Chameleon: A self-evolving, fully-adaptive resource arbitrator for storage systems

Sandeep Uttamchandani, Li Yin, Guillermo A. Alvarez, John Palmer, Gul Agha

Research output: Contribution to conferencePaperpeer-review


Enterprise applications typically depend on guaranteed performance from the storage subsystem, lest they fail. However, unregulated competition is unlikely to result in a fair, predictable apportioning of resources. Given that widespread access protocols and scheduling policies are largely best-effort, the problem of providing performance guarantees on a shared system is a very difficult one. Clients typically lack accurate information on the storage system’s capabilities and on the access patterns of the workloads using it, thereby compounding the problem. CHAMELEON is an adaptive arbitrator for shared storage resources; it relies on a combination of self-refining models and constrained optimization to provide performance guarantees to clients. This process depends on minimal information from clients, and is fully adaptive; decisions are based on device and workload models automatically inferred, and continuously refined, at run-time. Corrective actions taken by CHAMELEON are only as radical as warranted by the current degree of knowledge about the system’s behavior. In our experiments on a real storage system CHAMELEON identified, analyzed, and corrected performance violations in 3-14 minutes—which compares very favorably with the time a human administrator would have needed. Our learning-based paradigm is a most promising way of deploying large-scale storage systems that service variable workloads on an ever-changing mix of device types.

Original languageEnglish (US)
Number of pages14
StatePublished - 2005
Event2005 USENIX Annual Technical Conference - Anaheim, United States
Duration: Apr 10 2005Apr 15 2005


Conference2005 USENIX Annual Technical Conference
Country/TerritoryUnited States

ASJC Scopus subject areas

  • General Computer Science


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