to compose services in cloud computing. They focus
on user’s functional and non-functional requirements.
Thus, an algorithm is applied to automate the selec-
tion of optimal services and to realize service com-
position Another approach to build a decentralized
framework based on self organized agents is deter-
mined in (Garcia and Sim, 2010). Authors used con-
tract net protocol for dynamic communications tech-
nical knowledge of acquintance networks for incom-
plete information about existing services. In (Zeng
et al., 2009), authors solve the problem of cloud ser-
vice composition using a service matching algorithm
(SMA). It is based on QoS to filter services and per-
form the matching algorithm of services based on the
semantic similarity between input and output param-
eters to achieve this composition.
We notice that these works do not provide appropriate
support for the concept of context and progress sce-
narios during cloud services searching and matching.
Moreover, they do not retain the concept of the cloud
which is the multi-tenancy nature during composition
where a single instance is shared between tenants.
4.2 Cloud Service Adaptation
The approach presented in (Truyen et al., 2012) de-
scribes a framework for tenant-specific customization
and manages changes of software-tenant SaaS appli-
cations. To achieve this, this framework is based on
the context oriented programming to provide flexible
SaaS services for multi-tenants suitable to the context
information. In (Papakos et al., 2010), authors de-
velop a middleware called VOLARE which monitors
resources and context of mobile devices, and dynam-
ically suits the cloud services. This approach allows a
reliable services discovery and cost reduction at run-
time to fit in the current context of the consumers. In
(La and Kim, 2010), authors propose a framework for
context-awareness services for mobile computing. In
fact, this framework allows the capture of the con-
text, determines the specific adaptation and executes
the right services.
However, these efforts focus only on the customiza-
tion of a single service but no solution has been pro-
posed to resolve the adaptation of composed cloud
service. Also, they deal with the personalization of
responses at a high level but not the personalization
of services at a lower level before the composition to
provide more flexibility of applications.
To overcome these issues, we propose in this paper a
primal tenant cloud service composition middleware
to address all aspects of the context awareness starting
from the dynamic services matching until to adapta-
tion.
5 INITIAL APPROACH
In this section we present, in detail, our multi-tenants
context-aware cloud service composition middleware.
Indeed (as it is shown in Figure 2), we aim to focus on
two steps of the life cycle: (1) services matching and
(2) adaptation of a composed service. Each step con-
tains components, and their related communications.
5.1 Tenant Users
Tenant users layer is composed of multiple tenant
applications deployed on portable multimedia player
and mobile device such as Android or SmartPhone.
Thus, we consider that a tenant can be a single user
or a whole company consisting of a collection of dis-
tinct users which can access to his flexible services
whenever he wants and wherever he is.
5.2 Service Matching
A composition engine is a multi-tenant service ex-
ecuting on the cloud platform. It parses the tenant
query and uses the contexts information. Then, it
can be able to lookup its cloud services from repos-
itories component using a given policy (such as the
nearest data center to tenant location, the fastest re-
sponse if the tenant is running, and so on) and choose
the most appropriate composition that suits the ten-
ant’s context. In fact, once the services are selected,
the composition engine is responsible for defining a
sequence of services to be executed and supervising
the composed service. The composition engine can
decide which concrete services are included in the dy-
namic composition, and how many instances of each
concrete service are needed in order to satisfy the ten-
ant’s context. Therefore, this component can dynam-
ically update the composition and substitute any con-
crete service at any time with another service based
on the feedback of the tenant’s runtime context data.
5.3 Service Adaptation
In context-aware composite service provisioning,
adaptation can be achieved by splitting it into sepa-
rate services and tailoring them for the given context.
Tenant specific customization can be specified as in-
dependent software variations according to some cat-
egories of context such as tenant profile, environment
information and so on. For that, we propose the run-
time behavior adaptation component which is called
tenant context adaptor in order to suit the composi-
tion, by retrieving context information from the Con-
text Base registry, and updating it to new situations.
CLOSER2013-3rdInternationalConferenceonCloudComputingandServicesScience
388