TOWARDS AUTONOMIC MARKET MANAGEMENT IN CLOUD

COMPUTING INFRASTRUCTURES

Ivan Breskovic, Michael Maurer, Vincent C. Emeakaroha, Ivona Brandic

Distributed Systems Group, Information Systems Institute, Vienna University of Technology, Vienna, Austria

orn Altmann

TEMEP, Department of Industrial Engineering, College of Engineering, Seoul National University, Seoul, South Korea

Keywords:

Service level agreement, SLA management, Electronic markets, Autonomic computing.

Abstract:

Cloud computing markets face challenges, such as a large variety of different resource types in the market.

A large variety of resource types results in a low number of matches of ask and bids. Consequently, the

market has a low liquidity, which is economically inefﬁcient and can lead to market failure. To counteract

this problem, SLA templates (i.e., templates for electronic contracts) have been introduced. However, until

now, SLA templates were static, not able to reﬂect changes in users’ requirements. In this paper, we apply

the adaptive SLA mapping approach for deriving public SLA templates from private SLA templates of users.

To achieve the adaptation of SLA templates, we combine clustering algorithms with adaptation methods. The

result is a set of new public SLA templates, which reﬂect the market situation more accurately. This way,

we enable marketplaces to automatically adapt to observed changes of market conditions. For the simulation-

based evaluation of our approach, we formalize a utility and cost model, calculate the overall net utility, and

assess the scalability of the approach. Our results show that the use of clustering algorithms can signiﬁcantly

improve the performance of the adaptive SLA mapping approach.

1 INTRODUCTION

Cloud computing is the result of the convergence

of several concepts, ranging from virtualization, dis-

tributed application design, Grid computing, and en-

terprise IT management. It enables a ﬂexible ap-

proach for deploying and using applications (Buyya

et al., 2009). In such a paradigm, hardware re-

sources, software resources, and data are offered as

on-demand services to the user in a pay-as-you go

model. Functional and non-functional requirements

of those services, termed quality of service (QoS) re-

quirements, are expressed and negotiated by means of

service level agreements (SLAs). Those are contracts

in Cloud resource markets and are signed between a

customer and a service provider.

For the successful implementation of the Cloud

computing paradigm, well-developed hardware and

software resource markets are fundamental (Risch

et al., 2010). One essential feature of a well-

developed market is liquidity of its goods. Illiq-

uid goods have the risk of not being sellable or pur-

chasable when needed, driving potential market par-

ticipants away. Therefore, well-developed resource

markets must enable immediate execution of standard

orders, which is possible through a sufﬁciently large

number of participants trading (Samuelson and Nord-

haus, 2005).

Many electronic marketplaces suffer from chal-

lenging situations, such as a highly dynamic evolution

of user requirements for goods. Due to the large re-

source variability in Cloud markets and still low num-

ber of market participants, a small difference in a sin-

gle user requirement might prevent a match. This can

result in very low percentage of matching of asks (i.e.,

consumers’ requirements) and bids (i.e., providers’

offers), making the market unattractive to both con-

sumers and providers.

To counteract this problem, SLA templates have

been introduced. They are documents comprising all

required elements of an SLA but without QoS val-

ues assigned (Brandic et al., 2010). By creating pub-

licly available SLA templates stored in searchable

registries, ﬁnding an appropriate trade partner is sim-

Breskovic I., Maurer M., C. Emeakaroha V., Brandic I. and Altmann J..

TOWARDS AUTONOMIC MARKET MANAGEMENT IN CLOUD COMPUTING INFRASTRUCTURES.

DOI: 10.5220/0003391200240034

In Proceedings of the 1st International Conference on Cloud Computing and Services Science (CLOSER-2011), pages 24-34

ISBN: 978-989-8425-52-2

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

pliﬁed. However, since the generation of SLA tem-

plates has been static until now, they cannot efﬁciently

react to market changes that require the adaptation of

resource attributes within the SLA template.

To confront this challenge, we investigate auto-

nomic creation and management of SLA templates

with the goal of attracting participants to trade and,

therefore, increasing the liquidity of goods. In de-

tail, the approach presented in this paper is based on

SLA mapping. SLA mappings are XSLT documents,

which bridge the differences between two SLA tem-

plates by deﬁning mappings between non-matching

parts of the two SLAs. A service consumer can there-

fore deﬁne mappings between his private SLA tem-

plate and the publicly available SLA template.

In this paper, in particular, we derive new public

SLA templates by utilizing clustering methods and

economic mechanisms, adapting public SLA tem-

plates to changes in demand in the market. This

method leads to the automation of resource markets.

By including economic mechanisms into Cloud com-

puting infrastructures, they can automatically adapt to

changes of market conditions. With the help of a sim-

ulation, we demonstrate the beneﬁts of our approach.

In particular, we show that this SLA management ap-

proach brings three additional beneﬁts for market par-

ticipants: (1) It allows market participants to keep

their existing private SLA templates that may already

be used in other business processes; (2) It enables par-

tipants to impact the evolution of Cloud resource mar-

kets; (3) It delivers better results for the Cloud market

as a whole. We show this beneﬁt by formalizing and

applying a measure for assessing publicly available

SLA templates within our simulation.

Concluding, the main contributions of this paper

are: (1) the introduction of an approach for applying

clustering algorithms to group similar SLA mappings

and to enable autonomic market management; (2) the

formalization of a measure for evaluating the SLA

mapping approach by determining the utility and the

cost to users; and (3) the evaluation of the proposed

mapping approach using an experimental testbed.

The remainder of the paper is organized as fol-

lows. Section 2 describes related work. Section 3 de-

ﬁnes the SLA mapping approach and introduces the

idea of applying clustering algorithms to group sim-

ilar SLA mappings. Section 4 introduces clustering

algorithms and adaptation methods used for gener-

ating new public SLA templates. The formalization

of the utility and cost model to assess the beneﬁts of

the approach is given in Section 5. A description of

the simulation environment and evaluation results are

presented and discussed in Section 6. Section 7 con-

cludes the paper.

2 RELATED WORK

Over the past several years, many large IT companies

have entered the utility computing market by offer-

ing different types of services. We divide them in

three groups, based on types of resources offered: (1)

group of service providers who offer their comput-

ing resources on a pay-per-use basis in infrastructure-

as-a-service model, such as Amazon EC2

and S3

(2) providers who offer their computing resources

in combination with their own software components,

such as Google Apps

and Salesforce.com

, and (3)

group of providers offering not only a deployment

platform, but an application development platform as

well, such as Microsoft Azure

and Salesforce.com.

However, although these providers offer a large vari-

ety of services, they usually sell a single type of re-

sources and do not cover ﬂexible SLAs.

In addition to this, several research projects inves-

tigate SLA template generation (Oldham and Verma,

2006; Dobson and Sanchez-Macian, 2006; Green,

2006). As reported in (Kar

anke and Kirn, 2007), most

projects use SLA speciﬁcations, based on WSLA and

WS-Agreement, which lack support for economic

attributes and negotiation protocols. To compen-

sate these shortcomings, (Oldham and Verma, 2006)

introduce utilization of semantic Web technologies

based on WSDL-S and OWL for enhancement of

WS-Agreement speciﬁcations to achieve autonomic

SLA matching. Similar to that, (Green, 2006) in-

troduces an ontology-based SLA formalization where

OWL and SWRL are chosen to express the ontolo-

gies. (Dobson and Sanchez-Macian, 2006) suggest

producing a uniﬁed QoS ontology applicable to the

main scenarios such as QoS-based Web Services se-

lection, QoS monitoring, and QoS adaptation.

Several research projects have also discussed the

implementation of system resource markets (Buyya

et al., 2001; Nimis et al., 2008; Neumann et al.,

2008). GRACE (Buyya et al., 2001) developed a

market architecture for Grid markets and outlined a

market mechanism, while the good itself (i.e., com-

puting resource) has not been deﬁned. Moreover,

the process of creating agreements between con-

sumers and providers has not been addressed. The

SORMA project (Nimis et al., 2008) has also con-

sidered open Grid markets. They identiﬁed several

market requirements, such as allocative efﬁciency,

budget-balance, truthfulness, and individual rational-

http://aws.amazon.com/ec2/

http://aws.amazon.com/s3/

http://www.google.com/apps/

http://www.salesforce.com/

http://www.microsoft.com/windowsazure/

TOWARDS AUTONOMIC MARKET MANAGEMENT IN CLOUD COMPUTING INFRASTRUCTURES

SaaS/PaaS/IaaS

SQL databases

Initial

template

Template

v.1

Template

v.2

Template

v.3

Step 5: Group mappings into clusters

Step 3: Lookup

Private

template

Service consumer

Step 4: Define mappings

Step 1: Assign service to a template

Applications

Infrastructure

Private

template

Step 2: Define mappings

Service provider

Step 6: Apply learning methods

Figure 1: The SLA mapping process.

ity (Neumann et al., 2008). However, they have not

considered that a market can only function efﬁciently

if there is a sufﬁciently large liquidity of its goods.

Overall, most of the computing resource market

research did not deﬁne the tradable good, or they

worked only with simpliﬁed deﬁnitions of goods.

Furthermore, most of the existing approaches in SLA

management use static SLA templates and, therefore,

do not consider market changes. This means that the

issue of maintaining a sufﬁciently high liquidity in

electronic market has not been addressed yet.

3 COMPUTING RESOURCE

MARKETS

In this section, we present the SLA mapping approach

and introduce the idea of applying clustering algo-

rithms to group SLA mappings.

3.1 The SLA Mapping Approach

The SLA mapping process is presented in Figure 1.

In the step 1, a service provider assigns his service

(e.g., infrastructure resources) to a particular public

SLA template. Since it is sometimes not possible to

ﬁnd a perfect match between a private SLA template

and a public SLA template, service provider can de-

ﬁne mappings to bridge the differences between two

templates (step 2).

In the next step, a service consumer can look for

appropriate Cloud services. Since it might happen

that a consumer cannot change his private template

due to existing business processes, legal issues, or

other reasons, he can deﬁne SLA mappings to bridge

the differences between his private template and the

public template assigned to the service (step 4).

Both service provider and service consumer can

specify two types of SLA mappings:

1. Ad-hoc SLA Mapping Type deﬁnes translation

between a parameter existing in the user’s private

SLA template and the public SLA template. We

distinguish simple ad-hoc mappings, i.e., map-

ping of different values for an SLA element (e.g.,

a mapping between the names “CPUCores” and

“NumberOfCores” of an SLA parameter, or a

mapping between two different values of a service

level objective), and complex ad-hoc mappings.

The later one maps between different functions

used for calculating a value of an SLA parame-

ter (e.g., deﬁning a mapping for a metric unit of a

value of an SLA parameter such as “Price” from

“EUR” to “USD” can translate one function for

calculating price to another one).

2. Future SLA Mapping Type deﬁnes a wish for

adding (or deleting) a new SLA parameter that is

supported by the private template to a public SLA

template. Unlike ad-hoc mapping, future mapping

cannot be applied immediately.

Figure 2 represents a sample public SLA template

and a service consumer’s private SLA template. Con-

sumer’s private template differs from the public tem-

plate in four ways: (1) the name of the SLA param-

eter “Price”, (2) the unit of a value of the parame-

ter “Performance” (i.e., the parameter metric), (3) the

parameter “Availability”, which does not exist in the

consumer’s private SLA template, and (4) the SLA

parameter “ClockSpeed”, which exists in the con-

sumer’s private template but not in the public SLA

template. Therefore, the consumer will create four

SLA mappings to bridge these difference, namely (i)

a simple ad-hoc mapping to map the difference of

the parameter name, (ii) a complex ad-hoc mapping

for deﬁning a mapping function for measuring per-

CLOSER 2011 - International Conference on Cloud Computing and Services Science

formance, (iii) a deleting wish (future mapping) for

wishing for the deletion of the “Availability” parame-

ter from the public SLA template, and (iv) an adding

wish (future mapping) for adding a new parameter

“ClockSpeed” into the public SLA template.

3.2 Evolution of Public SLA Templates

After a certain period of time, SLA mappings are

evaluated in order to adapt public SLA templates to

the current market trends. By utilizing adaptation

methods, we enable the creation of new branches of

public templates that reﬂect consumers’ needs more

precisely. For example, a general template for internal

medicine applications can be substituted by more spe-

cialized templates on cardiology and oncology. The

adaptation process is executed in two steps. First,

by applying clustering algorithms, similar SLA map-

pings are grouped (step 5 in Figure 1). For exam-

ple, one group of SLA mappings could be for those

templates that name their parameters in a certain lan-

guage. In the second step (i.e., step 6 in Figure 1),

adaptation methods are applied to each of the groups.

Those methods analyze the SLA mappings and de-

cide about the content of new public SLA templates.

At the end of the process, new public templates are

generated. With this approach, public SLA templates

are adapted according to the need of market partici-

pants. This increases the likelihood that new requests

of consumers will need less SLA mappings, reducing

the cost of consumers and making the market more

attractive.

(Cost, EUR)

(Performance, GHz)

(ClockSpeed, GHz)

(Price, EUR)

(Performance, MHz)

(Availability, %)

Simple ad-hoc mapping:

Cost → Price

Complex ad-hoc mapping:

GHz → MHz

Future mapping (adding wish)

+ (ClockSpeed, GHz)

Future mapping (deleting wish)

- (Availability, %)

User‘s SLA template

SLA

registry

Public SLA template

Figure 2: An example of the SLA mapping process.

After new public SLA templates have been gener-

ated, users can decide if they want to use the newly

generated templates or if they prefer keeping the old

ones. Note, although new templates reﬂect their re-

quirements, users have to deﬁne new SLA mappings,

which incur cost.

4 IMPLEMENTATION ISSUES

In this section, we discuss the algorithms and meth-

ods implemented for grouping similar SLA mappings

and generating new public SLA templates. Besides,

we formalize a measure of (dis)similarity of two SLA

templates by utilizing an n-tuple SLA representation.

4.1 Clustering Algorithms for Grouping

SLA Mappings

For the purpose of grouping similar sets of SLA map-

pings, we apply two clustering algorithms: DBSCAN

and k-means. In our clustering algorithms, an item for

clustering is a set of consumer’s SLA mappings, and

a cluster centroid is a newly generated public SLA

template for a group of consumers (i.e., private SLA

templates).

DBSCAN is a data clustering algorithm that is

based on the density distribution of nodes and ﬁnds

an appropriate number of clusters (Ester et al., 1996).

The ε-neighborhood of a point p is deﬁned as a set of

points that are not farther away from p than a given

distance ε. A point q is directly density-reachable

from the point p, if q is in the ε-neighborhood of p,

and the number of points in the ε-neighborhood of q is

bigger than MinPts. A cluster satisﬁes two properties:

(1) each two points are mutually density-reachable,

and (2) if a point is mutually density-reachable to any

point of the cluster, it is part of the cluster as well.

K-means is a clustering algorithm that, given an

initial set of k means, assigns each observation to a

cluster with the closest mean. It then calculates new

means to be centroids of observations in the clusters

and stops when the assignments no longer change

(MacQueen, 1967). A frequent problem in k-means

algorithm is the estimation of the number k. Within

this paper, we explain two implemented approaches.

1. Rule-of-Thumb is a simple but very effective

method in which k is set to

N/2, where N is

the number of entities (Mardia and Kent, 1980).

2. Hartigan’s Index is an internal index introduced in

(Hartigan, 1975). Let W(k) represents the sum of

squared distances between cluster members and

cluster centroid for k clusters. When grouping

n items, the optimal number k is chosen so that

the relative change of W (k) multiplied with the

correction index γ(k) = n −k −1 does not signiﬁ-

cantly change for k + 1, i.e.,

H(k) = γ(k)

W (k) −W(k + 1)

W (k + 1)

< 10

The threshold 10 shown in Hartigan’s index is also

TOWARDS AUTONOMIC MARKET MANAGEMENT IN CLOUD COMPUTING INFRASTRUCTURES

used in our simulations. It is “a crude rule of

thumb” suggested by Hartigan (Hartigan, 1975).

4.1.1 Computing Distance between SLA

Templates

In order to utilize clustering algorithms, the measure

of distance between two clustering items must be de-

ﬁned, as well as the distance between a clustering

item and a cluster centroid. As already mentioned in

the previous section, an item for clustering is a set of

consumer’s SLA mappings, while a cluster centroid

is an SLA template for the given group of consumers.

Since having knowledge about the content of a public

SLA template and about the SLA mappings a con-

sumer created, we can exactly reconstruct consumer’s

private SLA template. Therefore, both the distance

between two clustering items as well as between an

item and a cluster centroid can be reduced to comput-

ing distances between two SLA templates. For this

purpose, we introduce the n-tuple representation of

SLAs.

An SLA consists of SLA parameters with SLA

metrics and service level objectives (SLOs) associ-

ated to the parameters. We distinguish between the

structure of an SLA, i.e., the list of parameters with

their names and SLA metrics, and the values of an

SLA, i.e., a list of numerical, boolean and string val-

ues of SLOs and SLA attributes. We introduce the

n-tuple representation of an SLA, where ﬁrst n − 1

elements contain values of the SLA, while the last

element contains the SLA structure. By using such

a representation, we can deﬁne the distance between

two SLA templates as an n-tuple, where ﬁrst n − 1

elements contain the differences between the two val-

ues of each of the parameters, while the ﬁnal element

contains a value representing the difference between

the structures of the SLAs.

To formalize, we observe two SLA templates T

and T

deﬁned by their SLA values {α,β,γ,...} and

the SLA structure ∆.

= (α

,β

,γ

,...,∆

),T

= (α

,β

,γ

,...,∆

)

The n-tuple D

representing the distance between

two SLA templates is deﬁned as:

= ( f (α

,α

), f (β

,β

), f (γ

,γ

),...,F(∆

,∆

))

The result of the function f for calculating the differ-

ence between two SLA values α

and α

depends on

the type of its arguments and is deﬁned as follows.

f (α

,α

) =











|α

− α

|, if α

and α

are numerical

0, if α

and α

are not

numerical and α

= α

1, if α

and α

are not

numerical and α

6= α

The distance between the structures of SLA tem-

plates is expressed as a number of differences be-

tween properties of SLA templates. This value is cal-

culated by iterating through all SLA parameters con-

tained by at least one of the SLA templates, calculat-

ing the distance between the SLA templates with re-

spect to the parameters. We deﬁne the distance func-

tion F calculating the difference between structures

∆

and ∆

of two SLA templates T

and T

F(∆

,∆

) =

∑

p∈T

∪T

)

where the distance between two SLA parameters of

two SLA templates with respect to its properties (i.e.,

its name and metric) is deﬁned as:

) =











0, if name and metric of p are

the same in T

and T

1, if T

or T

does not contain p

1, if only name or metric of p

differs in T

and T

2, if both name and metric of p

differ in T

and T

After the result tuple has been calculated, it can

be used to generate a single numerical value repre-

senting the distance between the two SLA templates.

In order to do so, the result tuple must be normalized

beforehand so that the tuple elements can be mutu-

ally comparable. Normalization is executed on each

of the n tuple elements separately, where a value of

an element is divided by a range of possible values

for the element (maximum value minus the minimum

value). Then, the ﬁnal value representing the distance

between SLAs can be computed by a simple function

(e.g., summation of element values).

Note, in this paper, we discuss only the ﬁnal ele-

ment of the distance tuple, i.e., the difference between

structures of two SLA templates. We plan to consider

the values of SLA templates in our future work.

4.2 Adaptation Methods for Evolving

Public SLA Templates

For adaptation of public SLA templates to react to

market changes, we utilize three adaptation methods.

These methods analyze submitted SLA mappings and

for each SLA parameter determine whether the cur-

rent parameter name and metric should be changed,

as well as if a new parameter should be added or an

existing one deleted.

To demonstrate the methods, we use the follow-

ing example. The example considers the evolution

of an SLA parameter π occuring in an initial pub-

lic SLA template T

init

when adapting the template

CLOSER 2011 - International Conference on Cloud Computing and Services Science

based on SLA mappings of 100 service consumers.

The value of the parameter π in the initial template

is (Price,EU R), where the ﬁrst member of the tuple

represents the parameter name, and the second mem-

ber represents the metric unit for expressing the pa-

rameter value (see Table 1). The example also con-

siders an SLA parameter µ which has not been deﬁned

in the initial public SLA template. Let 20 consumers

express a wish for deleting the parameter π from T

init

and the rest of them deﬁne mappings according to the

distribution presented in Table 1. Note that the last

column of the table represents the number of non-

mapped values, i.e., the number of private SLA tem-

plates containing the same value as in the public SLA

template. Let 75 consumers express a wish for adding

the parameter µ to the public template, with a name

and unit by the distribution depicted in Table 2.

Table 1: Distribution of mappings for the SLA parameter π.

a) Name of the SLA parameter π

Name: Cost Charge Rate (Price)

Mappings: 15% 15% 40% (30%)

b) Unit of the SLA parameter π

Name: USD GBP YPI (EUR)

Mappings: 38% 2% 40% (20%)

Table 2: Distribution of mappings for the SLA parameter µ.

a) Name of the SLA parameter µ

Name: MemoryConsumption Consumption

Mappings: 70% 30%

b) Unit of the SLA parameter µ

Name: Mbit Gbit Tbit

Mappings: 5% 55% 40%

The maximum method selects the option which

has the highest number of SLA mappings (i.e., the

maximum candidate). This candidate will then be

used in the new SLA template. If there is more than

one maximum candidate with the same number of

SLA mappings, one of them is chosen randomly. In

the given example, only 20% of consumers wished

for deleting the parameter π, which is not sufﬁciently

high. Therefore, the parameter stays in the template

with “Rate” as the new parameter name because of

the highest number of mappings (40% in compari-

son to 30% who did not create mappings, i.e., who

want to keep the name “Price”). The price will be

expressed in Japanese Yens due to the highest num-

ber of mappings. The parameter µ will also be in the

new template, since there are more than 50 wishes for

this parameter, and parameter name and unit will be

(MemoryConsumption,Gbit).

In order to lower the requirements for selecting

the maximum candidate, the threshold method in-

troduces a threshold value. In this method, the value

gets chosen if it is used more than the given thresh-

old and has the highest count. If more than one value

satisﬁes the conditions, one of them is chosen ran-

domly. Throughout the evaluation in this paper, we

ﬁx the threshold to 60%. In the given example, nei-

ther the mappings for the name nor for the unit of the

parameter π exceeds the threshold value. As for the

parameter µ, it will be introduced in the new public

template with the properties chosen according to the

maximum method.

The signiﬁcant-change method is a modiﬁca-

tion of the maximum-percentage-change method in-

troduced in (Maurer et al., 2010). In this method, an

SLA parameter property is only changed, if the per-

centage difference between the highest count value

and the current public SLA template value exceeds a

given threshold, which we assume to be signiﬁcant at

> 15%. In the given example, 40 consumers have

the name “Cost” for the “Price” parameter, while 30

consumers use the same name as in the public SLA

template. Since the percentage difference of 33% is

higher than the given threshold, “Cost” will be chosen

as the new name for the parameter. As the new unit,

“YPI” will be chosen. Since “MemoryConsumption”

does not exist in the old public SLA template, the de-

cision about this SLA parameter is made as described

for the maximum method.

5 FORMALIZATION OF THE

UTILITY AND COST MODEL

As already mentioned in Section 1, the approach pre-

sented in this paper brings many beneﬁts to market

participants. However, it also incurs cost. Namely,

whenever a public SLA template has been adapted,

the users of the template have to deﬁne new SLA map-

pings. Within this section, we investigate these costs

and assess the beneﬁts of our approach. For this pur-

pose, we introduce the utility and cost model.

In the following, we observe a set of SLA pa-

rameters P

N,F

, where possible names of a parame-

ter α ∈ P

N,F

are N(α) = {A,A

,...}, and the set

of possible metrics is F(α) = { f

, f

,...}. Let

there be a set of consumers’ private SLA templates

C = {c

,...,c

} and two public SLA templates: the

initial public SLA template T

init

, and the newly gen-

erated public SLA template (for the given set of con-

sumers) T

new

For each SLA template p ∈ C ∪ {T

init

new

}, we

deﬁne the relationship between a certain SLA param-

eter with its name and metric as

SLA

: P

N,F

→

[

α∈P

N,F

N(α) ∪

0 ×

[

α∈P

N,F

F(α) ∪

TOWARDS AUTONOMIC MARKET MANAGEMENT IN CLOUD COMPUTING INFRASTRUCTURES

where a parameter of a template is represented as a

tuple (name,metric), i.e., for an SLA parameter α in

an SLA template p it holds

SLA

(α) 7→ (N(α), F(α))

If the value of α in a template p is SLA

(α) = (A, f

then we can deﬁne the projection function Π as

(SLA

(α)) = A, Π

(SLA

(α)) = f

with the abbreviation

(α) = Π

(SLA

(α)), F

(α) = Π

(SLA

(α))

Additionally, we deﬁne the function χ to determine if

an SLA template p contains an SLA parameter α:

(α) =

(

0, if p does not contain α

1, if p contains α

Finally, we can deﬁne the utility function for a

consumer c and for an SLA parameter α as follows.

(α) =











0, χ

(α) 6= χ

new

(α) ∨ χ

(α) = χ

new

(α) = 0

A, χ

(α) = χ

new

(α) = 1∧

(α) 6= N

new

(α) ∧ F

(α) 6= F

new

(α)

B, χ

(α) = χ

new

(α) = 1∧

(α) = N

new

(α) ∧ F

(α) 6= F

new

(α)∨

(α) 6= N

new

(α) ∧ F

(α) = F

new

(α))

C, χ

(α) = χ

new

(α) = 1∧

SLA

(α) = SLA

new

(α)

As stated in the deﬁnition of the utility function, the

consumer gains no utility for an SLA parameter if

it does not exist in one of the SLA templates (con-

sumer’s private template or the public template). If

it does exist, the utility can be A (if both name and

metric for an SLA parameter differ in user’s private

SLA template and the public SLA template), B (if

only name or metric differ), or C (if templates do not

differ with respect to the parameter).

The cost function for a consumer c and an SLA

parameter α is deﬁned as follows.

−

(α) =











0, SLA

new

(α) = SLA

init

(α)∨

SLA

(α) = SLA

new

(α)∨

(χ

(α) = χ

new

(α) = χ

init

(α) = 1∧

(α) = N

new

(α) ∧ F

new

(α) = F

init

(α)∨

new

(α) = N

init

(α) ∧ F

(α) = F

new

(α)))

D, χ

init

(α) 6= χ

new

(α) ∧ χ

(α) 6= χ

new

(α)

E, otherwise

The cost function states that a user has no cost if he

does not have to create new SLA mappings i.e., if the

parameter properties are the same in his private tem-

plate and in the newly generated public template, or

the new template did not change with respect to the

parameter. If a user has to create a future mapping,

the cost is equal to E. Finally, if a user has to create

ad-hoc mappings for name, metric, or both, the cost

is equal to D.

At the end, we deﬁne the overall utility U

and

the overall cost U

−

for all service consumers C as:

∑

c∈C

∑

α∈P

N,F

(α), U

−

∑

c∈C

∑

α∈P

N,F

−

(α)

The overall net utility U

is then calculated as

= U

−U

−

(1)

Note that the values for the utility and the cost

functions are not strictly deﬁned, but considering the

effort or beneﬁt it should hold that 0 ≤ A ≤ B ≤ C and

0 ≤ D ≤ E.

6 EVALUATION

In this section, we measure and investigate the ben-

eﬁts and costs of deﬁning SLA mappings and adapt-

ing public SLA templates to the current needs of mar-

ket participants, using the utility and cost model pre-

sented in Section 5. Moreover, we assess the scalabil-

ity of the approach and discuss the quality of newly

generated public SLA templates.

6.1 Simulation Environment

For the simulation of our approach, we designed

a testbed within the VieSLAF environment. VieS-

LAF is a framework for semi-automated management

of SLA mappings and generation of SLA templates

(Brandic et al., 2010). The major VieSLAF compo-

nents are the knowledge base and the SLA mapping

middleware.

1. The knowledge base is used for storing and man-

aging public SLA templates. Browsing and cre-

ating SLA templates is enabled over searchable

repositories implemented in Microsoft SQL 2008

DBMS with a Web service front-end to provide

the interface for the SLA mapping management.

2. The SLA mapping middleware is based on sev-

eral Windows Communication Foundation ser-

vices for administration (e.g., creating SLA tem-

plates), accounting (e.g., creating consumer ac-

counts), querying (e.g., ﬁnding an appropriate

SLA template), management of SLA mappings

(e.g., deﬁning mappings for a user), and adapta-

tion of public SLA templates.

Both knowledge base and the registry services can be

accessed only with appropriate access rights depend-

ing on three access roles: service consumer, service

provider, and registry administrator.

CLOSER 2011 - International Conference on Cloud Computing and Services Science

We describe the VieSLAF architecture and the

testbed used for the simulation based on Figure 3.

The simulation process is conducted in several steps.

First, a service provider assigns his service (e.g., soft-

ware, resources, infrastructure) to a public SLA tem-

plate using VieSLAF’s registry services. Service con-

sumers than fetch the public template (step 2) and

generate SLA mappings to map the differences be-

tween their private templates and the public SLA tem-

plate (step 3). After the end of the mapping process,

the clustering algorithms are applied and consumers’

SLA mappings are grouped (step 4). For each of the

groups of SLA mappings, new public SLA templates

are generated and assigned to the consumers (step 5).

Service

consumers

Private SLA

templates

Knowledge base

Accounting

service

Querying

service

SLA mapping

service

Adaptation

service

Administration

service

Service

provider

Initial public SLA

template

1. Assign service to the template

2. Fetch the SLA template

Create SLA mappings

4. Apply clustering and

adaptation methods

New public SLA

templates

5. Assign templates to consumers

6. Measure overall

net utility

Figure 3: Simulation testbed.

The process is evaluated by investigating the over-

all net utility (step 6). For evaluation purposes, all de-

scribed clustering algorithms and adaptation methods

for creating new public SLA templates are used and

compared. The number of service consumers creating

SLA mappings is varying from 100 to 10000. The ini-

tial public SLA template is static and contains 8 SLA

parameters, while the consumers’ private SLA tem-

plates are generated randomly at the beginning of the

evaluation process and can contain up to 11 SLA pa-

rameters, where, for each SLA parameter, consumers

can choose between 5 names and 2 metrics.

6.2 Evaluation Results

6.2.1 Overall Net Utility

To assess the beneﬁts and costs of the SLA mapping

approach, we use the overall net utility deﬁned by

Equation (1), where the values for the constants of the

utility and cost model are ﬁxed as depicted in Table

3. For evaluation purposes, we simulate 500 service

consumers creating SLA mappings to a public SLA

template. In order to compare results achieved with

different methods, all presented clustering algorithms

and adaptation methods are used and compared. After

the new SLA templates are generated, the overall net

utility is calculated, representing the difference be-

tween gains and costs of the SLA mapping approach.

The simulation results are depicted in Figure 4.

Using the results depicted in the graph, we can

compare the approach described in this paper with our

previous work on SLA mapping described in (Maurer

et al., 2010), where we did not utilize clustering algo-

rithms, but instead applied adaptation methods to cre-

ate a single new public SLA template for all market

participants. The ﬁrst column of the graph presents

the overall net utility achieved when creating a single

new public template, while the rest of the columns

represent results achieved by utilizing different clus-

tering algorithms. As it can be seen from the graph,

by generating only one public SLA template for all

service consumers, the overall net utility is signiﬁ-

cantly lower than in the other cases. This is due to

creating more new public SLA templates that reﬂect

consumers’ needs more precisely.

Table 3: Values of the utility and cost function variables

used for the evaluation.

Variable A B C D E

Value 1 2 3 1 2

2000"

4000"

6000"

8000"

10000"

12000"

Maximum"method"

Threshold"method"

Signiﬁcant<change"method"

Overall'net'u+lity'

NO"CLUSTERING"

DBSCAN"

KMEANS"ROT"

KMEANS"HARTIGAN"

Figure 4: Evaluation results: overall net utility.

When comparing the overall net utility gained by

utilizing different adaptation methods, we can con-

clude that the best results are achieved by using the

maximum method. Although the threshold method

causes signiﬁcantly lower number of changes (due to

the high threshold), and therefore very low cost, the

maximum method adapts public template more dy-

namically and achieves higher utility rate. Highest

overall net utility is achieved by the k-means cluster-

ing algorithm with the rule-of-thumb method, due to

creating large number of very speciﬁc clusters.

The utility rate highly depends on the number of

generated clusters. The more clusters are created, the

higher utility will be achieved. This relation can be

seen when combining the results depicted in Figure

4 with Table 4, representing the number of clusters

TOWARDS AUTONOMIC MARKET MANAGEMENT IN CLOUD COMPUTING INFRASTRUCTURES

per clustering algorithm and per number of service

consumers creating SLA mappings. The maximum

rate of overall net utility for n service consumers can

be achieved by creating n clusters i.e., by generating

one public SLA template per consumer. In this case,

public SLA templates would be equal to consumers’

private templates. Moreover, the utility rate would be

maximum, and the cost would be equal to 0. How-

ever, by raising the number of new public SLA tem-

plates in the market, the liquidity of its goods is at

the lowest point. Therefore, we must ﬁnd a balance

between keeping low cost of creating new SLA map-

pings and ensuring high liquidity of market goods.

The problem of ﬁnding the optimal number of new

public SLA templates to be introduced to the market

will be addressed in our future work.

Table 4: Number of generated clusters per algorithm and

number of service consumers.

DBSCAN k-means (RoT) k-means (H)

100 5 7 3

200 5 10 3

300 6 12 3

500 6 15 5

1000 7 22 6

2000 7 31 6

5000 8 50 6

10000 10 69 10

Since the number of generated clusters inﬂuences

the overall net utility, we conclude that both the over-

all net utility and the dataset properties determine

the quality of newly generated public SLA templates.

Therefore, it is not sufﬁcient to investigate only the

utility gained, but also the properties of the simula-

tion dataset that may have inﬂuenced the results of

the utility measurements. For this purpose, we intro-

duce the measure of compactness. In our discussion

of dataset properties, C represents a set of clusters,

where C

∈ C is a cluster, and C

its centroid. R

is a

clustering item, and D(R

) a distance between two

items. |C| represents the total number of clusters, and

| represents a number of items in a cluster C

Compactness is reciprocal of average distance

between consumers’ private SLA templates within

clusters, and is formally deﬁned as follows.



|C|

∑

C j∈C



|(|C

| − 1)

∑

∈C

∑

∈C

D(R

)





−1

Since a cluster represents a group of consumers with

similar requirements, high compactness implies well

formed market. Figure 5 depicts the compactness

rates achieved by utilizing clustering algorithms. For

this purpose, we have simulated SLA mappings spec-

iﬁed by different number of service consumers, vary-

ing from 100 to 10000. The adaptation method used

for the evaluation is the maximum method.

High utility is common for compact clusters, since

all members of the cluster have similar properties, and

the cluster centroid i.e., newly generated SLA tem-

plate, reﬂects those properties more precisely. On

the other side, items of a less compact cluster differ

in a larger extent, and it is probable that the utility

achieved by creating a new public SLA template will

be lower. High compactness can be achieved by creat-

ing large number of smaller clusters and is maximized

when there is one cluster per item.

As depicted in Figure 5, the k-means algorithm

with the Hartigan’s index achieves lowest rate of com-

pactness. This is due to creating small number of gen-

eral clusters, where items mutually differ in a large

extent. Therefore, as it can be seen on Figure 4, this

algorithm results in low overall net utility. k-means

algorithm with the-rule-of-thumb achieves larger rate

of compactness, and consequently larger rate of over-

all net utility, due to large number of speciﬁc clusters.

0.1$

0.11$

0.12$

0.13$

0.14$

0.15$

0.16$

0$ 2000$ 4000$ 6000$ 8000$ 10000$

Compactness+

Number+of+consumers+

DBSCAN$

KMEANS$ROT$

KMEANS$HARTIGAN$

Figure 5: Evaluation results: compactness.

6.2.2 Scalability

For Cloud environments, it is of great importance that

the resource markets are highly scalable. In order to

assess the scalability of our approach, we measured

execution time of the clustering process for differ-

ent number of users creating SLA mappings, varying

from 100 to 10000. Results are depicted in Figure 6.

100000"

200000"

300000"

400000"

500000"

600000"

0" 2000" 4000" 6000" 8000" 10000"

!"#$%&'()&*#)+*,-)

.%*/#0)'1)$'(,%*#0,)

DBSCAN"

KMEANS"ROT"

KMEANS"HARTIGAN"

Figure 6: Evaluation results: execution time.

As showed in the graph, the k-means algorithm

with the Hartigan’s index takes most time to compute

CLOSER 2011 - International Conference on Cloud Computing and Services Science

groups of SLA mappings. This is due to the nature

of the Hartigan’s index method, in which the clus-

tering algorithm runs in several iterations and com-

putes different number of clusters before it ﬁnds the

optimal number k. The best performance is achieved

by the DBSCAN algorithm. This result is also not

surprising, since unlike the k-means algorithm, it cre-

ates smaller number of clusters and does not compute

cluster centroids, which incurs cost.

The results show that the approach presented in

this paper is highly scalable. It is important to notice

that the clustering process in the worst scenario takes

less than 0.08% of the total simulation execution time.

This percentage decreases with the rising of the num-

ber of service consumers. Furthermore, the execution

time of k-means algorithm with the Hartigan’s index,

which is far the highest, can be improved by intro-

ducing heuristics for determining the initial number

k, which we will consider in our future work.

7 CONCLUSIONS AND FUTURE

WORK

In this paper, we have extended the SLA mapping ap-

proach by introducing the idea of applying clustering

algorithms to group users’ SLA mappings. This en-

ables the automatic creation of new public SLA tem-

plates, paving the way for autonomic market manage-

ment. In particular, we introduced the n-tuple SLA

representation to compute the difference between two

SLA templates, utilized two clustering algorithms for

grouping SLA mappings, and used three adaptation

methods for generating new SLA templates. We have

investigated the utility and cost to users when apply-

ing the adaptive SLA mapping approach. Our simula-

tion results show that our approach facilitates contin-

uous market evolution and adaptation of public SLA

templates, responding to market trends and changes.

In the future, we will consider heuristic methods

for improving the efﬁciency of clustering algorithms

for grouping SLA mappings and investigate the full

possibilities of the n-tuple representation of SLAs.

Also, we will address the problem of ﬁnding the op-

timal number of new public SLA templates to be in-

troduced to the market, in order to ensure liquidity on

the market while keeping a high utility rate.

ACKNOWLEDGEMENTS

The work described in this paper is supported by

the Vienna Science and Technology Fund under the

grant agreement ICT08-018 Foundations of Self-

Governing ICT Infrastructures (FoSII), and by the

National Research Foundation of the Ministry of Ed-

ucation, Science and Technology of Korea under the

grant K21001001625-10B1300-03310.

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