Automated Throughput Optimization of Cloud Services Via Model-Driven Adaptation
Cloud computing promises easy access, low entry cost and elasticity. However, elastic service provisioning is usually delivered via service replication, which must be supervised manually, hand-picking, the services to replicate and ensuring their proper load balance. Automated service provisioning, i.e., the function of automatically scaling the services to cope up with their runtime demand, is a research challenge in cloud computing. In this paper, the authors include such scalability analysis early in its development cycle, right at the design stage. They propose a model-driven approach where various QoS parameters can be simulated and analyzed using the e-Motions tool. Additionally, the model is automatically transformed to fit the given throughput requirements by replicating the services which cause the bottleneck.