the decreased demand. 
       FIG.  4    depicts an example of a block diagram  400  of interactions between the AI scaler  132  and a non-disposable application  112  according to an embodiment and is described in reference to  FIGS.  1 - 4   . The AI scaler  132  operates external to the cloud environment  105 , and the non-disposable application  112  is configured to execute within the cloud environment  105 . The AI scaler  132  can monitor the non-disposable application  112  and predictively scale up  402  the non-disposable application  112  by creating one or more additional instances  404  of the non-disposable application  112  to meet predicted increases in demand. The AI scaler  132  can monitor and predict demands on the non-disposable application  112  based on demand predictions  148 . When the demands on the non-disposable application  112  are predicted to drop, such as an anticipated idle period or after a peak demand is likely to reduce, the AI scaler  132  can predictively scale down  406  the non-disposable application  112  with reduced instances  408  to align with the predicted decrease in demand. The predicted decrease can result in blocking the servicing of new transactions to allow existing transactions to complete before the scale down  406  and continue servicing of new transactions after the scale down  406  is completed. In some embodiments, the AI scaler  132  can also perform reactive/nonpredictive scaling in response to an unexpected change in demand. As demand changes continue over time, the AI scaler  132  can continue to refine constraints and rules  146  used to make the demand predictions  148 . 
       FIG.  5    depicts a block diagram of types of data that can be captured in usage history  138  of the database  140  of  FIG.  1   . As an example, the usage history  138  can include a plurality of records  502 A- 502 N to capture information, such as a record identifier  504 , an application identifier  506 , a running time  508 , a processor usage  510 , a memory usage  512 , a disk usage  514 , virtual machine usage  516 , and various quotas  518 . The record identifier  504  can be used to track unique records  502 A- 502 N and sequencing of historical data. The application identifier  506  can indicate which of the non-disposable applications  112  is associated with a particular copy of a record  502 A- 502 N. The running time  508  can indicate how long the non-disposable application  112  has been running since instantiation or other event. The processor usage  510  can indicate a processing load as a relative value, ratio, instruction execution throughput, or other such processing metric of the processor resources  116  consumed by the non-disposable application  112 . The memory usage  512  may indicate a total amount of volatile memory of memory resources  118  consumed by the non-disposable application  112 . The disk usage  514  can indicate a total amount of non-volatile memory of disk resources  120  consumed by the non-disposable application  112 . The virtual machine use  516  can indicate types and amounts of virtual machine resources  122  consumed by the non-disposable application  112 . The quotas  518  may indicate allocation limits for one or more of the processor resources  116 , memory resources  118 , disk resources  120 , and virtual machine resources  122 . Quotas  518  may be defined per application instance or aggregated across multiple instances of the non-disposable application  112 . Quotas  518  can also account for effects of disposable applications  110  on utilization of the resources  114 . 
     Although the example of  FIG.  5    depicts various types of data that can be captured in records  502 A- 502 N, it will be understood that many variations are contemplated. For example, values can represent a time sequence history or can blend/average data over time. As an example, values may be peak values, current values, minimum values, average values, weighted values, or the like. Further, other types of data can include host address, ports, network resource usage, inter-application communication records, and other such data. 
       FIG.  6    depicts a simplified example of a dashboard  600  as a simplified visual example of the dashboard  150  of  FIG.  1   . The dashboard  600  may include a combination of trends  602 , data values  604 , gauges  606 , allocation splits  608 , and the like. The trends  602  may illustrate changes in usage of resources  114  of  FIG.  1    for one or more non-disposable applications  112  of  FIG.  1    over a period of time. The data values  604  can support inspection of detailed values captured in the usage history  138  of  FIG.  1    or from other sources. The gauges  606  can indicate current performance level or usage of the resources  114  relative to a maximum allowable value, such as quotas  518  of  FIG.  5   . The allocation splits  608  may display relative allocations of resources  114  between multiple non-disposable applications  112 , for instance, as a pie chart. Other types of indicators on the dashboard  600  are contemplated. As one example, a utilization heat map can be depicted with color coding or other visual indications to identify how close utilization of one or more of the resources  114  is to one or more resource limits at time intervals over a selected date/time range. The dashboard  600  can adjust a display parameter of one or more values that exceed one or more resource limits on one or more visual depictions (e.g., green &lt;50%, yellow 51% to 59%, orange 60% to 74%, red &gt;=75%). The utilization heat map can be selectable to display average memory and/or processing resource utilization across discrete time intervals that may be user configurable/adjustable. 
       FIG.  7    depicts a training and prediction process  700  according to some embodiments. The training and prediction process  700  can include a training process  702  that analyzes training data  704  to develop trained models  706  as examples of a pattern detector  710  and demand predictor  712 . The training process  702  can use labeled or unlabeled data in the training data  704  to learn features, such as a resource usage and correlation between applications. The training data  704  can include a set of historical resource utilizations and other data to establish a ground truth for learning coefficients/weights and other such features known in the art of machine learning to develop trained models  706 . The trained models  706  can include a family of models to identify specific types of features from logs  708  and/or usage data  709 . The logs  708  can be extracted directly from the cloud environment  105  of  FIG.  1    or log files  136  of  FIG.  1   . The usage data  709  can comprise fields extracted from the records  502 A- 502 N of  FIG.  5    as part of usage history  138  of  FIG.  1   . The trained models  706  can include the pattern detector  710  as part of the AI scaler  132  of  FIG.  1    to produce the profiles and patterns  144  of  FIG.  1   . The demand predictor  712  can be part of the AI scaler  132  to produce demand predictions  148 . Other such models and further subdivision of the trained models  706  can be incorporated in various embodiments. 
     The trained models  706  can output a confidence determination  714  indicating a confidence level of a detected pattern or demand prediction. Result postprocessing  716  can determine an action to take based on the confidence and whether a scale up or scale down is likely needed presently or at a predicted future time. Result postprocessing  716  may also format results for display in the dashboard  150  of  FIG.  1    or for triggering of alerts and other events by the system management server  102  of  FIG.  1   . 
     Turning now to  FIG.  8   , a process flow  800  is depicted according to an embodiment. The process flow  800  includes a number of steps that may be performed in the depicted sequence or in an alternate sequence. The process flow  800  may be performed by the system  100  of  FIG.  1   . In one embodiment, the process flow  800  is performed by the system management server  102  of  FIG.  1    in combination with the cloud environment  105  and database server  134 . Although the example of process flow  800  is described in reference to the system management server  102 , the process flow  800  applies to any combination of depicted servers, as well as one or more additional servers (not depicted). The process flow  800  is described in reference to  FIGS.  1 - 8   . 
     At step  802 , the AI scaler  132  can identify a cloud application  108  in the cloud environment  105  as a non-disposable application  112 . Identifying the non-disposable application  112  can include performing a multi-factor check of the cloud application  108  to determine whether the cloud application  108  complies with a plurality of design constraints to support dynamic deployment in the cloud environment  105 . The design constraints can be defined in the constraints and rules  146 . The cloud application  108  can be identified as a disposable application  110  based on determining that the cloud application  108  complies with the design constraints to support dynamic deployment in the cloud environment  105 , or the cloud application  108  can be identified as the non-disposable application  112  based on complying with less than a predetermined number of the design constraints (e.g., less than twelve). 
     At step  804 , the AI scaler  132  can monitor a plurality of instances of the non-disposable application  112  running in the cloud environment  105 . Monitoring the instances of the non-disposable application  112  running in the cloud environment  105  can include monitoring usage of one or more resources  114  associated with the non-disposable application  112  in the cloud environment  105 . The one or more resources  114  can include a combination of one or more of processor resources  116 , memory resources  118 , disk resources  120 , and virtual machine resources  122 . Monitoring the plurality of instances of the non-disposable application  112  running in the cloud environment  105  can be performed at a predetermined time interval through an API  130  of the cloud environment  105 . 
     At step  806 , the AI scaler  132  can determine that a number of the instances of the non-disposable application  112  should be modified based on one or more demand predictions  148  of the AI scaler  132 . The one or more demand predictions  148  can be based on detecting one or more patterns indicative of a resource demand increase or a resource demand decrease. The AI scaler  132  can comprise a software service executed remotely from the cloud environment  105 . The AI scaler  132  can be trained with a plurality of test loads in training data  704  applied to the one or more resources  114  of the cloud environment  105 . 
     At step  808 , the AI scaler  132  can adjust the number of the instances of the non-disposable application  112  running in the cloud environment  105  based on the one or more demand predictions  148 . At step  810 , the AI scaler  132  can modify an allocation of one or more resources  114  of the cloud environment  105  associated with adjusting the number of the instances of the non-disposable application  112 . Steps  808  and  810  can be performed in any order or combined. For example, modification of the allocation of resources in step  810  can be performed in response to a request to change the number of instances of the non-disposable application  112  prior to execution of new instances of the non-disposable application  112 . Further, as resource needs associated with a change in the number of instances of the non-disposable application  112  result in a modified resource demand, the one or more resources  114  can be adjusted to meet the change during execution. In some embodiments, an auto-scaler  128  associated with the cloud environment  105  can be used to scale up and scale down a cloud application  108  based on determining that the cloud application  108  is a disposable application  110 . An alert can be issued based on detection by the AI scaler  132  of one or more irregular patterns of resource demands. The alert can be output to the dashboard  150  or sent to an administrator system. Irregular patterns can be identified based on a level of deviation from one or more expected patterns of resource demands. The patterns can be captured and updated as part of the profiles and patterns  144 . 
     Where log files  136  are used, a plurality of results of monitoring the instances of the non-disposable application  112  can be written into a log file  136  at a first interval. The AI scaler  132  can analyze content of the log file  136  to identify one or more patterns at a second interval. For instance, log files  136  can be populated by taking samples every few minutes while pattern observation may be in intervals of hours. 
     Turning now to  FIG.  9   , a process flow  900  is depicted according to an embodiment. The process flow  900  includes a number of steps that may be performed in the depicted sequence or in an alternate sequence. The process flow  900  may be performed by the system  100  of  FIG.  1   . In one embodiment, the process flow  900  is performed by the system management server  102  of  FIG.  1    in combination with the cloud environment  105  and database server  134 . The process flow  900  can comprise an extension of process flow  800  of  FIG.  8   . The process flow  900  is described in reference to  FIGS.  1 - 9   . 
     At step  902 , the AI scaler  132  can log a usage history  138  of the one or more resources  114  by the non-disposable application  112 . At step  904 , the AI scaler  132  can analyze the usage history  138  to determine a normalized usage profile and an idle usage profile of the one or more resources  114  by the non-disposable application  112 . The normalized usage profile can adjust a time scale that may differ between sampling intervals and may use averaging and interpolation to smooth the results. The idle usage profile can indicate parameters and conditions associated with a lower utilization of the resources  114 . 
     At step  906 , the AI scaler  132  can identify one or more patterns based on the normalized usage profile and an idle usage profile. The patterns can include, for example, identifying peak loading, average loading, and minimum load conditions over a period of time. Slope transitions between the different points in the usage profiles can assist with predicting trending in a particular direction (e.g., demand increasing or decreasing over time). The normalized usage profile, idle usage profile, and one or more patterns can be stored in the profiles and patterns  144 . 
     At step  908 , the AI scaler  132  can establish one or more of scale-up rules and scale-down rules based on the one or more patterns. The one or more of the scale-up rules and the scale-down rules can include a time component. For example, a time-of-day and day-of-week can impact when demand patterns are more likely. Scale-up rules may include localized and global parameters to determine a likely impact to other cloud applications  108  if scaling up is predictively performed. Similarly, scale-down rules can use localized and global parameters to determine whether scaling down will likely result in a net benefit by freeing resources  114  for other cloud applications  108  to use. The scale-up rules and scale-down rules can be stored in the constraints and rules  146 . The constraints and rules  146  may also be viewable and editable by administrators to support customization, analysis, and testing scenarios. 
     Turning now to  FIG.  10   , a process flow  1000  is depicted according to an embodiment. The process flow  1000  includes a number of steps that may be performed in the depicted sequence or in an alternate sequence. The process flow  1000  may be performed by the system  100  of  FIG.  1   . In one embodiment, the process flow  1000  is performed by the system management server  102  of  FIG.  1    in combination with the cloud environment  105  and database server  134 . The process flow  1000  can expand upon the process flow  900  of  FIG.  9   . The process flow  1000  is described in reference to  FIGS.  1 - 10   . 
     At step  1002 , the AI scaler  132  can adjust one or more of the scale-up rules and the scale-down rules based on observing a change in one or more patterns. At step  1004 , the AI scaler  132  can apply the one or more of the scale-up rules to initiate an increase in the number of the instances of the non-disposable application  112  running in the cloud environment  105  prior to observing an increased demand of the one or more resources  114  of the cloud environment  105  associated with the non-disposable application  112 . 
     At step  1006 , the AI scaler  132  can apply the one or more of the scale-down rules to initiate a decrease in the number of the instances of the non-disposable application  112  running in the cloud environment  105  prior to observing a decreased demand of the one or more resources of the cloud environment  105  associated with the non-disposable application  112 . 
     At step  1008 , the AI scaler  132  can pause at least one of the instances of the non-disposable application  112  to prevent one or more new transactions from being entered based on initiating the decrease in the number of the instances of the non-disposable application  112  running in the cloud environment  105 . At step  1010 , the AI scaler  132  can wait for one or more of the instances of the non-disposable application  112  to finish. At step  1012 , the AI scaler  132  can reduce the number of the instances of the non-disposable application  112  running in the cloud environment  105  based on the waiting. 
     Technical effects include increasing cloud environment resource allocation efficiency through predictive scaling of non-disposable applications that would not otherwise support scaling in the cloud environment. 
     It will be appreciated that aspects of the present invention may be embodied as a system, method, or computer program product and may take the form of a hardware embodiment, a software embodiment (including firmware, resident software, micro-code, etc.), or a combination thereof. Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon. 
     One or more computer readable medium(s) may be utilized. The computer readable medium may comprise a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may comprise, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In one aspect, the computer readable storage medium may comprise a tangible medium containing or storing a program for use by or in connection with an instruction execution system, apparatus, and/or device. 
     A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may comprise any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, and/or transport a program for use by or in connection with an instruction execution system, apparatus, and/or device. 
     The computer readable medium may contain program code embodied thereon, which may be transmitted using any appropriate medium, including, but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. In addition, computer program code for carrying out operations for implementing aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer, or entirely on the remote computer or server. 
     It will be appreciated that aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products, according to embodiments of the invention. It will be understood that each block or step of the flowchart illustrations and/or block diagrams, and combinations of blocks or steps in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks. The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     In addition, some embodiments described herein are associated with an “indication”. As used herein, the term “indication” may be used to refer to any indicia and/or other information indicative of or associated with a subject, item, entity, and/or other object and/or idea. As used herein, the phrases “information indicative of” and “indicia” may be used to refer to any information that represents, describes, and/or is otherwise associated with a related entity, subject, or object. Indicia of information may include, for example, a code, a reference, a link, a signal, an identifier, and/or any combination thereof and/or any other informative representation associated with the information. In some embodiments, indicia of information (or indicative of the information) may be or include the information itself and/or any portion or component of the information. In some embodiments, an indication may include a request, a solicitation, a broadcast, and/or any other form of information gathering and/or dissemination. 
     Numerous embodiments are described in this patent application, and are presented for illustrative purposes only. The described embodiments are not, and are not intended to be, limiting in any sense. The presently disclosed invention(s) are widely applicable to numerous embodiments, as is readily apparent from the disclosure. One of ordinary skill in the art will recognize that the disclosed invention(s) may be practiced with various modifications and alterations, such as structural, logical, software, and electrical modifications. Although particular features of the disclosed invention(s) may be described with reference to one or more particular embodiments and/or drawings, it should be understood that such features are not limited to usage in the one or more particular embodiments or drawings with reference to which they are described, unless expressly specified otherwise. 
     Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise. On the contrary, such devices need only transmit to each other as necessary or desirable, and may actually refrain from exchanging data most of the time. For example, a machine in communication with another machine via the Internet may not transmit data to the other machine for weeks at a time. In addition, devices that are in communication with each other may communicate directly or indirectly through one or more intermediaries. 
     A description of an embodiment with several components or features does not imply that all or even any of such components and/or features are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the present invention(s). Unless otherwise specified explicitly, no component and/or feature is essential or required. 
     Further, although process steps, algorithms or the like may be described in a sequential order, such processes may be configured to work in different orders. In other words, any sequence or order of steps that may be explicitly described does not necessarily indicate a requirement that the steps be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously despite being described or implied as occurring non-simultaneously (e.g., because one step is described after the other step). Moreover, the illustration of a process by its depiction in a drawing does not imply that the illustrated process is exclusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to the invention, and does not imply that the illustrated process is preferred. 
     “Determining” something can be performed in a variety of manners and therefore the term “determining” (and like terms) includes calculating, computing, deriving, looking up (e.g., in a table, database or data structure), ascertaining and the like. 
     It will be readily apparent that the various methods and algorithms described herein may be implemented by, e.g., appropriately and/or specially-programmed computers and/or computing devices. Typically, a processor (e.g., one or more microprocessors) will receive instructions from a memory or like device, and execute those instructions, thereby performing one or more processes defined by those instructions. Further, programs that implement such methods and algorithms may be stored and transmitted using a variety of media (e.g., computer readable media) in a number of manners. In some embodiments, hard-wired circuitry or custom hardware may be used in place of, or in combination with, software instructions for implementation of the processes of various embodiments. Thus, embodiments are not limited to any specific combination of hardware and software. 
     A “processor” generally means any one or more microprocessors, CPU devices, computing devices, microcontrollers, digital signal processors, or like devices, as further described herein. 
     The term “computer-readable medium” refers to any medium that participates in providing data (e.g., instructions or other information) that may be read by a computer, a processor or a like device. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical or magnetic disks and other persistent memory. Volatile media include DRAM, which typically constitutes the main memory. Transmission media include coaxial cables, copper wire and fiber optics, including the wires that comprise a system bus coupled to the processor. Transmission media may include or convey acoustic waves, light waves and electromagnetic emissions, such as those generated during RF and IR data communications. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory chip or cartridge, a carrier wave, or any other medium from which a computer can read. 
     The term “computer-readable memory” may generally refer to a subset and/or class of computer-readable medium that does not include transmission media such as waveforms, carrier waves, electromagnetic emissions, etc. Computer-readable memory may typically include physical media upon which data (e.g., instructions or other information) are stored, such as optical or magnetic disks and other persistent memory, DRAM, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory chip or cartridge, computer hard drives, backup tapes, Universal Serial Bus (USB) memory devices, and the like. 
     Various forms of computer readable media may be involved in carrying data, including sequences of instructions, to a processor. For example, sequences of instruction (i) may be delivered from RAM to a processor, (ii) may be carried over a wireless transmission medium, and/or (iii) may be formatted according to numerous formats, standards or protocols, such as Bluetooth™, TDMA, CDMA, 3G. 
     Where databases are described, it will be understood by one of ordinary skill in the art that (i) alternative database structures to those described may be readily employed, and (ii) other memory structures besides databases may be readily employed. Any illustrations or descriptions of any sample databases presented herein are illustrative arrangements for stored representations of information. Any number of other arrangements may be employed besides those suggested by, e.g., tables illustrated in drawings or elsewhere. Similarly, any illustrated entries of the databases represent exemplary information only; one of ordinary skill in the art will understand that the number and content of the entries can be different from those described herein. Further, despite any depiction of the databases as tables, other formats (including relational databases, object-based models and/or distributed databases) could be used to store and manipulate the data types described herein. Likewise, object methods or behaviors of a database can be used to implement various processes, such as the described herein. In addition, the databases may, in a known manner, be stored locally or remotely from a device that accesses data in such a database. 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one more other features, integers, steps, operations, element components, and/or groups thereof.