Performance scalability of n-tier application in virtualized cloud environments: Two case studies in vertical and horizontal scaling
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The prevalence of multi-core processors with recent advancement in virtualization technologies has enabled horizontal and vertical scaling within a physical node achieving economical sharing of computing infrastructures as computing clouds. Through hardware virtualization, consolidated servers each with specific number of core allotment run on the same physical node in dedicated Virtual Machines (VMs) to increase overall node utilization which increases profit by reducing operational costs. Unfortunately, despite the conceptual simplicity of vertical and horizontal scaling in virtualized cloud environments, leveraging the full potential of this technology has presented significant scalability challenges in practice. One of the fundamental problems is the performance unpredictability in virtualized cloud environments (ranked fifth in the top 10 obstacles for growth of cloud computing). In this dissertation, we present two case studies in vertical and horizontal scaling to this challenging problem. For the first case study, we describe concrete experimental evidence that shows important source of performance variations: mapping of virtual CPU to physical cores. We then conduct an experimental comparative study of three major hypervisors (i.e., VMware, KVM, Xen) with regard to their support of n-tier applications running on multi-core processor. For the second case study, we present empirical study that shows memory thrashing caused by interference among consolidated VMs is a significant source of performance interference that hampers horizontal scalability of an n-tier application performance. We then execute transient event analyses of fine-grained experiment data that link very short bottlenecks with memory thrashing to the very long response time (VLRT) requests. Furthermore we provide three practical techniques such as VM migration, memory reallocation, soft resource allocation and show that they can mitigate the effects of performance interference among consolidate VMs.