SERVICES GROUPINGS FOR STRATEGIC CONSUMPTION
Service deployments of multiple services are grouped together according to common usage history. Services are proposed for a user account where one service of a services group is deployed and another service of the services group is new, or not deployed by the user account. Usage factors specific to a user account a weighted for ranking recommendation of certain services within service groups have at least one deployed service and at least one new service.
1 . A computer-implemented method (CIM) comprising:
recording a historical data set including information indicative of: (i) identities of a plurality of cloud services, (ii) a plurality of cloud services accounts that respectively use at least some cloud services of the plurality of cloud services, (iii) usage of the plurality of services respectively by the plurality of cloud services accounts, and (iv) a plurality of performance metrics relating to the plurality of cloud services when used in various combinations in a cloud; and
identifying, by machine logic, a recommended cloud service group based on the historical data set, where the recommended cloud service group is constituted by a subset of the cloud services identified in the historical data set, such that the subset of cloud services in the recommended cloud services group: (i) are dependent upon each other, and (ii) perform favorably on at least one performance metric, of the plurality of performance metrics, when used together in combination.
2 - 18 . (canceled)
19 . The CIM of claim 1 further comprising:
recommending use of the recommended service group to a first cloud services user for use in a cloud.
20 . The CIM of claim 1 wherein the plurality of performance metrics include at least one of the following: (i) provision time; (ii) uptime; (iii) downtime; (iv) fail-over rates; (v) fewest number of clicks; (vi) network latency; and (vii) user experience via feedback.
21 . The CIM of claim 1 wherein the plurality of performance metrics include all of the following: (i) provision time; (ii) uptime; (iii) downtime; (iv) fail-over rates; (v) fewest number of clicks; (vi) network latency; and (vii) user experience via feedback.
22 . A computer program product (CPP) comprising:
a set of storage device(s); and
computer code stored collectively in the set of storage device(s), with the computer code including data and instructions to cause a processor(s) set to perform at least the following operations:
recording a historical data set including information indicative of: (i) identities of a plurality of cloud services, (ii) a plurality of cloud services accounts that respectively use at least some cloud services of the plurality of cloud services, (iii) usage of the plurality of services respectively by the plurality of cloud services accounts, and (iv) a plurality of performance metrics relating to the plurality of cloud services when used in various combinations in a cloud, and
identifying, by machine logic, a recommended cloud service group based on the historical data set, where the recommended cloud service group is constituted by a subset of the cloud services identified in the historical data set, such that the subset of cloud services in the recommended cloud services group: (i) are dependent upon each other, and (ii) perform favorably on at least one performance metric, of the plurality of performance metrics, when used together in combination.
23 . The CPP of claim 22 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s):
recommending use of the recommended service group to a first cloud services user for use in a cloud.
24 . The CPP of claim 22 wherein the plurality of performance metrics include at least one of the following: (i) provision time; (ii) uptime; (iii) downtime; (iv) fail-over rates; (v) fewest number of clicks; (vi) network latency; and (vii) user experience via feedback.
25 . The CPP of claim 22 wherein the plurality of performance metrics include all of the following: (i) provision time; (ii) uptime; (iii) downtime; (iv) fail-over rates; (v) fewest number of clicks; (vi) network latency; and (vii) user experience via feedback.
26 . A computer system (CS) comprising:
a processor(s) set;
a set of storage device(s); and
computer code stored collectively in the set of storage device(s), with the computer code including data and instructions to cause the processor(s) set to perform at least the following operations:
recording a historical data set including information indicative of: (i) identities of a plurality of cloud services, (ii) a plurality of cloud services accounts that respectively use at least some cloud services of the plurality of cloud services, (iii) usage of the plurality of services respectively by the plurality of cloud services accounts, and (iv) a plurality of performance metrics relating to the plurality of cloud services when used in various combinations in a cloud, and
identifying, by machine logic, a recommended cloud service group based on the historical data set, where the recommended cloud service group is constituted by a subset of the cloud services identified in the historical data set, such that the subset of cloud services in the recommended cloud services group: (i) are dependent upon each other, and (ii) perform favorably on at least one performance metric, of the plurality of performance metrics, when used together in combination.
27 . The CS of claim 26 wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s):
recommending use of the recommended service group to a first cloud services user for use in a cloud.
28 . The CS of claim 26 wherein the plurality of performance metrics include at least one of the following: (i) provision time; (ii) uptime; (iii) downtime; (iv) fail-over rates; (v) fewest number of clicks; (vi) network latency; and (vii) user experience via feedback.
29 . The CS of claim 26 wherein the plurality of performance metrics include all of the following: (i) provision time; (ii) uptime; (iii) downtime; (iv) fail-over rates; (v) fewest number of clicks; (vi) network latency; and (vii) user experience via feedback.