Patent Publication Number: US-8990383-B1

Title: Elastic service level agreement for metrics accuracy computation

Description:
BACKGROUND 
     The availability and relatively low cost of data storage enables people or entities to store large amounts of data while operating their businesses or performing other computing operations. For example, a large retailer may store data for each transaction that the retailer performs. The retailer may use this data for computations, such as to generate reports for total sales, net revenue, inventory, and other important metrics for the retailer. To perform these computations, the retailer may need access to computing resources that are capable of processing large amounts of transaction data. When the retailer processes thousands or even millions of transactions per day, the cost of computation may become increasingly expensive based on the number of computing resources necessary to perform the computations. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same reference numbers in different figures indicate similar or identical items. 
         FIG. 1  is a schematic diagram of an illustrative computing environment to use an elastic service level agreement (SLA) for metrics accuracy computation. 
         FIG. 2  is a flow diagram of an illustrative process to determine an elastic SLA for tasks based on available resources. 
         FIG. 3  is a flow diagram of an illustrative process to schedule a job to recompute metrics based on an elastic SLA and to provide messaging when available computing resources cannot achieve the SLA. 
         FIG. 4  is a flow diagram of another illustrative process to schedule a job to recompute metrics based on an elastic SLA. 
     
    
    
     DETAILED DESCRIPTION 
     Overview 
     This disclosure is directed, among other things, to scheduling work to available computing resources according to an elastic service level agreement (SLA). The available computing resources may be a fixed allocation of computing resources, such as a fixed number of servers, computation time, computation cost, or other measurable amount of computing services. For example, the available resources may be determined by a cost of the computing resources for a given time period. The computing resources may be used to perform various computational tasks such as generate reports from large amounts of data. For example, the computational tasks may be performed for a retailer and may be used to generate sales reports for given time periods or intervals. 
     The computational tasks may be performed in accordance with an elastic SLA. The SLA may define an accuracy, confidence level, or goal that is used when determining a frequency of a computational task. The elastic SLA may be selected or modified based on a relative difficulty (e.g., resource intensiveness) of the computational task, an expected or requested accuracy of the computation task, or other predetermined design constraints. For example, a first task may include computing sales for a first time period, such as for a current day. The first task may have a first SLA, such as 99.5, which may target a 99.5% accuracy value for data resulting from the first task at any given time when the data is accessed by a user or entity. The first task (order metrics, etc.) may be recomputed many times during the day to update the data and maintain the 99.5 SLA. In contrast, a second task may include computing sales for a second time period, such as for a current week. The second task may have a second SLA, such as 98.2, which may be a lower SLA than the first SLA. The second SLA may result in the second task being performed less frequently than the first task. The first and second tasks may also include priority values, which may or may not be based on the respective SLAs. For example, when both the first task and second task are due to be computed because each have reached or are predicted to reach an SLA defining a respective metrics accuracy threshold, then the SLA and/or other factors may be used to prioritize one task over the other. In some embodiments, tasks may also be performed concurrently and/or in parallel. 
     In accordance with various embodiments, the SLAs may be determined based on the available computing resources so that the respective tasks can be performed to achieve their respective SLAs. The SLAs may be updated periodically based on various factors, such as increased workflow, modified design requirements, and/or for other reasons, and thus are elastic. 
     The techniques and systems described herein may be implemented in a number of ways. Example implementations are provided below with reference to the following figures. 
     Illustrative Environment 
       FIG. 1  is a schematic diagram of an illustrative computing environment  100  to use an elastic service level agreement (SLA) for metrics accuracy computation. The environment  100  may include a host  102  that includes servers  104  that perform computations using data  106 . The servers  104  may be configured in a distributed or non-distributed computing system, such as in a cloud service, a server farm, or other type of configuration, which may enable the servers to process large amounts of work (computations), and may enable scalability of the servers based on various constraints, such as capacity, cost, and other types of constraints. The data  106  may be continually, periodically, or randomly updated based on various events, such as a transaction with a user, sales, or other types of events. 
     The servers  104  may provide data to clients  108  via a network  110 . The clients  108  may include various types of different clients, which may be part of a same organization or independent from one another. Each client may request and/or require different types of data from the host  102 . For example, a first client  108 ( 1 ) may request sales data at a given service level agreement (SLA), while a second client  108 ( 2 ) may request sales data at a second SLA and another client  108 ( n ) may request the sales data at yet another SLA or at one of the other SLAs. Thus, each client may be able to specify or select an SLA that is appropriate for the client&#39;s needs. The SLA may be used to determine a cost of associated with use of the servers  104 , and thus a lower SLA may be associated with a lower cost to a client having the lower SLA. 
     The host  102  may manage the SLAs such that the SLAs are selected to enable the host, via the servers  104 , to process predefined jobs that, when processed, update metrics. For example, the host  102  may perform a particular job that includes computation of metrics from the data  106 . The computations may be repeated for different segments of time (time intervals) to refresh the metrics and maintain an SLA that defines an accuracy for the metrics. For example, a first segment of time may include a current day with a first SLA that is higher than a second SLA corresponding to a second segment of time that includes a current week, and so forth. In this example, the host  102  may assume that today&#39;s metric (the first segment) has a greater need for accuracy as defined by the higher SLA. The host  102  may determine or assign the first SLA, the second SLA, and any other SLAs such that the host  102  is capable of performing the predefined jobs according to the SLAs. The SLAs may be adjusted, and thus elastic, when the host  102  is unable to process the predefined jobs in accordance with the SLAs or for other reasons, such as because of changes in the available resources (servers, etc.) used by the host  102 . 
     The servers  104  may include one or more processor(s)  112  and computer-readable media  114 . The processor(s)  112  interact with the computer-readable media  114  to execute instructions and facilitate operation of the servers  104 . The computer-readable media  114  may include software programs or other executable modules that may be executed by the processor(s)  112 . Examples of such programs or modules include operating systems, applications, and so forth. The computer-readable media  114  may also be used to store data. 
     The computer-readable media  114  may include volatile memory (such as RAM), nonvolatile memory, removable memory, and/or non-removable memory, implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. Also, the processor(s)  112  may include onboard memory, which in some instances may be the computer-readable media  114 . Some examples of storage media that may be included in the computer-readable media  114  and/or processor(s)  112  include, but are not limited to, random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disk (CD-ROM), digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the server  104 . Any such computer-readable media may be part of the server  104 . 
     In accordance with one or more embodiments, the computer-readable media  114  may include a manager  116 . The manager  116  may interact with the data  106  to manage computation of the metrics in accordance with the elastic SLAs and predefined restrictions on use of computing resources and/or availability of the computing resources. The manager  116  may include a metrics module  118 , an SLA module  120 , and a scheduling module  122 , each described in turn. 
     The metrics module  118  may initiate jobs to process the data  106  to update and maintain metrics, such as order metrics, for the clients  108 . The metrics module  118  may define the metrics as having different inputs, different amounts of the data  106  (e.g., different time intervals of the data), or other variations. The metrics module  118  may also monitor a metrics accuracy of the metrics. For example, the metrics module  118  may track new events (e.g., orders, etc.) for one or more time intervals that, until a recomputation is performed, reduce a current accuracy of the metrics. 
     The SLA module  120  may determine an SLA for each of the metrics updated, maintained, or monitored by the metrics module  118 . The SLA may define a metrics accuracy threshold for the metrics that, when reached or predicted to be reached, triggers a recomputation of the metrics. The SLA module  120  may determine the SLAs so that the metrics may be computed using a predetermined amount of computational resources. For example, the SLA module  120  may create SLAs, and then determine whether the metrics can be maintained in accordance with the SLAs using the predetermined amount of computational resources. When the metrics cannot be maintained in accordance with the SLAs, then the SLA module  120  may adjust the SLAs automatically or with human input (e.g., alert administrator, etc.). 
     The scheduling module  122  may schedule the jobs to be performed to update the metrics in accordance with the SLAs using the computational resources. The scheduling module  122  may determine the availability of the computational resources to process the recomputation of the metrics. When the scheduling module  122  is unable to meet the SLA for a metric, the then the scheduling module  122  may transmit a message to an administrator and/or request an update to the SLAs by the SLA module  120  and/or request additional computational resources. In some embodiments, when the scheduling module  122  determines that the computational resources are underutilized, the scheduling module may transmit a message to the SLA module  120  to indicate that the SLAs may be increased or otherwise may report the utilization of the computational resources. 
     Illustrative Operations 
       FIGS. 2-4  describe illustrative processes to provide the elastic SLA. The processes are illustrated as a collection of blocks in a logical flow graph, which represent a sequence of operations that can be implemented in hardware, software, or a combination thereof. In the context of software, the blocks represent computer-executable instructions stored on one or more computer-readable storage media that, when executed by one or more processors, perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures, and the like that perform particular functions or implement particular abstract data types. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described blocks can be combined in any order and/or in parallel to implement the process. The processes are described with reference to the environment  100  and may be performed by the servers  104 . Of course, the processes may be performed in other similar and/or different environments. 
       FIG. 2  is a flow diagram of an illustrative process  200  to determine an elastic SLA for metrics based on available computational resources. 
     At  202 , the metrics module  118  may determine the metrics that are to be maintained by the servers  104  through the processing of jobs. A job may be a computation of a metric from the data  106 , and may be processed many times during a time interval. As time progresses, the job may be performed over and over to update the metrics. Thus, the job may be a predetermined set of computations, which may be executed or computed by the computing resources many times over a given time interval to update or maintain the metrics. 
     At  204 , the scheduling module  122  may determine an availability of computational resources that can be used to maintain the metrics determined at the operation  202 . The availability of computational resources may be a capacity of the computational resources based on physical constraints, monetary constraints, or other types of constraints on use of the computing resources. 
     At  206 , the SLA module  120  may assign an elastic SLA having an accuracy threshold for the metrics determined by the metrics module  118  at the operation  202 . For example, the SLA module  120  may select an SLA for metric that enables the metric to be maintained with the other metrics using the available computing resources determined by the operation  204 . In some embodiments, the SLA module  120  may determine the SLAs for the metrics using an iterative approach, thereby assigning an SLA to each metric and then determining the computational resources necessary to achieve the particular SLA for that metric. The SLA module  120  may adjust the SLAs when the SLAs cannot be achieved by the computational resources and/or underutilize the computational resources. 
     At  208 , the scheduling module  122  may verify that the metrics can be maintained in accordance with the assigned SLA based at least in part on the SLAs and the availability of the computational resources. For example, the scheduling module  122  may use the SLAs to assign a frequency of computation of the various jobs based on the SLAs and the availability of the computational resources. The scheduling module  122  may provide feedback to the SLA module  120  to indicate when the computational resources are over utilized or underutilized to enable the SLA module  120  to adjust the SLAs, thereby creating elastic SLAs. 
       FIG. 3  is a flow diagram of an illustrative process  300  to schedule a job to recompute the metrics based on an elastic SLA and to provide messaging when available computing resources cannot achieve the SLA. The elastic SLA may be determined using the process  200  described above. 
     At  302 , the SLA module  120  may identify the SLA having a threshold for one or more metrics. 
     At  304 , the metrics module  118  may monitor the metrics using the SLAs identified at the operation  302 . For example, the metrics module  118  may determine an estimated accuracy of the metrics in view of a metrics accuracy threshold defined by the SLA for the metrics. The monitoring may involve monitoring orders placed after a last computation of the metrics and/or may involve monitoring other data. 
     At  306 , the metrics module may determine whether the metrics accuracy of the metrics reaches or is predicted to reach the metrics accuracy threshold of the SLA. For example, in some instances the metrics may be scheduled for recomputation (e.g., processing a corresponding job) when the accuracy threshold is reached or exceeded. The metrics may also be scheduled for recomputation when the metrics accuracy threshold of the SLA is about to be reached (e.g., converging on SLA or predicted to reach SLA). In this situation a lower threshold or buffer maybe used to track a cutoff point which may trigger recomputation of the metrics via processing of a corresponding job. When the metrics accuracy of the metrics reaches or is predicted to reach the metrics accuracy threshold of the SLA at  306 , then the process may advance to the operation  308 , otherwise the monitoring may continue at the operation  304  via a loop. 
     At  308 , the scheduling module  122  may determine the availability of the scheduled computational resources. For example, the computational resources may include queues that may receive the jobs for processing, and may dispatch jobs to recompute the metrics to available computational resources or portions of the computational resources. The scheduling module  122  may also reallocate the computational resources using the queues to adjust processing power to focus on particular jobs, such as jobs having a higher priority than other jobs. At  310 , the scheduling module  122  may determine whether the computations resources are available to perform the job to recompute the metrics in accordance with the SLA. When the scheduling module  122  is able to identify available ones of the computational resources, then following the “yes” route from the decision operation  308 , the scheduling module  122  may schedule the job at  312  to recompute the metrics. The scheduling of the job may be based at least in part on the SLA and the availability of the computational resources. In some embodiments, the scheduling of the jobs to recompute the metrics may include a priority of the metrics. For example, if two metrics are both scheduled to be recomputed and await available computational resources, one of the jobs may have priority over the other job. The priority may be based on the SLA or on other factors. In some embodiments, the jobs may be performed (metrics recomputed) concurrently or in parallel. For example, when both jobs include recomputation of metrics that include a same time interval, the computations may be performed concurrently. When the available resources have availability to process two or more jobs at a same time (or overlapping time), then the jobs may be performed in parallel. 
     However, when the scheduling module  122  is not able to identify available ones of the computational resources, then following the “no” route from the decision operation  310 , the scheduling module  122  may transmit a message at  314  to an administrator indicating that the SLA cannot be maintained with the currently available resources. The administrator may adjust the SLA, add more computational resources, or take other actions. Processing may continue at  302  in some embodiments. 
     In some embodiments, a metric may not converge on the threshold, may not reach the SLA, or be predicted to reach the SLA as determined at the operation  306 . In these instances, the metric may not be updated and no job may be scheduled at the operation  312 . Thus, the process  300  may continually loop between operations  304  and  306  when a metric has reached a steady state and no longer needs recomputation because the accuracy does not change. This may occur for order metrics for a time interval in the past, such as metrics for orders that occurred between five and ten years in the past (or other older time intervals). 
       FIG. 4  is a flow diagram of another illustrative process  400  to schedule a job to recompute metrics based on an elastic SLA. The operations  402 - 408  may be similar or the same as the operations  302 - 308 , respectively. 
     At  410 , the scheduling module  122  may determine whether the computations resources are available to perform the job while maintaining the SLA for the metrics. When the scheduling module  122  is not able to identify available ones of the computational resources, then following the “no” route from the decision operation  408 , the scheduling module  122  may transmit a message to an administrator at  412  indicating that the SLA cannot be maintained for the metrics with the currently available computing resources. 
     At  414 , the SLA module  120  may determine whether the SLAs can be adjusted to accommodate recomputation of the metrics in light of the available computational resources. When the SLAs can be adjusted (following the “yes” route from the decision operation  412 ), then the SLA module  120  may adjust the SLA at  416  to reduce a frequency of the recomputation of the metrics in view of the availability of the computational resources. However, when the SLAs cannot be adjusted for various reasons (following the “no” route from the decision operation  414 ), then the process  400  may continue at a decision operation  418 . 
     At  418 , the scheduling module may determine whether additional computational resources may be added to enable maintaining the SLAs and scheduling the job to recompute the metrics. When additional computational resources are available and able to be obtained (e.g., due to budget constraints, business rules, etc.) (following the “yes” route from the decision operation  418 ), then the scheduling module  122  may obtain the additional resources at  420 . For example, the additional resources may be added in a scalable system at an increased cost. However, when additional computational resources are not available or not able to be obtained (e.g., due to cost constraints or other constraints) (following the “no” route from the decision operation  418 ), then the process may advance to the operation  404  and processing may continue as described above. 
     When the scheduling module  122  is able to identify available ones of the computational resources at the decision operation  410  (following the “yes” route from the decision operation  410 ), or following the operations  416  or  420 , then the scheduling module  122  may schedule the job at  422 . 
     Illustrative Order Processing Example 
     The following example is provided to highlight a possible use case to implement elastic SLAs in an order processing environment to maintain order metrics based on the elastic SLAs. Although the example discusses SLAs specific to a retail environment, the SLAs may be used in other environments in accordance with the processes described above. 
     An order transaction system may compute real-time or near real-time order metrics for one or more marketplaces, such as electronic marketplaces, brick-and-mortar marketplaces, or both. The order transaction system may take into consideration that orders processed by the system may change over time, such as by changing of a price, item quantity, and/or condition. Thus, the order transaction system may have to update order metrics periodically (i.e., recompute by processing a job) in accordance with a service level agreement (SLA) for the order metrics. 
     In some embodiments, the order metrics may be computed for various intervals of time, such as hourly, daily, weekly, monthly, annually, and/or for other intervals of time. Each interval of time may define a different job. Because the orders may change over time due to returns, corrections, exchanges, or for other reasons, each of these jobs (time periods) may be recomputed periodically to maintain an SLA corresponding to the computational job. 
     The computational resources may be fixed based on budgetary constraints, physical constraints, and/or other types of constraints. Thus, the SLAs may be tailored to enable processing the jobs within a capacity of the computational resources. 
     In accordance with various embodiments, the SLAs may be computed per time intervals. For example, in a first time interval, three events may occur in the order system: 1) 1 item, $7 total; 2) 2 items, $10 total; and 3) 1 item, $3 total. In this first time interval, the total price is $20 with 4 total items. In a second interval that includes the first interval plus additional time, the following events may occur: 4) 2 items, $8 total, and 5) 1 item, $6 total. Thus, the second interval may include a total price of $34 and 7 total items. 
     The metrics accuracy confidence level (MACL) may be defined by 
     
       
         
           
             
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     In the example order data above, at event time 2 (second interval), even if 5 events have occurred, the order system may only have the metrics computed for the first 3 metrics because the order system has not yet recomputed the order metrics. Thus, the MACL[OT1, OT2]=3/5=0.6. Price accuracy may be computed in the same way, and thus result in Price accuracy [OT1, OT2]=20/34=0.58. Items accuracy may be computed as Items accuracy [OT1, OT2]=4/7=0.57. 
     However, in a large scale system, hundreds, thousands, or even millions of evens may occur each hour or other time interval. The confidence level may converge to a value of one (confident level→1) when no new events occur. 
     An elastic SLA enables use of a variable and assignable accuracy per time interval. The elastic SLA may specify better accuracy for some intervals than other intervals. For example a most recent interval (e.g., last hour, etc.) may include a greater SLA than a second most recent interval (e.g., last two hours, etc.), and so forth. An administrator&#39;s priorities may change over time so the SLA is elastic and can be redefined to adjust distribution of the computational resources (e.g., computational power) in order to achieve the revised SLA. In addition, an amount of data may increase over time, which may result in adjustment of the SLAs and/or an expansion of the available computational resources. 
     CONCLUSION 
     Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as illustrative forms of implementing the claims.