Cloud Workload Deployment Unit Allocation via Context Auditing
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Solution Overview
Problem
In cloud computing, existing methods lack an efficient mechanism for selectively allocating deployment units across multiple clouds based on specific context requirements, leading to suboptimal resource utilization and performance.
Innovation Solution
A method and system for selectively allocating deployment units among multiple clouds by identifying and auditing clouds that satisfy the context-specific requirements of each deployment unit, creating a deployment plan to automatically allocate units to the most suitable clouds, ensuring optimal resource utilization and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deployment units are allocated to clouds without context-based selection, then allocation process is simple, but resource utilization and performance are suboptimal
Solution Approach 1:
The workload is divided into multiple deployment units, each with its own context requirements. The system segments the allocation process into identifying individual deployment unit contexts, auditing candidate clouds against those contexts, and creating targeted deployment plans for each unit, thereby enabling precise resource matching without overwhelming complexity
Solution Approach 2:
The system changes the allocation approach from generic to context-specific by introducing context parameters (performance requirements, compliance needs, cost constraints) as selection criteria. This parameter-based filtering enables optimized resource utilization while maintaining manageable complexity through structured evaluation
2Measurement precision
If multiple clouds are audited for each deployment unit context, then allocation accuracy improves, but processing time and complexity increase
Solution Approach 1:
The system performs preliminary actions by identifying context requirements for each deployment unit before conducting audits. By pre-defining what to look for (performance metrics, compliance standards, cost parameters), the auditing process becomes more focused and efficient, reducing unnecessary evaluation time while maintaining high selection accuracy
Solution Approach 2:
Different clouds are evaluated against specific context requirements relevant to each deployment unit. Rather than applying uniform evaluation criteria, the system tailors the audit focus to local needs (e.g., security compliance for sensitive data, performance metrics for compute-intensive workloads), improving accuracy without uniformly increasing processing time across all scenarios
3Reliability
If deployment units are allocated based on specific context requirements, then performance and cost-effectiveness improve, but system complexity increases
Solution Approach 1:
The system creates a universal deployment plan framework that handles multiple context types (performance, compliance, cost) through a single integrated process. The audit mechanism and deployment plan structure remain consistent across different deployment units, providing reliability through standardized procedures while accommodating diverse requirements through configurable context parameters
Solution Approach 2:
The system introduces an intermediary deployment plan that mediates between context requirements and cloud selection. This intermediate artifact translates complex context criteria into actionable allocation decisions, improving performance optimization while managing system complexity through structured information transformation
Data Source
AI summary
A method of selectively allocating a plurality of deployment units among a plurality of clouds. The method can include identifying a first context of a first deployment unit of a workload and identifying a second context of a second deployment unit of the workload. Based on the first context, a first of the plurality of clouds that satisfies at least one requirement indicated by the first context can be identified and the first deployment unit can be automatically allocated to the first cloud. Based on the second context, a second of the plurality of clouds that satisfies at least one requirement indicated by the second context can be identified, wherein the first cloud does not satisfy the requirement indicated by the second context, and the second deployment unit can be automatically allocated to the second cloud.