BRAHMA: An intelligent framework for automated scaling of streaming and deadline-critical workflows

Abstract

The prevalent use of multi-component, multi-tenant models for building novel Software-as-a-Service (SaaS) applications has resulted in wide-spread research on automatic scaling of the resultant complex application workflows. In this paper, we propose a holistic solution to Automatic Workflow Scaling under the combined presence of Streaming and Deadline-critical workflows, called AWS-SD. To solve the AWS-SD problem, we propose a framework BRAHMA, that learns workflow behavior to build a knowledge-base and leverages this info to perform intelligent automated scaling decisions. We propose and evaluate different resource provisioning algorithms through CloudSim. Our results on time-varying workloads show that the proposed algorithms are effective and produce good cost-quality trade-offs while preventing deadline violations. Empirically, the proposed hybrid algorithm — combining learning and monitoring, is able to restrict deadline violations to a small fraction (3–5%), while only suffering a marginal increase in average cost per component of 1–2% over our baseline naïve algorithm, which provides the least costly provisioning but suffers from a large number (35–45%) of deadline violations.