ASSESSING REPUTATION AND TRUST IN SCIENTIFIC GRIDS
Andrea Bosin, Nicoletta Dessi
Dipartimento di Matematica e Informatica,Universita' degli Studi di Cagliari
72,Via Ospedale, Cagliari, Italy
Balachandar Ramachandriya Amarnath
Dipartimento di Matematica e Informatica Universita' degli Studi di Cagliari, 72,Via Ospedale, Cagliari, Italy
and INFN sezione di Cagliari, Monserrato,Italy
Keywords: Grid Computing, Trust, Reputation, Data mining.
Abstract: Up to now, reputation and trust are widely acknowledged to be important for business environments, but
little attention has been placed in security aspects of Grids devoted to scientific applications. This paper
addresses these aspects and proposes both a Reputation Management System and a mechanism for assessing
the reputation of computing resources belonging to a scientific Grid. Because the overall performance of the
Grid depends on the quality of service given by each single resource, the resource reputation is a measure
of trustworthiness, in the sense of reliability and is asserted on a set of properties, namely the resource
operative context, that express the resource capability in providing available and reliable services. Unlike
the business applications, we are not interested in assessing the reputation of a single resource but in finding
a set of resource that have similar capabilities of successfully executing a specific job. Toward this end, the
paper proposes a mechanism that assesses reputation by clustering Grid resources in groups that exhibit
similar behavioural patterns and share similar operative contexts. Simulation results show the effectiveness
of the proposed approach and the possible integration of such a model in a real Grid.
1 INTRODUCTION
Grid computing involves the formation of virtual
organizations where different institutions pool
together their computational resources and diverse
and large groups of geographically distributed users
seek to share and use networked resources in
coordinated fashion (Foster et al,2002). From user’s
perspective, a Grid is a collaborative problem
solving environment that must guarantee the quality
of job execution. In scientific Grids, a Grid
scheduler hides the complexities of the Grid
computing environment from a scientist. It discovers
resources that the user can access, maps application
jobs to resources, starts execution and monitors the
execution progress of tasks. As such, the Grid
scheduler provides a reliable local distributed
execution environment and maximizes resource
utilization of a local site, but it does not enforce
absolute control over the reputation of distributed
resources. Usually, reputation is what is generally
said or believed about a person or thing character or
standing . In business transactions, the concept of
reputation has been proposed as a mechanism for
addressing the problem of evaluating whether a
network node acts consistently. Usually operative on
P2P networks, a reputation system calculates the
global reputation score of a peer by considering
feedbacks from all other peers who have interacted
in the past, with this peer. In this paper, we apply a
similar reference scheme for evaluating reputation in
scientific Grids: reputation is not a static property of
a single resource but rather it is an assessment based
on experience that is shared through the Grid and it
augments and decays with time and frequency of
interactions. Due to the diversity of computational
resources and the complex interactions that can
occur between different nodes, analytical modelling
of resource reputation appears impracticable and
often not effective since the Grid scheduler is not
interested in assessing the reputation of each single
resource but in finding a set of resources that have
similar capabilities of successfully executing a
235
Bosin A., Dessi N. and Ramachandriya Amarnath B. (2008).
ASSESSING REPUTATION AND TRUST IN SCIENTIFIC GRIDS.
In Proceedings of the Tenth International Conference on Enterprise Information Systems - ISAS, pages 235-238
DOI: 10.5220/0001691202350238
Copyright
c
SciTePress
specific job. Towards this end, this paper proposes
an heuristic mechanism that computes the single
resource reputation by considering a set of attributes
that we refer as Resource Operative Context (ROC) .
Usually derived from log-files, ROC attributes
allows for evaluating the resource behaviour with
time. To avoid information queries on single
resource behaviour and to obtain information faster
and more reliable, we propose to cluster resources
that exhibit similar behavioural patterns and share
similar operative contexts. Based on this
information, the Grid scheduler may limit the
number of resources that are potentially capable of
providing efficient job execution. The paper presents
preliminary results concerning the implementation
of the proposed approach in a simulated Grid
environment.
2 RELATED WORK
A survey of existing methodologies for trust and
reputation in grid environment is presented in (G.C.
Silaghi et al, 2007) that discusses many reputation
based trust management systems and their suitability
for grids. The complexities of enterprise grid
environment and the importance of QoS in grid are
discussed in (D.A.Menasce, & E.Casalicchio, 2004)
that remarks the need of considering SLAs and cost
constraints in the grid scheduling. Concepts related
to trust and reputation are validated by mathematical
definitions in (W.Xinhua et al, 2005). (B.Ma et al,
2006) present an approach to compute and compare
the trustworthiness of entities in the same
autonomous and different domains. In (Thamarai
Selvi Somasundaram et al,2007), a general purpose
trust management system for computational grids is
presented , based on several information that can be
obtained directly by the grid scheduler. A model of
trust aspects in executing collaborative distributed
services is presented in (C. Argiolas et al. ,2008)
3 ASSESSING REPUTATION
The reputation of a resource reveals its reliability in
executing jobs. Previous section emphasizes the
importance of resource’s past behaviour for
assessing its reputation. However, in an open and
decentralized scientific Grid, there is not a
centralized authority which collects and maintains
reputation information. Additionally, users only
submit their jobs and are not asked for a feedback.
This makes it impossible for the grid scheduler to
have updated reputation information about the whole
network, since some Grids may have thousand of
resources. Possible approaches could be using
distributed data structures or evaluating reputation
by local knowledge on the interested resource. Our
proposal considers the evaluation of the reputation
as a centralized activity supported by a Reputation
Management System (RMS) , depicted in figure 1,
that periodically interacts with every grid resource.
RMS is intended to support the grid scheduler in
selecting resources by assessing their reputation and
to calculate the delay in executing each user job. The
RMS can be configured to be either proactive,
active, or passive. In a proactive configuration , the
RMS will actively stop all communication with
resources that are not conformant to the level of
reputation required by the Grid scheduler. With
active and passive status, the RMS will not intervene
as strongly as with proactive configuration but it will
report non compliant resource behaviours to the Grid
Scheduler. In a passive configuration, the RMS will
only log task behaviour while the resource
conformance to the agreed reputation level will be
verified at a later time. The RMS includes a
Performance Module (PM) that estimates the
resource’s reputation as follows. It takes periodic
data on resources from the scheduler log file and
structures this information in relational tables whose
attributes express basic characteristic of the
resource behaviour that we globally refer as the
Resource Operative Context (ROC). Attributes
differ in a number of ways. For example , they can
be of different types (i.e. quantitative or qualitative)
and may contain explicit relationships to one
another. A Database Management System is
responsible for the storage and the management of
ROC information. Hence, each ROC is an instance
of a relation table that represents the resource
operative status with respect to time. As mentioned
earlier, we are not interested in evaluating the most
accurate reputation of each single resource, but in
clustering resources that exhibit similar behaviours
in executing jobs. To reduce the effort required to
achieve this, the RMS uses an application that
consists of a computation that queries periodically
the databases and carries out further analysis on the
retrieved data. Specifically, this analysis is based on
a more general technique known as instance-based
learning which uses retrieved data as a training set
for the k-means algorithm (J.A.Hartigen &
M.A.Wong, 1979) which is a popular clustering
technique. Taking into consideration the scale of the
considered attribute, each cluster can be labelled by
ICEIS 2008 - International Conference on Enterprise Information Systems
236
a qualitative reputation level (i.e. very high, high,
medium, sufficient, bad) that classifies each resource
according to its past behaviour.
To assure better availability and reliability in
executing job, the RMS interacts with the Grid
scheduler that periodically asks for resource
reputation levels as a decision support parameter in
scheduling resources for task submissions.
A
B
C
D
E
Grid
Scheduler
With Reputation
Management System
User
Submits job
Computes reputation
and updates
Database
A
B
C
D
E
Grid
Scheduler
With Reputation
Management System
User
Submits job
Computes reputation
and updates
Database
Figure 1: The Reputation Management System.
4 A CASE STUDY
Towards a validation of the proposed approach, we
implemented and tested a prototypical version of the
RMS in a scientific Grid that we simulated by using
GridSim (S. Venugopal et al., 2004).We simulated
25 grid resources and submitted 1500 jobs to each
resource. For the purpose of simulation, we created
grid resources that are similar in nature with respect
to operating system (in our case, Solaris operating
system), and consist of same amount of computing
power. Moreover, we simulated jobs with similar
complexity so that every job has the same length,
file size and also produces an equal volume of
output. Even if not fully compliant with a real
scientific Grid environment, these requirements
allows us to evaluate the behaviour of each
individual resource in executing jobs. The RMS
database was simulated by creating a
resource_model file for every resource. For the sake
of simplification, we considered two attributes
namely the number of successful jobs and the
execution time taken by a resource to complete a
job. The number of successful jobs determines the
reliability and quality of the resource in executing a
given job successfully. In real grid environment, a
job will fail due to many reasons that often depend
on the improper maintenance of the site by the
resource provider. This behaviour is highly
unwanted in the grid environment and it will affect
the reputation of the resource significantly.
The execution time is the amount of time taken
by the grid resource to complete the execution of the
submitted job. This attribute reveals the reliability of
the resource in executing the job in time because,
during the registration in a grid, a participating
resource provider would agree to contribute a fixed
amount of computing power. However, if it fails in
providing the committed computing power while
executing a job, the execution will take more time
than estimated. This delay in job execution will
eventually lower the reputation of the resource.
Step 1: Introducing Job Failures
Our first experiment aimed at assessing the
reliability of a resource with respect to successful
execution of submitted jobs. We submitted 1500
jobs to every resources using traditional First Come
First Served scheduling mechanism and we
randomly simulated jobs whose execution will fail
by a resource. Figure 2 shows the experiment
results.
Figure 2: Number of Successes by each resource.
Step 2 : Introducing Delay in Job Execution
In our second experiment we tried to evaluate the
reliability of the resource with respect to executing
the job in time. With our experience in grid, some
computing resources shut down during job execution
and this issue originates a delay that affects the
reputation of the resource. In this experiment, we
simulated random delays in job executions as
follows. The resource with id 25 was given no delay
and it executed all the jobs in 331ms. The delay
caused by the remaining resources was evaluated
with respect to this execution time, being jobs of
similar complexity. Figure 3 shows the results of this
experiment.
ASSESSING REPUTATION AND TRUST IN SCIENTIFIC GRIDS
237
Figure 3: Execution time taken by each resource.
Bad
(Cluster2)
Sufficient
(Cluster4)
Medium
(Cluster 0)
High
(Cluster3)
VeryHigh
(Cluster1)
15 16
14
24
18
19
23
21
8
9
10
20
11
3
5
6
7
22
1
4
17
12
13
2
25
Bad
(Cluster2)
Sufficient
(Cluster4)
Medium
(Cluster 0)
High
(Cluster3)
VeryHigh
(Cluster1)
15 16
14
24
18
19
23
21
8
9
10
20
11
3
5
6
7
22
1
4
17
12
13
2
25
Figure 4: Reputation Levels.
Step 3: Assessing Reputation Levels
Results of previous steps provided a basis for
grouping resources into five clusters, each labelled
with a reputation level (i.e. very high, high, medium,
sufficient, bad). In detail, we stored both the values
of success and execution time attributes in a single
dataset that served as a training set for the
application of the k-means algorithm. Every cluster
represents the category of resource which will
eventually help the grid scheduler to make resource
selection based on their reputation score. Figure 4
shows the experiment results.
5 CONCLUSIONS
The key objective of this paper was to incorporate
the reputation in decision making for scheduling
resources . The proposed RMS considers the past
resource behaviour and takes advantage from a data
mining approach to extract knowledge from the
operative context that measures the resource
capability in providing available and reliable
services. The merit of the proposed approach lies in
the fact that the reputation is automatically asserted
from data that directly incorporates information
about the resource behaviour. As future work, we
plan to further specify possible properties of the
ROC and to implement the proposed reputation
model in a real scientific Grid.
ACKNOWLEDGEMENTS
Part of this work was supported by MIUR under the
project PON-Cybersar.
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