Patent Publication Number: US-2020293008-A1

Title: Controlling tenant services based on tenant usage performance indicators

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     The present application is a continuation of and claims priority of U.S. patent application Ser. No. 15/609,262, filed May 31, 2017, the content of which is hereby incorporated by reference in its entirety. 
    
    
     BACKGROUND 
     Computing systems are currently in wide use. Some computing systems include service computing systems that host services. Such services can include, for instance, electronic mail services, calendar services, and services that provide productivity applications, such as work processing applications, spreadsheet applications, presentation applications, among others. Such computing systems can also include document sharing and management systems, on-line storage systems, enterprise resource management and customer relationship management applications, among a wide variety of others. 
     Each of these different types of services or applications are sometimes referred to as a workload. For instance, an electronic mail application may be referred to as a first workload, while a document sharing and management system may be referred to as a second workload. 
     Some such computing systems also host workloads for tenants, which can be organizations that each have a plurality of users. When hosting workloads for different tenants, it can be difficult to know which types of services should be provided to which types of tenants in order to enhance their usage of the hosted workloads. It can also be difficult to control those services so that they are provided to the appropriate tenants, at the appropriate times. 
     In some current computing systems, for instance, it can be difficult to assign a priority to different services, and corresponding tenants. This is because it can be difficult to identify whether a particular tenant wishes to obtain a workload, wishes to adopt a new workload or a new feature, or wishes to grow the tenancy of a workload or feature that the tenant already subscribes to. Some prior systems have attempted to obtain this information by conducting manual surveys with a particular tenant. However, this is often infeasible due to its cost and its questionable accuracy. This information can also quickly become stale. Similarly, even if it is accurate, this type of information simply conveys the current status of a tenant but does not give an indication as to the tenant&#39;s potential to use workloads or features, etc. 
     The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter. 
     SUMMARY 
     A control system controls tenant services to various tenants by obtaining tenant attributes for each tenant, with respect to a particular workload. A model is generated that models tenant usage performance for a set of best performing tenants. The model is then applied to a remainder of the tenants to obtain a metric indicative of a likely tenant capacity for incremental usage of the workload. The control system controls the services provided to the tenant based upon the likelihood of adoption metric. 
     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 of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of one example of a computing system architecture. 
         FIG. 2  is a block diagram showing one example of a tenant metric generator system, in more detail. 
         FIG. 3  is a block diagram showing one example of a control signal generator, in more detail. 
         FIGS. 4A and 4B  (collectively referred to herein as  FIG. 4 ) show a flow diagram illustrating one example of the operation of the architecture shown in  FIG. 1 , in controlling services provided to a tenant. 
         FIG. 5  is a block diagram showing one example of the architecture illustrated in  FIG. 1 , deployed in a cloud computing architecture. 
         FIGS. 6-8  show examples of mobile devices that can be used in the architectures shown in the previous figures. 
         FIG. 9  is a block diagram showing one example of a computing environment that can be used in the architectures shown in the previous figures. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  is a block diagram of one example of a computing system architecture  100 . Architecture  100  illustratively includes tenant computing systems  102 - 104  which can be connected to tenant monitoring and analysis computing system  106  as well to tenant service computing system  108  over network  110 . Network  110  can be any of a wide variety of different types of networks, such as wide area network, a local area network, a cellular communication network, a near field communication network, or any other of a wide variety of networks or combinations of networks. Before describing architecture  100  in more detail, a brief description of some of the items in architecture  100 , and their operation, will first be provided. 
     In the example shown in  FIG. 1 , tenant computing system  102  is shown generating user interfaces  112 - 114 , which may have user input mechanisms, for interaction by users  116 - 118 . Users  116 - 118  interact with the user input mechanisms on user interfaces  112 - 114  in order to control and manipulate tenant computing system  102 , and possibly some components of tenant service computing system  108 . Users  116 - 118  can interact directly with system  102  or through a network, such as network  110 . 
     In the example shown in  FIG. 1 , tenant computing system  102  can include one or more processors or servers  120 , data store  122 , workload functionality  124 , attribute generator logic  126 , and it can include a wide variety of other items  128 . Workload functionality  124  illustratively hosts one or more workloads (such as applications) that can be used to operate on, and interact with, data stored in data store  122 . Users  116 - 118  can interact with user interfaces  112 - 114  in order to perform workload functionality using workload functionality logic  124 . For example, where the workload is an electronic mail application, users  116 - 118  can interact with user interfaces  112 - 114  in order to perform e-mail functionality, such as send and receive e-mail messages, configure folders and filters, etc. 
       FIG. 1  also shows that, in one example, tenant computing system  104  generates user interfaces  130 - 132  (which may also have user input mechanisms) for interaction by users  134 - 136 . Users  134 - 136  illustratively interact (either directly or over a network) with the user input mechanisms on user interfaces  130 - 132  in order to control and manipulate computing system  104 , and perhaps some items of tenant service computing system  108 . In the example illustrated, tenant computing system  104  can be similar to tenant computing system  102 , and therefore it can host one or more applications for the users  134 - 136 . In the example shown in  FIG. 1 , it is assumed that tenant computing system  104  is similar to tenant computing system  102 , and therefore only tenant computing system  102  is described in more detail. This is done by way of example only. 
     Tenant service computing system  108  is shown generating user interfaces  140 - 142 , with user input mechanisms  144 - 146 , respectively, for interaction (either directly or over a network) by users  148 - 150 . In one example, users  148 - 150  interact with user input mechanisms  144 - 146  in order to control and manipulate tenant service computing system  108 . Tenant service computing system  108  can be used to perform services for tenants  102 - 104 . Thus, tenant service computing system  108  can include one or more processors or servers  152 , tenant service functionality logic  154 , users interface logic  156 , data store  158 , and it can include a wide variety of other items  160 . In one example, tenant service users  148 - 150  interact through mechanisms  144 - 146  with tenant service functionality logic  154  in order to perform services for the various tenants using tenant computing systems  102 - 104 . The services can include services which are used in order to increase tenant satisfaction with a particular hosted workload or feature, to increase tenant usage performance Increasing usage performance can include such things as increasing tenant adoption of a particular hosted workload or feature, increasing tenant engagement with a hosted workload or feature, or to otherwise enhance the experience of a particular tenant with respect to one or more hosted workloads or features. Tenant service users  148 - 150  may thus use computing system  108  to interact with tenants or users of tenants through tenant computing systems  102 - 104 . 
     Tenant monitoring and analysis computing system  106 , in the example shown in  FIG. 1 , illustratively includes one or more processors or servers  170 , data store  172 , tenant metric generator system  174 , control signal generator  176 , and it can include a wide variety of other items  178 . Tenant metric generator system  174  can include model building logic  180 , model application logic  182 , tenant priority generator logic  184 , and it can include a wide variety of other items  186 . Tenant monitoring and analysis computing system  106  generates a metric indicative of a likelihood that a particular tenant will adopt or increase its engagement or usage of a particular workload or feature. It also illustratively generates a metric indicative of what that means, in terms of raw capacity (e.g., the number of users or seats) for that particular tenant. It can generate these metrics across different workloads, for different tenants, and it can aggregate the metrics for different workloads, and for each tenant, in order to obtain one or more overall metrics corresponding to each tenant, indicative of an incremental capacity that the tenant has for the one or more workloads, and a likelihood that the tenant will reach that capacity. Control signal generator  176  illustratively generates one or more control signals that are provided to tenant service computing system  108 , based upon the metrics. The control signals can be used to identify and control various services that may be conducted for the various tenants. 
     In generating the metrics for each tenant, model building logic  180  illustratively receives tenant attributes for a variety of different tenants. Model building logic  180  then builds and trains a model that models the performance of a subset of those tenants (such as the best performing tenants in terms of workload or feature usage). Model application logic  182  then applies that model to the remaining tenants in order to identify the metrics indicative of how likely those tenants are to adopt or increase usage of a particular workload or feature, and tenant priority generator logic  184  illustratively generates a rank order of tenants based on the metrics generated for each tenant. 
       FIG. 2  is a block diagram showing one example of tenant metric generator system  174  in more detail.  FIG. 2  shows that model building logic  180  illustratively includes tenant attribute identifier logic  188 , size determination logic  190 , size bucketing logic  192 , top performer identifying logic  194 , model generator logic  196 , and it can include a wide variety of other items  198 .  FIG. 2  also shows that model generator logic  196  can output a likelihood to adopt (LTA) model  200 . 
       FIG. 2  shows that, in one example, model application logic  182  can include bucket/tenant/workload selection logic  202 , maximum active rate prediction logic  204 , headroom identifier logic  206 , current active rate identifier logic  208 , and it can include a wide variety of other items  210 . Tenant priority generator logic  184 , in the example shown in  FIG. 2 , can include entitlement size identifier logic  212 , incremental workload opportunity identifier logic  214 , total tenant incremental opportunity generator logic  216 , tenant level workload sequencing logic  218 , tenant ranking logic  220 , and it can include other items  222 . Before describing the overall operation of architecture  100 , and tenant metric generator system  174 , in more detail, a brief description of some of the items in system  174 , and their operation, will first be described. 
     Tenant attribute identifier logic  188  illustratively obtains or otherwise identifies various tenant attributes. These can include demographic or other attributes. Some examples include the size of the tenant, the deployment rate at which a particular workload is deployed, an active rate indicative of how many users are active on the deployed workload, etc. Some other attributes are discussed below with respect to  FIG. 4 . 
     Size determination logic  190  determines a potential size of the tenant and generates a corresponding size indicator. The size may be quantified in a variety of different ways, such as in the potential number of seats that may be used for the workload, or the size of the tenant in terms of employees in certain roles, etc. Size bucketing logic  192  illustratively divides all of the tenants for which attributes are being obtained, into size buckets. The size buckets may be predetermined size buckets, in which tenants that have a size indicator falling within a range corresponding to a bucket, are placed in that bucket. The size buckets may also be dynamically determined based on one or more statistics indicative of the various number of tenants that may fall into different buckets. Size bucketing logic  192  divides the tenants being considered into the different size buckets, based upon their size indicator. 
     Top performer identifying logic  194  illustratively identifies a set of top performing tenants in each size bucket. The top performing tenants may be identified based on any of a wide variety of different usage performance criteria. For instance, they may be identified as top performers based upon the proportionate share of users (indicated based on the size indicator) that have adopted and are meaningfully engaged with a particular workload or feature. They may be identified as top performers in a wide variety of other ways as well. In addition, the “top” performers may be defined as those having usage performance criteria in a particular percentile (such as the first decile) of the tenants in each size bucket, or they may be identified in a wide variety of other ways as well. The determination as to which tenant is a “top” performer may be determined based on whether the usage performance criteria exceed predefined thresholds, or the thresholds may be determined dynamically. All of these examples and others are contemplated herein. 
     Once the top performers are identified in each size bucket, then model generator logic  196  illustratively generates a model that models (or predicts) the usage performance of the top performers, based upon the attributes obtained or identified for each of those tenants. The model may be a regression model, or another model that is trained to predict the usage performance of the top performing tenants, based upon those attributes. 
     Model application logic  182  then applies that model to the remaining tenants in each bucket. Thus, bucket/tenant/workload selection logic  202  illustratively selects a size bucket, and selects a tenant to be analyzed, from that size bucket. The selected tenant is illustratively not one of the top performing tenants identified in that size bucket (as those tenants were the ones used to build and train the model). Instead, it is illustratively one of the remaining tenants in that size bucket. Logic  202  also illustratively selects a particular workload for which the analysis is to be performed. For instance, a given tenant may be analyzed based on a number of different workloads, or features. Thus, logic  202  selects one of those workloads for analysis, for this particular tenant. 
     Maximum active rate prediction logic  204  then illustratively predicts a maximum active rate for this tenant, for the given workload. For instance, assume that the tenant has purchased one hundred seat licenses for the workload. Based upon application of the model  200 , the predicted maximum active percent for this client, for the selected workload, may be, for instance, 92%. That is, the model indicates that, given the attributes of the selected tenant, it is likely that 92% of the eligible users will adopt and engage with the workload being analyzed. Thus, the predicted maximum active rate for this tenant, for this workload, is 0.92. 
     Current active rate identifier logic  208  illustratively identifies the current active rate for this particular tenant, for the selected workload. For instance, of the one hundred licenses that have been purchased, it may be that thirty-two of those are actively being used. Thus, the current active rate, in such an example, is 0.32. 
     Headroom identifier logic  206  illustratively identifies the incremental rate for which the usage performance of this particular tenant can be improved, in the selected workload. In one example, this is done by subtracting the current active rate identifier identified by logic  208  from the maximum active rate predicted by logic  204 . Continuing with the example discussed above, the headroom generated by headroom identifier logic  206  would thus be 0.92-0.32=0.60. 
     Once model application logic  182  has applied model  200  to a tenant, for each of its workloads, it illustratively selects a next tenant and performs the same analysis for the newly selected tenant, for the relevant workloads. This continues until each of the remaining tenants (those tenants other than the top performers) in the selected bucket have been analyzed. The process can then be repeated for each of the remaining tenants in another size bucket. Also, it will be noted that processing can be performed on tenants in different orders. For instance, the system need not necessarily process every tenant in a given size bucket before it moves on to processing other tenants. Similarly, it need not process all workloads for a particular tenant before it moves on to processing other tenants and their workloads. In addition, some of the processing can be performed in parallel so that multiple tenants are processed at the same time. All of these and other processing architectures are contemplated herein. 
     Tenant priority generator logic  184  then prioritizes the tenants based upon the metrics generated by applying the model to those tenants. In doing so, it identifies a raw incremental opportunity or incremental growth potential for each of the tenants. Thus, it takes the headroom and multiplies it by a number of eligible seats corresponding to the tenant to identify the likely number of seats that could be added for (e.g., that could adopt or increase usage of) a particular workload or feature. Thus, entitlement size identifier logic  212  identifies the total number of seats, for a particular workload, that a tenant is entitled to. This can be identified in variety of different ways. For instance, it may be identified by considering all licenses that are currently active, as well as a portion of those that are inactive. It may be identified in a wide variety of other ways as well. Incremental workload opportunity identifier logic  214  then combines the headroom generated by headroom identifier logic  206 , for this tenant and this workload, with the entitlement size identified by logic  212 . Logic  214  thus applies the headroom to the entitlement size identified by logic  212  to obtain a total number of incremental seats that are likely to be added (an incremental increase of usage performance, such as a number of seats that are likely to adopt a workload or feature or increase usage or engagement with the workload or feature), for this particular client, and this particular workload. 
     Total tenant incremental opportunity generator logic  216  illustratively combines the incremental workload opportunities identified by logic  214 , for each client, across all workloads. This gives an overall workload opportunity identifier for this particular tenant. 
     Tenant level workload sequencing logic  218  illustratively breaks down the incremental opportunity identifier, per workload, for each tenant. It can output a sequence indicator identifying which workload should be concentrated on first, for this particular tenant, based upon the incremental opportunity identified for each workload, for a given tenant. 
     Tenant ranking logic  220  then illustratively generates a ranked list of tenants, based upon the various metrics identified. The ranked list of tenants can be ranked in a variety of different ways. For instance, the tenants may be ranked based on the total tenant incremental opportunity identifier generated by logic  216 , or they can be ranked based on a particular incremental opportunity identifier for a particular workload. They can be ranked in a wide variety of other ways as well. 
     The total tenant incremental opportunity identifier generated by logic  216 , the incremental workload opportunity identifier identified by logic  214 , and the tenant level workload sequence identifier generated by logic  218  can all be output as metrics by system  174 . It will be appreciated, though, that the headroom identifier generated by logic  206 , the maximum active rate prediction identifier output by logic  204 , or any of the other metrics or criteria or attributes identified, obtained or generated by system  174  can be output as well. This information is output to control signal generator  176 , which uses it to generate control signals that can be provided to tenant service computing system  108  so that tenant service users  148 - 150  can perform desired services for the tenant (or so they can be performed automatically), based upon the control signals. 
       FIG. 3  shows a block diagram of one example of control signal generator  176 , in more detail. In the example shown in  FIG. 3 , control signal generator  176  illustratively includes service engagement identifier logic  240 , service engagement controller  242 , and it can include a wide variety of other items  244 . Service engagement identifier logic  240  illustratively identifies the particular services that may be conducted for a particular tenant, based upon the inputs received from tenant metric generator system  174 . For instance, given those signals, it may be that a certain feature adoption program should be run for a particular tenant. The service engagement identifier generated by logic  240  can be provided to service engagement controlled  242  which generates control signals to control portions of tenant service computing system  108  to provide those services. For instance, the control signals output by controller  242  may be interface control signals that control user interface logic  156  in order to surface the ranked list of tenants, the total incremental opportunity identifiers identified for each tenant, the incremental workload opportunity identifiers identified for each workload, and for each tenant, the tenant level workload sequencing identifiers generated by logic  218 , or other items. The control signals may control tenant service functionality logic  154  to perform certain services, automatically, for the tenant, or to provide a notification to tenant service users  148 - 150  that such services should be provided, or have been provided. The control signals can be used to control a wide variety of other things as well. 
       FIGS. 4A and 4B  (collectively referred to herein as  FIG. 4 ) show a flow diagram illustrating one example of the operation of architecture  100  in generating the various tenant metrics and control signals.  FIGS. 1-4  will now be described in conjunction with one another. 
     Tenant attribute identifier  188  first obtains attributes for one or more tenants. This is indicated by block  250  in the flow diagram of  FIG. 4 . In one example, attribute generator logic  126 , on each tenant computing system  102 - 104 , collects and provides those attributes to tenant attribute identifier logic  188 . The attributes can be from inactive tenants (which may also be referred to as cold start tenants) where attribute generator logic  126  may not exist or may not be providing many attributes. This is indicated by block  252 . The attributes can be obtained from active clients, that are active on workloads that may be monitored and analyzed using computing system  106 . In that case, the attributes may be provided by logic  126  on the tenant computing system, or in other ways. Receiving attributes from active tenants is indicated by block  254 . The attributes can include a wide variety of different types of attributes. For instance, they can include a workload identifier  256  that identifies particular workloads being used by the tenant. They can include tenant size indicators  258  indicative of tenant size (such as seat size, license size, etc.). They can include a number of seats purchased attribute  260  that identifies a particular number of seats purchased for one or more different workloads. They can include a deployment rate indicator  262  indicative of how many of the purchased seats have been deployed. They can include an active rate indicator  264  indicative of the rate at which the deployed seats are active. They can include a days of engagement attribute  266  indicative of how many days a particular user in the tenant has been engaged with a workload or feature. They can include a wide variety of other demographic attributes, or other attributes as well, and this is indicated by block  268 . 
     Based upon the attributes obtained, size determination logic  190  generates a size indicator indicative of a size of the tenant. This is indicated by block  270 . The size can be measured in a variety of different ways, such as being based on the number of seats purchased attribute  260 , the tenant size attribute  258  (which may be indicative of employee size, sales volume, etc.). 
     Size bucketing logic  192  then divides the tenants under analysis into buckets or groups based on their size. This is indicated by block  272 . 
     Top performer identifying logic  194  then identifies the top performing tenants, in each bucket, based on a particular performance metric. This is indicated by block  274 . The top performing tenants may be identified as a predefined portion of the tenants (such as the top decile)  276 . It may be defined based on a wide variety of other criteria as well. The usage performance criteria can also vary widely and include the active rate indicator  264 , the active rate combined for all workloads as indicated by block  278 , or it can be a wide variety of other usage performance criteria as well, and this is indicated by block  280 . 
     Logic  180  then controls model generator logic  196  to generate and train a model that predicts the usage performance criteria of the top performing tenants, in each bucket, based upon the tenant attributes. This is indicated by block  282 . The model may be a regression model  284 , and it may be trained to predict performance for each tenant, across all workloads as indicated by block  286 , or on a per-workload basis, as indicated by block  288 . The model can be built and trained in other ways as well, and this is indicated by block  290 . 
     Model application logic  182  then applies the model to the remaining tenants (other than the top performing tenants) in each bucket to generate a predicted achievable performance metric for each tenant. This is indicated by block  292 . In doing so, bucket/tenant/workload selection logic  202  selects a size bucket as indicated by block  294 , a tenant as indicated by block  296  and a workload as indicated by block  298 . It then applies the model to the selected tenant, for the selected workload, to generate a predicted achievable performance metric. It can apply the model, across all workloads, or it can apply the model on a per-workload basis. This is indicated by blocks  298  and  300 . 
     In one example, the predicted achievable performance metric indicates a likely usage performance that may be achieved for this tenant. For instance, it may be a predicted maximum active rate metric indicative of a rate of adoption or usage of a particular product or feature within the tenant. The predicted maximum active rate metric is indicated by block  302 . The predicted achievable performance metric can be any of a wide variety of other metrics or combinations of metrics as well, and this is indicated by block  304 . 
     In one example, headroom identifier logic  206  identifies a headroom indicator indicative of an incremental opportunity corresponding to each of the remaining tenants in each bucket, for each workload, based upon the predicted achievable performance metric. This is indicated by block  306 . In one example, logic  206  generates the headroom indicator by taking the predicted achievable performance metric and subtracting the current value of the same performance metric. For instance, again continuing with the example discussed above, assume that a tenant has paid for one hundred licenses. Assume that the predicted achievable performance metric corresponds to the predicted percent (or rate) of the available licenses that will be active, given the model. Assume that is 92% (or 0.92). Assume further that the current active rate identifier logic  208  has identified a currently active rate (the percent or rate of users of available licenses that are currently active), and that is 32% (or 0.32). Thus, the headroom (or the incremental opportunity available for this particular tenant and workload) is equal to 0.92−0.32=0.60. Generating the headroom indicator using logic  206 , in this way, is indicated by block  308 . The headroom indicator can be generated for each remaining tenant in each size bucket, and for each workload. An indicator of an incremental opportunity corresponding to each remaining tenant can be generated in other ways as well, and this is indicated by block  310 . 
     Tenant priority generator logic  184  then generates a priority indicator for each of the remaining tenants in each size bucket. Entitlement size identifier logic  212  first identifies a total entitlement size (such as the total possible number of eligible seats) for each remaining tenant, in each bucket, for each workload. This is indicated by block  312 . The total possible seat size may be identified in a variety of different ways. For instance, it may be the total number of licenses purchased for this workload, by this tenant. It may be determined in other ways as well. 
     Incremental workload opportunity identifier logic  214  then applies the headroom indicator to the total entitlement size for each remaining tenant, in each bucket, for each workload, in order to obtain an incremental workload opportunity indicator indicative of an incremental opportunity for each workload and each tenant. This is indicated by block  314 . For instance, the incremental workload opportunity indicator for a particular workload and a particular tenant may be obtained by taking the headroom indicator and multiplying it by the total entitlement size for this workload, and this tenant. Assume, for example, that the total number of seats purchased by a particular tenant, for a given workload, is 1,000 seats. Assume that the headroom indicator is 0.60. Then, the incremental workload opportunity indicator would be 0.60×1000=600. This gives an indication as to the number of seats which this particular tenant will likely add, given its headroom for this workload and given the total seats that the tenant is entitled to, for this workload. Obtaining the incremental workload opportunity indicator in this way is indicated by block  316 . It can be obtained in other ways as well, and this indicated by block  318 . 
     Tenant level workload sequencing logic  218  then generates a per-tenant workload sequence based on the incremental workload opportunity indicated for each workload. This is indicated by block  320 . By way of example assume that the incremental workload opportunity indicator for a first workload for a given tenant is 600. This indicates that the incremental number of seats that may adopt or increase usage of a feature or product is 600, for this tenant and this workload. Assume that the same indicator for the same tenant but a second workload is 1,200. This would mean that the incremental opportunity for adding seats for the second workload is 1,200. Thus, tenant level workload sequencing logic  218  generates a sequence indicating that the incremental opportunity for the first workload for this tenant is less than that for the second workload. This may provide the tenant service users  148 - 150  additional information in deciding how to approach various tenants. It may also cause control signal generator  176  to generate different control signals so that different tenant service functionality is performed by logic  154 , for the tenants. 
     Total tenant incremental opportunity generator logic  216  then combines the incremental workload opportunity indicators to obtain an overall incremental opportunity indicator for each tenant. For instance, if a particular tenant is being analyzed with respect to five different workloads. Then the incremental workload opportunity indicator for each of those five workloads, for that tenant, may be combined to obtain an overall incremental opportunity for the tenant. By way of example, assume that the incremental opportunity for each of the five workloads is 600, 1,000, 400, 800 and 200, respectively. Then those incremental workload opportunity indicators may be combined (such as by adding them) to obtain a total incremental opportunity indicator of 3,000, which indicates the total number of seats, for this tenant, that may adopt or increase usage of a workload or feature. Combining the incremental workload opportunity indicators to obtain an overall incremental opportunity indicator for each tenant is indicated by block  322  in the flow diagram of  FIG. 4 . 
     Tenant ranking logic  220  then illustratively generates a ranked list of tenants, based upon one or more of the incremental opportunity indicators. This is indicated by block  324 . For instance, it can be based on the overall incremental opportunity indicator as indicated by block  326 , or it can be based on other information as indicated by block  328 . 
     Control signal generator  176  receives the various information generated by tenant metric generator system  174 . Based on these signals, service engagement identifier logic  240  identifies tenant services that may be used to better engage tenants, based upon the ranked list. This is indicated by block  330 . By way of example, it may be that some tenant services that are used to automatically upgrade tenants, or that are used to offer upgrades to tenants, or that are used to train or otherwise perform services for a tenant, may be identified by service engagement identifier logic  240 . These are only examples. 
     Service engagement controller  242  then generates one or more control signals to control the service engagement identified by service engagement identifier logic  240 . This is indicated by block  332 . By way of example, the control signals may be provided to user interface logic  156  to surface the rank ordered list of tenants. This is indicated by block  334 . They may be provided to user interface logic  156  to surface the various metrics generated by system  174  either in addition to, or instead of, the rank ordered list. The metrics may include, for instance, the overall incremental opportunity and the incremental workload opportunity indicators. This is indicated by block  336 . They may include the maximum active rate indicator predicted by logic  204 . They may include a wide variety of other metrics as well. 
     In response to receiving the control signals, tenant service computing system  108  can automatically identify and perform the services, based upon the control signals. This is indicated by block  338 . The control signals can be generated to control the service engagement in a wide variety of other ways as well, and this is indicated by block  340 . 
     It can thus be seen that the present discussion provides a description that significantly enhances a computing system architecture. It automatically obtains tenant attributes and builds and trains a model based upon the highest performing tenants. The model is then applied to the remaining tenants to generate metrics indicative of likely adoption and usage of workloads or features. The metrics can be used to automatically generate control signals to control another computing system (controllable elements) based on the metrics. 
     It will be noted that the above discussion has described a variety of different systems, components and/or logic. It will be appreciated that such systems, components and/or logic can be comprised of hardware items (such as processors and associated memory, or other processing components, some of which are described below) that perform the functions associated with those systems, components and/or logic. In addition, the systems, components and/or logic can be comprised of software that is loaded into a memory and is subsequently executed by a processor or server, or other computing component, as described below. The systems, components and/or logic can also be comprised of different combinations of hardware, software, firmware, etc., some examples of which are described below. These are only some examples of different structures that can be used to form the systems, components and/or logic described above. Other structures can be used as well. 
     The present discussion has mentioned processors and servers. In one embodiment, the processors and servers include computer processors with associated memory and timing circuitry, not separately shown. They are functional parts of the systems or devices to which they belong and are activated by, and facilitate the functionality of the other components or items in those systems. 
     Also, a number of user interface displays have been discussed. They can take a wide variety of different forms and can have a wide variety of different user actuatable input mechanisms disposed thereon. For instance, the user actuatable input mechanisms can be text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. They can also be actuated in a wide variety of different ways. For instance, they can be actuated using a point and click device (such as a track ball or mouse). They can be actuated using hardware buttons, switches, a joystick or keyboard, thumb switches or thumb pads, etc. They can also be actuated using a virtual keyboard or other virtual actuators. In addition, where the screen on which they are displayed is a touch sensitive screen, they can be actuated using touch gestures. Also, where the device that displays them has speech recognition components, they can be actuated using speech commands. 
     A number of data stores have also been discussed. It will be noted they can each be broken into multiple data stores. All can be local to the systems accessing them, all can be remote, or some can be local while others are remote. All of these configurations are contemplated herein. 
     Also, the figures show a number of blocks with functionality ascribed to each block. It will be noted that fewer blocks can be used so the functionality is performed by fewer components. Also, more blocks can be used with the functionality distributed among more components. 
       FIG. 5  is a block diagram of architecture  100 , shown in  FIG. 1 , except that its elements are disposed in a cloud computing architecture  500 . Cloud computing provides computation, software, data access, and storage services that do not require end-user knowledge of the physical location or configuration of the system that delivers the services. In various embodiments, cloud computing delivers the services over a wide area network, such as the internet, using appropriate protocols. For instance, cloud computing providers deliver applications over a wide area network and they can be accessed through a web browser or any other computing component. Software or components of architecture  100  as well as the corresponding data, can be stored on servers at a remote location. The computing resources in a cloud computing environment can be consolidated at a remote data center location or they can be dispersed. Cloud computing infrastructures can deliver services through shared data centers, even though they appear as a single point of access for the user. Thus, the components and functions described herein can be provided from a service provider at a remote location using a cloud computing architecture. Alternatively, they can be provided from a conventional server, or they can be installed on client devices directly, or in other ways. 
     The description is intended to include both public cloud computing and private cloud computing. Cloud computing (both public and private) provides substantially seamless pooling of resources, as well as a reduced need to manage and configure underlying hardware infrastructure. 
     A public cloud is managed by a vendor and typically supports multiple consumers using the same infrastructure. Also, a public cloud, as opposed to a private cloud, can free up the end users from managing the hardware. A private cloud may be managed by the organization itself and the infrastructure is typically not shared with other organizations. The organization still maintains the hardware to some extent, such as installations and repairs, etc. 
     In the example shown in  FIG. 5 , some items are similar to those shown in  FIG. 1  and they are similarly numbered.  FIG. 5  specifically shows that computing system  102  can be located in cloud  502  (which can be public, private, or a combination where portions are public while others are private). Therefore, users  116 ,  118 ,  134 ,  136 ,  148 ,  150  use user devices  504 ,  506 ,  508 ,  510 ,  512  and  514  to access those systems through cloud  502 . 
       FIG. 5  also depicts another example of a cloud architecture.  FIG. 5  shows that it is also contemplated that some elements of architecture  100  can be disposed in cloud  502  while others are not. By way of example, data stores  122 ,  158  and  172  can be disposed outside of cloud  502 , and accessed through cloud  502 . In another example, computing system  106  (or other items) can be outside of cloud  502 . Regardless of where they are located, they can be accessed directly by device  504 - 514 , through a network (either a wide area network or a local area network), they can be hosted at a remote site by a service, or they can be provided as a service through a cloud or accessed by a connection service that resides in the cloud. All of these architectures are contemplated herein. 
     It will also be noted that architecture  100 , or portions of it, can be disposed on a wide variety of different devices. Some of those devices include servers, desktop computers, laptop computers, tablet computers, or other mobile devices, such as palm top computers, cell phones, smart phones, multimedia players, personal digital assistants, etc. 
       FIG. 6  is a simplified block diagram of one illustrative example of a handheld or mobile computing device that can be used as a user&#39;s or client&#39;s hand held device  16 , in which the present system (or parts of it) can be deployed.  FIGS. 7-8  are examples of handheld or mobile devices. 
       FIG. 6  provides a general block diagram of the components of a client device  16  that can run components of architecture  100  or that interacts with architecture  100 , or both. In the device  16 , a communications link  13  is provided that allows the handheld device to communicate with other computing devices and under some embodiments provides a channel for receiving information automatically, such as by scanning. Examples of communications link  13  include an infrared port, a serial/USB port, a cable network port such as an Ethernet port, and a wireless network port allowing communication though one or more communication protocols including General Packet Radio Service (GPRS), LTE, HSPA, HSPA+ and other 3G and 4G radio protocols, 1×rtt, and Short Message Service, which are wireless services used to provide cellular access to a network, as well as Wi-Fi protocols, and Bluetooth protocol, which provide local wireless connections to networks. 
     In other examples, applications or systems are received on a removable Secure Digital (SD) card that is connected to a SD card interface  15 . SD card interface  15  and communication links  13  communicate with a processor  17  (which can also embody processors or servers from other FIGS.) along a bus  19  that is also connected to memory  21  and input/output (I/O) components  23 , as well as clock  25  and location system  27 . 
     I/O components  23 , in one embodiment, are provided to facilitate input and output operations. I/O components  23  for various embodiments of the device  16  can include input components such as buttons, touch sensors, multi-touch sensors, optical or video sensors, voice sensors, touch screens, proximity sensors, microphones, tilt sensors, and gravity switches and output components such as a display device, a speaker, and or a printer port. Other I/O components  23  can be used as well. 
     Clock  25  illustratively comprises a real time clock component that outputs a time and date. It can also, illustratively, provide timing functions for processor  17 . 
     Location system  27  illustratively includes a component that outputs a current geographical location of device  16 . This can include, for instance, a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. It can also include, for example, mapping software or navigation software that generates desired maps, navigation routes and other geographic functions. 
     Memory  21  stores operating system  29 , network settings  31 , applications  33 , application configuration settings  35 , data store  37 , communication drivers  39 , and communication configuration settings  41 . Memory  21  can include all types of tangible volatile and non-volatile computer-readable memory devices. It can also include computer storage media (described below). Memory  21  stores computer readable instructions that, when executed by processor  17 , cause the processor to perform computer-implemented steps or functions according to the instructions. Similarly, device  16  can have a client system  24  which can run various applications or embody parts or all of architecture  100 . Processor  17  can be activated by other components to facilitate their functionality as well. 
     Examples of the network settings  31  include things such as proxy information, Internet connection information, and mappings. Application configuration settings  35  include settings that tailor the application for a specific enterprise or user. Communication configuration settings  41  provide parameters for communicating with other computers and include items such as GPRS parameters, SMS parameters, connection user names and passwords. 
     Applications  33  can be applications that have previously been stored on the device  16  or applications that are installed during use, although these can be part of operating system  29 , or hosted external to device  16 , as well. 
       FIG. 7  shows one example in which device  16  is a tablet computer  600 . In  FIG. 7 , computer  600  is shown with user interface display screen  602 . Screen  602  can be a touch screen (so touch gestures from a user&#39;s finger can be used to interact with the application) or a pen-enabled interface that receives inputs from a pen or stylus. It can also use an on-screen virtual keyboard. Of course, it might also be attached to a keyboard or other user input device through a suitable attachment mechanism, such as a wireless link or USB port, for instance. Computer  600  can also illustratively receive voice inputs as well. 
       FIG. 8  shows that the device can be a smart phone  71 . Smart phone  71  has a touch sensitive display  73  that displays icons or tiles or other user input mechanisms  75 . Mechanisms  75  can be used by a user to run applications, make calls, perform data transfer operations, etc. In general, smart phone  71  is built on a mobile operating system and offers more advanced computing capability and connectivity than a feature phone. 
     Note that other forms of the devices  16  are possible. 
       FIG. 9  is one example of a computing environment in which architecture  100 , or parts of it, (for example) can be deployed. With reference to  FIG. 9 , an example system for implementing some embodiments includes a general-purpose computing device in the form of a computer  810 . Components of computer  810  may include, but are not limited to, a processing unit  820  (which can comprise processors or servers from previous FIGS.), a system memory  830 , and a system bus  821  that couples various system components including the system memory to the processing unit  820 . The system bus  821  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus. Memory and programs described with respect to  FIG. 1  can be deployed in corresponding portions of  FIG. 9 . 
     Computer  810  typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer  810  and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media is different from, and does not include, a modulated data signal or carrier wave. It includes hardware storage media including both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk 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 computer  810 . Communication media typically embodies computer readable instructions, data structures, program modules or other data in a transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer readable media. 
     The system memory  830  includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM)  831  and random access memory (RAM)  832 . A basic input/output system  833  (BIOS), containing the basic routines that help to transfer information between elements within computer  810 , such as during start-up, is typically stored in ROM  831 . RAM  832  typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit  820 . By way of example, and not limitation,  FIG. 9  illustrates operating system  834 , application programs  835 , other program modules  836 , and program data  837 . 
     The computer  810  may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,  FIG. 9  illustrates a hard disk drive  841  that reads from or writes to non-removable, nonvolatile magnetic media, and an optical disk drive  855  that reads from or writes to a removable, nonvolatile optical disk  856  such as a CD ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The hard disk drive  841  is typically connected to the system bus  821  through a non-removable memory interface such as interface  840 , and optical disk drive  855  are typically connected to the system bus  821  by a removable memory interface, such as interface  850 . 
     Alternatively, or in addition, the functionality described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Program-specific Integrated Circuits (ASICs), Program-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc. 
     The drives and their associated computer storage media discussed above and illustrated in  FIG. 9 , provide storage of computer readable instructions, data structures, program modules and other data for the computer  810 . In  FIG. 9 , for example, hard disk drive  841  is illustrated as storing operating system  844 , application programs  845 , other program modules  846 , and program data  847 . Note that these components can either be the same as or different from operating system  834 , application programs  835 , other program modules  836 , and program data  837 . Operating system  844 , application programs  845 , other program modules  846 , and program data  847  are given different numbers here to illustrate that, at a minimum, they are different copies. 
     A user may enter commands and information into the computer  810  through input devices such as a keyboard  862 , a microphone  863 , and a pointing device  861 , such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  820  through a user input interface  860  that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). A visual display  891  or other type of display device is also connected to the system bus  821  via an interface, such as a video interface  890 . In addition to the monitor, computers may also include other peripheral output devices such as speakers  897  and printer  896 , which may be connected through an output peripheral interface  895 . 
     The computer  810  is operated in a networked environment using logical connections to one or more remote computers, such as a remote computer  880 . The remote computer  880  may be a personal computer, a hand-held device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer  810 . The logical connections depicted in  FIG. 9  include a local area network (LAN)  871  and a wide area network (WAN)  873 , but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. 
     When used in a LAN networking environment, the computer  810  is connected to the LAN  871  through a network interface or adapter  870 . When used in a WAN networking environment, the computer  810  typically includes a modem  872  or other means for establishing communications over the WAN  873 , such as the Internet. The modem  872 , which may be internal or external, may be connected to the system bus  821  via the user input interface  860 , or other appropriate mechanism. In a networked environment, program modules depicted relative to the computer  810 , or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,  FIG. 9  illustrates remote application programs  885  as residing on remote computer  880 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used. 
     It should also be noted that the different embodiments described herein can be combined in different ways. That is, parts of one or more embodiments can be combined with parts of one or more other embodiments. All of this is contemplated herein. 
     Example 1 is a computing system, comprising: 
     model building logic that receives a plurality of sets of tenant attributes, each set being indicative of attributes corresponding to a tenant computing system, and trains a model to predict usage performance relative to a workload, indicated by a usage performance indicator, of a first subset of the plurality of tenants, based on the sets of attributes corresponding to the first subset of the plurality of tenants; 
     model application logic that applies the model to a second subset of the plurality of tenants, different from the first subset of tenants, to obtain an incremental usage performance indicator corresponding to each tenant in the second subset of tenants, the usage performance indicator being indicative of a likely incremental increase in usage of the workload for the corresponding tenant in the second subset of tenants; and 
     a control signal generator that generates a control signal to control a controllable logic element based on at least one of the incremental usage performance indicators. 
     Example 2 is the computing system of any or all previous examples and further comprising: 
     tenant priority generator logic configured to generate a ranked list of tenants, ranking the tenants in the second subset of tenants based at least in part on the incremental usage performance indicator corresponding to each of the tenants in the second subset of tenants. 
     Example 3 is the computing system of any or all previous examples wherein the tenant priority generator logic comprises: 
     entitlement size identifier logic configured to identify an entitlement size indicator, indicative of a number of users that are entitled to use the workload, for each tenant in the second subset of tenants; and 
     an incremental workload opportunity identifier configured to, for each tenant in the second subset of tenants, identify an incremental workload opportunity indicator indicative of a size of increase of usage of the workload likely achievable for the tenant, based on the entitlement size indicator for the tenant and the usage performance indicator corresponding to the tenant. 
     Example 4 is the computing system of any or all previous examples wherein the model application logic is configured to generate a usage performance indicator for each tenant in the second subset of tenants for each of a plurality of different workloads. 
     Example 5 is the computing system of any or all previous examples wherein the entitlement size identifier logic is configured to identify an entitlement size indicator for each of the tenants in the second subset of tenants, for each of a plurality of different workloads. 
     Example 6 is the computing system of any or all previous examples wherein the incremental workload opportunity identifier is configured to, for each tenant in the second subset of tenants, identify an incremental workload opportunity indicator indicative of a size of increase of usage of each of the plurality of workloads likely achievable for the tenant, based on the entitlement size indicators for the tenant and the usage performance indicators corresponding to the tenant and each of the workloads. 
     Example 7 is the computing system of any or all previous examples wherein the tenant priority generator logic comprises: 
     total tenant incremental opportunity generator logic configured to generate a total tenant incremental opportunity indicator, for each tenant in the second subset of tenants, indicative of a combination of the incremental workload opportunity indicators for the plurality of different workloads for each tenant. 
     Example 8 is the computing system of any or all previous examples wherein the tenant priority generator logic comprises: 
     tenant level workload sequencing logic configured to generate a workload sequencing indicator, for each tenant in the second subset of tenants, based on the incremental workload opportunity indicators for the tenant. 
     Example 9 is the computing system of any or all previous examples wherein the model application logic comprises: 
     maximum active rate prediction logic configured to apply the model to the tenants in the second subset of tenants to obtain, as the incremental usage performance indicator, a likely maximum usage rate indicator indicative of a likely maximum rate of usage of the workload. 
     Example 10 is the computing system of any or all previous examples wherein the model application logic comprises: 
     headroom identifier logic that identifies a headroom indicator indicative of a difference between the likely maximum rate indicator and a current rate indicator indicative of a current usage rate indicator indicative of a current rate of usage of the workload, for the tenant, wherein the incremental workload opportunity identifier is configured to apply the headroom indicator to the entitlement size indicator to obtain the incremental workload opportunity indicator for the workload. 
     Example 11 is the computing system of any or all previous examples wherein the model building logic is configured to train the model based on the attributes from the first subset of tenants that meet a performance threshold relative to the workload, and wherein the second subset of tenants do not meet the performance threshold relative to the workload. 
     Example 12 is the computing system of any or all previous examples wherein the controllable logic element comprises: 
     user interface logic configured to surface the incremental usage performance indicators. 
     Example 13 is the computing system of any or all previous examples wherein the plurality of different tenants have different sizes indicated by a size indicator and wherein the model building logic divides the tenants into a plurality of different size groups and trains a separate model, corresponding to each size group, based on the attributes for a first subset of the tenants in each size group, the model application logic being configured to apply the model, corresponding to each size group, to a second subset of the tenants in that size group. 
     Example 14 is a computer implemented method, comprising: 
     receiving a plurality of sets of tenant attributes, each set being indicative of attributes corresponding to a tenant computing system; 
     training a model to predict usage performance relative to a workload, indicated by a usage performance indicator, of a first subset of the plurality of tenants, based on the sets of attributes corresponding to the first subset of the plurality of tenants; 
     applying the model to a second subset of the plurality of tenants, different from the first subset of tenants, to obtain an incremental usage performance indicator corresponding to each tenant in the second subset of tenants, the usage performance indicator being indicative of a likely incremental increase in usage of the workload for the corresponding tenant in the second subset of tenants; and 
     generating a control signal to control a controllable logic element based on at least one of the incremental usage performance indicators. 
     Example 15 is the computer implemented method of any or all previous examples and further comprising: 
     generating a ranked list of tenants, ranking the tenants in the second subset of tenants based at least in part on the incremental usage performance indicator corresponding to each of the tenants in the second subset of tenants. 
     Example 16 is the computer implemented method of any or all previous examples wherein training a model comprises: 
     training the model based on the attributes from the first subset of tenants that meet a performance threshold relative to the workload, and wherein applying the model comprises applying the model to the second subset of tenants do not meet the performance threshold relative to the workload. 
     Example 17 is the computer implemented method of any or all previous examples wherein generating a control signal comprises: 
     generating the control signal to control user interface logic to surface the incremental usage performance indicators. 
     Example 18 is the computer implemented method of any or all previous examples wherein the plurality of different tenants have different sizes indicated by a size indicator and wherein training the model comprises: 
     dividing the tenants into a plurality of different size groups; and 
     training a separate model, corresponding to each size group, based on the attributes for a first subset of the tenants in each size group, and wherein applying the model comprises applying the model, corresponding to each size group, to a second subset of the tenants in that size group. 
     Example 19 is a computing system, comprising: 
     model building logic that receives a plurality of sets of tenant attributes, each set being indicative of attributes corresponding to a tenant computing system, and trains a model to predict usage performance relative to a workload, indicated by a usage performance indicator, of a first subset of the plurality of tenants, based on the sets of attributes corresponding to the first subset of the plurality of tenants; 
     model application logic that applies the model to a second subset of the plurality of tenants, different from the first subset of tenants, to obtain an incremental usage performance indicator corresponding to each tenant in the second subset of tenants, the usage performance indicator being indicative of a likely incremental increase in usage of the workload for the corresponding tenant in the second subset of tenants; 
     tenant priority generator logic configured to generate a ranked list of tenants, ranking the tenants in the second subset of tenants based at least in part on the incremental usage performance indicator corresponding to each of the tenants in the second subset of tenants; and 
     a control signal generator that generates a control signal to control a controllable logic element based on at least one of the incremental usage performance indicators and the ranked list of tenants. 
     Example 20 is the computing system of any or all previous examples wherein the tenant priority generator logic comprises: 
     entitlement size identifier logic configured to identify an entitlement size indicator, indicative of a number of users that are entitled to use the workload, for each tenant in the second subset of tenants; and 
     an incremental workload opportunity identifier configured to, for each tenant in the second subset of tenants, identify an incremental workload opportunity indicator indicative of a size of increase of usage of the workload likely achievable for the tenant, based on the entitlement size indicator for the tenant and the usage performance indicator corresponding to the tenant. 
     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 above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.