Cloud Nodal Work Assignments for Data Redundancy
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Solution Overview
Problem
Data redundancy mechanisms in cloud computing networks lead to network congestion, excessive resource consumption, and high energy usage due to the maintenance of multiple redundant copies of data.
Innovation Solution
A cloud computing network implements a policy-driven approach to efficiently distribute redundant work items by continuously re-evaluating and modifying work assignments based on replication assignment skews, regional/zonal/clusteral storage targets, and leadership penalties, thereby optimizing hardware and software operations, network traffic, and energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If redundant copies of data are maintained to ensure availability and reliability, then data reliability is improved, but network bandwidth consumption increases
Solution Approach 1:
The patent implements dynamic work assignments that are continuously re-evaluated and modified based on changing network conditions, node availability, and replication assignment skews. This allows the system to adaptively optimize data placement and transfer operations, reducing unnecessary network bandwidth consumption while maintaining the required number of redundant copies for data reliability.
Solution Approach 2:
The system continuously monitors replication assignment skews and uses this feedback information to regenerate work assignments. By incorporating feedback loops that track the distribution of redundant copies across nodes and adjust assignments accordingly, the system minimizes excessive data transfers and optimizes network bandwidth utilization while preserving data availability.
2Reliability
If redundant copies of data are maintained to ensure availability and reliability, then data reliability is improved, but computer resource consumption increases
Solution Approach 1:
The patent employs dynamic work assignments that are continuously re-evaluated based on current system state, node capacity, and replication needs. This dynamic approach allows the system to optimize resource utilization by assigning redundant copy maintenance tasks only to nodes that have available capacity, thereby reducing overall computer resource consumption while maintaining data reliability through appropriate redundancy levels.
Solution Approach 2:
The system modifies work assignments based on changing parameters such as node availability, storage capacity, and replication assignment skews. By dynamically adjusting assignment parameters rather than maintaining static redundant copy distributions, the system reduces unnecessary computer resource consumption while preserving the required data availability and reliability.
3Productivity
If work assignments are repeatedly re-evaluated to implement incremental improvements, then network performance is improved, but energy consumption increases
Solution Approach 1:
The patent implements periodic re-evaluation of work assignments at strategically determined intervals rather than continuous monitoring. This periodic approach allows the system to capture significant performance improvements through incremental optimizations while avoiding the excessive energy consumption that would result from continuous re-evaluation and regeneration of work assignments.
Data Source
AI summary
Nodal work assignments efficiently distribute server work items, such as storing redundant copies of electronic data. A cloud computing network establishes a policy that governs how and where the redundant copies are stored cloud computing nodes (such as by region, zone, and cluster targets). The cloud computing network repeatedly or continuously re-evaluates the work assignments based on replication assignment skews and/or leadership penalties. The nodal work assignments thus minimize hardware and software operations, network traffic, and electrical energy consumption.