arXiv subjects

Saranya Chandrasekaran

Publications and source records attributed to Saranya Chandrasekaran.

2 recordsLinked to original sources

arXiv2026

Elastic Scheduling of Intermittent Query Processing in a Cluster Environment

Many applications process a stream of tuples over a window duration, and require the results within a specified deadline after the end of the window. For such scenarios, processing tuples intermittently (in batches) instead of eagerly processing tuples as they arrive significantly reduces the overall cost. Earlier work on intermittent query processing has addressed only fixed environments. In this paper, we propose scheduling schemes for batched processing of tuples, in an elastic parallel environment, scaling nodes up or down. Our scheduling schemes ensure to meet the deadlines, while incurring minimum cost. Our schemes also handle multiple concurrent queries, the arrival of new queries, and input rate variations. We have implemented our schemes on top of Apache Spark, in the AWS EMR environment, and evaluated performance with both TPC-H and Yahoo Streaming datasets. Our experimental results show that our scheduling algorithms significantly outperform alternatives, such as using a fixed set of nodes without elasticity, or using Spark streaming.

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arXiv2024

Scheduling of Intermittent Query Processing

Stream processing is usually done either on a tuple-by-tuple basis or in micro-batches. There are many applications where tuples over a predefined duration/window must be processed within certain deadlines. Processing such queries using stream processing engines can be very inefficient since there is often a significant overhead per tuple or micro-batch. The cost of computation can be significantly reduced by using the wider window available for computation. In this work, we present scheduling schemes where the overhead cost is minimized while meeting the query deadline constraints. For such queries, since the result is needed only at the deadline, tuples can be processed in larger batches, instead of using micro-batches. We present scheduling schemes for single and multi query scenarios. The proposed scheduling algorithms have been implemented as a Custom Query Scheduler, on top of Apache Spark. Our performance study with TPC-H data, under single and multi query modes, shows orders of magnitude improvement as compared to naively using Spark streaming.

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