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CCA: Cost-Capacity-Aware Caching for In-Memory Data Analytics Frameworks.

Seongsoo Park1, Minseop Jeong2, Hwansoo Han2

  • 1Department of Electrical and Computer Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Korea.

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This summary is machine-generated.

This study introduces a new caching scheme for in-memory data analytics frameworks. The proposed method optimizes intermediate data caching, significantly boosting performance in cloud-based IoT and wearable device data processing.

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Area of Science:

  • Computer Science
  • Data Engineering
  • Cloud Computing

Background:

  • Cloud offloading of data analytics for IoT and wearable devices is increasing.
  • Optimizing data analytics frameworks is crucial for processing large volumes of sensed data.
  • Caching intermediate data speeds up iterative computations in data analytics.

Purpose of the Study:

  • To propose an application-specific, cost-capacity-aware caching scheme for in-memory data analytics frameworks.
  • To automate the selection of optimal caching strategies, removing the need for manual programmer intervention.
  • To enhance the performance of data processing from IoT and wearable devices in the cloud.

Main Methods:

  • Developed a cost model using representative inputs.
  • Utilized execution flow analysis from DAG schedules to identify caching candidates.
  • Implemented the scheme in Apache Spark and evaluated on HiBench benchmarks.

Main Results:

  • The proposed caching scheme improved performance by 27% with sufficient cache memory.
  • Performance increased by 11% even with insufficient cache memory compared to original benchmarks.
  • Automated caching selection enhanced efficiency in Apache Spark.

Conclusions:

  • The application-specific, cost-capacity-aware caching scheme effectively optimizes intermediate data caching.
  • Automated caching selection in data analytics frameworks offers significant performance gains.
  • This approach is vital for efficient cloud-based processing of large-scale IoT and wearable device data.