Joint Cloud Storage And Processing Allocation Under Data Regulations
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Solution Overview
Problem
Conventional approaches fail to efficiently address data placement for storage and processing in cloud environments while adhering to varying data regulations and optimizing operational costs, leading to suboptimal selection of cloud service providers and data centers.
Innovation Solution
A method and system for data regulations-aware cloud storage and processing service allocation that utilizes a joint optimization model to generate feasible allocations and compute transfer costs, considering data regulations and tiered pricing, using Multi-Criteria Decision Making (MCDM) to rank migration pairs and achieve near-optimal solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If data is placed in geographically distributed data centers to reduce transfer costs and improve QoS, then transfer cost and service quality are improved, but the number of selected data centers increases leading to increased overall operational cost
Solution Approach 1:
The patent merges multiple data sources into fewer data center locations by evaluating trade-offs between transfer costs and storage costs. The joint optimization model determines optimal consolidation strategies where multiple user data sources are assigned to shared data centers, reducing the total number of active data centers while balancing transfer and storage cost implications.
2Productivity
If data is consolidated into fewer data centers to reduce operational cost, then overall cost is reduced, but transfer cost and QoS may increase
Solution Approach 1:
The patent changes the optimization parameters by introducing a joint cost function that combines both transfer cost and storage cost with adjustable weighting factors. This allows the system to dynamically adjust the balance between consolidation benefits and transfer penalties based on specific operational requirements, finding optimal consolidation ratios that minimize total cost.
3Loss of energy
If conventional greedy approaches are used to select nearest data centers, then transfer cost is minimized, but storage cost optimization is not ensured given tier pricing policy
Solution Approach 1:
The patent creates a universal optimization framework that simultaneously handles both transfer cost minimization and storage cost optimization under tier pricing policies. The joint optimization model serves multiple objectives at once, evaluating both transfer distance/cost and storage tier benefits together, rather than treating them as separate sequential decisions.
4Adaptability or versatility
If data placement decisions are made without considering data regulations, then operational flexibility is improved, but compliance with data regulations cannot be ensured
Solution Approach 1:
The patent applies preliminary action by incorporating data regulation constraints into the optimization model before making placement decisions. The system pre-processes regulation requirements and integrates them as hard constraints in the joint optimization framework, ensuring compliance is built into the solution space from the beginning rather than added as a post-processing filter.
Data Source
AI summary
The present disclosure a method for data regulations-aware cloud storage and processing service allocation. Conventional approaches fail to address the technical problem of data placement for storage as well as processing, considering multiple criteria. Further, the conventional approaches fail to address compliance with data regulations, tier pricing policy for multiple Cloud Service Providers (CSPs) which impacts storage and processing center selection and constraint satisfaction. The present disclosure proposes a joint optimization model for the selection of storage and processing services from multiple cloud service providers, taking into practical consideration of data regulations and tiered pricing, which has not been addressed in the prior art. To solve this hard multi-objective combinatorial optimization problem, the present disclosure utilizes a cost-reduction-based algorithm for obtaining optimal solution.