By offering storage services in several geographically distributed data centers, cloud computing platforms enable applications to offer low latency access to user data. However, application developers are left to deal with the complexities associated with choosing the storage services at which any object is replicated and maintaining consistency across these replicas.In this paper, we present SPANStore, a key-value store that exports a unified view of storage services in geographically distributed data centers. To minimize an application provider's cost, we combine three key principles. First, SPANStore spans multiple cloud providers to increase the geographical density of data centers and to minimize cost by exploiting pricing discrepancies across providers. Second, by estimating application workload at the right granularity, SPANStore judiciously trades off greater geo-distributed replication necessary to satisfy latency goals with the higher storage and data propagation costs this entails in order to satisfy fault tolerance and consistency requirements. Finally, SPANStore minimizes the use of compute resources to implement tasks such as two-phase locking and data propagation, which are necessary to offer a global view of the storage services that it builds upon. Our evaluation of SPANStore shows that it can lower costs by over 10x in several scenarios, in comparison with alternative solutions that either use a single storage provider or replicate every object to every data center from which it is accessed.

Developers of cloud-connected mobile apps need to ensure the consistency of application and user data across multiple devices. Mobile apps demand different choices of distributed data consistency under a variety of usage scenarios. The apps also need to gracefully handle intermittent connectivity and disconnections, limited bandwidth, and client and server failures. The data model of the apps can also be complex, spanning inter-dependent structured and unstructured data, and needs to be atomically stored and updated locally, on the cloud, and on other mobile devices.In this paper we study several popular apps and find that many exhibit undesirable behavior under concurrent use due to inadequate treatment of data consistency. Motivated by the shortcomings, we propose a novel data abstraction, called a sTable, that unifies a tabular and object data model, and allows apps to choose from a set of distributed consistency schemes; mobile apps written to this abstraction can effortlessly sync data with the cloud and other mobile devices while benefiting from end-to-end data consistency. We build Simba, a data-sync service, to demonstrate the utility and practicality of our proposed abstraction, and evaluate it both by writing new apps and porting existing inconsistent apps to make them consistent. Experimental results show that Simba performs well with respect to sync latency, bandwidth consumption, server throughput, and scales for both the number of users and the amount of data.

Existing cloud computing platforms leave it up to applications to deal with the complexities associated with data replication and propagation across data centers. In our work, we propose the CSPAN key-value store to instead export a unified view of storage services in several geographically distributed data centers. To minimize the cost incurred by application providers, we combine two principles. First, CSPAN spans the data centers of multiple cloud providers. Second, CSPAN judiciously trades off the lower latencies and the higher storage and data propagation costs based on an application's anticipated workload, latency goals, and consistency requirements.

Existing cloud computing platforms leave it up to applications to deal with the complexities associated with data replication and propagation across data centers. In our work, we propose the CSPAN key-value store to instead export a unified view of storage services in several geographically distributed data centers. To minimize the cost incurred by application providers, we combine two principles. First, CSPAN spans the data centers of multiple cloud providers. Second, CSPAN judiciously trades off the lower latencies and the higher storage and data propagation costs based on an application's anticipated workload, latency goals, and consistency requirements.