  Elasticity in resource allocation is still a relevant problem in cloud
computing. There are many academic and white papers which have investigated the
problem and offered solutions.\textbf{Unfortunately, there are scant evidence
of determining scaling number dynamically.} Elasticity is defined as the
ability to adapt with the changing workloads by provisioning and
de-provisioning Cloud resources. We propose ALVEC, a novel model of resource
allocation in Cloud data centers, inspired by population dynamics and
Mathematical Biology, which addresses dynamic allocation by auto-tuning model
parameters. The proposed model, governed by a coupled differential equation
known as Lotka Volterra (LV), fares better in Service level agreement (SLA)
management and Quality of Services (QoS). We show evidence of true elasticity,
in theory and empirical comparisons. Additionally, ALVEC is able to predict the
future load and allocate VM's accordingly. The proposed model, ALVEC is the
first example of unsupervised resource allocation scheme.
