Name
Abstract | ||
|---|---|---|
For in-memory computing frameworks such as Apache Spark [5, 6], objects (i.e., the intermediated data) can be accommodated in the main memory for speeding up the execution process. In this paper, we propose a cost-aware object management method for in-memory computing frameworks. When the main memory space of any worker node is not enough to accommodate the new computed or the retrieved object, we first pick appreciate objects which are already accommodated in the main memory as candidates for eviction and then evict objects with the minimal sum of the creation cost and the maximum sum of the occupied main memory space. According to the experimental results, we can achieve the goal under the 80/20 and 50/50 principles.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3167132.3167409 | SAC 2018: Symposium on Applied Computing
Pau
France
April, 2018 |
Keywords | Field | DocType |
In-Memory Computing Frameworks, Cost-Aware Object Management, Storage Management, Memory Management | Spark (mathematics),Computer science,In-Memory Processing,Memory management,Storage management,Eviction,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-1-4503-5191-1 | 0 | 0.34 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chin-Hsien Wu | 1 | 419 | 47.93 |
| Chien-Wei Chen | 2 | 0 | 0.34 |
| Kai-Chun Wang | 3 | 0 | 0.34 |