Optimization of cloud egress tasks
An embodiment includes computing a computational cost of a job, using a first machine learning algorithm. The job may include an original amount of an egress of data from a cloud computing environment. The embodiment includes determining, using a second machine learning algorithm, the amount of the egress of data corresponding to the job has a computer business criticality that exceeds a threshold level of business criticality. The embodiment includes analyzing a current egress plan used in computing the computational cost of the job. The embodiment includes reconfiguring the current plan to a second egress plan to reduce the computational cost of the job. The embodiment includes implementing a second plan such responsive to execution of the job, the second plan causes data egress behavior to change from the original egress of data behavior. A modified egress behavior causes an effective reduction in the egress cost of the job.
1 . A computer-implemented method comprising:
computing a computational cost of a job, using a first machine learning component, the job comprising an original amount of an egress of data from a cloud computing environment;
determining, using a second machine learning component, the amount of the egress of data corresponding to the job has a computed criticality that exceeds a threshold level of criticality for a cycle wherein the computed criticality comprises computing an impact of the amount of the egress of data corresponding to the job from the cloud computing environment for the cycle;
responsive to the amount of the egress of data corresponding to the job has the computed criticality that exceeds the threshold level of criticality for the cycle, causing to keep a current egress plan or to reconfigure the current egress plan to a second egress plan, analyzing a current egress plan used in computing the cost of the job and reconfiguring the current egress plan to the second egress plan to reduce an egress cost of the job; and
implementing a second plan such that responsive to execution of the job, the second plan causes data egress behavior to change from the original egress of data behavior, wherein a modified egress behavior causes an effective reduction in the egress cost of the job.
2 . The computer-implemented method of claim 1 , wherein the first machine learning component takes into account historical data and a set of user defined factors.
3 . The computer-implemented method of claim 1 , wherein reconfiguring the current plan comprises an alternate time for the job.
4 . The computer-implemented method of claim 1 , wherein the first machine learning component comprises historical data stored in a job repository comprising a priority and an impact.
5 . The computer-implemented method of claim 1 , further comprising comparing the original egress of data job to a threshold of the criticality.
6 . The computer-implemented method of claim 1 , further comprising optimizing the cost the computational cost in relation to a threshold of the criticality.
7 . The computer-implemented method of claim 1 , wherein the second egress plan comprises predicting the impact wherein predicting the impact is based on seasonality and revenue of past jobs in the cycle.
8 . The computer-implemented method of claim 1 , wherein the second egress plan comprises an alternative cloud storage provider.
9 . The computer-implemented method of claim 1 , further comprising providing an option to choose the original egress of data job or the second egress plan wherein the second egress plan having an excess cost over the original egress of data job is chosen.
10 . A computer program product comprising one or more computer readable storage medium, and program instructions collectively stored on the one or more computer readable storage medium, the program instructions executable by a processor to cause the processor to perform operations comprising:
computing a computational cost of a job, using a first machine learning component, the job comprising an original amount of an egress of data from a cloud computing environment;
determining, using a second machine learning component, the amount of the egress of data corresponding to the job has a computed criticality that exceeds a threshold level of criticality for a cycle wherein the computed criticality comprises computing an impact of the amount of the egress of data corresponding to the job from the cloud computing environment for the cycle;
responsive to the amount of the egress of data corresponding to the job has the computed criticality that exceeds the threshold level of criticality for the cycle, causing to keep a current egress plan or to reconfigure the current egress plan to a second egress plan, analyzing a current egress plan used in computing the cost of the job and reconfiguring the current egress plan to the second egress plan to reduce an egress cost of the job; and
implementing a second plan such that responsive to execution of the job, the second plan causes data egress behavior to change from the original egress of data behavior, wherein a modified egress behavior causes an effective reduction in the egress cost of the job.
11 . The computer program product of claim 10 , wherein the stored program instructions are stored in a computer readable storage device in a data processing system, and wherein the stored program instructions are transferred over a network from a remote data processing system.
12 . The computer program product of claim 10 , wherein the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system, further comprising:
comparing the original amount of egress of data job to a threshold of the criticality.
13 . The computer program product of claim 10 , wherein the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system, further comprising:
providing an option to choose the original amount of the egress of data job or the second egress plan wherein the second egress plan having an excess cost over the original egress of data job is chosen.
14 . The computer program product of claim 10 , wherein the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system, further comprising:
optimizing the computation cost in relation to a threshold of the criticality.
15 . The computer usable program product of claim 13 , wherein the computer usable code is stored in a computer readable storage device in a server data processing system, and wherein the computer usable code is downloaded over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system.
16 . A computer system comprising a processor and one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by the processor to cause the processor to perform operations comprising:
computing a computational cost of a job, using a first machine learning component, the job comprising an original amount of an egress of data from a cloud computing environment;
determining, using a second machine learning component, the amount of the egress of data corresponding to the job has a computed criticality that exceeds a threshold level of criticality for a cycle wherein the computed criticality comprises computing an impact of the amount of the egress of data corresponding to the job from the cloud computing environment for the cycle;
responsive to the amount of the egress of data corresponding to the job has the computed criticality that exceeds the threshold level of criticality for the cycle, causing to keep a current egress plan or to reconfigure the current egress plan to a second egress plan, analyzing a current egress plan used in computing the cost of the job and reconfiguring the current egress plan to the second egress plan to reduce an egress cost of the job; and
implementing a second plan such that responsive to execution of the job, the second plan causes data egress behavior to change from the original egress of data behavior, wherein a modified egress behavior causes an effective reduction in the egress cost of the job.
17 . The computer system of claim 16 , wherein the first machine learning component takes into account historical data and a set of user defined factors.
18 . The computer system of claim 16 , wherein reconfiguring the current plan comprises an alternate time for the job.
19 . The computer system of claim 16 , further comprising:
further comprising optimizing the cost in relation to a threshold of the criticality.
20 . The computer system of claim 16 , further comprising providing an option to choose the original egress of data job or the second egress plan wherein the second egress plan having an excess cost over the original egress of data job is chosen.