Abstract
A number of graph processing platforms have emerged recently as a result of the growing demand on graph data analytics with complex and large-scale graph structured datasets. These platforms have been tailored for iterative
graph computations and can offer an order of magnitude performance gain over generic data-flow frameworks like Apache Hadoop and Spark. Nevertheless, the increasing availability of such platforms and their functionality overlap necessitates a comparative study on various aspects of the platforms, including applications, performance and energy efficiency. In this work, we focus on the energy efficiency aspect of some large scale graph processing
platforms. Specifically, we select two representatives, e.g., Apache Giraph and Spark GraphX, for the comparative study. We compare and analyze the energy consumption of these two platforms with PageRank, Strongly Connected
Component and Single Source Shortest Path algorithms over five different realistic graphs. Our experimental results demonstrate that GraphX outperforms Giraph in terms of energy consumption. Specifically, Giraph consumes 1.71 times more energy than GraphX on average for the mentioned algorithms
graph computations and can offer an order of magnitude performance gain over generic data-flow frameworks like Apache Hadoop and Spark. Nevertheless, the increasing availability of such platforms and their functionality overlap necessitates a comparative study on various aspects of the platforms, including applications, performance and energy efficiency. In this work, we focus on the energy efficiency aspect of some large scale graph processing
platforms. Specifically, we select two representatives, e.g., Apache Giraph and Spark GraphX, for the comparative study. We compare and analyze the energy consumption of these two platforms with PageRank, Strongly Connected
Component and Single Source Shortest Path algorithms over five different realistic graphs. Our experimental results demonstrate that GraphX outperforms Giraph in terms of energy consumption. Specifically, Giraph consumes 1.71 times more energy than GraphX on average for the mentioned algorithms
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct |
| Publisher | ACM |
| Pages | 1287-1294 |
| ISBN (Print) | 978-1-4503-4462-3 |
| DOIs | |
| Publication status | Published - 2016 |
| MoE publication type | A4 Article in a conference publication |
| Event | ACM International Joint Conference on Pervasive and Ubiquitous Computing - Heidelberg, Germany Duration: 12 Sep 2016 → 16 Sep 2016 |
Conference
| Conference | ACM International Joint Conference on Pervasive and Ubiquitous Computing |
|---|---|
| Abbreviated title | UbiComp |
| Country | Germany |
| City | Heidelberg |
| Period | 12/09/2016 → 16/09/2016 |