Failures of any type are common in current datacenters, partly due to the higher scales of the data stored. As data scales up, its availability becomes more complex, while different availability levels per application or per data item may be required. In this paper, we propose a self-managed key-value store that dynamically allocates the resources of a data cloud to several applications in a costefficient and fair way. Our approach offers and dynamically maintains multiple differentiated availability guarantees to each different application despite failures. We employ a virtual economy, where each data partition (i.e. a key range in a consistent-hashing space) acts as an individual optimizer and chooses whether to migrate, replicate or remove itself based on net benefit maximization regarding the utility offered by the partition and its storage and maintenance cost. As proved by a game-theoretical model, no migrations or replications occur in the system at equilibrium, which is soon reached when the query load and the used storage are stable. Moreover, by means of extensive simulation experiments, we have proved that our approach dynamically finds the optimal resource allocation that balances the query processing overhead and satisfies the availability objectives in a cost-efficient way for different query rates and storage requirements. Finally, we have implemented a fully working prototype of our approach that clearly demonstrates its applicability in real settings.
In REF , the authors propose a key-value store named Skute, which consists in dynamically allocating the resources of a data cloud to several applications in a cost effective and fair way using game theoretical models.
A self-organized, fault-tolerant and scalable replication scheme for cloud storage
SoCC '10
Computer Science