A reliable and cost-efficient auto-scaling system for web applications using heterogeneous spot instances. (April 2016)

• Record Type: Journal Article
• Authors: Qu, Chenhao; Calheiros, Rodrigo N.; Buyya, Rajkumar
• Is Part Of: Journal of network and computer applications. Volume 65(2016)
• Page Start: 167
• Page End: 180
• Publication Date: 2016-04
• Subjects: Cloud computing -- Auto-scaling -- Web application -- Fault tolerant -- Cost -- QoS -- Spot instance; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2016.03.001
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Cloud providers sell their idle capacity on markets through an auction-like mechanism to increase their return on investment. The instances sold in this way are called spot instances. In spite that spot instances are usually 90% cheaper than on-demand instances, they can be terminated by provider when their bidding prices are lower than market prices. Thus, they are largely used to provision fault-tolerant applications only. In this paper, we explore how to utilize spot instances to provision web applications, which are usually considered as availability-critical. The idea is to take advantage of differences in price among various types of spot instances to reach both high availability and significant cost saving. We first propose a fault-tolerant model for web applications provisioned by spot instances. Based on that, we devise novel cost-efficient auto-scaling polices that comply with the defined fault-tolerant semantics for hourly billed cloud markets. We implemented the proposed model and policies both on a simulation testbed for repeatable validation and Amazon EC2. The experiments on the simulation testbed and EC2 show that the proposed approach can greatly reduce resource cost and still achieve satisfactory Quality of Service (QoS) in terms of response time and availability.