US-12619476-B2 - Optimization of cloud egress tasks

US12619476B2US 12619476 B2US12619476 B2US 12619476B2US-12619476-B2

Abstract

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.

Inventors

Dharma Teja Atluri
Neelamadhav Gantayat
Avirup Saha
Sampath Dechu

Assignees

INTERNATIONAL BUSINESS MACHINES CORPORATION

Dates

Publication Date: 20260505
Application Date: 20230628

Claims (20)

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.

Description

BACKGROUND The present invention relates generally to creating recommendations for cloud-based business tasks. More particularly, the present invention relates to a method, system, and computer program for optimizing egress of data jobs from cloud storage systems. The cost and effect on the business criticality are taken into consideration when making recommendations for performing the task of moving data. Cloud storage is a computing model that enables storing data and files on the internet through a provider that can be accessed either through the public internet or a dedicated private network. The provider of the cloud storage securely stores, manages, and maintains the storage servers, infrastructure, and network to ensure a user has access to the data when needed. Often a large amount of data can be stored ranging from gigabytes (GB) to hundreds of terabytes (TB). Many cloud storage providers do not charge for adding data to the storage system. However, costs for removing data can quickly add up causing huge unknown costs that are not charged until the end of a month or predetermined billing cycle. Cloud storage providers usually do not charge to transfer data into a cloud storage system which is called ingress. Ingress is when data leaves a network and goes to an external location. The network may be the network of a business. The external location may be a cloud storage system. When a user decides they need to access their data, the user will start an egress process. Egress occurs when a user's application writes data out to the user's internal network. Egress also occurs when a user repatriates data back to the user's on-premise environment. Data egress fees are one of the cloud's biggest hidden costs. Egress in the world of computer networking is traffic exiting an entity such as a cloud storage provider. Ingress is traffic entering the boundary of an entity such as a cloud storage provider. Ingress and egress can also refer to the movement of traffic into and out of a network, respectively. Large egress fees, those costing thousands of dollars per year, can hurt both large and small businesses. Cloud billing as part of public cloud adoption is considered a difficult concept to understand by both business leaders and Information Technology (IT) experts. Cloud storage providers include Amazon Web Service®, Azure®, Google Cloud and many other companies that provide storage options for businesses that can be accessed through the public internet or dedicated private networks (Amazon Web Service, Azure, are trademarks owned by their respective owners in the United States and other countries). The cloud storage providers place a limit on how much data can be transferred out of the cloud egress for free or lower cost. SUMMARY The illustrative embodiments provide for optimization of cloud egress costs. 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 also 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 also includes analyzing a current egress plan used in computing the computational cost of the job. The embodiment also includes reconfiguring the current plan to a second egress plan to reduce the computational cost of the job. The embodiment also 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. An embodiment includes a computer usable program product. The computer usable program product includes a computer-readable storage medium, and program instructions stored on the storage medium. An embodiment includes a computer system. The computer system includes a processor, a computer-readable memory, and a computer-readable storage medium, and program instructions stored on the storage medium for execution by the processor via the memory. BRIEF DESCRIPTION OF THE DRAWINGS The novel features believed characteristic of the invention are set forth in the appended claims. The invention itself, however, as well as a preferred mode of use, further objectives, and advantages thereof, will best be understood by reference to the following detailed description of the illustrative embodiments when read in conjunction with the accompanying drawings, wherein: FIG. 1 depicts a block diagram of a computing environment in accordance with an illustrative embodiment; FIG. 2 depicts a block diagram of a system overview in accordance with an illustrative embodiment; FIG. 3 depicts a flowchart of an example algorithm for calculating optimized egress of data in accordance with an il