Kaa: Evaluating Elasticity of CloudHosted DBMS Autoscaling is able to change the scale of an application at runtime. Understanding the application characteristics, scaling impact as well as the workload, an autoscaler aligns the acquired resources to match the current workload. For distributed Database Management Systems (DBMS) forming the backend of many largescale cloud applications, it is currently an open question to what extent they support scaling at runtime. In particular, elasticity properties of existing distributed DBMS are widely unknown and difficult to evaluate and compare. This paper presents a comprehensive methodology for the evaluation of the elasticity of distributed DBMS. On the basis of this methodology, we introduce a framework that automates the full evaluation process. We validate the framework by defining significant elasticity scenarios for a case study that comprises two DBMS for writeheavy and readheavy workloads of different intensities. The results show that scalable distributed DBMS are not necessarily elastic and that adding more instances to a cluster at runtime may even decrease the experienced performance.

Dynamic Provisioning of Service Composition in a MultiTenant SaaS Environment Multitenant service composition has become a common delivery model for business processes in cloud computing. To dynamically support the workload tenant variation, elasticity holds the promise of ensuring the quality of service (QoS) of the business process by providing the involved service instances at a low cost. However, integrating both of multitenancy and elasticity during service composition is a key problem for serving multiple tenants from a single process instance. Nowadays, existing approaches in the field of cloud service composition, although numerous, still fall short since they cannot adequately address issues related to supporting the scalability of the composed service and adapting it to the workload fluctuation. In this paper, we propose a holistic approach which makes the dynamic multitenant services matching and manages their elasticity in distributed business processes. This approach is based on a generic service pattern that integrates multitenancy property and handles elasticity at the process and service levels. Furthermore, we present elastic composition algorithms to compose multitenant cloud services and perform their elasticity through the proposed service pattern. The evaluation of our approach, compared to the baseline approach, proves that the latency taken to provide an elastic multitenant service composition and detect its SLA (Service Level Agreements) violation are reasonably short. We also show that the CPU overhead of using our approach is negligible. Furthermore, experimental results demonstrate the merits of our approach in terms of minimizing the memory consumption through the deployed service instances.