Abstract: As utility computing is widely deployed, organizations and researchers are turning to the next generation of cloud systems: federating public clouds, integrating private and public clouds, and merging resources at all levels (IaaS, PaaS, SaaS). Adaptive systems can help address the challenge of managing this heterogeneous collection of resources. While services and libraries exist for basic management tasks that enable implementing decisions made by the manager, monitoring is an open challenge. We define a set of requirements for aggregating monitoring data from a heterogeneous collections of resources, sufficient to support adaptive systems. We present and implement an architecture using stream processing to provide near-realtime, cross-boundary, distributed, scalable, fault-tolerant monitoring. A series of experiments demonstrate our prototype implementation provides low latency, low overhead, and high cost effectiveness.

This video demonstrates some of the front-end features of the MISURE monitoring system.