Data Digital Decoupling Framework Offloads Legacy Mainframe Workloads
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Solution Overview
Problem
Legacy systems, such as mainframes, face inefficiencies due to high consumption of MIPS/MSUs for read-only transactions, uncontrolled total cost of ownership, lack of skilled talent, limited analytics capabilities, tight coupling, and data management infrastructure deterioration, leading to constrained workloads and increased costs.
Innovation Solution
A data digital decoupling framework that discovers data in legacy systems, identifies high-cost queries, and offloads data to a target system for read-only transactions, optimizing MIPS/MSUs consumption by leveraging technologies and methodologies to build data and analytical systems that execute on top of legacy systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored and processed in legacy mainframe systems, then data processing capability and transaction handling are maintained, but MIPS/MSU consumption increases and system flexibility decreases
Solution Approach 1:
The system segments data into hot data (frequently accessed) and cold data (infrequently accessed), storing hot data in cloud-based object storage and cold data in legacy mainframe systems. This segmentation reduces MIPS/MSU consumption by offloading frequently accessed data from the mainframe while maintaining transaction handling capability for critical operations.
Solution Approach 2:
An intermediary data virtualization layer is introduced between applications and data storage, enabling transparent access to data regardless of its physical location. This mediator allows the system to maintain existing transaction workflows while reducing mainframe dependency by accessing data from cloud storage when appropriate.
2Stability of the object's composition
If all data is kept in the legacy system, then data consistency is maintained, but system complexity and cost of ownership increase
Solution Approach 1:
Different data retention strategies are applied to different data based on access patterns and criticality. Frequently accessed data is offloaded to cloud storage with appropriate synchronization mechanisms, while critical data remains in the legacy system. This local quality approach maintains data consistency where needed while reducing overall system complexity.
Solution Approach 2:
The system dynamically changes data location parameters based on access frequency and transaction requirements. Data that was previously always stored in the legacy system is now conditionally stored in cloud storage or legacy systems based on measured access patterns, reducing complexity while maintaining consistency.
3Reliability
If mainframe systems are used for read-only transactions, then transaction processing is reliable, but resource utilization is inefficient
Solution Approach 1:
The system creates copies of frequently accessed data in cloud-based object storage, allowing read-only transactions to access data from the cloud copy rather than querying the mainframe. This copying strategy maintains transaction reliability while dramatically improving resource utilization by offloading read operations from the mainframe.
Solution Approach 2:
Data is pre-loaded into cloud storage based on predicted access patterns and transaction requirements. This preliminary action ensures that frequently accessed data is already available in the cloud when needed, eliminating the need for mainframe processing of read-only transactions and improving overall resource efficiency.
Data Source
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AI summary
Methods, systems, and computer-readable storage media for determining for each query in a set of high-cost queries, an access pattern to data objects accessed by the query in a legacy system, determining for each query in a set of low-cost queries, an access pattern to each data object accessed by the query in the legacy system, providing a first set of design patterns representative of first data objects of the legacy system to be offloaded to the target system and a second set of design patterns representative of second data objects of the legacy system to remain on the legacy system, and executing at least one design pattern of the first set of design patterns to offload one or more first data objects to the target system.