It is not. The organic material isn't the part that gets used up, but rather the electrodes. The energy does not really come from the potato, just the chemistry environment. If you take the electrodes out and put them in a new potato, I don't think that will get you a fresh battery.
I think the intelligence work referred here is not within the scope of war as defined in war criminal.
But it could be collaborator to foreign intelligence, or agent for foreign intelligence, which already is punishable. Now the knowingly or unknowingly factor is important usually is qualifying these crimes.
Mesos made a ton of new contributions to distributed resource management. Resource offers and application integration can allow high cluster utilization and efficiency. Giving applications the opportunity to take part in resource allocation and scheduling was similar to the Exokernel design, and led to many interesting middleware architectures.
Mesos also introduced Dominant Resource Fairness for allocating resources to competing applications, which has become an important concept in CS and specifically computational economics in its own right.
Mesos achieved the rare combination of using CS research towards a widely deployed operational system. Its C++ code is simple enough to understand and hack, and seemed like one of those projects that you can/want to reimplement on your own "in a few days".
Are there examples of high-utilization, large-scale Mesos deployments? Mesos didn't even gain over-commit until 2015, so it seems like it was generally behind the state of the art.
OK but what was the utilization? I'm not really sure K8s is state-of-the-art either. There are published research papers about very-large-scale clusters with 80%+ resource utilization.
In our production experience, utilization had far more to do with the service owners (or autoscalers/auto-tuners) correctly choosing the cgroups and CPU scheduler allocations, as well as the kernel settings for cgroup slicing and CPU scheduler. We had Mesos clusters with 3% utilization and have Kubernetes clusters with 95%+ utilization. But we also have Kubernetes clusters with <10% utilization.
To be fair, Kubernetes right now only schedules relatively small clusters. But it turns out that the majority of the world is not Facebook or Google and only needs relatively small clusters.
There must be some folks from Criteo lurking here. I'm an ex-Criteo'er and if memory serves we had something on the order of 10K nodes running mesos/marathon. We did all kinds of silly things to it, like running very CPU intensive .NET/Core apps.
We also ran HiveServer2 and the Hive Metastore in Mesos, though that wasn't super CPU intensive (that was a pain, but mostly due to our Kerberos deployment).
The general use case of Mesos/Marathon always worked for us just fine (self-executable JVM apps), though there was plenty of Mesos hate at Criteo (and eventually Kubernetes spun up, though I left about a year ago and don't know its footprint).
PS, Hi Greg S! Hi Maxime B! <-- if you're reading :).
IIRC you could always overcommit in Mesos using DRF weights and accepting resource offers in your application. I could be wrong.
The larger point is that Mesos introduced a new, exciting way to do truly distributed allocation (where the cluster manager (i.e., Mesos) and various applications coordinated and cooperated in how they use computing resources). In contrast, Kubernetes is centralized, pretty vanilla, and I would love to know what new ideas it has introduced (from an algorithmic and architecture perspective).
Twitter. From generic caches to ad serving; from stream processing to video encoding, all high utilization applications of either one or multiple schedulable resources.
These jobs had their allotted quotas, per team, giving them above >70% utilization in their logical slice of the cluster. E.g. video processing team gets 20,000 nodes globally. They stack (co-locate) their tasks (interpret: set of processes) however they want.
Granted Twitter operated one big shared Mesos+Aurora offering for everything*, the whole cluster high utilization wouldn't give much flexibility to absorb load, or do reasonable capacity planning (which was an entire org in itself) when you own and operate those machines and data centers. I can't comment much on the 20-30% figure given in MesosCon, it's been more than 5 years since I was last privy to these figures.
I worked for Twitter up until 2017 and when I was there it was much higher than 20-30%, definitely >50%. It's very possibly changed since then, but at least at that point in time Twitter was running Mesos on many thousands of machines.
In almost all cases, the support of these giant corporations is towards feature-sets that are inherently self-serving. In many cases, they wield too much power and hijack the direction of the opensource projects. For example, there's plenty of optimizations for server-side CPU performance on Linux, often to the detriment of desktop interactive performance (see Con Kolivas' scheduler and his 10+ year long struggle).
You can apply this logic for any wasteful activity. Lets boil the ocean with coal-fired plants, because something something production vs. consumption.
Sorry, why is this downvoted? There is a serious argument to be had about the source of income for surveillance and tracking driven websites. If the oft-used line of "users being products" is true, then where's our profit?
