ALVEC: Auto-scaling by Lotka Volterra Elastic Cloud: A QoS aware Non Linear Dynamical Allocation Model

TL;DR

ALVEC introduces a novel, biologically inspired model for dynamic cloud resource allocation that improves SLA management, predicts future loads, and is the first unsupervised scheme of its kind.

Contribution

It presents the first unsupervised, biologically inspired model for dynamic cloud resource allocation using Lotka Volterra equations.

Findings

Better SLA and QoS management demonstrated

Accurate load prediction capabilities shown

First unsupervised resource allocation model introduced

Abstract

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.