A Reliable and Cost-Efficient Auto-Scaling System for Web Applications Using Heterogeneous Spot Instances

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 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 auto-scaling polices 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 the real platform against the benchmarks 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

Dynamic provisioning of cloud resources based on workload prediction

© Springer Nature Singapore Pte Ltd. 2019. Most of the businesses nowadays have started using cloud platforms to host their software applications. A cloud platform is a shared resource that provides various services like software as a service (SAAS), infrastructure as a service (IAAS) or anything as a service (XAAS) that is required to develop and deploy any business application. These cloud services are provided as virtual machines (VM) that can handle the end-user’s requirements. The cloud providers have to ensure efficient resource handling mechanisms for different time intervals to avoid wastage of resources. Auto-scaling mechanisms would take care of using these resources appropriately along with providing an excellent quality of service. The researchers have used various approaches to perform auto-scaling. In this paper, a framework based on dynamic provisioning of cloud resources using workload prediction is discussed