CSLA: A LANGUAGE FOR IMPROVING CLOUD SLA

MANAGEMENT

Yousri Kouki and Thomas Ledoux

ASCOLA Research Team, EMN-INRIA, LINA, Ecole des Mines de Nantes, Nantes, France

Keywords:

Cloud Computing, Elasticity, Quality-of-Service (QoS), Service Level Agreement (SLA), SLA Violations.

Abstract:

Cloud computing is a paradigm for enabling remote, on-demand access to a set of conﬁgurable computing

resources as a service. The pay-per-use model enables service providers to offer their services to customers in

different Quality-of-Service (QoS) levels. Service Level Agreement (SLA) is a negotiated agreement between

a service provider and a customer where QoS parameters specify the quality level of service that the service

provider have to guarantee. However, due to the dynamic nature of the Cloud and its instability, some SLA

violations can occurred and the service providers can be charged for penalties.

In this paper, we aim at addressing the Cloud instability to better control SLA management (in particular SLA

violations) and indirectly the Cloud elasticity. We propose CSLA, a new SLA language directly integrating

some features dealing with QoS uncertainty and Cloud ﬂuctuation. In our evaluation, we present a novel

proﬁt model for service provider and new algorithms (for admission control and scheduling) to meet SLA

requirements (e.g. prevent SLA violations) while tackling scalability and dynamic issues.

1 INTRODUCTION

According to NIST (Hogan and al., 2011), Cloud

computing is a model for enabling convenient, on-

demand network access to a shared pool of conﬁg-

urable computing resources as services. Based on

an elasticity property, it typically involves provision-

ing of dynamically scalable and often virtualized re-

sources.

The pay-per-use model enables service providers

to offer their services to customers in different

Quality-of-Service (QoS) levels. These QoS param-

eters are used to compose some bipartisan Service

Level Agreement (SLA) between a service provider

and a service consumer. Given that Cloud architec-

tures are usually composed in several XaaS layers,

SLAs are characterized at various levels in this hier-

archy to ensure the expected QoS for different stake-

holders.

Historically, SLA has been used since the 1980s

in a variety of areas (Networking, Web Services,

etc.). Whereas SLA in utility computing systems

becomes an important research challenge, existing

SLA solutions do not tackle Cloud characteristics

such as elasticity, scalability. Initiatives such as

SLA@SOI (Wieder et al., 2011) – based on the WS-

Agreement standard (Andrieux and al., 2007) – de-

ﬁne a consistent SLA-management framework and a

SLA model without capturing some Cloud speciﬁci-

ties. Indeed, the Cloud paradigm is based on elastic-

ity concept and on-demand model, whereas Web Ser-

vices paradigm is focused on interoperability but not

on scalability issues. In Cloud computing, a SLA has

to be suitable for multiple layers with heterogeneous

and volatile resources in a highly dynamic environ-

ment. Moreover, performance of Cloud services may

ﬂuctuate due to the dynamic Internet environment,

which makes the QoS inherently uncertain. To the

best of our knowledge, current SLA solutions are not

able to propose a SLA language that can cope with

the dynamic nature of clouds, the multiple QoS pa-

rameters, the broad and ﬂuctuate network access from

many end-users.

In this paper, we aim at addressing Cloud unstabil-

ity to better control SLA management (in particular

SLA violations) and Cloud scalability. We propose

CSLA, a new SLA language to allow SLA manage-

ment strategies to be more ﬂexible and ﬁnally Cloud

computing to be more elastic. The unstabilility is ad-

dressed by means of new features directly integrating

in our language. These scalability properties allows

CSLA to:

• cope with the error rate in SLAs so as to enable a

service provider to continue operating properly in

586

Kouki Y. and Ledoux T..

CSLA: A LANGUAGE FOR IMPROVING CLOUD SLA MANAGEMENT.

DOI: 10.5220/0003956405860591

In Proceedings of the 2nd International Conference on Cloud Computing and Services Science (CLOSER-2012), pages 586-591

ISBN: 978-989-8565-05-1

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

the case of some violation;

• adapt the provider proﬁt model to dynamic en-

vironment and QoS uncertainty. A novel proﬁt

model, based on properties for scalability, is de-

ﬁned;

• help a service provider to better conﬁgure Cloud

infrastructure. New conﬁguration policies (e.g.,

scheduling, admission control, resource alloca-

tion, etc.) is proposed for conﬁguration of Cloud

services to meet SLA requirements, while tack-

ling scalability and dynamic issues.

The rest of paper is organized as follows. Related

work are described in Section 2. Section 3 introduces

an overview of the main concepts in CSLA. Section

4 presents the beneﬁts of CSLA. Section 5 addresses

conclusion and future work.

2 RELATED WORK

A SLA is a formal contract between service con-

sumers and providers, it speciﬁes one or more Service

Level Objectives (SLOs) to guarantee that the service

quality is delivered to satisfy pre-agreed consumers’

expectations. SLA management is important in util-

ity computing systems because it helps to improve the

customer satisfaction level and to deﬁne clear rela-

tionship between parties.

Signiﬁcant level of research in SLAs languages

has been performed by the Web services commu-

nity. Several languages, such as SLAng (Lamanna

et al., 2003), WSLA (Ludwig et al., 2003) and WS-

Agreement (Andrieux and al., 2007), have been pro-

posed for establishing agreement between two par-

ties using a XML-based language. All these works

have contributed signiﬁcantly to the standardisation

of SLA. However, none meets the needs for Cloud

computing environment and particularly the elasticity

concept.

In the Cloud community, there has been many pro-

posals on SLA management. The main novelty of

the European project SLA@SOI (Wieder et al., 2011)

consists of a reference architecture for multi-layer,

multi-domain SLA management. The SLA@SOI ap-

proach deﬁnes a holistic view for the management of

SLAs and implements a SLA management framework

that can be easily integrated into a service-oriented in-

frastructure. Speciﬁcally, the project aims to enable

automatic negotiation of personalised SLAs across

multiple providers.

The Foundation of Self-governing ICT Infrastruc-

tures (FoSII) research project (Emeakaroha and al.,

2010) proposes solutions for autonomic management

Figure 1: CSLA MetaModel.

of SLAs in the Cloud. One interesting aspect of this

project is the prevention of SLA violations. The self-

management interface speciﬁes operations for sensing

future SLA violation threats based on resource usage

experiences and predeﬁned thresholds.

The project Contrail (CONTRAIL, 2012) pro-

poses a technology to manage SLAs in Clouds fed-

erations. Reusing SLA@SOI framework as a starting

point, the focus is to provide support for the full life-

cycle of SLAs (including the Quality of Protection).

To conclude, current work in Cloud computing are

essentially focused on SLA management at differents

levels of the Cloud services stack and for different re-

source types. However, there are no proposal to deal

with the Cloud unstability or the elasticity concept.

To this end, we propose to extend current deﬁnitions

of the SLA by introducing new features such as Fuzzi-

ness on the SLA parameter and a Conﬁdence on SLO

and SLA. In addition, we include a Penalty model in

the agreement.

3 CSLA LANGUAGE

In this section, we present an overview of the CSLA

language which has been inﬂuenced by related work

in particular WSLA and SLA@SOI.

3.1 CSLA Meta Model

A UML class diagram (see Figure 1) represents the

CSLA:ALANGUAGEFORIMPROVINGCLOUDSLAMANAGEMENT

587

Figure 2: Penalty model.

most important conceptual object types and some of

their relationships. An agreement is speciﬁed in the

form of an instance of the class CloudSLA. This in-

stance is composed of parties and obligations. A

party (typically a provider) implements some func-

tionalities, exposed as one or more services. Also, a

party might rely on services provided by other parties.

To describe this, a party can indicate the services it re-

lies on using references. Services represent the com-

mon understanding of the contracting parties of the

structure of the service, in terms of operations, ser-

vice parameters and SLA parameter that are the basis

of the SLA. Obligations section is based on guarantee

(SLO). For each guarantee, we deﬁne requirements,

conﬁdence and penalty. A guarantee describes a sim-

ple expression or a composite expression. A simple

expression is a SLO. It is characterized by a SLAPa-

rameter, Threshold, Comparator and Fuzziness. A

composite expression is composed of other expres-

sions. The combination of guarantees is done by us-

ing the set operators deﬁned in the class Operator.

3.2 CSLA Properties for Elasticity

Fuzziness, Conﬁdence and Penalty are the novel prop-

erties to deal with the scalability issues.

The degree of fuzziness deﬁnes the acceptable

margin around the threshold value of a SLA param-

eter, whereas the Conﬁdence level deﬁnes the per-

centage of compliance of SLO clauses. Furthermore,

CSLA includes Penalty model that allows the penal-

ties applied in case of SLA violation.

The SLA violation penalty model is similar to

other related works such as (Irwin et al., 2004). The

request earns a maximum value if it executes immedi-

ately and completes within its minimum run time (see

Figure 2). The value decreases linearly with queuing

delay. The value may decrease to a negative num-

ber, indicating a penalty. We model the SLA violation

penalty as linear function : P = α +βdt where β is the

penalty rate and dt is delay time.

3.3 Examples

We present an example of SLA between a SaaS

Figure 3: SLA example.

provider and its consumer. We describe only the obli-

gations section of SLA to demonstrate our contribu-

tion : for a maximum ﬁnancial cost of 0.35 e/request

to a SaaS business intelligence service, the response

time must be less than 5 seconds with acceptable mar-

gin less than 2 seconds and the availability of the ser-

vice must be greater than 98% with acceptable mar-

gin less than 2%. SLOs guaranteed on at least 97%

of requests to the SaaS service i.e. clauses must be

met for 97% of requests to the SaaS service. If more

than 3% of requests to the SaaS service violate SLOs,

a penalty of p e/violated request is applied. Penalty

value p depends on delay time : p = α + βdt where

β=15.

In this example, we treat a composite SLO that

is composed of three simple SLOs. The ﬁrst SLO is

about the ﬁnancial cost, the second one is about re-

sponse time and the third one is about availability.

In summary, we present a SLA support Fault-

tolerance property via the Fuzziness, the Conﬁdence

and the Penalty model. These parameters enable a

service provider to cope with ﬂuctuation and QoS un-

certainty and better controled SLA violations.

4 EVALUATION

Thanks to its features Fuzziness, Conﬁdence and

Penalty model, CSLA allows Cloud providers to pro-

pose ﬂexible proﬁt model and new SLA management

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588

Table 1: Resources details (Windows, On-Demand).

Type Small Large Extra Large

Price/hour e0.12 e0.48 e0.96

Table 2: Pre-deﬁned clauses (100% conﬁdence).

Bronze Silver Gold

Price e/month 140 170 210

Min Response time (s) 8 5 4

Table 3: Pre-deﬁned clauses (differents conﬁdence).

Bronze Silver Gold

Price e/month 120 150 190

Min Response time (s) 8 5 4

SLA Conﬁdence (%) 95 97 99

strategies. This section describes the evaluation of

those features as well as the prototype implemented

to evaluate them.

4.1 CSLA Prototype

The CSLA implementation uses Model Driven Ar-

chitecture (MDA) approach. Furthermore, we imple-

mented a modeling tool using ObeoDesigner in order

to easily deﬁne SLA graphical representations as dia-

grams with rich user interactions hiding the complex-

ity of an XML ﬁle. Obeo Designer allows to create the

graphical modeling workbenches that support CSLA

vocabulary. It supports our domain model based on

UML speciﬁc to the Cloud environment. We create

our code generators based on Acceleo.

The code generation tool is able to produce the

SLA in XML format which can then be parsed by the

Cloud provider. We only describe the obligations sec-

tion of SLA to show our contribution (see Figure 3).

4.2 Proﬁt Model

We deﬁne a ﬂexible proﬁt model for Cloud providers.

Based on Fuzziness, Conﬁdence and Penalty model,

our language handles the elasticity of Cloud, which

is traditionally not considered. The goal is to prevent

SLA violations to avoid penalties that are costly to

providers. Thanks to the Conﬁdence property of the

SLA, some violations can be ”absorbed” and ﬁnally

these violations are not considered as real violations.

From the consumers point of view, it is not necessar-

ily a problem since the Cloud provider proposes new

prices for rental according to the Conﬁdence level (see

Table 2 and Table 3). Prices (e/month) were ﬁxed

arbitrary but can be calibrated with experience and

statistics.

Let us suppose that a SaaS provider sign an agree-

ment with its consumer. The properties deﬁned in the

SLA are as follows: for a maximum ﬁnancial cost of

150 e/month to the SaaS business intelligence ser-

vice, the request response time must be less than 5

seconds with acceptable margin less than 2 seconds.

SLOs guaranteed on at least 97% of requests to the

SaaS service. If more than 3% of requests to the SaaS

service violate SLOs, a penalty of p e/violated re-

quest is applied. Penalty value p depends on delay

time : p = α + βdt where β=15.

Let the total number of consumer queries be 100.

SaaS provider capability to cope and perform under a

violation is presented by the following examples :

• Fuzziness: let a response time of a request be

equal to 6 seconds. This request is considered like

not violated since 6 < 5 + 2.

• Conﬁdence: let the number of processed requests

be 97 and the number of violated requests be 3.

The penalty is equal to 0 because the conﬁdence

is 97%.

• Penalty: let the response time of a request be 8

seconds. The penalty will be equal to p(8 − 7) =

p(1).

The total proﬁt earned by the SaaS provider for serv-

ing the total consumers requests is deﬁned in equation

pro f it =

∑

i=1

PrServ

−Cost (1)

where C is the total number of consumers, PrServ

the service price and Cost the total cost incurred to

the SaaS provider by serving the total consumers re-

quests.

The total cost is deﬁned by equation 2:

Cost =

∑

j=1

V mCost

∑

k=1

PenaltyCost

(2)

where M is the total number of VMs hosting the SaaS

application, V mCost

is the cost of V M

, P is the to-

tal number of penalties-consumer and PenaltyCost

the cost of consumer penalties.

The VM cost depends on the price of VM and

rental duration ( see equation 3).

V mCost

= PrV M

.duration

(3)

PrV M is per instance-hour consumed for each

VM instance. duration

is the sum of instance-hour

of VM j.

The economic model for costing the rental price value

of each VM instance is out of scope of this paper.

CSLA:ALANGUAGEFORIMPROVINGCLOUDSLAMANAGEMENT

589

Figure 4: Number of SLA violations.

Figure 5: Provider proﬁt.

The penalty cost is based on conﬁdence property

and penalty model (see equation 4, 5 and 6).

PenaltyCost

∑

l=p

Penalty

(4)

where Penalty

is the penalty of violation l, V is

the total number of violation of consumer k and C is

the conﬁdence of consumer k.

∑

− (100 −C

) (5)

where p

is the number of violations that are con-

sidered.

Penalty

= α + β.dt (6)

where β is the penalty rate and dt is delay time

A Cloud provider can maximize the proﬁt by re-

ducing the resource cost, which depends on the num-

ber and type of initiated VMs. To this end, we use

scheduling algorithms such as MinAvailCapacity

designed to minimize the number of VMs by utiliz-

ing already initiated VMs. If there are more than one

initiated VM with enough available capacity to pro-

cess the query q, then the query q is assigned to the

machine with minimum available capacity.

We compare our ﬂexible proﬁt model, which takes

into account different conﬁdences and supports fuzzi-

ness (see Table 3) with the previous proﬁt model (see

Table 2). We examine algorithms with 1000 cus-

tomers. All values are summarized in Table 1, Ta-

ble 2 and Table 3. To evaluate the impact on the per-

formance of our proposed model, we vary arrival rate

from 100 to 1000 requests per second. Five differ-

ent types of request arrival rate are used. All of re-

sults present the average obtained by 10 experiment

runs. Results (see Figure 4 and Figure 5) show that,

our proﬁt model is better when compared to the pre-

vious model. Figure 4 shows that our proﬁt model

minimize the number of (considered) SLA violations

during variation of request arrival rate then the proﬁt

provider increases as it can be seen from Figure 5.

4.3 SLA Management Strategies and

Conﬁguration Policies

With CSLA, new SLA management strategies are also

possible. We propose scheduling and resource allo-

cation algorithms for SaaS provider to maximize his

proﬁt.

SaaS provider has to manage the multiple con-

sumers’demand and obligations described in the

SLAs. In other words, it has to balance between

the cost minimization and satisfaction of the SLA re-

quirements. To achieve this goal, we propose an al-

gorithm based on Fuzziness, Conﬁdence and Penalty

model.

A service provider can maximize the proﬁt by re-

ducing the resource cost, which depends on the num-

ber and type of initiated VMs. We propose a new al-

gorithm designed to minimize the number of VMs by

using Fuzziness, Conﬁdence and Penalty model. Our

algorithm is based on MinAvailCapacity and it in-

volves two main phases:

• Admission control based on conﬁdence.

• Scheduling based on fuzziness and penalty model.

In admission control phase, the idea is to control

SLA violations so as to increase the percentage of

not considered violations. Whereas, in the schedule

phase, we schedule the new request in manner to ben-

eﬁt from fuzziness and support the penalty model.

We examine algorithms with 1000 customers.

Values are summarized in Table 1 and Table 3. To

evaluate the impact on the performance of our pro-

posed algorithm, we vary three parameters: arrival

rate, VM capacity and penalty rate factor. All of re-

sults present the average obtained by 10 experiment

runs. In each experiment, we vary one parameter.

Due to the limitation of paper space, we only show

the results of variation of arrival rate from 100 to

1000 requests per second. Five different types of re-

quest arrival rate are used. Simulation results show

CLOSER2012-2ndInternationalConferenceonCloudComputingandServicesScience

590

Figure 6: Number of initiated VMs.

Figure 7: Provider proﬁt.

Figure 8: Penalty cost.

that, our algorithm – using CSLA properties – opti-

mized cost savings better when compared to standard

MinAvailCapacity. As Figure 6 shows, when the re-

quest arrival rate varies from small to very large, our

algorithm performs better to reduce the number of ini-

tiated VMs. As it can be seen from Figure 7, during

the variation of request arrival rate, the provider proﬁt

increases because the number of VMs is reduced. Fig-

ure 8 shows that our algorithm minimize the penalty

cost during variation of request arrival rate due to con-

ﬁdence.

5 CONCLUSIONS

Cloud computing proposes on-demand resources in a

highly dynamic and volatile environment. Therefore,

current SLA proposals should addressed inherently

the scalability and the instability issues. This paper

presents CSLA, a SLA language for improving SLA

management in Cloud computing. The instability is

addressed by means of new features directly integrat-

ing in our language. CSLA is a XML-based language

that supports several properties allowing a more con-

trolled SLA violations. Simulation results show that

the algorithm based on CSLA properties maximizes

provider proﬁt better when compared to the previous

algorithms.

We intend to continue this work in several ways.

First, we would like to deﬁne pricing policies that

consider CSLA properties. To this end, we need more

experiments to deduce statistics for the price adjuste-

ment. Second, we continue to propose new SLA man-

agement strategies and conﬁguration policies to max-

imise the provider proﬁt. Finally, we intend to sup-

port negotiation phase to improve customer satisfac-

tion levels.

ACKNOWLEDGEMENTS

This work is supported by the MyCloud project

(ANR-10-SEGI-010).

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