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Contents

About this book

Cloud brokering

Part I. Value-added services in cloud brokering

Cloud performance and placement in cloud brokering

Introduction

Cloud performance evaluation

Motivations and challenges

Studies related to cloud providers performance evaluation

Cloud Virtual Machine (VM) characterization

Placement in cloud brokering

Non-functional requirements-based placement

Application aware placement

Conclusion

Towards a figure of merit for cloud performance

Introduction

Performance evaluation

Evaluation methodology

Experimental setup

Provisioning time

Computation benchmarks performance

Memory benchmarks performance

Storage benchmarks performance

Variability

Figure of merit of VM cloud performance

Mean and radar plot as figures of merit

Simple figure of merit

Figure of merit based on Analytic Hierarchy Process

Case study: CPU-intensive application

Summary

An exact approach for optimizing placement in cloud brokering

Introduction

Goal programming

An exact approach for the Placement problem

Parameters

Variables

Goal

Constraints

Case study: Online trading platform

Part II. A new pricing model in cloud brokering

The Pay-as-you-book pricing model

Introduction

Pricing models in cloud computing

Advance Reservations

Advance Reservation specified by cloud providers

Advance Reservation specified by end-users

Pay-as-you-book

Initial scheduling of Advance Reservations

Pricing and rewarding end-users

Resource allocation policies

Case Study: A Virtual cloud Provider maximizing revenues through the Pay-as-you-book pricing model

Experimental setup

Results and analysis

Summary

Conclusion and future works

Appendix A. Studies related to cloud providers performance

Appendix B. Cloud performance evaluation: details and extended results

Related issues to the performance evaluation

VM configurations

Benchmark duration

Performance-price correlation with a simple figure of merit of Cloud performance

Correlation among VM sizes from different cloud providers

Correlation among different VM sizes from a single cloud provider

Bibliography

List of Figures

List of Tables

About this book

Service arbitrage enables advanced services in cloud brokering by taking advantage of two or more cloud provider offers. This allows cloud brokers to simplify the vast number of offers by categorizing the features and benefits of each cloud provider in order to match consumer needs with an ideal set of cloud providers. In the first part of this book entitled "Value-added services in cloud brokering", a comprehensive, state of the art study has been carried out on cloud performance evaluations and placement in cloud brokering (Chapter 1). Then, a method is proposed to calculate cloud performance through a single figure of merit based on the mapping of the physical features of a Virtual Machine (VM) to their respective performance capacities (Chapter 2). Finally, an exact placement approach for optimizing the distribution of cloud infrastructure across multiple providers is proposed (Chapter 3). Parameters such as price, VM configuration, VM performance, network latency and availability are considered for that purpose.

Nowadays, pay-as-you-go and reserved pricing dominate the way consumers acquire cloud resources from legacy cloud providers at the infrastructure level. However, the introduction of cloud brokers may induce the commoditization of cloud infrastructures. Facing such an evolution, new pricing models are necessary to capture potential consumers or untapped market segments. The second part of this book entitled "A new pricing model for cloud brokering" focuses on the design of a pricing model for cloud brokering, called pay-as-you-book (Chapter 4). Pay-as-you-book is based on two types of information. The first type consists of the forecast of users’ job requests. The second one consists of the ability of cloud brokers to take advantage of such advanced reservations. With this aim in view, a study comparing three resource allocation policies under pay-as-you-book is carried out. The aim of this book is to contribute to the design of new value-added services and pricing models for cloud brokering. The majority of the investigations and original results presented in this manuscript have been achieved and obtained in the context of the CompatibleOne [1] research project supported by the French Ministry of Industry. Its objective was to demonstrate the feasibility of a cloud brokering intermediation platform integrating and adapting the various software solutions proposed by the industrial and academic partners of the project. This platform provides a single point for service consumption in order to avoid vendor lock-in. This book has three objectives:

• The first one is to present a single figure of merit for cloud VMs performance based on the application profile.
• The second one is to propose an exact approach for allocation of VMs across multiple cloud providers based on different optimization criteria.
• The third one is to describe a pricing model for cloud brokering, called pay-as-you-book.

The contribution of this book can be itemized as the following:

• A method to calculate a figure of merit for VM cloud performance. The originality of this figure of merit is to offer a single value to express VM cloud performance that is based on the type of application to be deployed. Thus, end-users may in a straightforward manner compare and select the best cloud provider in which to deploy an application.
• The formulation of Mixed-Integer Linear Programming for placement of VMs across multiple cloud providers. The originality of this approach is in associating the heterogeneity of cloud providers’ offers with their respective performance. This approach may be applied to the optimization of cost, performance, cost-performance and disaster recovery scenarios.
• The description of pay-as-you-book, a pricing model between pay-as-you-go and subscription. Pay-as-you-book consists of paying and reserving time-slots of VMs in advance without a fixed fee to subscribe to the service and without a long-term commitment, avoiding vendor lock-in, while obtaining lower prices than in pay-as-you-go. Pay-as-you-book may be applied in scenarios with predictable workloads. Through simulations, it has been shown why a model such as pay-as-you-book is not convenient for cloud providers. However, cloud brokers reselling cloud infrastructure may create attractive service offerings based on pay-as-you-book.
  

This book consists of an overview of the following conference publications:

1. F. Díaz-Sánchez, S. Al Zahr, M. Gagnaire, J-P. Laisné, J. Marshall. "CompatibleOne: Bringing cloud as a Commodity". IEEE International Conference on cloud Engineering (IC2E), Boston, US, May. 2014.
2. F. Díaz-Sánchez, S. Al Zahr, and M. Gagnaire. "An Exact Placement Approach for Optimizing Cost and Recovery Time under Faulty Multi-cloud Environments". IEEE cloudCom Conference, Bristol, UK, Dec. 2013.
3. F. Díaz Sánchez, E. Doumith, S. Al Zahr and M. Gagnaire. "An Economic Agent Maximizing cloud Providers Revenues Under Pay-as-you-Book Pricing Model". Conference on the Economics of Grids, clouds, Systems, and Services (GECON), Berlin, Germany, Nov. 2012.
4. F. Díaz, E. Doumith and M. Gagnaire. "Impact of Resource over-Reservation (ROR) and Dropping Policies on cloud Resource Allocation". IEEE cloudCom Conference, Athens, Greece, Nov. 2011.
5. F. Díaz-Sánchez, M. Gagnaire, J. Marshall and J-P. Laisné. "COSCHED: A Scheduling Agent Maximizing cloud Broker’s Revenues under the CompatibleOne Architecture". The 11th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA-13), Melbourne, Australia, Jul. 2013.

Cloud brokering

The role of cloud Brokers in the near future of cloud computing has been identified by Gartner as a major market trend: “By 2015, cloud Brokers will represent the single largest category of growth in cloud computing, moving from a sub-$1 billion market in 2010 to a composite market counted in the hundreds of billions of dollars.” [2]. This prediction seems to be reinforced by the amount of funding raised by some cloud brokering companies: Rightscale US$47.3m in three rounds1, 6fusion US$10m in two rounds, cloud Cruiser US$7.6m in two rounds, Zimory Systems US$7.2m in two rounds and Gravitant US$3.7m in one round [3]. One of the main reasons, behind this high economic expectation, is the highly heterogeneous current cloud market constituted by many cloud providers. Each cloud provider exhibits different interfaces, pricing models and value-added services. To help the end-user cope with such a fragmented ecosystem, cloud brokers have emerged as an intermediary third-party that provides unified self-service access to multiple cloud providers. Thus, by being a single point for service consumption, cloud brokers provide interoperability and portability of applications across multiple cloud providers. Besides this inherent role, current cloud brokers provide other value-added services to cloud consumers, such as the following: Advanced management by using tools beyond the stacks offered by cloud providers (e.g. consolidated billing, infrastructure monitoring, disaster recovery, SLA enforcement), elasticity management in order to automatically scale up or down infrastructure resources based on the workload and service arbitrage with the aim of taking advantage of two or more cloud provider offerings (e.g. cost optimization). These services can be overlayed, enabling new cloud computing scenarios such as cloud bursting or cloud marketplaces (Figure 1). These new scenarios may be beneficial for both end-users and cloud providers. In the case of cloud bursting, end-users have the possibility of extending their computing facilities by moving the development of applications or the non-mission-critical applications to public clouds. In the case of a cloud marketplace scenario, end-users have access to multiple cloud providers through a single interface, while cloud providers may sell spare infrastructure capacity. Cloud brokers are expected to drive creation of

Figure 1: Evolution and dependency of value-added services in cloud brokering

value through advanced value-added services enabling new cloud computing scenarios. The price of cloud computing resources varies around 20% between cloud providers, while the difference in performance between cloud providers remains unknown or less studied [3]. Due to the fact that cloud brokers are able to deploy a workload in any cloud provider, the measurement of performance for cloud providers and the placement of cloud resources based on a cost-performance relationship may be supported by cloud brokers in future value-added services. Moreover, the commoditization of infrastructure resources will increase the cloud adoption rate by simplifying the purchase of cloud computing resources. As cloud computing resources are traded like any other commodity (e.g. wheat, oil, iron) the currently fragmented cloud market will flatten. This will open the door to new pricing models in which cloud brokers not only act as intermediaries but also as liquidity providers, negotiating volume discounts from cloud providers and guaranteeing resource availability to end-users.

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Esta obra se editó en Ediciones USTA, Departamento Editorial de la Universidad Santo Tomás. 

2016

To Leta and Days.

Part I

Value-added services in cloud brokering

Chapter 1

Cloud performance and placement in cloud brokering

Introduction

As the number of cloud computing services increases, so does the interest of consumers to be able to compare these services in order to choose those best adapted to their needs. This chapter focuses on the performance issues related to cloud provider evaluation and on the role of cloud brokers in the automatic optimization of resource allocation across multiple cloud providers. This chapter is structured as follows. Section "cloud performance evaluation". presents a survey of the current studies related to cloud performance evaluation. The motivation for and challenges behind the evaluation of cloud provider performance is also detailed in this section. Section "Placement in cloud brokering" describes the current methods used to allocate resources in cloud brokering. The studies are classified into two categories: placement based on non-functional requirements and application-aware placement.

Cloud performance evaluation

Motivations and challenges