QoSMig: adaptive rate-controlled migration of bulk data in storage systems

Abstract

Logical reorganization of data and requirements of differentiated QoS in information systems necessitate bulk data migration by the underlying storage layer. Such data migration needs to ensure that regular client I/Os are not impacted significantly while migration is in progress. We formalize the data migration problem in a unified admission control framework that captures both the performance requirements of client I/Os and the constraints associated with migration. We propose an adaptive rate-control based data migration methodology, QoSMig, that achieves the optimal client performance in a differentiated QoS setting, while ensuring that the specified migration constraints are met QoSMig uses both long term averages and short term forecasts of client traffic to compute a migration schedule. We present an architecture based on Service Level Enforcement Discipline for Storage (SLEDS) that supports QoSMig. Our trace-driven experimental study demonstrates that QoSMig provides significantly better I/O performance as compared to existing migration methodologies.