Cost optimization of virtual infrastructures in dynamic multi‐cloud scenarios

Abstract

SummaryThe number of providers in the cloud computing market is increasing at a rapid pace. They offer a wide range of pricing schemes, different types of instance, or even different value‐added features to differ from other competitors, making the cloud market more complex. Cloud brokering enable users to choose the best cloud provider for their needs and avoid particular providers' lock in. Moreover, the use of multiple clouds offers several benefits such as scalability of services, improvement in fault tolerance, or cost reduction. However, users still find difficult the decision of where to deploy their resources as they have to handle too much information. The use of cloud brokering mechanisms is useful to reduce the complexity of using a multi‐cloud environment. In this paper, we show a cloud broker architecture for deploying virtualized servers across available clouds. This architecture uses a scheduling module to obtain an optimal placement of a virtual infrastructure while making it transparent for users. We focus our investigation on dynamic cloud scenarios in terms of variable resource prices, taking into account the virtual machine migration overhead issue. We evaluate the performance of several use cases applied in real scenarios, and we show the improvement potential of using brokering mechanisms in dynamic deployments compared with static ones. Copyright © 2012 John Wiley & Sons, Ltd.

Application based brokering algorithm for optimal resource provisioning in multiple heterogeneous clouds

“…In recent years, researchers and cloud brokers have focused on developing models and methods for federated cloud service and product selection based on either minimizing the total deployment cost or maximizing the QoS. Cloud service provisioning and cost optimization based on static demand, price and availability have been discussed in [6][7][8][9][10][11], where [12,13] deal with the uncertainty in the demand, price and availability of cloud services.…”

“…Simarro et al proposed a methodology in [12] which considers the dynamic price and demand of cloud service in contrast to the static one in [7]. The authors also considered cloud migration overhead issues for service provisioning among multiple clouds.…”

“…The general form of the optimization algorithm is formulated in Eqs. (12) to (23). The goal of the objective function Eq.…”

Recent developments in high‐performance computing and simulation: distributed systems, architectures, algorithms, and applications

“…‘Cost Optimization of Virtual Infrastructures in Dynamic Multi‐Cloud Scenarios’ by Jose Luis Lucas Simarro, Rafael Moreno‐Vozmediano, Ruben S. Montero, and Ignacio M. Llorente presents a so‐called cloud broker architecture: an architecture that is responsible for deploying virtual resources (virtualized servers) across compute clouds. By taking into account migration overhead costs in a dynamic cloud scenario, several use cases are investigated, demonstrating that using brokering mechanisms in dynamic deployments shows clear advantages over static deployments in cloud environments.…”

A Scalable and Distributed Cloud Brokering Service

“…Properties Limitations RightScale select, migrate and monitor different Clouds missing negotiation and optimization features CloudSwitch make simpler the process of migrating an application or workload to the Cloud missing proper brokerage service JamCracker aggregates and distributes on-demand services through a global ecosystem of Service Providers missing a proper brokerage service [9] based on a Cloud service register catalog where it performs a search of the desired proposal missing a proper brokerage service [12] focused on the scheduler only cost optimization OPTIMIS under development only cost optimization [14] optimize placement of VMs across multiple cloud providers dynamic cloud scheduling not supported analyzes the problem, whose solution's requirements, approach and implementation are then investigated in Sections 4, 5 and 6. Sections 7 and 8 concludes with an overview and interpretation of the results.…”