A MAS TO MANAGE AND MONITOR SLA

FOR CLOUD COMPUTING

A Draft Position Paper

Benjamin G

ateau

Centre de Recherche Public Henri Tudor, SISE Unit - Service Science & Innovation Dept

29 Av. John F. Kennedy, L1855 Luxembourg, Luxembourg

Keywords:

Dynamic infrastructure, Normative organization, SLA management.

Abstract:

The More than a technological solution, the Cloud Computing is also an economical advantage and will play

an important roles in next years. However, in order to ensure a QoS commitment between a provider and a

customer, Service Level Agreements (SLA) describe a set of non-functional requirements of the service the

customer is buying. SLA is the best way to ensure QoS. In this paper, we use a MAS to manage SLA by

monitoring the respect of service in the context of Cloud Computing.

1 INTRODUCTION

Evolution of high level ICT infrastructures in Europe

brings some difﬁculties due by a complex manage-

ment and the need of more and more energy. One

current fashionable solution to this problem is the use

of cloud computing services. Cloud computing is a

ICT model allowing an easy access through networks

to mutualised and conﬁgurable resources able to be

quickly activated and deactivated.

Cloud computing has become a mainstream tech-

nology offering mutualisation of IT infrastructures as

services along several paths such as Software (SaaS),

Platform (PaaS), and Infrastructure (IaaS). Compa-

nies such as Amazon, Microsoft, IBM, and Google,

to name but a few, offer such services, which rely

on virtualization and pay-as-you-go business models.

Flexibility and elasticity are also important features

of Cloud Computing made possible by the concept of

Dynamic Infrastructure.

Dynamic Infrastructure is an information technol-

ogy paradigm concerning the design of data centers

so that the underlying hardware and software can re-

spond dynamically to changing levels of demand in

more fundamental and efﬁcient ways than before. The

basic premise of Dynamic Infrastructures is to lever-

age server virtualization technology to pool comput-

ing resources wherever possible, and then to allocate

these resources on-demand. This allows for load bal-

ancing and is a more efﬁcient approach than keeping

massive computing resources in reserve to run tasks

that take place, for example, once a month. The

potential feature beneﬁts include enhancing perfor-

mance, scalability system availability and uptime, and

the ability to perform routine maintenance on either

physical or virtual systems all while minimizing in-

terruption to business operations and reducing cost

for IT. Dynamic Infrastructures also provide the fun-

damental business continuity and high availability re-

quirements to facilitate cloud or grid computing.

Figure 1: VMware Infrastructure example (source:

VMware).

Fig. 1 illustrates an example of dynamic infras-

tructure based on VMware solution. Physical servers

of the datacenter stores virtual machines that can be

loaded, managed and conﬁgured remotly by the cus-

tomer of the service (Iaas kind in this case). For that

the customer previously speciﬁes his needs in term of

687

Gâteau B..

A MAS TO MANAGE AND MONITOR SLA FOR CLOUD COMPUTING - A Draft Position Paper.

DOI: 10.5220/0003558206870692

In Proceedings of the 1st International Conference on Cloud Computing and Services Science (MAS-2011), pages 687-692

ISBN: 978-989-8425-52-2

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

distribution (OS), CPU, memory, disk space, band-

with, etc. to deﬁne the virtual machine(s) he will pay

for (pay on demand and based on the conﬁguration he

ask for).

Figure 2: Moving VM in a VMware Infrastructure.

Dynamic infrastructures allow the use of resource

when it is needed. For instance, Fig. 2 represents the

case when clients of the service provider don’t use

their virtual machine at a certain time (night for in-

stance). It could happen that only few virtual ma-

chines are running on each physical machine. So

physical servers are not fully used and gathering vir-

tual machines on one physical server could permit

to save energy and use less resource. But by mov-

ing virtual machines, the provider must ensure that

the customers pay for the service he previously spec-

iﬁed and that the Quality of Service (QoS) is still the

same. QoS delivery affects the value of the service for

the client and signiﬁcantly depends on IaaS or PaaS

provider’s infrastructure. Therefore there is need to

divide responsibility/risk between XaaS provider and

customer. To describe the responsibilities a formal

description of non-functional requirements from the

client’s point of the view is required.

Service Level Agreements (SLA) permit to spec-

ify the needs and offers of customer and the service

provider. To summarize the have to deﬁne some SLA

in order to .

The aim of our work is to deﬁne a SLA infras-

tructure that provides the same guarantees and proofs

that we can ﬁnd e-contract management, with more

ﬂexibility. In this paper we are considering a datacen-

ter providing IaaS remotly an automatically (such as

GoGrid

or Cloud.com

for instance) through a web-

based application. This application mediates the in-

teractions between customers and providers. Agree-

ment between parties are represented as SLAs and are

speciﬁed with a set of web interfaces deﬁning value of

KPI that the provider has to achieve (king of rules or

obligations). To this aim, we will use an e-contract

www.gogrid.com

www.cloud.com

model to enable an agent-based execution and man-

agement of them on one hand, and, on the other hand,

we will add a monitoring system to control and en-

force the SLAs between provider and customers.

This paper is organized as follows. We make a

quick survey on SLA management in the next sec-

tion, then we present our normative multi-agent sys-

tem solution before proposing a solution to monitore

SLA with an electronic institution based on multi-

agent system for the cloud computing.

2 SLA MANAGEMENT

SLA describes a set of non-functional requirements

of the service the customer is buying. The agreement

usually contains also penalties when the requirements

are not met. An example of a non-functional require-

ment would be ”RTO - Return to Operation time for

a service in case of a failure. To describe a non-

functional requirement we needed an objective to be

achieved (e.g. RTO under two minutes ) and a set of

indicators that prove the objective is met (e.g. new in-

stance bootstrap time). The objective to be achieved

is called ”Service Level Objective (SLO) and the indi-

cators are called ”Key Performance Indicators (KPIs).

Service Level Objective (SLO) is the objective of ser-

vice quality that has to be achieved. It is represented

by a set of measurable KPIs with thresholds to decide

if the objective is fulﬁlled or not. The fulﬁllment of

an SLOs describes a state of service when all of the

SLOs key performance indicators are within a spec-

iﬁed thresholds. KPIs usually consist from one or

more raw monitored values including min, avg and

max specifying the scale. They can also represent

some aggregated measurement (e.g. average output)

within a sliding window that is combined from one or

more monitoring outputs. The provider has to be able

to measure and affect the KPIs otherwise it would not

make sense to guarantee them. The Cloud Comput-

ing infrastructures are usually large scale, therefore

SLAs need to be formally described to enable their

automated handling and protection.

Automated SLA protection is based on a set of

policy rules. Each policy rule is formed by one or

more conditions (KPI’s value matching pattern) and

one or more actions. KPIs are periodically evaluated

according to deﬁned policies. If one or more condi-

tions are met, then appropriate actions are triggered.

An example of the policy action can be increasing

number of service instance. The action is triggered

by a server load KPI matching the policy rule. This

enables to automatically keep the load KPI under cer-

tain value speciﬁed by the SLO and avoid violating

CLOSER 2011 - International Conference on Cloud Computing and Services Science

688

the general SLA. These kinds of rules are named elas-

ticity rules.

Speciﬁcation of the policies (rules and action) is a

complex task in large scale dynamic system. Analy-

sis of historical KPI data and triggered actions can be

used to specify new or modify existing policies. This

enables system adaptation and automated SLA pro-

tection evolution. System can for example learn from

historical data about periodical service load peaks

and generate speciﬁc rules to keep the system with

throughput speciﬁed in SLA . Another research chal-

lenge is the speciﬁcation of models able to describe

and simulate these kinds of dynamic systems.

The SLA can also contain certain penalties if one

or more of the SLOs is broken. The amount of a

penalty usually differs with the SLO guarantee class

(e.g. Bronze, Silver, Gold) . This allows to overbook

resources with a certain risk that some of the SLOs

will be broken (e.g. Some of the Bronze SLO are not

going to be fulﬁlled in favor of some Gold SLOs that

are connected with higher penalties). The system can

be then optimized to minimize the penalties and max-

imize the utilization.

With the development of the Internet and Virtual

Enterprises appearance, e-Services and e-Contracting

in general have gained an increasing interest in the

business domain. While some B2B applications han-

dle electronic contracts (e-contract) and digital signa-

tures as digitalized paper contracts (Laurikkala and

Tanskanen, 2002), an increasing number of them

aim at increasing their enactment and management

(e.g. (Koetsier et al., 2000)). We consider in this paper

that the management of a SLA deﬁning a set of SLO

which have to be respected is the same that manag-

ing an e-contract composed by a set of clauses repre-

sented by obligations which have to be also respected.

Indeed, in his PhD thesis relating to E-contract

modeling and e-enactement (Krishna, 2010), P. Radha

Krishna describe the clauses of an electronic contract

as obligations being part of a SLA. He also says:

“E-contract management solutions should maintain,

monitor and manage contract rules derived from

these SLAs. Contract parties should verify QoS pa-

rameters by performing an SLA monitoring, which in-

volves monitoring the performance status of the of-

fered service. The e-contract management system

could assess the SLA requirements and apply penal-

ties if there is any deviation.”

The SLA4D-Grid project (Wieder et al., 2009)

deﬁnes a SLA management layer on top of an ex-

isting infrastructure providing e-contracting capabil-

ities. The infrastructure speciﬁes, implemnts and de-

ploys a SLA-based service stack for e-Contracting.

The authors say: “The SLA4D-Grid project is design-

ing and implementing an SLA management layer. The

functions of the developments cover the complete SLA

lifecycle, including SLA design, contract establish-

ment, SLA provisioning, and SLA monitoring.”.

The SLA@SOI project (Comuzzi et al., 2010) is

a FP7 project dealing with the deﬁnition of a SLA

management framework. The consortium deﬁned a

reference architecture, speciﬁed a SLA template and

the methodology to translate SLAs inot monitoring

speciﬁcations (for the EVEREST environment). With

the RESERVOIR project, they the deﬁned how us-

ing cloud standards for the interoperability of cloud

frameworks.

In this global trend, multi-agent technologies have

been introduced to achieve the automation in creation,

execution and monitoring of e-contracts by agents on

behalf of users. Lots of work deal with negotiation

of the issues related to the content of contract and

to their execution(e.g. (Dignum and Sierra, 2001)).

The resulting contracts consist in digital agreement

between contractual parts where rights and duties in

terms of deliverables, costs and delays of the partic-

ipants are explicitly represented. However such con-

tracts often lead to inﬂexible relations between par-

ticipants. The obtained result is in contrary to the

requirements of open and dynamic system that are

stressed by the actual business paradigms aiming at

improving the competitiveness of companies like dy-

namic virtual enterprises and dynamic service out-

sourcing (Hoffner et al., 2001). Moreover, few of the

existing research works take into account the moni-

toring of contracts clauses (Padovan et al., 2002) that

bind agents together.

In (Boissier and G

ateau, 2007) we proposed an

Electronic Institution model based on MAS to man-

age electronic contract by specifying obligations. In

this paper we aim at doing the same for the manage-

ment of SLA for Cloud Computing. In the next sec-

tion we present M OISE

Inst

and S YNAI, normative or-

ganization modelling and middleware.

3 NORMATIVE MULTI-AGENT

SYSTEM

In this work we propose a multi-agent support for the

enactment and monitoring of the different SLO speci-

ﬁed in the SLA. SLAs specify the agreement between

customers and the provider concerning their partic-

ipation to the distributed execution of the job. A

SLA must describe both the functioning and the struc-

ture organizing this functioning. Moreover it con-

tains explicit legal dimensions bearing on the involved

participants. In order to take this into account we

A MAS TO MANAGE AND MONITOR SLA FOR CLOUD COMPUTING - A Draft Position Paper

689

propose to use a “normative organizational model”,

called M OISE

Inst

, to express SLA. This normative or-

ganizational model is accompanied by a specialized

“normative middleware”, called S YNAI

to monitor

and enforce legal aspects expressed in the SLAs.

3.1 Normative Organization Modelling

M OISE

Inst

ateau et al., 2007) is founded on the

M OISE

organizational model

(Hubner et al., 2002).

It is composed of the following components that are

used to specify an organisation of agents in terms of

structure, functioning, evolution and norms (OS of the

Fig. 3):

• A Structural Speciﬁcation (SS) deﬁnes: (i) the

roles that agents will play in the organization, (ii)

the relations between these roles in terms of au-

thority, communication or accointance, (iii) the

groups, additional structural primitives used to de-

ﬁne and organize sets of roles;

• A Functional Speciﬁcation (FS) deﬁnes global

business processes that can be executed by the dif-

ferent agents participating to the organization ac-

cording to their roles and groups;

• A Contextual Speciﬁcation (CS) speciﬁes, a pri-

ori, the possible evolution of the organization in

terms of a state/transition graph;

• A Normative Speciﬁcation (NS) deﬁnes the deon-

tic relations gluing the three independant speciﬁ-

cation (SS, FS, CS). This NS clearly states rights

and duties of each roles/groups of SS on sets of

goals (missions) of FS, within speciﬁc states of

CS.

A BNF

deﬁnition of OS is available in (G

ateau

et al., 2004) for SS and FS and in (G

ateau et al., 2005)

for CS and NS and in (G

ateau, 2007) for the whole

model.

3.2 Normative Organization

Middleware

In human societies, institutions deﬁne rules (North,

1990) that enclose all kinds of formal or informal

constraints that human beings use to interact. These

last years, electronic institutions have been intro-

duced in multiagent domain. They propose to model

these rules with normative systems (Jones and Carmo,

2001). Institutions are deﬁned as a set of agents,

S YNAI: SYstem of Normative Agents for Institution.

M OISE

: Model of Organization for multI-agent Sys-

tem.

BNF: Backus-Naur Normal Form.

Figure 3: M OISE

Inst

, normative organization speciﬁcation

model, and S YNAI, normative middleware.

which behave according to norms taking into account

their possible violation. The functioning of the agents

is supervised and controled with a set of institution

services (e.g. (Dellarocas, 2000)). The institution ser-

vices that support and use M OISE

Inst

are regrouped in

a speciﬁc “normative middleware” called S YNAI on

which the agents execute. This layer is in charge of :

(i) managing the life cycle of SS as entering/exiting of

agents within the organization, or requesting/leaving

of roles or groups by the agents, (ii) coordination of

the concurrent execution of FS as commitment to mis-

sions or achievement of goals, etc, (iii) dynamic and

evolution of the organization state through the CS,

(iv) the monitoring and supervision of norms of NS

activated/deactivated by the evolution of the organi-

zation.

Using both S YNAI and M OISE

Inst

, we are able to

deﬁne a SLA management system as a set of agents

whose behavior is ruled by an organization expressed

in M OISE

Inst

and controlled by an arbitration sys-

tem, implemented with S YNAI, that has the possibil-

ity to reward or to punish agents in case they respect

or not their agreements as expressed in SLAs. Using

M OISE

Inst

, agents used on the provider infrastructure

are able to reason and to take into account the differ-

ent SLAs described with M OISE

Inst

. The S YNAI plat-

form on its side take into account this speciﬁcation in

order to supervise and control virtual resources. Both

layers are based on an agent execution platform.

These four speciﬁcations form the Organizational

Speciﬁcation (OS). The Organizational Entity (OE) is

then built from the set of agents that have adopted a

role according to the SS of the OS. From this time the

S YNAI middleware manages and controls the func-

tioning of this OE (composed of active contexts, ac-

tive norms, current structure and current functioning)

by the way of different events corresponding to the

entry/exit of agents of the OE, adoption/leaving roles

or groups, change of context, commitment to mis-

sions, achievement of goals.

CLOSER 2011 - International Conference on Cloud Computing and Services Science

690

4 SLA MANAGEMENT

ARCHITECTURE PROPOSAL

The framework we propose is based on classic

VMware or other virtualisation architecture used by

a IaaS provider. In our solution, each physical

server is represented by an agent belonging to a

M OISE

Inst

organization. An organization speciﬁed

with M OISE

Inst

represents a SLA that a customer

could deﬁned. For instance, with help of a web in-

terface, the customer build his virtual network com-

posed of database server, web server, storage server

and application server linked between them. Each

component has its own characteristic as seen before

(CPU, memory, etc.). By drawing its conﬁguration,

the customer speciﬁes the Structural Speciﬁcation of

the Organization (a machine selected is a role that a

physical server will play through a virtual instance)

and the Functional and Normative Speciﬁcation (each

role will have the Obligation to achieve the character-

istics deﬁned for each virtual machine which could be

considered as the SLO of the SLA).

Figure 4: M OISE

Inst

as SLA template in Cloud Computing

architecture.

On the Fig. 4, we represents on the left the infras-

tructure coming from the datacenter. The agents (in

blue) are launched on each physical server and repre-

sent them. By creating speciﬁc virtual machines, they

instantiate the current structure of an organization (a

topology buyed by a customer, i.e. a OE instantiating

an OS).

The agent act via the S YNAI layer which monitor

the agent. The monitoring is used for:

• detecting that a virtual machine violates the SLA

for any reason and triggering the agent playing the

role (i.e. the physical machine hosting the virtual

machine) have to modify the VM conﬁguration in

order to respect a speciﬁc SLO of the SLA (a norm

of the OE)

• showing to the customer the state of the OE re-

garding the OS (transparency purpose)

5 CONCLUSIONS

Service Level Agreement (SLA) describes agreement

on non-functional requirements between provider and

customer. SLA consists of service level objectives

(SLOs) that are evaluated according to measurable

Key Performance Indicators (KPIs). Automatic SLA

protection enables further increase of the system uti-

lization and system proﬁt. In currently available sys-

tems only some basic SLAs like ”uptime over a time

period guarantee are available.

This part of cloud computing is target of intensive

research now. Formal ways of SLA description have

to be standardized. Algorithms and models for the

resources allocation, automated SLA protection and

evolution has to be found.

These areas are for example in the scope of

RESERVOIR (Resources and Services Virtualization

without Barriers) - EU FP7 funded project

backed

by consortium of industry and academy partners.

Future works will concern the speciﬁcation of

the OS with M OISE

Inst

and its implementation with

Utopia (Schmitt et al., 2010; Schmitt et al., 2011). A

national project funded by the Fond National of Re-

in Luxembourg will permits to also work on

security and risk through SLA management in cloud

computing. We aim at gathering monitoring QoS and

security in the same way.

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