Bassem EL ZANT and Maurice GAGNAIRE

Introduction

Objectives

Performance Evaluation

Radar performance figures of merit

Value figures of merit

Service-Oriented model for Cloud Service Providers selection

Conclusion

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Cloud computing is a big market

Huge number of Cloud Service Providers (CSPs)

Each CSP has its own offers and prices

The virtual machines (VMs) configurations are different among CSPs

CSPs do not provide quantitative information about the performance of

their services

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1. Measure the performance and the quality of Cloud services

2. Build Radar performance and value figures of merit

3. Build and implement a Service-oriented model for CSP selection based on

the customer requirements

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Table 1: VMs’ characteristics and prices

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Performance metrics Benchmarks (HB or LB)

CPU performance Pystone (HB) and Sysbench (LB)

Memory performance STREAM benchamrk (HB)

Disk I/O performance Bonnie Benchmark (HB)

Provisioning time Measured using a Timer (LB)

Mean Response Time (MRT) Ping the IP address (LB)

Variability via Relative Standard Deviation

(RSD)

RSD calculated for all the measurements

(LB)

Availability Cedexis website: www.cedexis.com (HB)

HB: Highest value Better, LB: Lowest value Better

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The score of one metric is scaled between two fixed values (X and Y).

X corresponds to the lower bound with the lowest score and Y

corresponds to the upper bound with the highest score.

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Table 2: The Cloud customer parameters

Table 3: The virtual machines characteristics

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Table 4: The performance and the quality of the offered Cloud services

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The objective function of our Service-Oriented model for Cloud Service

Providers selection is then given by the following:

Max [ ß1 * Performance – ß2 * Price]

Performance= Average performance

Price= Total price

ß1 and ß2 are the respective weights

ß1+ ß2=1

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Cloud based multi-service software platform providing Software as a Service

(SaaS) over Infrastructure as a Service (IaaS)

Multiple softwares are offered by the platform as a services such as Storage

service by SoftwareA and Panoramic photo viewer service by SoftwareB

The Storage service needs specific Disk storage capacities with certain Disk

storage performances to access the data stored using SoftwareA

The Panoramic photo viewer provided by SoftwareB let the user specify the images

that constitutes the panoramic picture. This necessitates a high CPU, Memory and

Disk storage capacities and performances

The customer enters the software requirements and the platform running our

model select the suitable CSP(s)

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CSPs do not provide quantitative information about their performances and quality of services

We measured the performances of eight different CSPs choosing four VM sizes from each CSP

We present our new Service-Oriented model for Cloud Service Providers selection. Our model

takes into account the requirements of the customers and the existing VMs configurations and

prices of the CSPs in the market with their respective performances

These inputs are computed using Mixed-Integer Linear Programming formulas that select the

suitable CSP(s) according to the specified requirements of the customers’ services

The requirements of the Cloud customers could be provided by one single CSP or could be

provided by multiple CSP

Our model makes the selection of the appropriate CSP(s) to deploy one service an easier task

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The ranking of the quality of service provided by the various Cloud Service

Providers mentioned in this paper must be considered as an indication.

Thus, different benchmarks could have driven to relatively different results.

In addition, our tests have been performed on specific data centers and within

specific time periods that could also impact the obtained results.

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