100 creates table 340 and stores table 340 in sustainability-based dataset 185. Table 340 includes entries to track releases of software code and their corresponding sustainability labels to indicate which releases of a particular software code are greener than other releases of software code.

Rows 350 and 355 show release 5 and 6 of Microservice A, which corresponds to a sustainability label of yellow and green, respectively. As such, release 6 of Microservice A is greener than release 5 of Microservice A. Rows 360 and 365 show release 2 and 3 of Microservice B, which corresponds to a sustainability label of Red and Yellow, respectively. Rows 370 and 375 show release 8 and 9 of App C, which both correspond to a sustainability label of Green.

FIG. 4 is a flow diagram of a method for assigning a sustainability label to software code to produce a sustainability-based dataset, in accordance with some embodiments of the present disclosure.

Method 400 may be performed by processing logic that may include hardware (e.g., circuitry, dedicated logic, programmable logic, a processor, a processing device, a central processing unit (CPU), a system-on-chip (SoC), etc.), software (e.g., instructions running/executing on a processing device), firmware (e.g., microcode), or a combination thereof. In some embodiments, at least a portion of method 400 may be performed by sustainability classifier 170, processing device 520 shown in FIG. 5, processing device 820 shown in FIG. 8, processing device 902 shown in FIG. 9, or a combination thereof.

With reference to FIG. 4, method 400 illustrates example functions used by various embodiments. Although specific function blocks (“blocks”) are disclosed in method 400, such blocks are examples. That is, embodiments are well suited to performing various other blocks or variations of the blocks recited in method 400. It is appreciated that the blocks in method 400 may be performed in an order different than presented, and that not all of the blocks in method 400 may be performed.

With reference to FIG. 4, method 400 begins at block 410, where processing logic deploys software code from a dataset into a computing environment, such as computing environment 150 shown in FIG. 1. At block 420, processing logic collects energy metrics of the software code executing in the computing environment. For example, referring to FIG. 1, energy metrics collector 160 collects energy metrics 165 of software code 155 executing in computing environment 150.

At block 430, processing logic determines a sustainability label for the software code based on the energy metrics. Referring to FIG. 1, sustainability classifier 170 receives energy metrics 165 and determines a sustainability label for the software code. In some embodiments, processing logic computes a sustainability value of the software code based on the energy metrics that indicate a performance per unit of time of the software code executing in the cloud environment. In some embodiments, processing logic identifies an amount of actions executed by the software code that consume a joule of energy per unit of time. Processing logic then uses the identified amount of actions to compute the sustainability value. In turn, processing logic determines the sustainability label of the software code based on comparing the sustainability value with one or more previous sustainability values corresponding to one or more previous releases of the software code.

At block 440, processing logic assigns the sustainability label to the software code to produce a sustainability-based dataset. Referring to FIG. 1, sustainability classifier 170 assigns one of labels 180 to one of the software codes to produce sustainability-based dataset 185.

FIG. 5 is a block diagram that illustrates an example system for producing a sustainability-based dataset based on collected software code energy metrics, in accordance with some embodiments of the present disclosure.

System 500 includes computer system 510, which includes processing device 520 and memory 530. Memory 530 stores instructions 535 that processing device 520 executes. During execution, processing device 520 deploys software code 540 from dataset 125 into a computing environment 150. Processing device 520 collects energy metrics 165 of the software code while executing in computing environment 150. In some embodiments, a Kepler tool, such as energy metrics collector 160, collects the energy metrics and provides the energy metrics to processing device 520.

Processing devices 520 determines a sustainability label 550 for the software code 540 based on the energy metrics 165 and, in turn, assigns the sustainability label to the software code 540 to produce a sustainability-based (SB) dataset 185. In some embodiments, processing device 520 assigns sustainability label 550 to software code 540 in dataset 125 to transform dataset 125 into a sustainability-based dataset.

FIG. 6 is a flow diagram of a method for assigning sustainability labels to software code releases based on sustainability information corresponding to previous releases of the software code, in accordance with some embodiments of the present disclosure.

Method 600 may be performed by processing logic that may include hardware (e.g., circuitry, dedicated logic, programmable logic, a processor, a processing device, a central processing unit (CPU), a system-on-chip (SoC), etc.), software (e.g., instructions running/executing on a processing device), firmware (e.g., microcode), or a combination thereof. In some embodiments, at least a portion of method 600 may be performed by sustainability classifier 170, processing device 520 shown in FIG. 5, processing device 820 shown in FIG. 8, processing device 902 shown in FIG. 9, or a combination thereof.

With reference to FIG. 6, method 600 illustrates example functions used by various embodiments. Although specific function blocks (“blocks”) are disclosed in method 600, such blocks are examples. That is, embodiments are well suited to performing various other blocks or variations of the blocks recited in method 600. It is appreciated that the blocks in method 600 may be performed in an order different than presented, and that not all of the blocks in method 600 may be performed.

With reference to FIG. 6, method 600 begins at block 610, where processing logic deploys a new release of software code from a dataset into a computing environment, such as computing environment 150 shown in FIG. 1. At block 620, processing logic computes a new sustainability value of the new release of software code based on energy metrics collected of the new release of software code while executing in the computing environment. As discussed above, in some embodiments, processing logic computes a sustainability value of the software code based on the energy metrics that indicate a performance per unit of time of the software code executing in the cloud environment. In some embodiments, processing logic identifies an amount of actions executed by the software code that consume a joule of energy per unit of time. Processing logic then uses the identified amount of actions to compute the sustainability value.

At block 630, processing logic ranks the new release of software code with one or more previous releases of software code based on comparing the new sustainability value with one or more previous sustainability values corresponding to the one or more previous releases of software code (see FIG. 7, table 730, and corresponding text for further details). At block 640, processing logic assigns a plurality of new sustainability labels to the new release of software code and the one or more previous releases of software code based on the ranking to produce/update a sustainability-based dataset (see FIG. 7, table 760 and corresponding text for further details). In some embodiments, processing logic adds a new table entry corresponding to the new release of software code into a sustainability table with a corresponding one of the plurality of new sustainability labels. The sustainability table further includes one or more previous table entries corresponding to the one or more previous releases of the software code and their corresponding one or more previous sustainability labels. Processing logic updates the one or more pervious sustainability labels with one more of the plurality of new sustainability labels. In some embodiments, processing logic removes one or more of the previous releases of software code from the sustainability table based on one or more criteria, such as maintaining sustainability information of the past two releases.

FIG. 7 is a block diagram that illustrates an approach of ranking software code releases based on sustainability information relative to their corresponding previous releases of software code, in accordance with some embodiments of the present disclosure.

Diagram 700 shows a ranking process and a labeling process performed by sustainability classifier 170 to rank and label new releases of software code relative to previous releases of software code. In some embodiments, sustainability classifier 170 re-labels previous releases of software code based on their corresponding rankings. For example, a previous release of software code may initially receive a low sustainability label, but after several releases of the software code, sustainability classifier 170 may determine that one of the previous releases should be assigned a higher sustainability label relative to the other releases. As such, sustainability classifier 170 may replace the original sustainability label of the previous release with a higher sustainability label (discussed in more detail below).

Table 710 includes sustainability information for software releases 1-5, which includes their corresponding sustainability values and their corresponding previously assigned sustainability labels. Table 710 may be stored in history store 175, sustainability-based dataset 185, or a combination thereof. When sustainability classifier 170 receives energy metrics 165 corresponding to release 6 of the software code, sustainability classifier 170 adds a table entry 715 with its corresponding sustainability value of 85. Entry 715 does not yet include a sustainability label for release 6 because, as discussed below, sustainability classifier 170 first ranks release 6 relative to releases 1-5.

Sustainability classifier 170 performs ranking process 720, which ranks releases 1-6 based on their corresponding sustainability values. Table 730 shows rankings 740, which ranks release 6 as 1^stdue to it having the highest sustainability value of 85. Releases 4, 3, 1, 5, and 2 are subsequently ranked based on their corresponding sustainability values of 82, 78, 75, 73, and 65.

Next, sustainability classifier 170 performs labeling process 750 on table 730. In some embodiments, sustainability classifier 170 partitions the table entries into thirds and ranks the third highest releases as “green,” the middle third releases as “yellow,” and the lower third releases as “red.” Table 760 shows that releases 6 and 4 are assigned a “G” sustainability label for green, releases 3 and 1 are assigned a “Y” sustainability label for yellow, and releases 5 and 2 are assigned an “R” label for red. As can be seen, release 5 was initially labeled as yellow in table 710, but is now re-labeled as R in table 760. Similarly, release 1 was initially labeled as green in table 710, but it is now labeled as yellow in table 760. By iteratively re-ranking and re-labeling releases of software code, sustainability classifier 170 continually maintains updated sustainability information of the various releases of software code.

FIG. 8 is a block diagram that illustrates an example system for ranking software code releases based on sustainability information relative to their corresponding previous releases of software code, in accordance with some embodiments of the present disclosure.

Diagram 800 shows computer system 810, which includes processing device 820 and memory 830. Memory 830 stores instructions 835 that processing device 820 executes. During execution, processing device 820 deploys a new release of software code 840 from a dataset 125 into a computing environment 150. Processing device 820 computes a new sustainability value 860 of the new release of software code 840 based on energy metrics 165 collected of the new release of software code 840 while executing in the computing environment 150.

Processing device 820 ranks the new release of software code 840 with one or more previous releases of software code 845 based on comparing the new sustainability value 860 with one or more previous sustainability values 850 corresponding to the one or more previous releases of software code 845.

Processing device 820 assigns a new sustainability label 870 to the new release of software code 840 and the one or more previous releases of software code 845 based on their corresponding ranking (see FIG. 7 and corresponding text for further details).

FIG. 9 illustrates a diagrammatic representation of a machine in the example form of a computer system 900 within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein for intelligently scheduling containers.

In alternative embodiments, the machine may be connected (e.g., networked) to other machines in a local area network (LAN), an intranet, an extranet, or the Internet. The machine may operate in the capacity of a server or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a server, a network router, a switch or bridge, a hub, an access point, a network access control device, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. In some embodiments, computer system 900 may be representative of a server.

Computer system 900 includes a processing device 902, a main memory 904 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM), a static memory 906 (e.g., flash memory, static random access memory (SRAM), etc.), and a data storage device 918 which communicate with each other via a bus 930. Any of the signals provided over various buses described herein may be time multiplexed with other signals and provided over one or more common buses. Additionally, the interconnection between circuit components or blocks may be shown as buses or as single signal lines. Each of the buses may alternatively be one or more single signal lines and each of the single signal lines may alternatively be buses.

Computer system 900 may further include a network interface device 908 which may communicate with a network 920. Computer system 900 also may include a video display unit 910 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device 912 (e.g., a keyboard), a cursor control device 914 (e.g., a mouse) and an acoustic signal generation device 916 (e.g., a speaker). In some embodiments, video display unit 910, alphanumeric input device 912, and cursor control device 914 may be combined into a single component or device (e.g., an LCD touch screen).

Processing device 902 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device may be complex instruction set computing (CISC) microprocessor, reduced instruction set computer (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or processor implementing other instruction sets, or processors implementing a combination of instruction sets. Processing device 902 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 902 is configured to execute sustainability labeling instructions 925, for performing the operations and steps discussed herein.

The data storage device 918 may include a machine-readable storage medium 928, on which is stored one or more sets of sustainability labeling instructions 925 (e.g., software) embodying any one or more of the methodologies of functions described herein. The sustainability labeling instructions 925 may also reside, completely or at least partially, within the main memory 904 or within the processing device 902 during execution thereof by the computer system 900; the main memory 904 and the processing device 902 also constituting machine-readable storage media. The sustainability labeling instructions 925 may further be transmitted or received over a network 920 via the network interface device 908.

The machine-readable storage medium 928 may also be used to store instructions to perform a method for intelligently scheduling containers, as described herein. While the machine-readable storage medium 928 is shown in an exemplary embodiment to be a single medium, the term “machine-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) that store the one or more sets of instructions. A machine-readable medium includes any mechanism for storing information in a form (e.g., software, processing application) readable by a machine (e.g., a computer). The machine-readable medium may include, but is not limited to, magnetic storage medium (e.g., floppy diskette); optical storage medium (e.g., CD-ROM); magneto-optical storage medium; read-only memory (ROM); random-access memory (RAM); erasable programmable memory (e.g., EPROM and EEPROM); flash memory; or another type of medium suitable for storing electronic instructions.

Example 1 is a method comprising: deploying software code from a dataset into a computing environment; collecting energy metrics of the software code while executing in the computing environment; determining, by a processing device, a sustainability label for the software code based on the energy metrics; and assigning the sustainability label to the software code to produce a sustainability-based dataset.

Example 2 is the method of Example 1, wherein the software code is a new release of the software code and the sustainability label is a new sustainability label, the method further comprising: computing a new sustainability value of the new release of software code based on the energy metrics, wherein the energy metrics indicate a performance per unit of time of the new release of software code while executing in the computing environment; and determining the new sustainability label of the new release of software code based on comparing the new sustainability value with one or more previous sustainability values corresponding to one or more previous releases of the software code.

Example 3 is the method of any of Examples 1-2, further comprising: adding a new table entry comprising the new sustainability label and corresponding to the new release of software code into a sustainability table, wherein the sustainability table further comprises one or more previous table entries corresponding to the one or more previous releases of the software code and their corresponding one or more previous sustainability labels.

Example 4 is the method of any of Examples 1-3, further comprising: identifying an amount of actions executed by the new release of the software code that consume a joule of energy per unit of time; and using the identified amount of actions to compute the new sustainability value.

Example 5 is the method of any of Examples 1-4, further comprising: ranking the new release of software code with the one or more previous releases of the software code based on their corresponding sustainability values; assigning, based on the ranking, the new sustainability label to the new release of software code; and assigning one or more updated sustainability labels to the one or more previous releases of software releases based on the ranking

Example 6 is the method of any of Examples 1-5, wherein, prior to deploying the software code, the method further comprises: extracting code collection information from a playbook, wherein the playbook comprises a scripted automation configuration that outlines one or more tasks to be executed; and determining that at least one of the one or more tasks correspond to the software code.

Example 7 is the method of any of Examples 1-6, wherein, prior to deploying the software code, a plurality of approved playbooks comprising the playbook are utilized by a large learning model (LLM, further comprising: training the LLM using the sustainability label and the software code to produce a trained sustainability-based LLM.

Example 8 is a system comprising: a processing device; and a memory to store instructions that, when executed by the processing device cause the processing device to: deploy software code from a dataset into a computing environment; collect energy metrics of the software code while executing in the computing environment; determine a sustainability label for the software code based on the energy metrics; and assign the sustainability label to the software code to produce a sustainability-based dataset.

Example 9 is the system of Example 8, wherein the software code is a new release of the software code and the sustainability label is a new sustainability label, and wherein the processing device, responsive to executing the instructions, further causes the system to: compute a new sustainability value of the new release of software code based on the energy metrics, wherein the energy metrics indicate a performance per unit of time of the new release of software code while executing in the computing environment; and determine the sustainability label of the new release of software code based on comparing the new sustainability value with one or more previous sustainability values corresponding to one or more previous releases of the software code.

Example 10 is the system of any of Examples 8-9, wherein the processing device, responsive to executing the instructions, further causes the system to: add a new table entry comprising the new sustainability label and corresponding to the new release of software code into a sustainability table, wherein the sustainability table further comprises one or more previous table entries corresponding to the one or more previous releases of the software code and their corresponding one or more previous sustainability labels.

Example 11 is the system of any of Examples 8-10, wherein the processing device, responsive to executing the instructions, further causes the system to: identify an amount of actions executed by the new release of the software code that consume a joule of energy per unit of time; and use the identified amount of actions to compute the new sustainability value.

Example 12 is the system of any of Examples 8-11, wherein the processing device, responsive to executing the instructions, further causes the system to: rank the new release of software code with the one or more previous releases of the software code based on their corresponding sustainability values; assign, based on the ranking, the new sustainability label to the new release of software code; and assign one or more updated sustainability labels to the one or more previous releases of software releases based on the ranking.

Example 13 is the system of any of Examples 8-12, wherein, prior to deploying the software code, the processing device, responsive to executing the instructions, further causes the system to: extract code collection information from a playbook, wherein the playbook comprises a scripted automation configuration that outlines one or more tasks to be executed; and determine that at least one of the one or more tasks correspond to the software code.

Example 14 is the system of any of Examples 8-13, wherein, prior to deploying the software code, a plurality of approved playbooks comprising the playbook are utilized by a large learning model (LLM), and wherein, prior to deploying the software code, the processing device, responsive to executing the instructions, further causes the system to: train the LLM using the sustainability label and the software code to produce a trained sustainability-based LLM.

Example 15 is a non-transitory computer readable medium, having instructions stored thereon which, when executed by a processing device, cause the processing device to: deploy software code from a dataset into a computing environment; collect energy metrics of the software code while executing in the computing environment; determine, by the processing device, a sustainability label for the software code based on the energy metrics; and assign the sustainability label to the software code to produce a sustainability-based dataset.

Example 16 is the non-transitory computer readable medium of Example 15, wherein the software code is a new release of the software code and the sustainability label is a new sustainability label, and wherein the processing device is to: compute a new sustainability value of the new release of software code based on the energy metrics, wherein the energy metrics indicate a performance per unit of time of the new release of software code while executing in the computing environment; and determine the sustainability label of the new release of software code based on comparing the new sustainability value with one or more previous sustainability values corresponding to one or more previous releases of the software code.

Example 17 is the non-transitory computer readable medium of any of Examples 15-16, wherein the processing device is to: add a new table entry comprising the new sustainability label and corresponding to the new release of software code into a sustainability table, wherein the sustainability table further comprises one or more previous table entries corresponding to the one or more previous releases of the software code and their corresponding one or more previous sustainability labels.

Example 18 is the non-transitory computer readable medium of any of Examples 15-17, wherein the processing device is to: identify an amount of actions executed by the new release of the software code that consume a joule of energy per unit of time; and use the identified amount of actions to compute the new sustainability value.

Example 19 is the non-transitory computer readable medium of any of Examples 15-18, wherein the processing device is to: rank the new release of software code with the one or more previous releases of the software code based on their corresponding sustainability values; assign, based on the ranking, the new sustainability label to the new release of software code; and assign one or more updated sustainability labels to the one or more previous releases of software releases based on the ranking.

Example 20 is the non-transitory computer readable medium of any of Examples 15-18, wherein, prior to deploying the software code, the processing device is to: extract code collection information from a playbook, wherein the playbook comprises a scripted automation configuration that outlines one or more tasks to be executed; and determine that at least one of the one or more tasks correspond to the software code.

Example 20 is the non-transitory computer readable medium of any of Examples 15-19, wherein, prior to deploying the software code, a plurality of approved playbooks comprising the playbook are utilized by a large learning model (LLM), and wherein the processing device is to: train the LLM using the sustainability label and the software code to produce a trained sustainability-based LLM.

Example 21 is an apparatus comprising: means to deploy software code from a dataset into a computing environment; collecting energy metrics of the software code while executing in the computing environment; means to determine a sustainability label for the software code based on the energy metrics; and means to assign the sustainability label to the software code in the dataset to produce a sustainability-based dataset.

Example 22 is the apparatus of Example 21, wherein the software code is a new release of the software code and the sustainability label is a new sustainability label, further comprising: means to compute a new sustainability value of the new release of software code based on the energy metrics, wherein the energy metrics indicate a performance per unit of time of the new release of software code while executing in the computing environment; and means to determine the new sustainability label of the new release of software code based on comparing the new sustainability value with one or more previous sustainability values corresponding to one or more previous releases of the software code.

Example 23 is the apparatus of any of Examples 21-22, further comprising: means to add a new table entry comprising the new sustainability label and corresponding to the new release of software code into a sustainability table, wherein the sustainability table further comprises one or more previous table entries corresponding to the one or more previous releases of the software code and their corresponding one or more previous sustainability labels.

Example 24 is the apparatus of any of Examples 21-23, further comprising: means to identify an amount of actions executed by the new release of the software code that consume a joule of energy per unit of time; and means to use the identified amount of actions to compute the new sustainability value.

Example 25 is the apparatus of any of Examples 21-24, wherein, prior to deploying the software code, further comprising: means to extract code collection information from a playbook, wherein the playbook comprises a scripted automation configuration that outlines one or more tasks to be executed; and means to determine that at least one of the one or more tasks correspond to the software code.