Two-Phase Data Group Allocation for Heterogeneous Node Balancing
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Solution Overview
Problem
Existing data allocation systems in distributed computing environments fail to efficiently balance load and utilize resources due to heterogeneous hardware configurations and dynamic workload patterns, leading to persistent imbalances and increased migration overhead during topology changes.
Innovation Solution
A two-phase allocation and correction scheme is employed, where data groups are initially assigned using a deterministic mapping function and subsequently updated based on resource metrics and convergence parameters to iteratively reallocate data groups to nodes with higher available capacity, reducing migration operations and improving load balancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a deterministic allocation scheme is used to assign data groups to servers, then allocation simplicity is improved, but load balancing deteriorates due to persistent imbalances and lack of adaptability to resource conditions
Solution Approach 1:
The allocation process is divided into two distinct phases: Phase 1 uses a deterministic mapping function for initial allocation, while Phase 2 applies a correction scheme with resource-aware algorithms. This segmentation allows each phase to optimize for its specific goal without compromising the other.
Solution Approach 2:
The system transitions from a static deterministic allocation to a dynamic two-phase approach where Phase 2 continuously adjusts allocations based on real-time resource metrics, workload patterns, and convergence parameters, enabling adaptability while maintaining initial allocation simplicity.
2Productivity
If allocations are updated based on resource metrics during topology changes, then load balancing is improved, but migration overhead increases due to a large number of data migrations
Solution Approach 1:
Phase 2 applies corrective allocations selectively rather than reassigning all data groups. The correction scheme targets only those allocations that deviate from optimal resource utilization, performing partial reallocations that reduce migration overhead while still improving load balancing.
Solution Approach 2:
The correction scheme uses resource metrics and convergence parameters as feedback to guide iterative reallocation decisions. This feedback mechanism enables the system to make targeted adjustments that improve load balancing with minimal migrations by focusing corrections only where needed.
3Adaptability or versatility
If a correction scheme is applied to update allocations based on resource metrics, then adaptability to dynamic environments is improved, but computational complexity increases
Solution Approach 1:
Phase 1 performs preliminary deterministic allocation using a mapping function, establishing an initial allocation state that Phase 2 can build upon. This preliminary action reduces the computational burden of Phase 2 by eliminating the need to evaluate all possible allocations from scratch.
Solution Approach 2:
The correction scheme adjusts allocation parameters iteratively based on resource metrics and convergence criteria. By changing parameters incrementally and using convergence thresholds to terminate iterations, the system achieves real-time adaptability while controlling computational complexity through bounded optimization.
Data Source
AI summary
Various examples, systems, and methods are disclosed relating to a hybrid allocation scheme for load-balanced data groups in distributed nodes. Some systems can allocate, using an allocation scheme corresponding to a first phase, a plurality of data groups to a plurality of nodes. Some systems can update, using a correction scheme corresponding to a second phase, at least one allocation of at least one data group of the plurality of data groups based at least on a convergence parameter and a resource metric. The resource metric corresponds to at least one resource indicator of a hardware configuration detected from at least one node of the plurality of nodes based at least on performance of at least one node command.