DCaaS: Data Consistency as a Service for Managing Data Uncertainty on the Clouds

Abstract

Ensuring data correctness over partitioned distributed database systems is a classical problem. Classical solutions proposed to solve this problem are mainly adopting locking or blocking techniques. These techniques are not suitable for cloud environments as they produce terrible response times; due to the long latency and faultiness of wide area network connections among cloud datacenters. One way to improve performance is to restrict access of users-bases to specific datacenters and avoid data sharing between datacenters. However, conflicts might appear when data is replicated between datacenters; nevertheless change propagation timeliness is not guaranteed. Such problems created data uncertainty on cloud environments. Managing data uncertainty is one of the main obstacles for supporting global distributed transactions on the clouds. To overcome this problem, this paper proposes an quota-based approach for managing data uncertainty on the clouds that guarantees global data correctness without global locking or blocking. To decouple service developers from the hassles of managing data uncertainty, we propose to use a new platform service (i.e. Data Consistency as a Service (DCaaS)) to encapsulate the proposed approach. DCaaS service also ensures SaaS services cloud portability, as it works as a cloud adapter between SaaS service instances. Experiments show that proposed approach realized by the DCaaS service provides much better response time when compared with classical locking and blocking techniques.