Online cloud services are widely deployed as Long-Running Applications (LRAs) hosted in containers. Placing LRA containers turns out to be particularly challenging due to the complex interference between co-located containers and the operation constraints in production clusters such as fault tolerance, disaster avoidance and incremental deployment. Existing schedulers typically provide APIs for operators to manually specify the container scheduling requirements and offer only qualitative scheduling guidelines for container placement. Such schedulers, do not perform well in terms of both performance and scale, while also requiring manual intervention.
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Li et al. (2021) studied this question.
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