Self-optimizing transactional data grids for elastic cloud environments∗

For several decades, relational databases have represented the indisputable reference solution for transactional data management. Over the last years, however, we have witnessed the proliferation of a new generation of in-memory, transactional data platforms, often referred to as NoSQL data grids. By relying on a simpler data model (key/value vs relational), lightweight application interfaces (embedded vs. JDBC/ODBC connections) and efficient mechanisms to achieve data durability (in-memory replication vs disk-based logging), NoSQL data grids are designed from the ground up to maximize the scalability of applications deployed on commodity, shared-nothing, distributed infrastructures, such as those typically offered by IaaS cloud providers. On the other hand, the inherently dynamic nature of elastic cloud computing environments raises the issue of how to ensuring the optimal efficiency of NoSQL data grids in face of fluctuations of the applications’ workloads and of the scale of the platform over which these data management platforms are deployed. This is an extremely relevant problem given that, in the usage-based model of the cloud, contenting with static configurations that achieve suboptimal (or not always optimal) performance means having to acquire a larger than required amount of resources to achieve predetermined QoS level, and thus incurring in higher operational costs. Further, manually identifying the optimal configuration of the many (often tightly interdependent) parameters of these platforms, in presence of rapidly fluctuating workloads, is an expensive, challenging and error prone task. In this talk I will present some of the recent results achieved in the area of self-optimization of NoSQL data grids within the context of Cloud-TM. Cloud∗This work has been partially supported by the project Cloud-TM (co-financed by the European Commission through the contract no. 257784), the FCT project ARISTOS (PTDC/EIAEIA/102496/2008) and by FCT (INESC-ID multiannual funding) through the PIDDAC Program Funds.