Prediction period notification program, information processing system, and prediction period notification method

Abstract

To enable a resource to be delivered at a required time.SOLUTION: A control server 1 collates stock management data 21 for storing a stock status of a server in a computer with customer management data 22 for storing servers used and the number of servers used on a customer side, calculates the number of days required till a delivery of the number of required servers when the customer side expands them, and notifies a server on the customer side of a prediction period that is recommended from the number of days required by the delivery and is used for capacity planning.SELECTED DRAWING: Figure 2A

Description

[Technical Field]

[0001] The present invention relates to a prediction period notification program and the like. [Background technology]

[0002] In response to an increase in users and improvements in service levels, the servers that make up the system are added (scaled out) to enhance the system's specifications.

[0003] The need to scale out a server can be determined using a capacity planning function. Capacity planning is the process of determining the resource configuration required for a system, and the capacity planning function is, for example, an application running on a server.

[0004] For example, a management server that manages a customer's system uses a capacity planning function to determine whether or not to scale out the servers in the system based on future changes in resource usage. If the management server determines that scaling out is necessary, it places an order with the vendor to deliver the required number of servers by the required time.

[0005] Furthermore, technologies relating to capacity planning functions have been disclosed (see, for example, Patent Documents 1 to 3). As an example, it is disclosed that for a computer environment in which multiple containers are deployed, resource usage in the computer environment from the present to a predetermined period of time is predicted based on regular fluctuation data obtained by breaking down collected resource usage measurements through time-series analysis processing, and containers in the computer environment are relocated based on the predicted resource usage values.

[0006] As another example, it is disclosed that for a physical server that has multiple virtual machines, virtual machines that can be moved to resolve resource shortages are identified and moved based on the resource usage of the virtual machines within a specified period. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Application Publication No. 2020-144669 [Patent Document 2] Japanese Patent Application Laid-Open No. 2014-6739 [Patent Document 3] Japanese Patent Application Laid-Open No. 2005-182697 Summary of the Invention [Problem to be solved by the invention]

[0008] However, when the forecast period for predicting resource usage used in the capacity planning function is fixed, there is a problem on the sales side that the delivery of the server may not be in time for the timing of the customer's expansion. For example, if the date when the resource will be depleted is a fixed forecast period from the present, inventory may decrease for some reason, such as a shortage of parts or a sudden large order, and the number of days required for delivery may be longer than the fixed forecast period. In such a case, the sales side may not be able to deliver the server in time for the timing of the customer's expansion.

[0009] The above-mentioned problem arises not only in the delivery of the computer itself, but also in the delivery of resources that make up the computer, such as the CPU, memory, and HDD.

[0010] One aspect of the present invention aims to enable resources to be delivered when needed. [Means for solving the problem]

[0011] In one aspect, the forecast period notification program causes a computer to execute the following process when calculating a recommended forecast period for capacity planning performed by a customer: compare inventory status information that stores server inventory status with customer information that stores the servers and number of servers used by the customer; calculate the number of days required for delivery of the number of units required for expansion by the customer; and notify the customer's server of the recommended forecast period obtained from the number of days required for delivery. [Effects of the Invention]

[0012] According to one embodiment, the required resources can be delivered when the customer needs them. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is a diagram illustrating an example of an information processing system according to an embodiment. [Figure 2A] FIG. 2A is a diagram (1) illustrating an example of the flow of a prediction period notification process according to the embodiment. [Figure 2B] FIG. 2B is a diagram (2) illustrating an example of the flow of the prediction period notification process according to the embodiment. [Figure 3] FIG. 3 is a block diagram illustrating an example of a functional configuration of the control server according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of inventory management data according to the embodiment. [Figure 5] FIG. 5 is a diagram illustrating an example of customer management data according to the embodiment. [Figure 6] FIG. 6 is a block diagram illustrating an example of a functional configuration of the system management server according to the embodiment. [Figure 7A] FIG. 7A is a diagram (1) illustrating an example of the recommended forecast period calculation process according to the embodiment. [Figure 7B] FIG. 7B is a diagram (2) illustrating an example of the recommended forecast period calculation process according to the embodiment. [Figure 7C] FIG. 7C is a diagram (3) illustrating an example of the recommended forecast period calculation process according to the embodiment. [Figure 7D]FIG. 7D is a diagram (4) illustrating an example of the recommended forecast period calculation process according to the embodiment. [Figure 8] FIG. 8 is a diagram illustrating an example of a flowchart of a prediction period notification process according to the embodiment. [Figure 9] FIG. 9 is a diagram illustrating an example of the effect of the prediction period notification process according to the embodiment. [Figure 10] FIG. 10 is a diagram illustrating an example of a computer that executes a prediction period notification program. [Figure 11] Figure 11 is a reference diagram showing the process up to ordering a server using the Capapula function. [Figure 12] FIG. 12 is a reference diagram for explaining a case where delivery is not made in time. DETAILED DESCRIPTION OF THE INVENTION

[0014] Hereinafter, embodiments of the forecast period notification program, information processing system, and forecast period notification method disclosed in the present application will be described in detail with reference to the accompanying drawings. However, the present invention is not limited to the embodiments. [Example]

[0015] First, the flow of using the capacity planning function to order servers within a system will be explained with reference to Figure 11. Capacity planning here refers to the process of determining the configuration of resources required for a system, and the capacity planning function is, for example, an application that runs on a server. Note that capacity planning will be abbreviated as "capapla" hereafter.

[0016] Figure 11 is a reference diagram showing the process up to ordering a server using the capacity planning function. As shown in Figure 11, the service provider (customer) manages the customer's system. The customer's system is equipped with multiple servers. The system management server uses the capacity planning function to determine whether or not to order a server based on future changes in resource usage for the customer's system. Examples of resources include CPUs, disks, and memory. For convenience, the resource here is assumed to be a CPU.

[0017] The customer sets the threshold used in the capacity planning function to 90% of the total amount of resources in advance ( <91> ). The CapaPla function predicts the change in CPU usage from the present to the forecast period ( <92> ) Here, the prediction period is a fixed value of 30 days. The shorter the prediction period, the higher the accuracy. For this reason, the customer sets a short fixed value (for example, 30 days) in the capacity planning function.

[0018] The CapaPla function will then send an alert if the prediction exceeds the threshold ( <93> ) In this example, the CPU usage will exceed the threshold 30 days into the forecast period from now, so an alert will be sent. The service provider (customer) will then ultimately decide whether or not to order a server, and if so, will order the required number of servers from the manufacturer ( <94> ) The manufacturer then receives the order and delivers to the customer before the customer's resources are depleted. In this case, the manufacturer needs to deliver to the customer within 30 days of the current forecast period when the resources are expected to be depleted.

[0019] However, there are cases where the manufacturer cannot meet the delivery deadline. Figure 12 is a reference diagram that explains what happens when delivery cannot be made on time. As shown in Figure 12, the forecast period is a fixed value of 30 days. The capacity planning function predicts that CPU usage will exceed the threshold on November 17th, 30 days into the forecast period from the present (October 17th), and that resources will be depleted. However, the manufacturer's delivery date is December 5th. This is because inventory may decrease for some reason, such as a shortage of parts or a sudden large order. In such cases, the manufacturer's delivery time will be more than 30 days from the present. In other words, if the forecast period used by the capacity planning function is fixed, there are cases where the server delivery will not be in time to meet the customer's expansion schedule.

[0020] Therefore, in the embodiment, a system will be described that enables a manufacturer to deliver products in time for the timing of customer increases.

[0021] [Example of an information processing system] 1 is a diagram illustrating an example of an information processing system according to an embodiment. As illustrated in FIG. 1, the information processing system 9 includes a control server 1, a customer system 5, and a system management server 3.

[0022] The system (customer) 5 includes multiple servers. Service users access the multiple servers included in the system (customer) 5 via a network to use the service. The system management server 3 is a server on the customer side and manages the system (customer) 5. The system management server 3 uses the capacity planning function 32A to determine whether or not to scale out the servers included in the system (customer) 5 based on future changes in resource usage within the system (customer) 5. The capacity planning function 32A predicts whether or not to scale out for a predetermined prediction period from the present. Then, if the system management server 3 determines to scale out based on the final decision of the service provider (customer), it places an order with the manufacturer (seller) to deliver the required number of servers by the prediction period.

[0023] The control server 1 is a server on the manufacturer (seller) side and is connected to inventory management data 21 and customer management data 22. The inventory management data 21 here stores the inventory status for each server model. The inventory status stores the number of units in stock for each future period. The customer management data 22 here stores the server model used by the customer, the number of units used, and the current forecast period of the capacity planning function, in association with each other. The control server 1 then uses the inventory management data 21 and the customer management data 22 to dynamically change the forecast period used in the capacity planning function 32A for each customer depending on the server inventory status. The control server 1 then notifies the system management server 3 of the corresponding customer of the changed forecast period.

[0024] [Prediction period notification process flow] Here, the flow of the prediction period notification process according to the embodiment will be described with reference to FIGS. 2A and 2B. FIGS. 2A and 2B are diagrams showing an example of the flow of the prediction period notification process according to the embodiment. As shown in FIG. 2A, the control server 1 acquires the inventory quantity of the server from the inventory management data 21 ( <1> The control server 1 acquires the model of the server used by the customer, the number of servers used, and the current forecast period of the capacity planning function 32A from the customer management data 22 ( <2> ) The control server 1 uses the acquired server inventory number and the number of servers used by the customer to calculate the number of days required for delivery of the number of servers required for the scale-out by the customer. Then, the control server 1 calculates the recommended forecast period from the number of days required for delivery ( <3> ) Then, the control server 1 notifies the recommended forecast period to the system management server 3 corresponding to the customer ( <4> The system management server 3, which has been notified of the recommended prediction period, uses the notified prediction period in the capacity planning function to predict future changes in resource usage.

[0025] 2B, when the resource is a CPU, the customer's system management server 3 uses the capacity planning function to predict future changes in CPU usage, with the threshold set to 90%.

[0026] The left diagram in Figure 2B shows the case where the current forecast period is a fixed 30 days. In this case, when the implementation date for the Capacity Planning function is October 17th, the function predicts that CPU usage will exceed 90% on November 17th, which is 30 days from October 17th, which is the current forecast period. However, if the manufacturer has 40 days until delivery, delivery will be more than 30 days after the implementation date of October 17th, and will not be in time for the customer's increase in capacity.

[0027] The right diagram in Figure 2B shows the case where the revised forecast period is 60 days. In this case, if the implementation date for the capacity planning function is 9 / 17, the capacity planning function predicts that CPU usage will exceed 90% on 11 / 17, which is 60 days from 9 / 17, the revised forecast period. If the manufacturer has 40 days until delivery, the delivery date will be 10 / 27, which is before the time (11 / 17) when CPU usage will exceed 90%. In other words, delivery will be in time for the customer's increase in capacity.

[0028] In this way, the control server 1 dynamically changes the forecast period of the capacity planning function according to the inventory status of the customer's servers, thereby enabling delivery of the servers at the time required by the customer.

[0029] [Control server functional configuration] Fig. 3 is a block diagram showing an example of a functional configuration of a control server according to an embodiment. As shown in Fig. 3, the control server 1 includes a control unit 10 and a storage unit 20. The control unit 10 includes a recommended forecast period calculation unit 11 and a notification unit 12. The storage unit 20 includes inventory management data 21 and customer management data 22. The recommended forecast period calculation unit 11 is an example of a calculation unit. The notification unit 12 is an example of a notification unit.

[0030] The inventory management data 21 stores the inventory status for each server model. An example of the inventory management data 21 will now be described with reference to FIG. 4. FIG. 4 is a diagram showing an example of inventory management data according to an embodiment. As shown in FIG. 4, the inventory management data 21 stores the number of inventory units for each future time period for each product name. The product name corresponds to the server model. The time period may be, for example, in weekly units, monthly units, or a combination of weekly and monthly units.

[0031] For example, if the product name is "XY2000M5," it will be stored as "0" for the period up to 11 / 25, "0" for the period up to 11 / 30, ..., "5" for the period up to 12 / 09, "15" for the period in December, and "186" for the period in January.

[0032] Returning to FIG. 3, the customer management data 22 stores customer information. An example of the customer management data 22 will now be described with reference to FIG. 5. FIG. 5 is a diagram showing an example of customer management data according to an embodiment. The customer management data 22 stores a server model, number of units, and current forecast period in association with the customer. The server model corresponds to the server product name. The number of units is the number of servers used in the customer's system 5. The current forecast period is the current forecast period used in the capacity planning function 32A implemented by the customer. The current forecast period is changed by the notification unit 12.

[0033] For example, if the customer is "Customer A," the server model is stored as "XY2000M5," the number is stored as "100," and the current forecast period is stored as "30 days."

[0034] Returning to FIG. 3, the recommended forecast period calculation unit 11 calculates, for each customer, a recommended forecast period to be used in the capacity planning function 32A implemented by the customer.

[0035] For example, the recommended forecast period calculation unit 11 compares the inventory management data 21 with the customer management data 22, and calculates the number of days required for delivery of the number of servers required for the scale-out by the customer. As an example, the recommended forecast period calculation unit 11 refers to the customer management data 22 and calculates the number of servers required when scaling out the servers used by the customer. The number of servers required when scaling out the servers is, for example, 10% of the number of servers used by the customer. However, the number of servers required when scaling out the servers is not limited to 10% of the number of servers used by the customer, but may be 0.5% or 20%, and can be changed as appropriate.

[0036] The recommended forecast period calculation unit 11 then references the inventory management data 21 and obtains the inventory quantity for each period of servers used by the customer.The recommended forecast period calculation unit 11 then searches for a period when the inventory quantity for each period of each server will exceed the calculated required quantity.The recommended forecast period calculation unit 11 then calculates the number of days required to deliver the required quantity from the period indicated by the search result and the date on which the recommended forecast period calculation was performed.

[0037] Then, the recommended forecast period calculation unit 11 calculates a number of days longer than the number of days required for delivery as the recommended forecast period. As an example, the recommended forecast period may be set in 30-day increments, in 10-day increments, or may be changed as appropriate. For example, when set in 30-day increments, if the number of days required for delivery is 45 days, the recommended forecast period is calculated as 60 days. When set in 10-day increments, if the number of days required for delivery is 45 days, the recommended forecast period is calculated as 50 days.

[0038] The notification unit 12 notifies the customer's system management server 3 of the recommended forecast period. For example, the notification unit 12 acquires the current forecast period associated with the target customer from the customer management data 22. If the recommended forecast period differs from the acquired current forecast period, the notification unit 12 notifies the customer's system management server 3 of the recommended forecast period. This allows the customer's system management server 3 to deliver the required server at the required time by using the forecast period recommended by the manufacturer in the capacity planning function. Meanwhile, the control server 1 can enable the customer to deliver the required server at the required time.

[0039] Then, the notification unit 12 updates the current prediction period associated with the target customer in the customer management data 22 to the recommended prediction period. Furthermore, if the recommended prediction period is the same as the acquired current prediction period, the notification unit 12 does not notify the customer's system management server 3 of the recommended prediction period. In this way, by not notifying the customer of the recommended prediction period, the notification unit 12 can eliminate wasted communication.

[0040] [System Management Server Functionality Configuration] Next, the functional configuration of the system management server 3 will be described with reference to Fig. 6. Fig. 6 is a block diagram showing an example of the functional configuration of the system management server according to the embodiment. As shown in Fig. 6, the system management server 3 has a control unit 30 and a storage unit 40. The control unit 30 has a reception unit 31, a resource usage prediction unit 32, and an alert notification unit 33. The storage unit 40 has a recommended prediction period 41.

[0041] The recommended forecast period 41 is a forecast period used for the capacity planning function and is a forecast period recommended by the manufacturer.

[0042] The receiving unit 31 receives the recommended prediction period from the control server 1. Then, the receiving unit 31 stores the recommended prediction period in the recommended prediction period 41 so that the recommended prediction period can be used for the capacity planning function.

[0043] The resource usage prediction unit 32 predicts resource usage using the recommended prediction period 41 for the capacity planning function. That is, the resource usage prediction unit 32 predicts resource usage from the implementation date of the capacity planning function to the recommended prediction period 41.

[0044] The alert notification unit 33 issues an alert when the resource usage exceeds a threshold based on the resource usage prediction. For example, the alert notification unit 33 issues an alert to the service provider when the resource usage exceeds the threshold. The service provider then ultimately determines whether or not to order servers, and if necessary, places an order for the required number of servers with the manufacturer.

[0045] [Example of recommended forecast period calculation process] An example of the recommended forecast period calculation process performed by the recommended forecast period calculation unit 11 in the control server 1 will now be described with reference to FIGS. 7A to 7D. FIGS. 7A to 7D are diagrams showing an example of the recommended forecast period calculation process according to the embodiment. The recommended forecast period calculation unit 11 calculates a forecast period recommended for each customer, for example, once a day, in the following manner. Here, a case will be described in which the recommended forecast period calculation unit 11 calculates the recommended forecast period for customer A on "12 / 4."

[0046] 7A, the recommended forecast period calculation unit 11 refers to the customer management data 22 and acquires the server model, number of units, and current forecast period used by customer A. Here, "XY2000M5" is acquired as the server model used by customer A, "100" as the number of units, and "30 days" as the current forecast period (see symbol a1).

[0047] Then, the recommended prediction period calculation unit 11 calculates the number of servers required for scale-out of customer A. The required number is assumed to be 10% of the number of servers used by the customer. In this case, since the number of servers for customer A is 100, the number of servers required for scale-out is calculated to be 10.

[0048] As shown in FIG. 7B, the recommended forecast period calculation unit 11 refers to the inventory management data 21 and obtains the inventory quantity for each period of the servers used by customer A. Then, the recommended forecast period calculation unit 11 searches for a period when the inventory quantity for each server and each period will exceed the number required for scale-out. In this example, the period when the inventory quantity of the server model "XY2000M5" used by customer A will exceed 10 units, which is the number required for scale-out, is the end of December (see a2).

[0049] Then, the recommended forecast period calculation unit 11 calculates the number of days required to deliver the required number of units from the time indicated by the search result and the implementation date of the recommended forecast period calculation. In this case, the time indicated by the search result is "12 / 31", and the implementation date of the recommended forecast period calculation is "12 / 4". Therefore, the number of days required to deliver is calculated as 27 days (=12 / 31-12 / 4).

[0050] The recommended forecast period calculation unit 11 then calculates a number of days longer than the number of days required for delivery as the recommended forecast period. Here, the recommended forecast period is set in 30-day increments. Since the number of days required for delivery is "27 days," the recommended forecast period is calculated to be "30 days."

[0051] The notification unit 12 then obtains the current prediction period associated with customer A from the customer management data 22. If the recommended prediction period differs from the current prediction period, the notification unit 12 notifies the system management server 3 of customer A of the recommended prediction period. In this case, the current prediction period associated with customer A is obtained as "30 days" from the customer management data 22 (see symbol a3). Therefore, since the recommended prediction period is the same as the obtained current prediction period, the notification unit 12 does not notify the capacity planning function of customer A of the recommended prediction period.

[0052] Figure 7C shows a case where the recommended forecast period changes. Here, we will explain the case where the recommended forecast period calculation unit 11 calculated the recommended forecast period for customer A on "12 / 13." The inventory management data 21 is as shown in the left diagram of Figure 7C.

[0053] As shown in FIG. 7C, the recommended forecast period calculation unit 11 refers to the inventory management data 21 and obtains the inventory quantity for each period of the servers used by customer A. Then, the recommended forecast period calculation unit 11 searches for a period when the inventory quantity for each server and each period will exceed the number required for scale-out. Here, the period when the inventory quantity of the server model "XY2000M5" used by customer A will exceed 10 units, which is the number required for scale-out, is the end of January (see a5).

[0054] Then, the recommended forecast period calculation unit 11 calculates the number of days required to deliver the required number of units from the time indicated by the search result and the implementation date of the recommended forecast period calculation. In this case, the time indicated by the search result is "1 / 31", and the implementation date of the recommended forecast period calculation is "12 / 13". Therefore, the number of days required to deliver is calculated as 48 days (=1 / 31-12 / 13).

[0055] The recommended forecast period calculation unit 11 then calculates a number of days longer than the number of days required for delivery as the recommended forecast period. Here, the recommended forecast period is set in 30-day increments. Since the number of days required for delivery is "48 days," the recommended forecast period is calculated to be "60 days."

[0056] As shown in FIG. 7D , the notification unit 12 acquires the current prediction period associated with customer A from the customer management data 22. If the recommended prediction period differs from the current prediction period, the notification unit 12 notifies the system management server 3 of customer A of the recommended prediction period. In this example, the current prediction period associated with customer A is acquired from the customer management data 22 as "30 days" (see reference symbol a6). Therefore, since the recommended prediction period differs from the acquired current prediction period, the notification unit 12 notifies the capacity planning function of customer A of the recommended prediction period of "60 days." Then, the notification unit 12 updates the current prediction period associated with customer A in the customer management data 22 from "30 days" to the recommended prediction period of "60 days" (see reference symbol a7).

[0057] This allows the system management server 3 of customer A to deliver the necessary servers at the required time by using the "60 days" forecast period recommended by the manufacturer for the capacity planning function. On the other hand, the control server 1 can deliver the necessary servers to customer A at the required time.

[0058] [Flowchart of forecast period notification process] Here, a flowchart of the prediction period notification process performed by the control server 1 according to the embodiment will be described with reference to Fig. 8. Fig. 8 is a diagram illustrating an example of a flowchart of the prediction period notification process according to the embodiment. It is assumed that the control server 1 calculates the recommended prediction period for a customer, for example, once a day.

[0059] Upon receiving the calculation request, the control server 1 acquires the model of the server used by the customer, the number of servers in use, and the current prediction period from the customer management data 22 (step S11). The control server 1 calculates the number of servers required when scaling out the model of the server used by the customer (step S12). For example, the number of servers required when scaling out is 10% of the number of servers currently in use.

[0060] Then, the control server 1 obtains the earliest date on which there is more inventory than the number of servers of the model used by the customer required for scale-out from the inventory management data 21 (step S13).Then, the control server 1 determines the number of days from the present (the date on which the recommended forecast period calculation was performed) to the obtained date as the number of days required for delivery (step S14).

[0061] Then, the control server 1 sets the recommended forecast period for the capacity planning function to the shortest number of days exceeding the number of days required for delivery in 30-day increments (step S15). The control server 1 determines whether the recommended forecast period is different from the customer's current forecast period (step S16). The customer's current forecast period may be obtained from the customer management data 22.

[0062] If it is determined that the recommended prediction period is the same as the customer's current prediction period (step S16; No), the control server 1 does not notify the customer of the recommended prediction period and terminates the prediction period notification process. After this, the customer's system management server 3 continues to predict resource usage using the already set recommended prediction period 41 in the capacity planning function.

[0063] On the other hand, if it is determined that the recommended prediction period is different from the customer's current prediction period (step S16; Yes), the control server 1 notifies the customer's capacity planning function of the recommended prediction period (step S17). In addition, the control server 1 updates the current prediction period in the customer management data 22 to the recommended prediction period (step S18). Then, the control server 1 ends the prediction period notification process. Thereafter, the customer's system management server 3 uses the updated prediction period in the capacity planning function to predict resource usage.

[0064] [Effect of forecast period notification processing] Here, the effect of the prediction period notification process according to the embodiment will be described with reference to Fig. 9. Fig. 9 is a diagram showing an example of the effect of the prediction period notification process according to the embodiment. As shown in Fig. 9, the system management server 3 uses "30 days" stored in the recommended prediction period 41 in the capacity planning function 32A to predict changes in CPU usage 30 days from 9 / 17 (present), as indicated by symbol b1. Here, it is predicted that CPU usage 30 days from 9 / 17 (present) will not exceed the threshold.

[0065] Assume that the manufacturer's inventory decreases on September 21st, causing the number of days required for delivery of a server to be extended from 20 to 38 days. Then, the control server 1 sets the recommended forecast period in 30-day increments to the shortest number of days exceeding the required number of days for delivery, "38 days." In this case, the control server 1 calculates the recommended forecast period as "60 days." The control server 1 then notifies the customer's system management server 3 of the recommended forecast period of "60 days" (b2). The system management server 3 then changes the recommended forecast period 41 from "30 days" to "60 days" on September 21st (b3).

[0066] The system management server 3 then uses the "60 days" changed to the recommended prediction period 41 in the capacity planning function 32A to predict changes in CPU usage 60 days from 9 / 21 (present) indicated by symbol b4. Here, it is predicted that CPU usage will exceed the threshold on 11 / 1 within the period from 9 / 21 (present) to 60 days from now (b5).

[0067] Therefore, if the service provider orders the servers required for scale-out from the manufacturer on September 21st, the servers can be delivered when needed (November 1st). On the other hand, the manufacturer can deliver the servers when the customer needs them.

[0068] [Effects of the Example] According to the above embodiment, the control server 1 compares the inventory management data 21, which stores the inventory status of computer resources, with the customer management data 22, which stores the resources and number of units used by the customer, and calculates the number of days required to deliver the required number of units when the customer adds more equipment. The control server 1 notifies the customer's system management server 3 of a forecast period to be used for capacity planning, which is recommended based on the number of days required for delivery. With this configuration, the system management server 3 can deliver the required resources at the required time by using the capacity planning function. Meanwhile, the control server 1 can deliver the required resources at the required time.

[0069] Furthermore, according to the above embodiment, the customer management data 22 stores the resources used by the customer, the number of units used, and the current forecast period used for capacity planning. If the forecast period recommended based on the number of days required for delivery differs from the current forecast period stored in the customer management data 22, the control server 1 notifies the customer-side system management server 3 of the forecast period recommended based on the number of days required for delivery, and updates the customer management data 22 with the recommended forecast period. With this configuration, the control server 1 can dynamically change the forecast period used for capacity planning depending on the inventory status of resources.

[0070] Furthermore, according to the above embodiment, the control server 1 does not notify the customer-side system management server 3 of the recommended prediction period if the prediction period recommended based on the number of days required for delivery is the same as the current prediction period stored in the customer management data 22. With this configuration, the control server 1 can eliminate wasted communication by not notifying the customer of the recommended prediction period.

[0071] Furthermore, according to the above embodiment, the control server 1 refers to the customer management data 22 and calculates a predetermined percentage of the number of units used as the number of units required when the customer expands the system. The control server 1 refers to the inventory management data 21 and acquires the inventory quantity of the resources used by the customer for each period. The control server 1 then searches for a period when the inventory quantity of the resources used by the customer for each period will exceed the calculated number. The control server 1 then calculates the number of days required to deliver the required number of units from the period indicating the search result and the date on which the calculation of the recommended forecast period was performed. With this configuration, the control server 1 can easily calculate the number of days required to deliver the required number of units to the customer.

[0072] [others] In the above embodiment, the components of the illustrated control server 1 and system management server 3 do not necessarily have to be physically configured as shown. In other words, the specific manner in which the control server 1 and system management server 3 are distributed and integrated is not limited to that shown in the drawings, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc. Furthermore, the storage unit 20 may be connected to the control server 1 as an external device via a network. The storage unit 40 may be connected to the system management server 3 as an external device via a network.

[0073] The various processes described in the above embodiments can be realized by executing a prepared program on a computer such as a personal computer or a workstation. An example of a computer that executes a prediction period notification program that realizes the same functions as the control server 1 shown in Fig. 3 will be described below. Fig. 10 is a diagram showing an example of a computer that executes a prediction period notification program.

[0074] 10, computer 200 includes a CPU (Central Processing Unit) 203 that executes various types of arithmetic processing, an input device 215 that accepts data input from a user, and a display device 209. Computer 200 also includes a drive device 213 that reads programs and the like from a storage medium, and a communication I / F (Interface) 217 ​​that transmits and receives data to and from other computers via a network. Computer 200 also includes a memory 201 that temporarily stores various types of information, and an HDD (Hard Disk Drive) 205. Memory 201, CPU 203, HDD 205, display control unit 207, display device 209, drive device 213, input device 215, and communication I / F 217 are connected by a bus 219.

[0075] The drive device 213 is, for example, a device for the removable disk 211. The HDD 205 stores a predicted period notification program 205a and predicted period notification processing related information 205b. The communication I / F 217 manages the interface between the network and the inside of the device, and controls the input and output of data from other computers. The communication I / F 217 can be, for example, a modem or a LAN adapter.

[0076] The display device 209 is a display device that displays a cursor, an icon, a toolbox, and data such as documents, images, and function information. The display device 209 can be, for example, a liquid crystal display or an organic EL (Electroluminescence) display.

[0077] The CPU 203 reads the forecast period notification program 205a, loads it into the memory 201, and executes it as a process. These processes correspond to the respective functional units of the control server 1. The forecast period notification processing related information 205b includes, for example, inventory management data 21 and customer management data 22. The removable disk 211, for example, stores each piece of information such as the forecast period notification program 205a.

[0078] It should be noted that the predicted period notification program 205a does not necessarily have to be stored in the HDD 205 from the beginning. For example, the program may be stored in a "portable physical medium" such as a flexible disk (FD), CD-ROM, DVD disk, magneto-optical disk, or IC card that is inserted into the computer 200. The computer 200 may then read and execute the predicted period notification program 205a from the medium.

[0079] Furthermore, the information processing system 9 described in the above embodiment can be applied when servers constituting the system are added when the system is on-premise and provides services to users. For example, a manufacturer (seller) can deliver the necessary resources to a service provider (customer) when necessary for addition or the like. [Explanation of symbols]

[0080] 1 Control Server 3 System Management Server 9. Information Processing Systems 10 Control Unit 11 Recommended forecast period calculation section 12 Notification Department 20 Memory section 21 Inventory Management Data 22 Customer Management Data 30 Control Unit 31 Reception 32 Resource Usage Prediction Unit 32A Capapla function 33 Alert notification section 40 Storage section 41 Recommended Forecast Period

Claims

1. comparing inventory status information that stores the inventory status of resources in the computer with customer information that stores the resources used by the customer and the number of units used, and calculating the number of days required for delivery of the number of units required for an expansion by the customer; A forecast period to be used for capacity planning, which is recommended based on the number of days required for the delivery, is notified to the customer's server. A prediction period notification program that causes a computer to execute a process.

2. The customer information stores the resources used by the customer, the number of units used, and the current forecast period used for the capacity planning; The notification process notifies the server of the customer of the predicted period recommended from the number of days required for delivery, if the predicted period recommended from the number of days required for delivery differs from the current predicted period stored in the customer information, and updates the customer information with the recommended predicted period.

2. The prediction period notification program according to claim 1,

3. The notification process further includes not notifying the server on the customer side of the recommended forecast period if the forecast period recommended based on the number of days required for delivery is the same as the current forecast period stored in the customer information.

3. The prediction period notification program according to claim 2.

4. The calculating process includes: Refer to the customer information and calculate a predetermined ratio of the number of units used as the number of units required when the customer side increases the number of units; Refer to the inventory status information to obtain the inventory quantity of the resources used by the customer for each period; A time period in which the inventory quantity of the resources used by the customer will exceed the calculated number is searched for; Calculate the number of days required to deliver the required quantity of units from the date of the search results and the date of the calculation of the recommended forecast period.

2. The prediction period notification program according to claim 1,

5. a first server on the seller side; A second server on a customer side, The first server comprises: a calculation unit that compares inventory status information that stores the inventory status of resources in a computer with customer information that stores resources used by a customer and the number of units used, and calculates the number of days required for delivery of the number of units required when the customer adds more equipment; a notification unit that notifies a server on a client side of a predicted period to be used for capacity planning, the predicted period being recommended based on the number of days required for delivery; The second server comprises: a prediction unit that predicts the usage amount of the usage resource by using the prediction period notified from the first server in the capacity planning; An information processing system comprising:

6. comparing inventory status information that stores the inventory status of resources in the computer with customer information that stores the resources used by the customer and the number of units used, and calculating the number of days required for delivery of the number of units required for an expansion by the customer; A forecast period to be used for capacity planning, which is recommended based on the number of days required for the delivery, is notified to the customer's server. A method for notifying a predicted period, the processing of which is executed by a computer.

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