Optimizing network virtualization in Xen
ATEC '06 Proceedings of the annual conference on USENIX '06 Annual Technical Conference
Dynamo: amazon's highly available key-value store
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
Benchmarking cloud serving systems with YCSB
Proceedings of the 1st ACM symposium on Cloud computing
Automated control for elastic storage
Proceedings of the 7th international conference on Autonomic computing
Understanding Performance Interference of I/O Workload in Virtualized Cloud Environments
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Proceedings of the VLDB Endowment
The SCADS director: scaling a distributed storage system under stringent performance requirements
FAST'11 Proceedings of the 9th USENIX conference on File and stroage technologies
Performance Analysis of Network I/O Workloads in Virtualized Data Centers
IEEE Transactions on Services Computing
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As one database offloading strategy, elastic key-value stores are often introduced to speed up the application performance with dynamic scalability. Since the workload is varied, efficient data migration with minimal impact in service is critical for the issue of elasticity and scalability. However, due to the new virtualization technology, real-time and low-latency requirements, data migration within cloud-based key-value stores has to face new challenges: effects of VM interference, and the need to trade off between the two ingredients of migration cost, namely migration time and performance impact. To fulfill these challenges, in this paper we explore a new approach to optimize the data migration. Explicitly, we build two interference-aware models to predict the migration time and performance impact for each migration action using statistical machine learning, and then create a cost model to strike a balance between the two ingredients. Using the load rebalancing scenario as a case study, we have designed one cost-aware migration algorithm that utilizes the cost model to guide the choice of possible migration actions. Finally, we demonstrate the effectiveness of the approach using Yahoo! Cloud Serving Benchmark (YCSB).