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To observe, analyze and control large scale distributed systems and the applications hosted on them, there is an increasing need to continuously monitor performance attributes of distributed system and application states. This results in application state monitoring tasks that require fine-grained attribute information to be collected from relevant nodes efficiently. Existing approaches either treat multiple application state monitoring tasks independently and build ad-hoc monitoring trees for each task, or construct a single static monitoring tree for multiple tasks. The authors argue that a careful planning of multiple application state monitoring tasks by jointly considering multi-task optimization and node level resource constraints can provide significant gains in performance and scalability.
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