Design and evaluation of automatic workflow scaling algorithms for multi-tenant SaaS

Current Cloud software development efforts to come up with novel Software-as-a-Service (SaaS) applications are, just like traditional software development, usually no longer built from scratch. Instead more and more Cloud developers are opting to use multiple existing components and integrate them in their application workflow. Scaling the resulting application up or down, depending on user/tenant load, in order to keep the SLA, no longer becomes an issue of scaling resources for a single service, rather results in a complex problem of scaling all individual service endpoints in the workflow, depending on their monitored runtime behavior. In this paper, we propose and evaluate algorithms through CloudSim for automatic and runtime scaling of such multi-tenant SaaS workflows. Our results on time-varying workloads show that the proposed algorithms are effective and produce the best cost-quality trade-off while keeping Service Level Agreements (SLAs) in line. Empirically, the proactive algorithm with careful parameter tuning always meets the SLAs while only suffering a marginal increase in average cost per service component of approximate to 5-8% over our baseline passive algorithm, which, although provides the least cost, suffers from prolonged violation of service component SLAs