Agile-Ant: Self-managing Distributed Cache Management for Cost Optimization of Big Data Applications
Hani Al-Sayeh, Muhammad Attahir Jibril, Kai-Uwe Sattler
摘要
Distributed in-memory processing frameworks accelerate application runs by caching important datasets in memory. Allocating a suitable cluster configuration for caching these datasets plays a crucial role in achieving minimal cost. We present Agile-ant, a self-managing framework that identifies important datasets and scales out the cluster memory to cache them on the fly without any human interaction, without any prior knowledge of the application, the characteristics of the input data, the specification of the computing resources and their utilization by multiple-tenants. We evaluate Agile-ant on various real-world applications. Compared with our baseline, Agile-ant reduces execution cost by 78.3 % on average and provides better performance than the related work.
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- Black or White? How to Develop an AutoTuner for Memory-based AnalyticsMayuresh Kunjir, Shivnath BabuSIGMOD 2020 · 被引用 65 次
- OPTIMUSCLOUD: Heterogeneous Configuration Optimization for Distributed Databases in the CloudAshraf Mahgoub, Alexander Medoff, Rakesh Kumar, Subrata Mitra 等USENIX ATC 2020 · 被引用 63 次
- Using Cloud Functions as Accelerator for Elastic Data AnalyticsHaoqiong Bian, Tiannan Sha, Anastasia AilamakiSIGMOD 2023 · 被引用 18 次
- Juggler: Autonomous Cost Optimization and Performance Prediction of Big Data ApplicationsHani Al-Sayeh, Bunjamin Memishi, Muhammad Attahir Jibril, Marcus Paradies 等SIGMOD 2022 · 被引用 16 次
- Detecting cache-related bugs in Spark applicationsHui Li, Dong Wang, Tianze Huang, Yu Gao 等ISSTA 2020 · 被引用 8 次
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