Distributed Deduplication Pipeline Bottleneck Balancing

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Solution Overview

Problem

Distributed clusters face challenges in scaling deduplication performance efficiently due to increased costs and resource bottlenecks, which are not effectively addressed by existing solutions.

Innovation Solution

Monitoring runtime performance of dedup pipelines across nodes to identify bottlenecks and reallocating machine resources to balance costs and eliminate stalls, ensuring overall dedup and compression ratios are optimized.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If deduplication is implemented in distributed cluster, then storage size is reduced, but processing capacity and cache requirements increase

Engineering Contradiction:

Improvestorage sizeVSAvoidprocessing capacity

Core Design Contradiction:

Quantity of substanceVSDevice complexity

Solution Approach 1:

The deduplication pipeline is divided into multiple stages (ingestion, preprocessing, deduplication, post-processing) that can be independently optimized and executed across different nodes in the distributed cluster, allowing storage reduction while distributing processing load

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces a time dimension by implementing asynchronous pipeline execution with staging areas, allowing data to flow through different processing stages at different rates, thus reducing the peak processing capacity required at any single moment

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Adaptability or versatility

If more machines are added to the cluster, then scalability is improved, but cost and maintenance increase

Engineering Contradiction:

ImprovescalabilityVSAvoidcost

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

Each node in the distributed cluster is designed to perform multiple functions (ingestion, preprocessing, deduplication, post-processing) based on available resources, allowing existing machines to be utilized more efficiently and reducing the need for additional hardware investments

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system dynamically allocates pipeline stages to different nodes based on real-time resource availability and performance metrics, enabling the cluster to adapt to changing conditions without requiring fixed infrastructure investments

Inventive Principle:

Principle #15Dynamics

3Quantity of substance

If deduplication pipeline is executed, then data duplication is eliminated, but transmission bandwidth is loaded

Engineering Contradiction:

Improvedata duplicationVSAvoidtransmission bandwidth

Core Design Contradiction:

Quantity of substanceVSLoss of energy

Solution Approach 1:

Preprocessing operations including chunking, fingerprinting, and initial deduplication checks are performed at the source node before data transmission, eliminating obvious duplicates locally and reducing the volume of data that needs to be transmitted across the network

Inventive Principle:

Principle #10Preliminary action

4Productivity

If pipeline stalls occur, then task completion time increases, but resource utilization becomes unbalanced

Engineering Contradiction:

Improvetask completion timeVSAvoidresource utilization balance

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system continuously monitors pipeline performance metrics including throughput, latency, and resource utilization at each stage, using this feedback to dynamically adjust resource allocation and identify bottlenecks that cause stalls, thereby improving task completion time while maintaining balanced resource utilization

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11262935B2Optimized distributed deduplication for distributed cluster

Publication Date: 2022.03.01 EMC IP HLDG CO LLC

AI summary

Distributed deduplication wherein runtime performance of dedup pipelines in all nodes is monitored. The bottleneck for each pipeline is identified and machine resources from different nodes are reallocated to seek to balance the costs of each stage of each task in each of the pipelines. While the overall cost for each task may remain the same, stalls may be eliminated such that the total cost to complete all the tasks is reduced. The global dedup ratio and the local compression ratio may be used to weight certain stage costs.