Cloud Program Operation Scheduling With Constraint-Based Service Selection

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Solution Overview

Problem

Conventional methods for executing program operations on cloud-based services do not provide multi-criteria optimization, leading to mismanaged resources, increased costs, and inefficiencies due to the inability to effectively schedule operations based on user and service provider constraints.

Innovation Solution

A method involving a machine learning algorithm that identifies optimal service component combinations for executing program operations on cloud-based services by considering user constraints such as deadlines and budgets, while accounting for service provider constraints like availability and pricing, to determine a suitable schedule that meets these criteria.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If high-end GPU is used to meet customer deadline, then processing speed is improved, but cost increases significantly

Engineering Contradiction:

Improveprocessing speedVSAvoidcost

Core Design Contradiction:

SpeedVSLoss of energy

Solution Approach 1:

The system dynamically changes service parameters (GPU type, storage capacity, network bandwidth) based on user constraints and real-time status to optimize the balance between processing speed and cost. The machine learning algorithm evaluates multiple parameter combinations to select the most cost-effective configuration that meets the deadline requirement.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system dynamically adjusts resource allocation and service component selection based on changing conditions including user constraints, service component status, and processing requirements. The scheduling is not static but adapts to real-time information about resource availability and performance characteristics.

Inventive Principle:

Principle #15Dynamics

2Volume of stationary object

If more storage space is allocated to customer, then storage capacity is improved, but resource utilization deteriorates due to unused storage

Engineering Contradiction:

Improvestorage capacityVSAvoidresource utilization

Core Design Contradiction:

Volume of stationary objectVSProductivity

Solution Approach 1:

The system selects storage capacity as a dynamic parameter based on the actual size of the dataset and user constraints. Rather than allocating fixed or excessive storage, the machine learning algorithm determines the optimal storage parameter that satisfies the customer's needs while maximizing resource utilization by allocating only what is necessary.

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If program operation is executed as single task, then simplicity is maintained, but scheduling flexibility and efficiency are reduced

Engineering Contradiction:

ImprovesimplicityVSAvoidscheduling efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system segments the program operation into multiple sub-operations that can be independently scheduled and executed. This segmentation enables the machine learning algorithm to optimize the execution schedule by assigning different sub-operations to different service components based on their status, constraints, and performance characteristics, thereby improving overall scheduling efficiency.

Inventive Principle:

Principle #1Segmentation

4Ease of operation

If conventional scheduling methods are used, then implementation simplicity is maintained, but multi-criteria optimization capability is lost

Engineering Contradiction:

Improveimplementation simplicityVSAvoidmulti-criteria optimization capability

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The system introduces a machine learning algorithm as an intermediary between the user's program operation request and the cloud service infrastructure. This intermediary evaluates multiple criteria including user constraints, service component status, processing requirements, and cost considerations to make intelligent scheduling decisions, thereby enabling multi-criteria optimization while abstracting the complexity from the user.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12430180B2Systems and methods for optimized execution of program operations on cloud-based services based on processing constraints

Publication Date: 2025.09.30 ACRONIS INT

AI summary

Disclosed herein are systems and method efficiently executing a program operation on a cloud-based service. In an exemplary aspect, a method comprises receiving a request to perform a program operation on a cloud-based service and at least one user constraint for performing the program operation, and determining a plurality of sub-operations that are comprised in the program operation. The method comprises identifying a plurality of service component combinations offered by the service provider that can execute the program operation, and identifying at least one processing constraint of each service component. The method comprises determining a service component combination from the plurality of service component combinations for executing the program operation based on the at least one user constraint and processing constraints. The method comprises executing the program operation by the determined service component combination.