A Framework for QoS-aware Execution of Workflows over the Cloud

TL;DR

This paper introduces SAVER, a QoS-aware framework for dynamically managing cloud resources to execute workflows with response time constraints, using passive monitoring and queueing network models to optimize resource allocation.

Contribution

It presents a novel algorithm that dynamically adjusts Web Service instances in the cloud based on observed response times, without requiring detailed knowledge of the workflows.

Findings

Validated through numerical simulations showing effective resource management.

Demonstrated ability to meet response time constraints under workload fluctuations.

Uses passive monitoring and queueing models for efficient resource allocation.

Abstract

The Cloud Computing paradigm is providing system architects with a new powerful tool for building scalable applications. Clouds allow allocation of resources on a "pay-as-you-go" model, so that additional resources can be requested during peak loads and released after that. However, this flexibility asks for appropriate dynamic reconfiguration strategies. In this paper we describe SAVER (qoS-Aware workflows oVER the Cloud), a QoS-aware algorithm for executing workflows involving Web Services hosted in a Cloud environment. SAVER allows execution of arbitrary workflows subject to response time constraints. SAVER uses a passive monitor to identify workload fluctuations based on the observed system response time. The information collected by the monitor is used by a planner component to identify the minimum number of instances of each Web Service which should be allocated in order to satisfy the response time constraint. SAVER uses a simple Queueing Network (QN) model to identify the optimal resource allocation. Specifically, the QN model is used to identify bottlenecks, and predict the system performance as Cloud resources are allocated or released. The parameters used to evaluate the model are those collected by the monitor, which means that SAVER does not require any particular knowledge of the Web Services and workflows being executed. Our approach has been validated through numerical simulations, whose results are reported in this paper.