Profiling memory vulnerability of big-data applications
- Rameshan, Navaneeth
- Birke, R.
- Navarro Moldes, Leandro
- Vlassov, Vladimir
- Urgaonkar, B.
- Kesidis, G.
- Schmatz, M.
- Chen, L. Y.
Publication date
January 2016
DOI Publisher
Institute of Electrical and Electronics Engineers (IEEE)Abstract
Motivated by the increasing popularity of hosting in-memory big-data analytics in cloud, we present a profiling methodology that can understand how different memory subsystems, i.e., cache and memory bandwidth, are susceptible to the impact of interference from co-located applications. We first describe the design of the proposed tool and demonstrate a case study consisting of five Spark applications on real-life data set.Peer ReviewedPostprint (published version
Add to ORKG