Imranul Hoque

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Parallel dataflow programs generate enormous amounts of distributed data that are short-lived, yet are critical for completion of the job and for good run-time performance. We call this class of data as <i>intermediate data</i>. This paper is the first to address intermediate data as a first-class citizen, specifically targeting and minimizing the effect of(More)
This paper takes a renewed look at the problem of managing intermediate data that is generated during dataflow computations (e.g., MapReduce, Pig, Dryad, etc.) within clouds. We discuss salient features of this intermediate data and outline requirements for a solution. Our experiments show that existing local write-remote read solutions, traditional(More)
Distributed graph analytics frameworks must offer low and balanced communication and computation, low preprocessing overhead, low memory footprint, and scalability. We present LFGraph, a fast, scalable, distributed, in-memory graph analytics engine intended primarily for directed graphs. LFGraph is the first system to satisfy all of the above requirements.(More)
Large-scale distributed systems are subject to churn, i.e., continuous arrival, departure and failure of processes. Analysis of protocols under churn requires one to use churn models that are tractable (easy to apply), realistic (apply to deployment settings), and general (apply to many protocols and properties). In this paper, we propose two new churn(More)
Attribute-based Access Control (ABAC) based on XACML can substantially improve the security and management of access rights on databases. However, existing implementations rely on high-level policy interpretation and are not as efficient as mechanisms natively supported by commodity databases. In this paper we explore advantages and challenges arising from(More)
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