Application specific data replication for edge services
WWW '03 Proceedings of the 12th international conference on World Wide Web
Idletime scheduling with preemption intervals
Proceedings of the twentieth ACM symposium on Operating systems principles
Analysis and evolution of journaling file systems
ATEC '05 Proceedings of the annual conference on USENIX Annual Technical Conference
TCON'95 Proceedings of the USENIX 1995 Technical Conference Proceedings
Dynamo: amazon's highly available key-value store
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
Write off-loading: practical power management for enterprise storage
FAST'08 Proceedings of the 6th USENIX Conference on File and Storage Technologies
PNUTS: Yahoo!'s hosted data serving platform
Proceedings of the VLDB Endowment
Queue - Scalable Web Services
Automated control of multiple virtualized resources
Proceedings of the 4th ACM European conference on Computer systems
Restrained utilization of idleness for transparent scheduling of background tasks
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Zeno: eventually consistent Byzantine-fault tolerance
NSDI'09 Proceedings of the 6th USENIX symposium on Networked systems design and implementation
Fast Eventual Consistency with Performance Guarantees for Distributed Storage
ICDCSW '12 Proceedings of the 2012 32nd International Conference on Distributed Computing Systems Workshops
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Systems have adopted the notion of eventual consistency which means that the targeted redundancy of data in the system is reached asynchronously, i.e., outside of the critical path of user traffic, so that performance of user traffic is impacted minimally. Here, we propose a scheduling framework that makes decisions about when to schedule the asynchronous tasks associated with new or updated data such that they are completed as soon as possible without violating user traffic quality targets. At the heart of the framework lies a learning methodology that extracts the characteristics of idle periods and infers the average amount of work to be filled during periods of idleness so that asynchronous tasks are completed transparently to the user. Extensive trace-driven evaluation shows the effectiveness and robustness of the proposed framework when compared to common practices.