arXiv subjects

Ravikant Dindokar

Publications and source records attributed to Ravikant Dindokar.

2 recordsLinked to original sources

arXiv2016

A Meta-graph Approach to Analyze Subgraph-centric Distributed Programming Models

Component-centric distributed graph processing platforms that use a bulk synchronous parallel (BSP) programming model have gained traction. These address the short-comings of Big Data abstractions/platforms like MapReduce/Hadoop for large-scale graph processing. However, there is limited literature on foundational aspects of the behavior of these component-centric abstractions for different graphs, graph partitioning, and graph algorithms. Here, we propose a analytical approach based on a meta-graph sketch to examine the characteristics of component-centric graph programming models at a coarse granularity. In particular, we apply this sketch to subgraph- and block-centric abstractions, and draw a comparison with vertex-centric models like Google's Pregel. First, we explore the impact of various graph partitioning techniques on the meta-graph, and next consider the impact of the meta-graph on graph algorithms. This decouples the unwieldy large graph and their partitioning specific artifacts from their algorithmic analysis. We use 5 spatial and powerlaw graphs as exemplars, four different partitioning strategies, and PageRank and Breadth First Search as canonical algorithms. These analysis over the meta-graphs provide a reliable measure of the expected number of supersteps, and the communication and computational complexity of the algorithms for various graphs, and the relative merits of subgraph-centric models over vertex-centric ones.

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arXiv2015

Elastic Resource Allocation for Distributed Graph Processing Platforms

Distributed graph platforms like Pregel have used vertex- centric programming models to process the growing corpus of graph datasets using commodity clusters. The irregular structure of graphs cause load imbalances across machines operating on graph partitions, and this is exacerbated for non-stationary graph algorithms such as traversals, where not all parts of the graph are active at the same time. As a result, such graph platforms, even as they scale, do not make efficient use of distributed resources. Clouds offer elastic virtual machines that can be leveraged to improve the resource utilization for such platforms and hence reduce the monetary cost for their execution. In this paper, we propose strategies for elastic placement of graph partitions on Cloud VMs for subgraphcentric programming model to reduce the cost of execution compared to a static placement, even as we minimize the increase in makespan. These strategies are innovative in modeling the graph algorithms behavior. We validate our strategies for several graphs, using runtime tra- ces for their distributed execution of a Breadth First Search (BFS) algorithms on our subgraph-centric GoFFish graph platform. Our strategies are able to reduce the cost of exe- cution by up to 42%, compared to a static placement, while achieving a makespan that is within 29% of the optimal

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