Categories
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-User Pipelines (Poster)”

We released a new technical report “Plumb: Efficient Processing of Multi-User Pipelines (Poster)”, by Abdul Qadeer and John Heidemann, as ISI-TR-731.  This work was originally presented at ACM Symposium on Cloud Computing (the poster abstract is available at ACM). The poster abstract with a small version of the poster is available at https://www.isi.edu/publications/trpublic/pdfs/isi-tr-731.pdf

aqadeer at SoCC 2018 Carlsbad CA

From the abstract:

As the field of big data analytics matures, workflows are increasingly complex and often include components that are shared by different users. Individual workflows often include multiple stages, and when groups build on each other’s work it is easy to lose track of computation that may be shared across different groups.

The contribution of this poster is to provide an organization-wide processing substrate Plumb that can be used to solve commonly occurring problems and to achieve a common goal. Plumb makes multi-user sharing a first-class concern by providing pipeline-graph abstraction. This abstraction is simple and based on fundamental model of input-processing-output but is powerful to capture processing and data duplication. Plumb then employs best available solutions to tackle problems of large-block processing under structural and computational skew without user intervention.

We expect to release the Plumb software this fall; please contact us if you have questions or interest in using it.

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.

 

Categories
DNS Papers Publications

new journal paper “Detecting Malicious Activity With DNS Backscatter Over Time” in IEEE/ACM ToN Oct, 2017

The paper “Detecting Malicious Activity With DNS Backscatter Over Time ” appears in EEE/ACM  Transactions on Networking ( Volume: 25, Issue: 5, Oct. 2017 ).

From the abstract:

Network-wide activity is when one computer (the originator) touches many others (the targets). Motives for activity may be benign (mailing lists, CDNs, and research scanning), malicious (spammers and scanners for security vulnerabilities), or perhaps indeterminate (ad trackers). Knowledge of malicious activity may help anticipate attacks, and understanding benign activity may set a baseline or characterize growth. This paper identifies DNS backscatter as a new source of information about network-wide activity. Backscatter is the reverse DNS queries caused when targets or middleboxes automatically look up the domain name of the originator. Queries are visible to the authoritative DNS servers that handle reverse DNS. While the fraction of backscatter they see depends on the server’s location in the DNS hierarchy, we show that activity that touches many targets appear even in sampled observations. We use information about the queriers to classify originator activity using machine learning. Our algorithm has reasonable accuracy and precision (70–80%) as shown by data from three different organizations operating DNS servers at the root or country-level. Using this technique we examine nine months of activity from one authority to identify trends in scanning, identifying bursts corresponding to Heartbleed and broad and continuous scanning of ssh.

This paper furthers our understanding of evolution of malicious network activities from an earlier work that:
(1) Why our machine-learning based classifier (that relies on manually collected labeled data) does not port across physical sites and over time.
(2) Secondly paper recommends how to sustain good learning score over time and provides expected life-time of labeled data.

An excerpt from section III-E (Training Over Time):

Classification (§ III-D) is based on training, yet training accuracy is affected by the evolution of activity—specific examples come and go, and the behavior in each class evolves. Change happens for all classes, but the problem is particularly acute for malicious classes (such as spam) where the adversarial nature of the action forces rapid evolution (see § V).

 

Some datasets used in this paper can be found here: