Multi-Cloud Workload Assignment via Stage Segmentation
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Solution Overview
Problem
Current workload implementations in cloud environments do not optimize resource allocation across different cloud vendors, leading to inefficiencies in resource usage and cyber resilience during workload execution, particularly in the face of system failures or malware attacks.
Innovation Solution
A method that determines and stores characteristics of multiple cloud vendors, divides workloads into logical stages, and assigns each stage to the most suitable cloud vendor based on comparisons of vendor characteristics, including running capability, cost, and security features, to optimize resource allocation and ensure cyber resilience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workload is implemented using a single cloud vendor or non-optimized multi-cloud approach, then implementation simplicity is maintained, but resource allocation efficiency and cyber resilience are suboptimal
Solution Approach 1:
The workload is divided into multiple logical stages that can be independently assigned to different cloud vendors. Each stage represents a discrete unit of work that can be optimized separately, allowing the system to achieve better resource allocation efficiency while managing complexity through structured decomposition of the overall workload.
Solution Approach 2:
The system dynamically changes assignment parameters by evaluating cloud vendor characteristics (such as performance metrics, cost, security features) and matching them to workload stage requirements. This parameter-based optimization enables efficient resource allocation while the automated evaluation process manages the complexity of multi-cloud coordination.
2Reliability
If workload data is secured and service level is maintained during implementation, then cyber resilience is improved, but resource requirements increase
Solution Approach 1:
Different cloud vendors are selected for different workload stages based on their specific strengths and characteristics. Security-critical stages are assigned to vendors with superior security features, while other stages may use vendors optimized for cost or performance. This local optimization achieves necessary cyber resilience without uniformly increasing resource requirements across all stages.
Solution Approach 2:
The system evaluates and changes security parameter assignments dynamically, matching security requirements of each workload stage with the security capabilities of available cloud vendors. This targeted approach ensures cyber resilience where needed while avoiding unnecessary resource consumption in less critical areas.
3Productivity
If cloud vendor characteristics are comprehensively analyzed and compared, then optimal workload assignment is achieved, but analysis time and computational overhead increase
Solution Approach 1:
Cloud vendor characteristics are pre-analyzed and stored in a database before workload assignment occurs. This preliminary action captures vendor capabilities, performance metrics, cost structures, and security features in advance, so that when workload assignment is needed, the system can quickly retrieve and compare preprocessed information rather than performing comprehensive analysis from scratch each time.
Solution Approach 2:
The system replaces manual or exhaustive analytical processes with automated computational evaluation using standardized criteria and algorithms. This substitution enables rapid comparison of cloud vendor characteristics against workload requirements, achieving optimal assignment while minimizing analysis time through systematic automated evaluation.
Data Source
AI summary
A computer-implemented method according to one aspect includes determining and storing characteristics of a plurality of cloud vendors; dividing a workload into a plurality of logical stages; determining characteristics of each of the plurality of logical stages; and for each of the plurality of logical stages, assigning the logical stage to one of the plurality of cloud vendors, based on a comparison of the characteristics of the plurality of cloud vendors to the characteristics of the logical stage. Data migration between the cloud vendors is performed during an implementation of the workload to ensure data is located at necessary cloud vendors during the corresponding tasks of the workload.