Identifying impactful service system problems via log analysis

Identifying impactful service system problems via log analysis He et al., ESEC/FSE’18

If something is going wrong in your system, chances are you’ve got two main sources to help you detect and resolve the issue: logs and metrics. You’re unlikely to be able to get to the bottom of a problem using metrics alone (though you might well detect one that way), so that leaves logs as the primary diagnosis tool. The online service at Microsoft used as the main case study in the paper produces dozens of Terabytes of logs every day.

Logs play a crucial role in the diagnosis of modern cloud-based online service systems. Clearly, manual problem diagnosis is very time-consuming and error-prone due to the increasing scale and complexity of large-scale systems.

Log3C analyses logs to look for indications of impactful problems, using correlated KPIs as a guide. It finds these needles in the haystack with an average precision of 0.877 and an average recall of 0.883. A distributed version of Log3C has been deployed and used in production at Microsoft for several years, both to support a massive online service (we are not told which one), and integrated into “Product B” where Continue reading

How to pick an off-site data-backup method

Everyone agrees that backups should be sent off site, but not everyone agrees on how that should be accomplished. The decision about which method to use will affect your recovery-time objective (RTO), recovery-point objective (RPO), risk level, and cost – so it’s rather important.To read this article in full, please click here(Insider Story)

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Now, to be perfectly clear, containers themselves aren’t some sort of magical automation sauce that changed everything. Containers are something of a totem for IT operations automation, for a few different reasons.

Unlike the Virtual Machines (VMs) that preceded them, containers don’t require a full operating system for every workload. A single operating system can host hundreds or even thousands of containers, moving the necessary per-workload RAM requirement from several gigabytes to a few dozen megabytes. Similarly, containerized workloads share certain basic functions – libraries, for instance – from the host operating system, which can make maintaining key aspects of the container operating environment easier. When you update the underlying host, you update all the containers running on it.

Unlike VMs, however, containers are feature poor. For example, they have no resiliency: traditional vMotion-like workload migration doesn’t exist, and we’re only just now – several years after containers went mainstream – starting to get decent persistent Continue reading

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