120 points by dist_sys_ninja 6 months ago flag hide 31 comments
johnsmith 6 months ago next
Excited to see this post on anomaly detection in distributed systems! I've been working on a similar project lately. What libraries/tools did you use for implementation?
originalposter 6 months ago next
@johnsmith we used a combination of Prometheus and Grafana for monitoring and alerting. Have you tried those out?
johnsmith 6 months ago prev next
@originalposter thanks for the recommendation. I'll give them a try. Btw, have you considered using machine learning techniques in your approach?
originalposter 6 months ago next
@johnsmith we did consider ML but decided against it due to the extra complexity and resources required. We might revisit that decision in the future though.
janedoe 6 months ago prev next
I'm interested in learning more about this topic. Can you recommend some resources or papers for further reading?
originalposter 6 months ago next
@janedoe Sure! Check out 'Anomaly Detection in Large Distributed Systems' by Krishnaswamy et al. and 'Streaming Analytics in Distributed Systems' by Kddi et al.
bobbuilder 6 months ago prev next
We built our own in-house solution based on machine learning techniques. It's been working great for us so far.
aliceai 6 months ago next
@bobbuilder can you share some details on how you implemented your ML-based solution? We've been considering a similar approach but haven't started yet.
bobbuilder 6 months ago next
@aliceai sure! We used a combination of decision trees and random forests to detect anomalies in our system. We also used historical data to train our models.
newuser 6 months ago prev next
I'm new to this field and was wondering if someone could explain what exactly anomaly detection is in the context of distributed systems?
charlescloud 6 months ago next
@newuser Anomaly detection in distributed systems refers to the process of identifying unexpected behavior or patterns in the system's performance metrics, such as CPU usage or network latency. It's used to detect potential issues before they become critical.
elizabethengineer 6 months ago prev next
We've been using statistical methods for anomaly detection, but we've been noticing some false positives. Any recommendations on how to improve our approach?
originalposter 6 months ago next
@elizabethengineer You could try tweaking your thresholds or using a moving average window for smoothing out the data. ML-based methods might also be worth exploring.
garygateway 6 months ago prev next
We use a third-party service for anomaly detection but have been experiencing some reliability issues. Any recommendations for alternative solutions?
originalposter 6 months ago next
@garygateway Check out tools like Datadog, SignalFx, and Dynatrace. They offer robust anomaly detection features and have good reputations in the industry.
heatherhost 6 months ago prev next
Can someone explain the difference between supervised and unsupervised anomaly detection methods, and when to use each one?
originalposter 6 months ago next
@heatherhost Sure! Supervised methods require labeled data and use it to train a model. They're ideal when you have known anomalies. Unsupervised methods, on the other hand, don't require labeled data and can detect unknown anomalies. They're useful for exploratory analysis and real-time monitoring.
ivaninfrastructure 6 months ago prev next
Great post! I'm curious how well your approach scales with larger systems and more data points.
originalposter 6 months ago next
@ivaninfrastructure Our approach has been working well for us in large-scale distributed systems, but we do load testing and performance optimization on a regular basis. It's important to continuously monitor and adjust the system to ensure optimal performance.
juliejet 6 months ago prev next
I'm wondering if this approach can be applied to real-time systems and what performance impact it might have.
originalposter 6 months ago next
@juliejet Yes, our approach can be applied to real-time systems, but it might require more resources and optimization. Real-time systems typically have stricter requirements for latency and throughput, so it's important to take that into account.
karloss 6 months ago prev next
How do you handle noisy data and outliers in your approach?
originalposter 6 months ago next
@karloss We use data cleaning and preprocessing techniques to remove outliers and reduce noise. We also use moving averages and standard deviation as part of our anomaly detection engine.
lauraleader 6 months ago prev next
Have you considered using deep learning techniques for anomaly detection in distributed systems?
originalposter 6 months ago next
@lauraleader Yes, we have considered using deep learning techniques. They can be powerful but also require more resources and training data. We opted for a simpler approach for our specific use case, but ML and DL are definitely worth considering in general.
mikemachine 6 months ago prev next
Are there any benchmarks or evaluations of your approach compared to other existing solutions?
originalposter 6 months ago next
@mike machine Yes, we conducted several experiments to evaluate our approach and compared it to other state-of-the-art solutions. We're planning to publish our results in a future paper. Stay tuned!
nancynetwork 6 months ago prev next
What's the typical false positive/negative rate of your approach?
originalposter 6 months ago next
@nancy network Our false positive rate is relatively low due to our careful selection of thresholds and data processing techniques. However, false negatives can still occur in complex scenarios. We're constantly working on improving our approach.
oliveroperator 6 months ago prev next
We're using a different approach for anomaly detection in our distributed system and have been experiencing false negatives. Any suggestions?
originalposter 6 months ago next
@oliver Operator Double-check your thresholds and data processing steps. Also, consider using ML-based methods for more robust anomaly detection.