The increasing popularity of large-scale distributed applications in datacenters has led to the growing demand of distributed application state monitoring. These application state monitoring tasks often involve collecting values of various status attributes from a large number of nodes. One challenge in such large-scale application state monitoring is to organize nodes into a monitoring overlay that achieves monitoring scalability and cost effectiveness at the same time. In this paper, we present REMO, a REsource-aware application state MOnitoring system, to address the challenge of monitoring overlay construction. REMO distinguishes itself from existing works in several key aspects. First, it jointly considers intertask cost-sharing opportunities and node-level resource constraints. Furthermore, it explicitly models the per-message processing overhead which can be substantial but is often ignored by previous works. Second, REMO produces a forest of optimized monitoring trees through iterations of two phases. One phase explores cost-sharing opportunities between tasks, and the other refines the tree with resource-sensitive construction schemes. Finally, REMO also employs an adaptive algorithm that balances the benefits and costs of overlay adaptation. This is particularly useful for large systems with constantly changing monitoring tasks. Moreover, we enhance REMO in terms of both performance and applicability with a series of optimization and extension techniques. We perform extensive experiments including deploying REMO on a BlueGene/P rack running IBM's large-scale distributed streaming system - System S. Using REMO in the context of collecting over 200 monitoring tasks for an application deployed across 200 nodes results in a 35-45 percent decrease in the percentage error of collected attributes compared to existing schemes.

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. We 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. In this paper, we present REMO, a REsource-aware application state MOnitoring system. REMO produces a forest of optimized monitoring trees through iterations of two phases, one phase exploring cost sharing opportunities via estimation and the other refining the monitoring plan through resource-sensitive tree construction. Our experimental results include those gathered by deploying REMO on a BlueGene/P rack running IBM's large-scale distributed streaming system - System S. Using REMO running over 200 monitoring tasks for an application deployed across 200 nodes results in a 35%-45% decrease in the percentage error of collected attributes compared to existing schemes.