COST-EFFECTIVE SERVICE TASK ASSIGNMENT IN MULTI-
DOMAIN DISTRIBUTED COMPUTING ENVIRONMENTS
Malamati Louta, Angelos Michalas, Ioannis Psoroulas
School of Electrical and Computer Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Str,
Athens, Greece
Evangelos Loutas
Department of Informatics, University of Thessaloniki, Thessaloniki, Greece
Keywords: Intelligent Agents, Service Task Assignment, Service Nodes, Domains, Resource Allocation
Abstract: Highly competitive and open environments should encompass mechanisms that will assist service providers
in accounting for their interests, i.e., offering at a given period of time adequate quality services in a cost
efficient manner which is highly associated to efficiently managing and fulfilling current user requests. In
this paper, service task assignment problem is addressed from one of the possible theoretical perspectives,
while new functionality is introduced in service architectures that run in open environments in order to
support the proposed solution. The pertinent problem aiming to find the most appropriate assignment of
service tasks to service nodes is concisely defined, mathematically formulated and empirically evaluated.
1 INTRODUCTION
The ongoing liberalisation and deregulation of the
telecommunication market will introduce new
actors. In principle, the main role of all players in
such a competitive environment will be to constantly
monitor the user demand, and in response to create,
promote and provide the desired services and service
features. According to a business model foreseen to
apply to the telecommunications world of the near
future, five main different entities can be identified,
namely, user, service provider, third party
application or/and content provider, broker and
network provider. The role of the third party
application or/and content provider is to develop and
offer applications or/and content. The role of the
service provider is to provide the means through
which the users will be enabled to access the
applications or/and content of third party application
or/and content providers. The broker assists business
level entities in finding other business entities.
Finally, the role of a network provider is to offer the
network connectivity needed for service provision.
Service provisioning in such open models is a
quite complex process since it involves various
diverse actors. The following are some key factors
for success. First, the efficiency with which services
will be developed. Second, the quality level, in
relation with the corresponding cost, of new
services. Third, the efficiency with which the
services will be operated, controlled, maintained,
administered, etc. The challenges outlined above
have brought to the foreground several new
important research areas. Some of them are the
elaboration on e-business concepts (Ghosh, 1998),
the specification of service architectures (SAs)
(Magedanz, 1997), the development of advanced
service creation environments (SCEs) (Tag, 1996)
and service features (e.g. the personal mobility
concept), and the exploitation of advanced software
technologies, (e.g. distributed object computing
(Vinoski, 1997) and intelligent mobile agents
(Morreale, 1998). The aim of this paper is, in
accordance with the cost-effective QoS provision
and the efficient service operation objectives, to
propose enhancements to the sophistication of the
functionality that can be offered by service
architectures in open competitive communications
environments.
In accordance with the SA concept and exploiting
advanced software paradigms, the service logic is
164
Louta M., Michalas A., Psoroulas I. and Loutas E. (2005).
COST-EFFECTIVE SERVICE TASK ASSIGNMENT IN MULTI- DOMAIN DISTRIBUTED COMPUTING ENVIRONMENTS.
In Proceedings of the Second International Conference on e-Business and Telecommunication Networks, pages 164-168
DOI: 10.5220/0001407901640168
Copyright
c
 SciTePress

realized by a set of autonomous co-operating
components, which interact through middleware
functionality that runs over Distributed Processing
Environments (e.g., Common Object Request
Broker Architecture - CORBA). Limited by techno-
economic reasons or considering administrative,
management and resilience/ redundancy purposes it
is assumed that each service provider deploys
service components realising service logic in
different service nodes, residing in the same and/or
different domains. In the context of this paper
domains represent different network segments.
Moreover, it can be envisaged that a service will in
general comprise a set of distinct service tasks,
which could be executed by different service nodes.
Highly competitive and open environments should
encompass mechanisms that will assist service
providers in accounting for their interests, i.e.,
offering at a given period of time adequate quality
services in a cost efficient manner which is highly
associated to efficiently managing and fulfilling
current user requests. Assume that a user wishes to
access a specific service offered by a service
provider. The service is composed by a distinct set
of service tasks. Each service task can be served by
various candidate service nodes (CSNs). Thus, a
problem that should be addressed is the assignment
of service tasks to the most appropriate service
nodes. In this paper, the pertinent problem is called
service task assignment. The aim of this paper is to
address the problem from one of the possible
theoretical perspectives and to show the software
architecture that supports its solution
This study is related to pertinent previous work in
the literature. Efficient resource utilisation and job
scheduling is gaining the attention of the researchers
as computational grids (interconnected networks of
super-computing centers) have become an emerging
trend on high performance computing (Special Issue,
2003). Most studies in the field of resource
allocation schemes aim at efficiently utillising the
CPU resources spread throughout a network.
Different global objectives could be considered,
such as minimization of mean service/task
completion time, maximization of resources
utilization (e.g., CPU utilization), minimization of
mean response ratio (Tanenbaum, 2001). The design
choices that the system architect has to face are quite
vast ranging from deploying centralized vs.
decentralized arrival and/or control systems,
adopting static vs. dynamic schemes, considering
different resource allocation strategies/algorithms
incorporating or not the task migration concept,
taking into account diverse load metrics, etc. (Stone,
1997). In general, many approaches have derived
and encourage the necessity of adaptive switching
between strategies and dynamic adjustment of
decision parameters (e.g., node’s load predefined
threshold, time interval upon which load information
exchange between the nodes should take place).
The approach in this paper is the following. Section
2 provides the software elements required for the
realization of the service task assignment process,
while our assumptions regarding the system model
are presented. Section 3 presents a concise definition
and mathematical formulation to the service task
assignment problem. Finally, section 4 gives future
plans and concluding remarks.
2 SOFTWARE ARCHITECTURE
Service Architectures (e.g., TINA Access Session
(Magedanz, 1997)) offer the framework for user
authentication and service invocation. The feature
that is not supported is the overall task of service
task assignment. The following key extensions are
made so as to cover the necessary functionality.
First, the Service Provider Agent (SPA) is
introduced and assigned with the role of selecting on
behalf of the service provider the best service task
assignment pattern. Second, the User Agent (UA) is
assigned with the role of promoting the service
request to the appropriate SPA. Third, the Service
Node Agent (SNA) is introduced and assigned with
the role of promoting the current load conditions of a
CSN. Finally, the Network Provider Agent (NPA) is
introduced and assigned with the task of providing
current network load conditions (i.e., bandwidth
availability) to the appropriate SPA. In other words,
the SPA interacts with the UA in order to acquire the
user preferences, requirements and constraints,
analyses the user request in order to identify the
service tasks constituting the service and their
respective requirements in terms of CPU, memory
and disk space, identifies the set of CSNs and their
respective capabilities, interacts with the SNAs of
the candidate service nodes so as to obtain their
current load conditions and with the NPAs so as to
acquire the network load conditions, and ultimately
selects the most appropriate service task assignment
pattern for the provision of the desired service.
Regarding the system model, we consider a set of
service nodes
SN
and a set of links
L
. Each service
node
SNn
i
∈
corresponds to a server, while each link
Ll ∈
corresponds to a physical link that
interconnects two nodes
SNnn
ji
∈ ,
. Our system
COST-EFFECTIVE SERVICE TASK ASSIGNMENT IN MULTI-DOMAIN DISTRIBUTED COMPUTING
ENVIRONMENTS
165

operates in a multi-tasking environment, i.e., several
tasks may be executed on a single service node
sharing its resources (e.g., CPU utilization, memory,
disk space). Let
i
D
denote a set of nodes grouped to
form a domain. A pattern for the physical
distribution of the related components to the service
task assignment scheme is given in Figure 1. Each
SPA controls the service nodes of a domain. Each
SNA is associated with each node, while each NPA
is associated with the network elements (e.g.,
switches or routers) necessary for supporting service
node connectivity. The SNA, NPA role (in a sense)
is to represent the service nodes or network
elements, respectively, and to assist SPA by
providing information on the availability of
resources of the service node/network element.
Domain state information (load conditions of the
service nodes of the particular domain and link
utilisation) is exchanged between the SPA and the
SNAs/NPAs residing in the specific domain, while
SPAs residing in different domains exchange their
domain state info. This approach increases
scalability as it reduces the requirements in terms of
computation, communication and storage. At this
point it should be noted that for simplicity reasons
the network elements needed for the service node
connectivity are not depicted in Figure 1.
In the scope of this paper we consider that the
service nodes constituting a specific domain are
interconnected by a local area network, while
different domains are interconnected by a wide area
network. In the current version of this study we limit
our attention to the cases where a service request
may be served by service nodes residing in a single
domain, since we consider that the cost imposed due
to information transfer through the WAN links is
big, diminishing the net benefit of possible efficient
resource utilisation. Thus, in our study, in case a
service request cannot be served by the service
nodes of a domain, it is transferred to the SPA of
another domain in order to handle the request.
However, the formulation of the service task
assignment problem is given in a general mode,
since the emergence of infrastructure (e.g., very high
performance Backbone Network Service (vBNS) ,
NSFnet topologies) and test-beds like Tera-Grid
(TeraGrid, 2003) promises remarkable network
bandwidth between distant sites, enabling thus load
balancing with minimal cost.
3 PROBLEM FORMULATION
User
u
wishes to use a given service
s
, which may
be decomposed in a set of distinct service tasks,
which will be denoted as
)(sST
. Among these service
features, those of interest to the user will be denoted
as
),( suST
, where
),( suST )(sST⊆
.
Let’s assume the existence of multiple service nodes
for the provision of service
s
, denoted by
)(sSN
},...,{
||1 k
nn=
. Each service node-
j
n
contains a
collection of components, denoted as
)(iA
j
n
, which
inter-work with other components that may reside in
the same or in a different service node in order to
accomplish each service task
)(sSTi ∈
. Let
j
n
A
and
C
be the total set of components residing in the
j
n
service node and the various service nodes in total,
respectively. Hence, the following relationship
holds:
CAiA
jj
nn
⊆⊆)(
. Each service task
()
sSTi ∈
may be executed on an associated set of possible
candidate service nodes, represented by the set
)(iSN
,
(
() suSTi ,∈
). Thus,
)(iSN
)(sSN⊆
. The service logic
deployment pattern adopted by service providers
determine each of these service node sets.
Task
i
, (
)(sSTi ∈
) requires for its completion
consumption of
)(ir
CPU
,
)(ir
mem
and
)(ir
disk
resources of
D2
D3
SPA
SNA SPA
SNA
SNA
SNA
SPA
NPA
NPA
SNA
NPANPA
NPA
SNA
SNA
D1
Figure 1: System Model and physical distribution of the service task assignment related components.
SNA
SNA
SNA
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166

service node(s)
j
n ))(( , iSNn
j
∈
. A realistic
assumption is that SPA being in charge of assisting
the service providers in the competitive
telecommunication market, has a solid interest in as
accurately as possible identifying the resources
)(ir
a
(where
} , ,{ diskmemCPUa ∈
) needed for the
provisioning of service task
i
in terms of CPU
utilization, memory and disk space. In this respect,
the SPA can be the entity that configures these
values based on the service feature characteristics,
user preferences and requirements, exploiting also
previous experience.
Let
D
c
denote the cost of involving service node
j
n
))(( , iSNn
j
∈
, in the service provision. For notation
simplicity it is assumed that the cost of involving a
service node in the solution is the same for all
service nodes. Notation may readily be extended.
The objective of our problem is to find a service task
assignment pattern, i.e., an assignment
() sA
ST
of
service tasks
i
(
),( suSTi ∈
) to service nodes
j
n
))(( , iSNn
j
∈
, that is optimal given the current load
conditions and number of service tasks being served
by each service node
j
n
, represented as
)(
j
pre
a
nr
and
)(
j
pre
nk
, respectively. The assignment should
minimise an objective function
()()
sAsf
ST
,
that
models the overall cost introduced due to network
resources consumption. Among the terms of this
function there can be the overall cost due to the
deployment of various service nodes to the service
provisioning process, the communication cost
introduced due to the interaction of the components
j
n
A
residing in
j
n
service node with the components
k
n
A
residing in service node
k
n
for the completion of
each service task
i
,
))(( sSTi ∈∀
, as well as the
management cost
)',( iic
M
introduced due to the
assignment of
)',( ii )(
2
sST∈
service tasks to different
service nodes
)(),(
2
'
iSNnn
jj
∈
.
The constraints of our problem are the following.
First, each service task
i
(
),( suSTi ∈
) should be
assigned to only one service node
j
n
,
))(( iSNn
j
∈
.
Second, the capacity constraints of each service
node should be preserved. Lets assume that
max
a
r
and
max
k
represent the maximum load and the maximum
number of tasks that a service node may handle. For
notation simplicity, these parameters are assumed to
be the same for each service node
j
n
,
))(( iSNn
j
∈
.
Thus, the constraints are
≤)(
j
post
a
nr
max
a
r
and
≤)(
j
post
nk
max
k
,
))(( iSNn
j
∈∀
, where
)(
j
post
a
nr
and
)(
j
post
nk
denote the potential load conditions of
service node
j
n
, after the service task assignment
process. Notation may readily be extended.
In order to describe the allocation
() sA
ST
of service
tasks to service nodes we introduce the decision
variables
()
jix
ST
,
(
),( suSTi ∈
,
)(iSNn
j
∈
) that take the
value 1(0) depending on whether service task
i
is (is
not) executed by service node-
j
n
. The decision
variables
()
jy
SN
assume the value 1(0) depending on
whether candidate service node
j
n
(
)(iSNn
j
∈
) is (is
not) deployed (involved in the solution). In addition,
we define the set of variables
()
'
,iiz
ST
(
()
2'
),(, suSTii ∈∀
)
that take the value 1(0) depending on whether the
service tasks
i
and
'
i
are (are not) assigned to the
same service node. Allocation
() sA
ST
may be
obtained by reduction to the following 0-1 linear
programming problem.
Service Task Assignment Problem:
Minimise
()() sAsf
TN
,
∑∑
∈∈
⋅⋅+⋅⋅=
)(},,{
max
)
)(
)(
1()(
sSNn diskmemoryCPUa
ja
j
pre
a
aSND
j
nr
nr
wbjyc
∑
∑
∈∈
⋅+
)()(
),(),(
sSTiiSNn
STj
j
jixniC
∑
∑
∈∈
−⋅+
)()('
))',(1()',(
sSTisSTi
STM
iiziic
(1),
where
),(
j
niC
denotes the communication cost
introduced in case
j
n
service node has undertaken
the responsibility for the execution of service task
i
(
),( suSTi ∈
),
subject to the constraints:
∑
∈
=
)(
1),(
iSNn
ST
j
jix
,
)(sSTi ∈∀
(2),
∑
∈
⋅≤⋅+
)(
max
)()(),()()(
sSTi
SNaSTaj
pre
a
jyjrjixirnr
,
)(sSNn
j
∈∀
(3),
∑
∈
⋅≤+
)(
max
)()(),()(
sSTi
SNSTj
pre
jyjkjixnk
,
)(sSNn
j
∈∀
(4)
At this point it should be noted that, in order for the
service providers to better utilize their resources, the
cost of each service node deployment introduced in
cost function (1) takes also into account the node’s
current load conditions in order to obtain a load
balancing solution. Parameters
b
,
)1( <b
, and
a
w
denote the relative significance of load balancing
and of each resource type
a
to the service provider.
Constraints (2), guarantee that each service task will
be assigned to one service node. Constraints (3) and
(4) guarantee that each service node will not have to
cope with more load and service tasks than those
dictated by its pertinent capacity constraint.
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ENVIRONMENTS
167

4 CONCLUSIONS
This paper presented a mechanism that will assist
service providers in efficiently managing and
fulfilling user requests. Specifically, the contribution
of this paper lies in the following areas. First, the
definition and mathematical formulation of (one
possible version) of the service task assignment
problem. Second, the presentation of a software
architecture, which is adopted for acquiring the best
service task configuration pattern, i.e., assignment of
service tasks to service nodes for efficient service
provisioning.
Initial results indicate that the proposed framework
produces good results in simple contexts where only
the service node deployment cost factor is
considered. Directions for future work include, but
are not limited to the following. First, the realisation
of further wide scale trials, so as to experiment with
the applicability of the framework presented
herewith. Second, the experimentation with alternate
approaches (e.g., market-based techniques) for
solving the service task assignment problem.
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