Beehive: A Framework for Graph Data Analytics on Cloud Computing Platforms

Tripathi, Anand; Padhye, Vinit; Sunkara, Tara Sasank

doi:10.1109/icppw.2014.50

Cited by 4 publications

(6 citation statements)

References 26 publications

(12 reference statements)

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“…Our initial design [18] supported reading and writing of an entire node object by a task computation. This imposed performance overheads due to the serialization cost when accessing a remote object.…”

Section: A Remote Data Access Mechanismsmentioning

confidence: 99%

“…In comparison to our previous work in [18], the Beehive system described here provides significantly better performance by supporting object caching, fine-grain operations on remote objects, and multi-threaded implementation of the transaction validation service. This system also supports fault-tolerance using checkpointing and recovery mechanisms.…”

Section: Introductionmentioning

confidence: 97%

“…Our work has been driven by the goal of developing a transaction-based model of parallel programming on cluster computers, utilizing optimistic execution techniques for harnessing amorphous parallelism in graph problems. We implemented this model in a parallel programming framework called Beehive [18]. It is intended for cluster computing of large problems for which the RAM of a single computer may not be sufficient.…”

Section: Introductionmentioning

confidence: 99%

“…Section IV presents the programming abstractions, Section V presents an overview of the system architecture. Section VI outlines the significant refinements of the initial design presented in [18]. In Section VII we illustrate parallel programming in this framework using a set of graph problems.…”

Section: Introductionmentioning

confidence: 99%

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A Transactional Model for Parallel Programming of Graph Applications on Computing Clusters

Tripathi¹,

Padhye

Sunkara

et al. 2017

2017 IEEE 10th International Conference on Cloud Computing (CLOUD)

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We present here the results of our investigation of a transactional model of parallel programming on cluster computing systems. This model is specifically targeted for graph applications with the goal of harnessing unstructured parallelism inherently present in many such problems. In this model, tasks for vertex-centric computations are executed optimistically in parallel as serializable transactions. A key-value based globally shared object store is implemented in the main memory of the cluster nodes for storing the graph data. Task computations read and modify data in the distributed global store, without any explicitly programmed message-passing in the application code. Based on this model we developed a framework for parallel programming of graph applications on computing clusters. We present here the programming abstractions provided by this framework and its architecture. Using several graph problems we illustrate the simplicity of the abstractions provided by this model. These problems include graph coloring, k-nearest neighbors, and single-source shortest path computation. We also illustrate how incremental computations can be supported by this programming model. Using these problems we evaluate the transactional programming model and the mechanisms provided by this framework.

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Section: A Remote Data Access Mechanismsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Transactional Model for Parallel Programming of Graph Applications on Computing Clusters

Tripathi¹,

Padhye

Sunkara

et al. 2017

2017 IEEE 10th International Conference on Cloud Computing (CLOUD)

Self Cite

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“…In this model, tasks for vertex‐centric computations are asynchronously executed in parallel as serializable transactions. The transactional model adopted here is supported by a parallel programming framework called Beehive described in the works of Tripathi et al Beehive executes on a cluster of computers, and the graph data is stored in the RAM of the cluster nodes. Parallel computations on graph data are performed using transactional tasks.…”

Section: Introductionmentioning

confidence: 99%

Incremental parallel computing for continuous queries in dynamic graphs using a transactional model

Tripathi

Sharma

Khandelwal

et al. 2018

Concurrency and Computation

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In dynamically evolving graphs, one may be interested in continuously observing certain properties of the graph. One approach for continuous monitoring is to re-execute the graph analytics program on the entire graph after it is updated. However, this approach can lead to high computation cost and latency in case of large graphs. An alternate approach, which we present in this paper, is to execute the analytics program only initially and then perform incremental computations for supporting continuous queries as the graph is modified. The goal of our work is to develop incremental parallel computing techniques to continuously monitor a graph as it is updated to check for the properties of interest. We present the results of our investigation of utilizing a transactional model of parallel programming for supporting continuous queries on dynamic and evolving graphs. In our model, the graph updates are performed as transactions, which trigger the execution of a set of transactional tasks to perform incremental computations. In our testbed system, the graph data is stored in the RAM of cluster nodes, and continuous queries involve the parallel execution of transactional tasks on the cluster nodes. Using a set of graph problems, we illustrate this approach and evaluate its performance.

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