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Adaptive memory reservation strategy for heavy workloads in the Spark environment.

Bohan Li1, Xin He1, Junyang Yu1

  • 1School of Software, Henan University, Kaifeng, Henan Province, China.

Peerj. Computer Science
|December 16, 2024
PubMed
Summary

An adaptive memory reservation (AMR) strategy effectively addresses memory bottlenecks in Apache Spark for heavy workloads. This approach significantly reduces execution time by optimizing task parallelism and memory allocation, improving computational efficiency.

Keywords:
Adaptive memory reservationSpark environmentStorage location selectionTask parallelism

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Area of Science:

  • Computer Science
  • Big Data Analytics
  • Distributed Systems

Background:

  • The Internet of Things (IoT) and Industry 2.0 drive demand for extensive data computing.
  • Apache Spark is a key Big Data platform due to its in-memory computing, but faces memory bottlenecks with heavy workloads.
  • These bottlenecks lead to resilient distributed datasets (RDD) eviction and degraded computational efficiency.

Purpose of the Study:

  • To propose an adaptive memory reservation (AMR) strategy to mitigate memory bottleneck issues in Apache Spark for heavy workloads.
  • To optimize task parallelism and memory allocation for efficient computational processing.

Main Methods:

  • Modeled optimal task parallelism by minimizing the disparity between blocked and regular task completion.
  • Determined optimal memory for task parallelism to create efficient execution memory space.
  • Implemented adaptive execution memory reservation with dynamic adjustments (compression/expansion) based on task progress.
  • Selected suitable storage locations for different RDD types considering cache location costs and real-time memory usage.

Main Results:

  • The proposed AMR strategy effectively alleviates execution memory pressure in Spark.
  • Extensive experiments validated the effectiveness of AMR compared to existing solutions.
  • AMR achieved an approximate reduction of 46.8% in execution time.

Conclusions:

  • The AMR strategy provides a robust solution for managing memory-intensive workloads in Apache Spark.
  • Optimized memory reservation and dynamic adjustments enhance Spark's computational efficiency and performance.