Pradeep Padala

Application-driven dynamic vertical scaling of virtual machines in resource pools

Most modern hypervisors offer powerful resource control primitives such as reservations, limits, and shares for individual virtual machines (VMs). These primitives provide a means to dynamic vertical scaling of VMs in order for the virtual applications to meet their respective service level objectives (SLOs). VMware DRS offers an additional resource abstraction of a resource pool (RP) as a logical container representing an aggregate resource allocation for a collection of VMs. In spite of the abundant research on translating application performance goals to resource requirements, the implementation of VM vertical scaling techniques in commercial products remains limited. In addition, no prior research has studied automatic adjustment of resource control settings at the resource pool level. In this paper, we present AppRM, a tool that automatically sets resource controls for both virtual machines and resource pools to meet application SLOs. AppRM contains a hierarchy of virtual application managers and resource pool managers. At the application level, AppRM translates performance objectives into the appropriate resource control settings for the individual VMs running that application. At the resource pool level, AppRM ensures that all important applications within the resource pool can meet their performance targets by adjusting controls at the resource pool level. Experimental results under a variety of dynamically changing workloads composed by multi-tiered applications demonstrate the effectiveness of AppRM. In all cases, AppRM is able to deliver application performance satisfaction without manual intervention.

Crowdsourced Resource-Sizing of Virtual Appliances

Using a population of VMware Virtual Center Virtual Appliances (VCVA) and their respective workloads we de- scribe techniques for constructing a model of their resource consumption and performance, specially memory requirements, and average operation-latency by mining logs of application (VCVA) performance. We use our model to provide sizing recommendations for the virtual appliance and identify features that can be used to provide rough estimates of expected memory consumption. We show results of better than 70% prediction accuracy (recall) for predicting Physical Memory Usage and better than 80% prediction accuracy (recall) for predicting the average latency of work- load operations. We describe modeling techniques from statistical machine learning that are amenable to representing complex, non-linear systems. Further, via the choice of techniques, we present an approach for reasoning about the limitations of our model, i.e., identifying when (and why) our model is expected to perform well and poorly.