A Framework for Trust Management
System in Computational Grids
Thamarai Selvi Somasundaram1, #R. Kumar1, R.A. Balachandar1, P. Balakrishnan1,
K. Rajendar1,J.S. SwarnaPandian1 , G. Kannan1 , R. Rajiv1, C.A. Prasath1
1
Department of Information Technology
Anna University, MIT Campus
Chromepet, Chennai – 600044, India
Abstract
The Grid approach provides the ability to access,
utilize, and control a variety of heterogeneous
resources distributed across multiple domains.
Choosing appropriate resources in such a
distributed and heterogeneous environment brings
up several challenges. Several attempts have been
made to select resources based on user
requirements and resources availability, but there
has been no attempts made in Grids to provide
selection of resources on the basis of trust. We
introduce a new framework that is based on the
concept of dynamic trust evaluation by taking into
account of User’s experiences and the Resource
provider’s efficiency.
1.
Introduction
Grid approach is evolving as an important trend in
high-performance computing. Trust plays an
important role in all grid computing interactions.
Grid computing, largely used by scientific and
academic communities, has been slow to enter the
commercial arena. A commercial grid means the
use of grid computing within the context of a
business or enterprise, rather than perhaps for
academic or research purposes. Consumer must
trust that the service provider will provide the
services they advertise and will not disclose private
consumer information. Trust of the service
provider's competence, availability, job efficiency
and reputation will influence the consumer’s
satisfaction in the selection of the service provider.
Service provider must trust that the consumer is an
authenticated and an authorized one, thereby
making him liable in payment of goods or services.
Thus, trust management has to be an intrinsic part
of commercial grids. However, business
transactions span across multiple organizations,
possibly in different countries and not all of these
domains may be trusted to the same extent.
Applications will need to be able to navigate
through these possibly inconsistent trust
relationships. There is a need for a general-purpose
trust management system for computational grids.
2. Background
A trust relationship occurs between a truster, the
subject that trusts a target entity, called the trustee.
A truster/trustee may be a collective entity such as
a company, committee or partnership. The essential
components of a trust relationship are: the truster,
the trustee, a specific context with associated level
of trust, and the conditions under which this
relationship becomes active [1]. There are several
definitions about trust in literature. In this paper,
we define trust as the quantified belief by a truster
with respect to the competence, honesty, security
and dependability of a trustee within a specified
context at a given time. Trust is not symmetric, so
this belief by the truster does not imply any similar
belief by the trustee. Distrust is the quantified
belief by a truster that a trustee is incompetent,
dishonest, not secure or not dependable within a
specified context. Quantification reflects that a
truster can have various degrees of trust (distrust),
which could be expressed as a numerical range or
as a discrete classification such as low, medium or
high. The context of a trust relationship is defined
as a set of actions with a trust level applying to all
the actions and a set of constraints, which must be
evaluated for the trust relationship to apply. Hence
evaluating the trustworthiness of a service provider
is the important factor when we design a new trust
model for commercial grids.
Traditionally there are two major classes of trust
models. The first class is the Central model (CM),
which has a central trust point. Every entity in the
Central model has the same opinion as what the
central trust point has. Reputation-based systems
[2], e. g. eBay [3], are the typical examples of the
CM model. The certificate authority (CA) based
trust model, which has been widely deployed in ecommerce [4], [5], [6], [9], [10] is the typical
example. The CM model works fine if the central
point is reliable and trustable. The second class is
the Transitive model (TM) [7] which has a
transitive trust chain. In TM, the recommendation
from the recommender is highly emphasized for
the trustworthiness. Actually the CM model can be
seen as a special case of the TM model with just on
transitive relationship and the recommendation
being totally trusted. However, making use of the
recommendation and other metrics are good for
developing a new TM model.
3. Related Work
Most of the work concerning trust has been
concentrated in the area of security/reputation.
These are mainly in the form of formal logics to
analyze cryptographic protocols for design flaws
and correctness. However, they are ill suited as
general models of trust as their applications are for
a specific domain. Trust models such as PGP [8]
and the X.509 [11], [12] as well as trust
management applications such PolicyMaker [13]
and KeyNote [14] are concerned identity trust.
These trust mechanisms do not consider the
behavior trust which changes over time.
A survey of trust in Internet applications is
presented in [1] and as part of this work a policy
specification language called Ponder [15]
supporting behavior trust was developed. Ponder
can be used to define authorization and security
management policies. Ponder being extended to
allow for more abstract and potentially complex
trust relationships between entities across
organizational domains.
Abdul-Rahman and Hailes categories the
trustworthy as four levels namely (“very
trustworthy”, “trustworthy”, “untrustworthy”, “and
very untrustworthy”). In our approach we utilized
the both subjective and objective natures, inorder
to reflect the evaluation of trust. Reputations are
also effectively used in electronic marketplaces as
a measure of the reliability of participants. For
instance, with eBay, buyers and sellers can express
their votes (-1, 0, or 1) for each other after each
transaction. Votes so collected are used by eBay to
provide cumulative ratings of users that are made
known to all participants. In systems like eBay,
reputations are associated with physical identities
and are managed at the eBay server.
Google, which is one of the well-known
applications, uses the notion of trust to display the
relevant information from internet web pages. It
retrieves and displays the relevant documents by
sorting the web pages according to the PageRank
of each web page. PageRank is one of the methods
that have been used by Google to determine page’s
relevance or importance. Also it employs the
principal eigenvector of the matrix to compute the
PageRank.
Reputation Systems such as CNET.com, EXP.com
and expertcentral.com compute reputations by
taking the feedback from experts and reviewers.
OpenPrivacy (www.openprivacy.org) introduces a
set of reputation services that can be used to create,
use, and calculate results from accumulated
opinions and reputations. Sierra, Talon, and Reptile
are OpenPrivacy projects that incorporate
reputations to enhance searching as well as to
discard unwanted information. The ReferralWeb
system helps to explore social networks.
The trust which has been concentrated in the area
of reputations are [4], [9], [16], [17], [18], [19] and
[20].
4. Relationship between Trust and Security
Trust and security are not the same areas in the
domain of e-Commerce and Grid. Unfortunately
trust is sometimes confused with PKI (Public key
Infrastructure). Trust models in PKI or ACL in
access control systems are called objective trust
models, where the objects specify strict trust
relationship between entities. But they occupy
much system resource and are not flexible as
expected. If such system collapses, damage can’t
be controlled. For these unconquerable problems of
objective trust models, subjective trust as a new
area and research field has gained momentum.
Distributed network, ubiquitous, mobile computing,
and rating systems for online communities, where
maintenance of explicit certification authorities is
not feasible anymore, have raised the research
interest in subjective trust models. Subjective trust
model describes the trust relationship between two
entities as similar as the relationship in social
network and defines trust relationship in a much
proper way. Subjective trust model only bring light
load and can adapt to flexible network environment.
Most of the existing systems follow the approach
of binary trust (Yes/No) values, which restricts the
expression of trust to a certain degree (trusted or
non-trusted). No previous interaction histories are
evaluated. In terms of calculation, this is a non-
calculative trust. The binary trust model fails to
reflect well the real situation in security. Trust
assessment may not be static. It may depend on
environmental context, amount of referral from
other trusted parties and the task being performed.
From the above discussion we can conclude that
the security and trust are two distinct concepts.
Security can be used to support trust by providing a
secure trusted environment, network and
communication so that the trusted computation can
take place. However, building trust in Grid
environments also helps to reduce aspects of
Security Risks [21].
5. Trust Management System
The Trust Management System proposed in this
research evaluates the trust value of all grid
resource providers and facilitates the selection of
suitable resource for job execution based on the
trust value. Though the literature proposes several
types of trust in grid environment, we classify trust
into five different types so that all types of trust
come under our classification. We classify the trust
using the three entities of the grid environment viz.,
the user, the resource broker and the resource
provider as,
 Service Provision Trust – It describes relying
party’s trust in a service provider. The trustor
trust the trustee to provide a service that does
not involve access to the trustor’s resource
 Equipment
provision
trust/Resource
provision trust – It describes trust in
principals for the purpose of accessing
resources owned by the relying party. A trustor
trusts to use resources that he owns or controls.
It measures whether a resource provided by
the resource provider is trustworthy. The QoS
offered by the resource will determine this
trust.
 Information provision trust – It refers to the
belief that information provided by the
information provider is reliable and accurate
 Broker’s trust – It is a measure of belief that
a resource broker has discovered a trustworthy
resource.
 User’s trust – It measures whether a resource
provider is willing to offer his resources to the
user. In this case,
the previous behavior of the user
while
payment may be considered for
this
trust.
In grid like environment, where resources from
diverse organizations are shared to solve a
computationally intensive problem which requires
trusted resource, the real challenge lies in the
determination of trustworthiness of the resource
provider. Hence, the emphasis is on establishing
equipment or resource provision trust. In this
project, we design and develop a trust management
system for establishing trust management system
for determining equipment provision trust.
5.1. Lifecycle of Trust Management System
A trust management system follows a specific
lifecycle that goes through several stages while
computing trust of a particular entity. The figure 1
shows a generalized lifecycle of a trust
management system that goes through several
stages.
 Trust Metric Identification
This is the first stage of a trust management system
in which the required trust metrics from which the
given trust of an entity can be defined is identified.
 Trust Metric Evaluation
In the next stage, a suitable methodology is applied
to determine the value of those metrics.
 Trust Metric Calculation
Once the values for all the metrics are computed,
the overall trust value is determined using the
values. It requires formalization of trust model
expressed in terms of the metrics identified. The
calculated trust is then stored in the database for
further use.
Trust
Metric
identification
Trust
Integration
TMS
Trust
Value
updation
Trust
Metric
Evaluation
Trust
Metric
Calculation
Figure 1: Lifecycle of Trust Management System
 Trust Value updation
Since, to reflect the dynamic nature of grid
environment where trust value will change rapidly
as the resources and users come and go, it is
mandatory to monitor and compute the metrics
periodically and calculate the trust value. This
value is updated in the database to ensure that the
trust management system always uses the current
trust value of the entity.
 Trust Integration
The calculated trust value is then used for making
decisions towards job scheduling, service access
and for other purpose depending on the type of the
trust established.
5.2. Block Diagram of the Trust Management
System
The Trust Management System proposed in this
project evaluates the trust value of all grid resource
providers and facilitates the selection of suitable
resource for job execution based on the trust value.
It computes trust value of a resource provider
based on the following three factors: Infrastructure of the organization that
provides a grid resource to the grid
 Feedback from the user after accessing
the resource and
 Performance metrics of the particular grid
resource.
evaluating their trust. The performance metrics
considered are as follows: Actual Execution Time
Actual Execution time is defined as the time
taken by the resource provider in executing a
job. It is the sum of the CPU time and the I/O
waiting time of a job. Actual Execution time
reflects the capability of resources in executing
a particular job.
 Availability
Availability is defined as the time during
which the resource provider is available over a
period of time. It brings out the difference
between the Uptime (the period of time, the
resource provider is ready for execution of
job) and the Downtime (the period of time, the
resource provider is not ready for the
execution of job). Hence this plays an
important role in evaluating the consistency of
the Resource provider.
 Number of Success
Success denotes the state of the job after being
executed by a
particular resource provider.
The accumulation (total) of success parameter
helps us in determining the success rate of the
resource provider over his past experience in
User
User Feedback
executing a job.
 Number of Failure
Trust
Failure denotes the state of the job after being
Manager
executed. More failure in executing a job
Resource
Resource
reflects the inefficiency of a resource provider.
Provider
The accumulation (total) of failure parameter
Registration
helps us in determining the resource provider
failure rate in the execution a job.
Trust
Trust

Bandwidth
Resource
Metrics
Computation
It can be defined as the speed with which data
Computation
can be sent to a target resource. It is measured
Performance
in megabits/seconds. The purpose of
considering this parameter is to determine the
resource provider's network performance since
it reflects the throughput of the communication
Gridway Metascheduler, Ganglia, NWS
link.

Latency
The amount of time in milliseconds, required
Figure 2: Block diagram of proposed Trust
to transmit a tcp message to a target resource.
Management system
The purpose of considered this parameter is to
The proposed trust management system that
determine the resource provider's network
establishes equipment or resource provision trust is
performance since it reflects the round trip
shown in the figure 2 that follows various stages of
time of the communication link.These
the lifecycle. It shows several modules that works
parameters are collectively called as resource
together to compute the trust value of the grid
performance parameters. These parameters
resource providers.
reflect the efficiency of the resource provider
Resource Performance Module
in executing a job and its network
This module obtains the performance metrics of
characteristics
every resource providers and uses them in
Resource Registration Module
This module obtains infrastructure information of
the resource provider during registration of the
resource in to the grid. We express this information
in terms of the following parameters: Governing body of the organization. This
parameter allows us to classify the
organization into public or private assuming
public organization has a greater trust over the
private organization.
 Registration number of the private organization.
These parameters are collectively called as
resource registration parameters and they
reflect the reputation of the resource provider
in the user community.
User Feedback Module
This module obtains the user’s feed back about a
particular resource provider by prompting him to
mention the level of satisfiability and willingness
to recommend the resource to others. With this
information, we classify the trust level of resource
providers in to following six categories: Excellent
 Very Good
 Good
 Medium
 Low
 Very Low
The two parameters namely the level of
satisfiability and willingness to recommend are
collectively called as user feedback parameters and
they reflect the behavior of resource provider with
user community.
Trust Metrics Computation
This module applies various methodologies to
compute the value of trust metrics received from
underlying resources.
Several tools such as
Ganglia, Network Weather Service were identified
to determine the values of the metrics and they are
integrated with the trust management system. The
values are sent to the trust computation module to
calculate overall trust value.
Trust Computation
This module gathers the input from all the above
modules and computes the overall trust of a
resource provider and stores it in the database. The
trust represents the trustworthy of the resource
provider at a given instant of time.
Trust Updation
This module periodically monitors the resource
performance metrics and computes the overall trust
value and updates in the database. Similarly, at the
end of every resource access, the user feedback and
resource performance metrics are obtained, and
overall trust value is computed and updated in the
database.
The trust value obtained from the trust
management system can be used for making
decisions in the grid environment. In this project,
the trust value of the resource provider is used to
identify suitable and most trusted resource for job
execution. Hence, the trust management system is
proposed to be integrated with a grid
metascheduler and thereby developing a grid
resource broker that can discover a suitable
trustworthy grid resource for job execution. The
proposed four layered trusted grid architecture is
shown in the figure 3.
Figure 3: Layered Architecture of Trusted
Grid
Fabric Layer
The Fabric layer deals with the resources available
in grid environment and defines the interface to
local resources, which may be shared. This
includes computational resources, data storage,
networks, catalogs, software modules, and other
system resources.
Grid Middleware Layer
This layer refers to the grid middleware that
incorporates
necessary
components
for
authentication, monitoring and discovery of grid
resources, execution of job in grid resources, file
transfer between grid resources.
Trust Layer
The trust layer is responsible for evaluating the
trust value of all the grid resource providers. This
layer periodically monitors the trust metrics and
obtains the values of those metrics from various
tools such as Ganglia, NWS, Metascheduler etc., It
computes overall trust value using the metrics and
stores them in the database. This trust value is used
to identify the most trusted resources for job
execution. Suitable grid resources that match the
job requirements are discovered and they are
ranked on the basis of their trust value. The
resource that has most trusted value is selected for
job execution.
Application layer
The application layer enables the use of resources
in a grid environment through several portlets. It
includes portlets for providing user feedback and
resource registration information. This information
is useful for evaluating trust value of the grid
resource provider. In addition to that, this layer
may include portlets that display availability of
resources, results of job execution and necessary
user interface components for job submission and
resource request.
The known issues in the Trust management system
are the evaluation of the trust parameters. The two
categories of parameters namely Resource
registration parameter and the User feedback
parameter can be obtained directly from the
Resource provider and the user respectively.
However to obtain the Resource performance
parameters a separate methodology must be
followed. The parameters namely the Actual
Execution time, Success and the failure of the job
are calculated using the information obtained from
a grid meta scheduler. The other parameters such
as the Availability, Latency and Bandwidth can be
calculated using the information obtained from the
Network Monitoring tools such as Network
Weather Servcie, Ganglia.
6. Research Issues and Future Work
We believe that the trust management in Grid has
not been explored properly. We have proposed the
new Trust management system for computational
grids chooses the optimal resources that are
available in the grid environment. According to our
information we did not find any literature which
points out the impact of trust on Grid environment
as whole. We believe our list of suggested
parameters provides a realistic and practical view
of a Grid.
We have suggested a complete Trust Management
System Architecture for Grid and discussed its
implementation issues. We are sure it will have
minimal overhead in terms of computation,
infrastructure, storage, and complexity
In future our intention is to implement the
suggested Trust Management System Architecture
and integrated with meta scheduler for better
performance of the resource selection and job
submission.
ACKNOWLDGEMENT
This research work is funded by Department of
Information Technology, New Delhi, India.
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