SCOPE: self-adaptive and policy-based data management middleware for federated clouds

A federated cloud storage setup which integrates and utilizes storage resources from multiple cloud storage providers has become an increasingly popular and attractive paradigm for the persistence tier in cloud-based applications (e.g., SaaS applications, IoT applications, etc). However, federated cloud storage setups are prone to run-time dynamicity: many dynamic properties impact the way such a setup is governed and evolved over time, e.g., storage providers enter or leave the market; QoS metrics and SLA guarantees may change over time; etc. In general, existing federated cloud systems are oblivious to dynamic properties of the underlying operational environment, resulting in both sub-optimal data management decisions and costly SLA violations. Additionally, due to the sheer complexity of cloud-based applications coupled with the heterogeneous and volatile nature of federated cloud setups, the complexity of building, maintaining, and expending such applications increases dramatically and therefore managing them manually is no longer simply an option. To address these concerns, we present SCOPE, a policy-based and autonomic middleware that provides self-adaptiveness for data management in federated clouds. We have validated SCOPE in the context of a realistic SaaS application, performed an extensive functional validation, and conducted a thorough experimental evaluation. The evaluation results demonstrate (i) the ability of the middleware to perform data management decisions that take into account the run-time dynamicity (i.e., dynamic properties) of a federated cloud storage setup to meet the promised SLAs, and (ii) the self-adaptive behavior of SCOPE without the need for operator intervention. In addition, our in-depth performance evaluation results indicate that the benefits are achieved with acceptable performance overhead, and as such highlight the applicability of the proposed middleware for real-world application cases.