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Papers Publications

new journal paper “Plumb: Efficient Stream Processing of Multi-User Pipelines” in the Journal of Software: Practice and Experience

We have published a new journal paper “Plumb: Efficient Stream Processing of Multi-User Pipelines” in Wiley’s Journal of Software: Practice and Experience, available at https://onlinelibrary.wiley.com/doi/10.1002/spe.2909

Plumb provides a new pipeline-graph abstraction that allows multiple users to specify workflows in which Plumb can detect and elimiate duplicate processing and handle processing skew due to unbalanced data or stages. The end result is that users get their results faster and a shared cluster is efficiently utilized.

From the abstract of our journal paper:

Operational services run 24×7 and require analytics pipelines to evaluate performance. In mature services such as DNS, these pipelines often grow to many stages developed by multiple, loosely-coupled teams. Such pipelines pose two problems: first, computation and data storage may be duplicated across components developed by different groups, wasting resources. Second, processing can be skewed, with structural skew occurring when different pipeline stages need different amounts of resources, and computational skew occurring when a block of input data requires increased resources. Duplication and structural skew both decrease efficiency, increasing cost, latency, or both. Computational skew can cause pipeline failure or deadlock when resource consumption balloons; we have seen cases where pessimal traffic increases CPU requirements 6-fold. Detecting duplication is challenging when components from multiple teams evolve independently and require fault isolation. Skew management is hard due to dynamic workloads coupled with the conflicting goals of both minimizing latency and maximizing utilization. We propose Plumb, a framework to abstract stream processing as large-block streaming (LBS) for a multi-stage, multi-user workflow. Plumb users express analytics as a DAG of processing modules, allowing Plumb to integrate and optimize workflows from multiple users. Many real-world applications map to the LBS abstraction. Plumb detects and eliminates duplicate computation and storage, and it detects and addresses both structural and computational skew by tracking computation across the pipeline. We exercise Plumb using the analytics pipeline for B-Root DNS. We compare Plumb to a hand-tuned system, cutting latency to one-third the original, and requiring 39% fewer container hours, while supporting more flexible, multi-user analytics and providing greater robustness to DDoS-driven demands.

This journal paper is joint work of Abdul Qadeer and  John Heidemann from USC/ISI.

Plumb is open source software and we will be interested in beta testers. Please contact us if you think it would be useful to manage your workflows over one or a cluster of computers.

Categories
Publications Technical Report

new technical report “Plumb: Efficient Processing of Multi-Users Pipelines (Extended)”

We released a new technical report “Plumb: Efficient Processing of Multi-Users Pipelines (Extended)”, by Abdul Qadeer and John Heidemann, as ISI-TR-727.  It is available at https://www.isi.edu/publications/trpublic/pdfs/isi-tr-727.pdf

Benefits of processing de-duplication

Benefits of data de-duplication

From the abstract:

Services such as DNS and websites often produce streams of data that are consumed by analytics pipelines operated by multiple teams. Often this data is processed in large chunks (megabytes) to allow analysis of a block of time or to amortize costs. Such pipelines pose two problems: first, duplication of computation and storage may occur when parts of the pipeline are operated by different groups. Second, processing can be lumpy, with structural lumpiness occurring when different stages need different amounts of resources, and data lumpiness occurring when a block of  input requires increased resources. Duplication and structural lumpiness both can result in inefficient processing. Data lumpiness can cause pipeline failure or deadlock, for example if differences in DDoS traffic compared to normal can require 6× CPU. We propose Plumb, a framework to abstract file processing for a multi-stage pipeline. Plumb integrates pipelines contributed by multiple users, detecting and eliminating duplication of computation and intermediate storage. It tracks and adjusts computation of each stage, accommodating both structural and data lumpiness. We exercise Plumb with the processing pipeline for B-Root DNS traffic, where it will replace a hand-tuned system to provide one third the original latency by utilizing 22% fewer CPU and will address limitations that occur as multiple users process data and when DDoS traffic causes huge shifts in performance.