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new conference paper “Efficient Processing of Streaming Data using Multiple Abstractions” at IEEE Cloud

We have published a new paper “Efficient Processing of Streaming Data using Multiple Abstractions” at the IEEE Cloud 2021 conference. (to be available at https://conferences.computer.org/cloud/2021/)

We show that one framework can efficiently support multiple abstractions. We provide three abstractions of Block, Windowed, and Stateful streaming and demonstrate that many application classes can be developed with ease, correctness, and low processing latency.

From the abstract of our paper:

Large websites and distributed systems employ sophisticated analytics to evaluate successes to celebrate and problems to be addressed. As analytics grow, different teams often require different frameworks, with dozens of packages supporting with streaming and batch processing, SQL and no-SQL. Bringing multiple frameworks to bear on a large, changing dataset often create challenges where data transitions—these impedance mismatches can create brittle glue logic and performance problems that consume developer time. We propose Plumb, a meta-framework that can bridge three different abstractions to meet the needs of a large class of applications in a common workflow. Large-block streaming (Block-Streaming) is suitable for single-pass applications that care about the temporal and spatial locality. Windowed-Streaming allows applications to process a group of data and many reductions. Stateful-Streaming enables applications to keep a long-term state and always-on behavior. We show that it is possible to bridge abstractions, with a common, high-level workflow specification, while the system transitions data batch processing and block- and record-level streaming as required. The challenge in bridging abstractions is to minimize latency while allowing applications to select between sequential and parallel operation, while handling out-of-order data delivery, component failures, and providing clear semantics in the face of missing data. We demonstrate these abstractions evaluating a 10-stage workflow of DNS analytics that has been in production use with Plumb for 2 years, comparing to a brittle hand-built system that has run for more than 3 years.

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

Plumb is open source software and will be available at: https://ant.isi.edu/software/plumb/index.html

Update 2021-09-26: This paper was given a “special paper award” at IEEE Conference on Cloud Computing 2021! Congratulations, Abdul!

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Publications Technical Report

new technical report “Peek Inside the Closed World: Evaluating Autoencoder-Based Detection of DDoS to Cloud ”

We have released a new technical report “Peek Inside the Closed World: Evaluating Autoencoder-Based Detection of DDoS to Cloud” as an ArXiv technical report 1912.05590, available at https://www.isi.edu/~hangguo/papers/Guo19a.pdf

We study 4 cloud IPs (SR1VP1 to 3 and SR2VP1) that are under attack. SR1VP3 sees a large number of mostly short DDoS events (71% of its 49 events being 1 second or less). SR1VP1 and SR1VP2 see smaller numbers of longer DDoS events (median duration for their 20 and 27 events are 121 and 140 seconds). SR2VP1 sees DDoS events of broad range of durations (from 1 second to more than 14 hours).

From the abstract of our technical report:

From the abstract:

Machine-learning-based anomaly detection (ML-based AD) has been successful at detecting DDoS events in the lab. However published evaluations of ML-based AD have only had limited data and have not provided insight into why it works. To address limited evaluation against real-world data, we apply autoencoder, an existing ML-AD model, to 57 DDoS attack events captured at 5 cloud IPs from a major cloud provider. To improve our understanding for why ML-based AD works or not works, we interpret this data with feature attribution and counterfactual explanation. We show that our version of autoencoders work well overall: our models capture nearly all malicious flows to 2 of the 4 cloud IPs under attacks (at least 99.99%) but generate a few false negatives (5% and 9%) for the remaining 2 IPs. We show that our models maintain near-zero false positives on benign flows to all 5 IPs. Our interpretation of results shows that our models identify almost all malicious flows with non-whitelisted (non-WL) destination ports (99.92%) by learning the full list of benign destination ports from training data (the normality). Interpretation shows that although our models learn incomplete normality for protocols and source ports, they still identify most malicious flows with non-WL protocols and blacklisted (BL) source ports (100.0% and 97.5%) but risk false positives. Interpretation also shows that our models only detect a few malicious flows with BL packet sizes (8.5%) by incorrectly inferring these BL sizes as normal based on incomplete normality learned. We find our models still detect a quarter of flows (24.7%) with abnormal payload contents even when they do not see payload by combining anomalies from multiple flow features. Lastly, we summarize the implications of what we learn on applying autoencoder-based AD in production.problme?Machine-learning-based anomaly detection (ML-based AD) has been successful at detecting DDoS events in the lab. However published evaluations of ML-based AD have only had limited data and have not provided insight into why it works. To address limited evaluation against real-world data, we apply autoencoder, an existing ML-AD model, to 57 DDoS attack events captured at 5 cloud IPs from a major cloud provider. To improve our understanding for why ML-based AD works or not works, we interpret this data with feature attribution and counterfactual explanation. We show that our version of autoencoders work well overall: our models capture nearly all malicious flows to 2 of the 4 cloud IPs under attacks (at least 99.99%) but generate a few false negatives (5% and 9%) for the remaining 2 IPs. We show that our models maintain near-zero false positives on benign flows to all 5 IPs. Our interpretation of results shows that our models identify almost all malicious flows with non-whitelisted (non-WL) destination ports (99.92%) by learning the full list of benign destination ports from training data (the normality). Interpretation shows that although our models learn incomplete normality for protocols and source ports, they still identify most malicious flows with non-WL protocols and blacklisted (BL) source ports (100.0% and 97.5%) but risk false positives. Interpretation also shows that our models only detect a few malicious flows with BL packet sizes (8.5%) by incorrectly inferring these BL sizes as normal based on incomplete normality learned. We find our models still detect a quarter of flows (24.7%) with abnormal payload contents even when they do not see payload by combining anomalies from multiple flow features. Lastly, we summarize the implications of what we learn on applying autoencoder-based AD in production.

This technical report is joint work of Hang Guo and John Heidemann from USC/ISI and Xun Fan, Anh Cao and Geoff Outhred from Microsoft

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Papers Publications

new conference paper “The Need for End-to-End Evaluation of Cloud Availability” in PAM 2014

The paper “The Need for End-to-End Evaluation of Cloud Availability” was published by PAM 2014 in Marina del Rey, CA (available at http://www.isi.edu/~zihu/paper/cloud_availability.pdf).

From the abstract:cloud_availability_blog

People’s computing lives are moving into the cloud, making understanding cloud availability increasingly critical. Prior studies of Internet outages have used ICMP-based pings and traceroutes. While these studies can detect network availability, we show that they can be inaccurate at estimating cloud availability. Without care, ICMP probes can underestimate availability because ICMP is not as robust as application-level measurements such as HTTP. They can overestimate availability if they measure reachability of the cloud’s edge, missing failures in the cloud’s back-end. We develop methodologies sensitive to five “nines” of reliability, and then we compare ICMP and end-to-end measurements for both cloud VM and storage services. We show case studies where one fails and the other succeeds, and our results highlight the importance of application-level retries to reach high precision. When possible, we recommend end-to-end measurement with application-level protocols to evaluate the availability of cloud services.

Citation: Zi Hu, Liang Zhu, Calvin Ardi, Ethan Katz-Bassett, Harsha Madhyastha, John Heidemann, Minlan Yu. The Need for End-to-End Evaluation of Cloud Availability. Passive and Active Measurements Conference (PAM). Los Angeles, CA, USA, March 2014.