Cluster Data Rebalancing for Heterogeneous Node Utilization
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Solution Overview
Problem
Data centers face low resource utilization due to heterogeneous node capabilities in clusters, leading to performance degradations and inefficient data distribution, especially in latency-sensitive environments.
Innovation Solution
A computer-implemented manager collects access pattern data to identify overutilized nodes and automatically rebalances data by moving it from overutilized nodes to underutilized ones, ensuring balanced resource utilization across the cluster.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is distributed evenly across all nodes in a heterogeneous cluster, then data placement simplicity is maintained, but performance degradation occurs due to mismatch between node capabilities and data processing load
Solution Approach 1:
The patent applies local quality by assigning different data placement strategies to different nodes based on their individual characteristics. High-performance nodes receive larger data chunks and more processing tasks, while low-performance nodes receive smaller chunks. This node-specific differentiation resolves the contradiction by maintaining operational simplicity through automated classification while optimizing performance according to each node's local capabilities.
Solution Approach 2:
The system dynamically changes the data distribution parameters based on node performance characteristics. Instead of using a fixed even-distribution parameter, the system adjusts data chunk sizes and placement ratios according to measured node performance metrics. This parameter adaptation allows the system to maintain simplicity in implementation while achieving performance optimization through dynamic parameter adjustment.
2Productivity
If data is placed according to node computation power in a heterogeneous cluster, then processing performance is improved, but system complexity increases due to need for continuous monitoring and redistribution
Solution Approach 1:
The patent applies preliminary action by classifying nodes into performance categories during initial cluster setup or during idle periods, before actual data processing begins. This advance classification eliminates the need for complex real-time monitoring and redistribution during active processing, thus improving performance while minimizing the complexity of continuous management interventions.
Solution Approach 2:
The system implements periodic performance assessment and data redistribution at scheduled intervals rather than continuously. This periodic action maintains performance optimization while significantly reducing system complexity by limiting management interventions to specific time points, allowing the system to operate in a simpler state between assessments.
3Productivity
If workloads are consolidated onto fewer machines to maximize utilization, then resource utilization efficiency is improved, but interference between workloads increases leading to performance degradation
Solution Approach 1:
The patent applies local quality by assigning different workload consolidation strategies to different nodes based on their performance characteristics. High-performance nodes are designated as consolidation targets for multiple workloads, while low-performance nodes maintain more isolated workload assignments. This differentiated approach maximizes utilization efficiency on capable nodes while preventing overload and interference on less capable nodes, thus maintaining workload execution stability.
4Reliability
If load balancing is applied to evenly spread compute workload across available machines, then service level agreement compliance is improved, but network bandwidth consumption increases due to frequent data transfers
Solution Approach 1:
The patent applies local quality by assigning data placement and workload distribution strategies tailored to each node's performance characteristics. High-performance nodes are designated as primary data storage locations and processing targets, reducing the need for data transfers to and from low-performance nodes. This node-specific differentiation maintains SLA compliance through adequate resource allocation while minimizing unnecessary network transfers and associated bandwidth consumption.
Data Source
AI summary
An embodiment includes a method for use in managing a cluster comprising a plurality of computers, each computer comprising at least one hardware processor coupled to at least one memory. The method comprises a computer-implemented manager: collecting access pattern data for the plurality of computers; based at least in part on the access pattern data, determining that at least a first one of the plurality of computers is overutilized; and responsive to determining that the first computer is overutilized, moving stored data from the first computer to at least a second one of the plurality of computers.