Engineering topics

Kumbhare, Alok

Publications and source records attributed to Kumbhare, Alok.

3 records

DOE OSTI2015

Fault-Tolerant and Elastic Streaming MapReduce with Decentralized Coordination

The MapReduce programming model, due to its simplicity and scalability, has become an essential tool for processing large data volumes in distributed environments. Recent Stream Processing Systems (SPS) extend this model to provide low-latency analysis of high-velocity continuous data streams. However, integrating MapReduce with streaming poses challenges: first, the runtime variations in data characteristics such as data-rates and key-distribution cause resource overload, that inturn leads to fluctuations in the Quality of the Service (QoS); and second, the stateful reducers, whose state depends on the complete tuple history, necessitates efficient fault-recovery mechanisms to maintain the desired QoS in the presence of resource failures. We propose an integrated streaming MapReduce architecture leveraging the concept of consistent hashing to support runtime elasticity along with locality-aware data and state replication to provide efficient load-balancing with low-overhead fault-tolerance and parallel fault-recovery from multiple simultaneous failures. Our evaluation on a private cloud shows up to 2:8 improvement in peak throughput compared to Apache Storm SPS, and a low recovery latency of 700 -1500 ms from multiple failures.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Efficient Extraction of High Centrality Vertices in Distributed Graphs

Betweenness centrality (BC) is an important measure for identifying high value or critical vertices in graphs, in variety of domains such as communication networks, road networks, and social graphs. However, calculating betweenness values is prohibitively expensive and, more often, domain experts are interested only in the vertices with the highest centrality values. In this paper, we first propose a partition-centric algorithm (MS-BC) to calculate BC for a large distributed graph that optimizes resource utilization and improves overall performance. Further, we extend the notion of approximate BC by pruning the graph and removing a subset of edges and vertices that contribute the least to the betweenness values of other vertices (MSL-BC), which further improves the runtime performance. We evaluate the proposed algorithms using a mix of real-world and synthetic graphs on an HPC cluster and analyze its strengths and weaknesses. The experimental results show an improvement in performance of upto 12x for large sparse graphs as compared to the state-of-the-art, and at the same time highlights the need for better partitioning methods to enable a balanced workload across partitions for unbalanced graphs such as small-world or power-law graphs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

GoFFish: A Sub-Graph Centric Framework for Large-Scale Graph Analytics1

Large scale graph processing is a major research area for Big Data exploration. Vertex centric programming models like Pregel are gaining traction due to their simple abstraction that allows for scalable execution on distributed systems naturally. However, there are limitations to this approach which cause vertex centric algorithms to under-perform due to poor compute to communication overhead ratio and slow convergence of iterative superstep. In this paper we introduce GoFFish a scalable sub-graph centric framework co-designed with a distributed persistent graph storage for large scale graph analytics on commodity clusters. We introduce a sub-graph centric programming abstraction that combines the scalability of a vertex centric approach with the flexibility of shared memory sub-graph computation. We map Connected Components, SSSP and PageRank algorithms to this model to illustrate its flexibility. Further, we empirically analyze GoFFish using several real world graphs and demonstrate its significant performance improvement, orders of magnitude in some cases, compared to Apache Giraph, the leading open source vertex centric implementation. We map Connected Components, SSSP and PageRank algorithms to this model to illustrate its flexibility. Further, we empirically analyze GoFFish using several real world graphs and demonstrate its significant performance improvement, orders of magnitude in some cases, compared to Apache Giraph, the leading open source vertex centric implementation.

Simmhan, Yogesh↗