Adil Yousif, Abdul Hanan Abdullah
Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, Johor, Malaysia
Muhammad Shafie Abd Latiff, Mohammed Bakri Bashir
Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, Johor, Malaysia
Grid computing, Resource selection, Resource management and bidding.
Resources in grid systems are heterogeneous, geographically distributed, belong to different administrative
domains and apply different management policies. To manage resources in grid computing, efficient resource
selection mechanism is required. In traditional non-reserved resource selection mechanisms, there is no guar-
antee that the job completion time will be as expected. On the other hand, in the reserved bidding process,
conventional resource selection mechanisms waste providers resources and hence affect the overall grid sys-
tem performance. We propose a new grid resource selection mechanism to achieve performance enhancement
in the resource allocation process.
Grid computing emerged in the middle of 1990s as a
wide-scale distributed system to offer dynamic coor-
dinated resources sharing and high performance com-
puting (Foster and Kesselman, 2004). Grid technolo-
gies have evolved over the past two centuries from
primary metacomputing into Open Grid Service Ar-
chitecture(OGSA) using the service oriented architec-
ture (SOA) Concepts.
Grid computing as defined previously differs with
current internet technologies, as the current inter-
net technologies handle the process of exchanging
data between internet users, but they do not sup-
port methods for coordinated employment of several
resources at different internet locations for compu-
tation (Iamnitchi and Foster, 2001). CORBA and
Enterprise Java as distributed computing technolo-
gies differ with grid computing as they allow shar-
ing of resources inside one institute or company but
not with other outer institutes or companies (Harold
and Loukides, 2000; Iamnitchi and Foster, 2001).
In Storage Service Providers(SSP) and Application
service Providers(ASP), Institutes are permitted to
serve outside clients but this occurs in restricted man-
ners, such as using special types of network connec-
tions (Iamnitchi and Foster, 2001). According to the
previous review current technologies do not handle
all resource categories or do not support the manage-
ment and flexibility needed to build the coordinated
resource sharing among several institutes and organi-
This paper is organized as follows: Section 2 de-
scribes the resource management in grid computing;
this is followed, in Section 3, by a discussion of the
related works in the area. Details of the proposed
mechanism are presented in Section 4; followed by
a conclusion and discussion.
2.1 Overview
Resources in grid systems are heterogeneous, geo-
graphically distributed, belong to different adminis-
trative domains and apply different management poli-
cies (Ahmed et al., 2007; Chang et al., 2011). Further-
more, grid management systems do not have full con-
trol over the resources that belong to the grid (Schni-
zler, 2007). Czajkowski et al. (2002) defined the term
resource to denote any capability that may be shared
and exploited in a networked environment. The re-
sources and services may differ in the form of func-
tionality that they offer to the user, but both are sim-
ilar in the way that they provide the functionalities
Yousif A., Hanan Abdullah A., Shafie Abd Latiff M. and Bakri Bashir M..
DOI: 10.5220/0003606202260229
In Proceedings of the 6th International Conference on Software and Database Technologies (ICSOFT-2011), pages 226-229
ISBN: 978-989-8425-76-8
2011 SCITEPRESS (Science and Technology Publications, Lda.)
Figure 1: Grid Resource Broker.
to the users (Foster and Kesselman, 2004; Andrieux
et al., 2004); hence we can use the term resource more
generally to indicate all types of ancient resources, as
well as services.
In conventional computing systems, such as PCs,
cluster systems and other high performance comput-
ing, resource management problem has been stud-
ied intensively and a number of different types of
management systems are implemented such as batch
scheduler, workflow engine and local operating sys-
tems. These systems have full control over resources
that follow them and also have a full understanding of
all information on those resources (Schnizler, 2007).
The local recourse management systems are not capa-
ble of managing grid computing resources since the
heterogeneity of resources and environments in grid
networks. Moreover, grid management systems do
not have the full control over the resources that they
manage (Schnizler, 2007). Resource management in
grid environments is a great challenge; this is due to
the heterogeneity of resources in grid environments
and besides that, the resources belong to diverse ad-
ministrativedomains and apply different management
policies (Wang et al., 2010).
2.2 Resource Brokers
In the decentralized resource broker systems, each
grid user has its own broker to access the avail-
able grid resources. All resources that participate in
the grid system must register in one or more index
servers. However, these index servers have the abil-
ity to register in other index servers. The discovery of
available grid resources is made by the users resource
brokers through communicating between client bro-
ker with one or multiple index servers and the result
of this process is a list of all available resources that
satisfied the user needs. If the resource broker needs
more information, it can contact the resource provider
directly. Before the submission of the user jobs, the
user broker select suitable resources from the avail-
able resources depending on some selection criteria
such as the Quality of service (QoS) requested by
the user, and locations and policies of the resource
providers (Krauter et al., 2002; Elmroth and Tords-
son, 2008). Figure 1 describes decentralized resource
broker process.
2.3 Co-allocation and Advance
In some scenarios grid clients request more than one
resource at the same time to perform a specific task,
part of those resources is meaningless to the client.
Accordingly, the resource allocation system should
find a way to provide those resources all together
as a bundle. The process for requesting a number
of homogeneous resources concurrently by the grid
clients is called Co-Allocation; therefore, the alloca-
tion manager should support a co-allocation mecha-
nism (Schnizler, 2007; Elmroth and Tordsson, 2009).
Advance reservation is an important issue arise
when grid clients request grid resources at a certain
time in future; for instance, a grid client demands pro-
cessor after two hours, and in some cases the grid
client needs resources for a certain period of time
Figure 2: Matchmaking Approach.
in the future, such as a client requested memory af-
ter two hours and for one hour. Some grid clients
need resources in a time in the future, but the time
is not well-defined; for instance, a grid client needs
resources after two or three hours. Therefore, the al-
location management system must comprise a way for
the grid clients to describe the time in which they need
the resources precisely; and this may also help the
resource providers to clarify at which times their re-
sources will be idle in the future (Elmroth and Tords-
son, 2008, 2009) .
Due to the heterogeneity of grid resources, there is
a need to describe those resources in a standard way
so that providers and grid clients can allocate them
in consistent way (Park and Kang, 2007; Yuesheng
et al., 2009). There are two types of resource se-
lection approaches for grid environments, matchmak-
ing approach and bidding based approach. In match-
making approach (Figure 2) resource providers de-
scribe and register their resources in the matchmaker
database. When a grid client requests resources to
perform tasks, it determines the properties of those
resources to the matchmaker (Yuesheng et al., 2009);
and the matchmaker will perform a matching algo-
rithm to match the resources to tasks and sends noti-
fication to the grid client and to the selected resource
provider (Wang et al., 2010). The resource providers
update their resources states to the matchmaker peri-
odically and any changes occur between the updating
processes is not reflected in the matchmaker. More-
over, the matchmaker like other centralized systems
represents a performance bottleneck and single point
of failure (Wang et al., 2010).
The idea behind bidding based approach arose to
avoid the matchmaking drawbacks. In the bidding
process when a grid client needs resources to execute
certain jobs, it forwards a call for proposal CFP mes-
sage to grid providers (Hongbo et al., 2005; Wang
et al., 2010). Upon receiving the CFP messages, re-
source providers decide whether or not to join the
bidding process according to their facilities and re-
sources characteristics. If a grid provider partici-
pated in a bidding process it dispatches a bid to the
client. The client assesses all the received bids and
chooses the best bid according to a specific bidding
algorithm (Habibi and Jafar, 2009; Vilajosana, 2008;
Wang et al., 2010).
Resource securing in grid resource allocation pro-
cess can be achieved in two different ways; In re-
served securing and non-reserved resource securing
(Wang et al., 2010). In reserved method the resource
providers keep the resources for the grid client’s bids;
so it can guarantee the future status of the resources
(Haji et al., 2006). However, this method wastes re-
sources because more than one resource provider may
keep their resources for a single bid and at the end the
grid client will select only one resource and reject the
others; and so those rejected resources are wasted at
the time that they have been kept for that client’s bid
(Wang et al., 2010).
On the other hand, in non-reserved resource secur-
ing method the resource providers do not keep the re-
sources for any bid; and hence the resource providers
can utilize their resources effectively; nevertheless,
this method does not promise the future status for the
resources; and hence there is no guarantee that the job
completion time will be as expected (Haji et al., 2005;
Yousif et al., 2011).
We propose a grid resource selection technique to
tackle the problems of conventional reserved and non
reserved bidding approaches; following is a brief de-
scription of our mechanism. In our mechanism when
a grid client requests resources, it create a call-for-
proposal CFP messages and forwards it to all avail-
able resource providers in the grid system and wait
for a certain time interval for bids. Upon receiving
the CFP messages, resource providers decide whether
or not to join the bidding process according to their
facilities and the resource’s characteristics. If a re-
source provider participated in a bidding process it
dispatches a bid to the client and reserves the corre-
sponding resources for that bid. The bids arrive at the
grid client one by one; and the clients have to assess
the received bids and make judgments immediately.
At the beginning the first bid arrives at the grid client
is considered as the best bid; and when the next bid
arrives at the client, the client compares the current
received bid with the best bid, if the current received
bid is better than the best bid the client will consider
ICSOFT 2011 - 6th International Conference on Software and Data Technologies
the current received bid as the best bid and notifies the
first resource provider that its bid is rejected and then
the provider will free the resources. However, if the
old best bid is better than the current received bid the
grid client simply sends reject notification message to
the new provider to free its resources.
This paper presents a new resource selection mecha-
nism in a bidding based grid system to enhance the
process of resource selection in an effective and effi-
cient way. The proposed mechanism tackles the prob-
lems arise when using traditional reserved and non-
reserved resource selection mechanisms. Our algo-
rithm keeps the most suitable resource for executing
the job as a promise, and therefore, the job comple-
tion time will be as estimated. Furthermore, since
the proposed mechanism reserves only one resource,
resource providers can utilize their resources effec-
tively. Now we are in the implementation phase, and
we plan to simulate the mechanism in a grid simulated
environmentusing GridSim as a simulator, to evaluate
the performance of the proposed mechanism.
Ahmed, A., Latiff, M., Bakar, K., and Rajion, Z. (2007).
Adaptive resources selection framework for grid en-
abled visualization pipeline. IJCSNS, 7(12):114.
Andrieux, A., Czajkowski, K., Dan, A., Keahey, K., Lud-
wig, H., Pruyne, J., Rofrano, J., Tuecke, S., and Xu,
M. (2004). Web services agreement specification (ws-
Chang, R. S., Lin, C. F., and Chen, J. J. (2011). Selecting
the most fitting resource for task execution. Future
Generation Computer Systems, 27(2):227–231.
Czajkowski, K., Foster, I., Kesselman, C., Sander, V., and
Tuecke, S. (2002). Snap: A protocol for negotiating
service level agreements and coordinating resource
management in distributed systems. pages 153–183.
Elmroth, E. and Tordsson, J. (2008). Grid resource bro-
kering algorithms enabling advance reservations and
resource selection based on performance predictions.
Future Generation Computer Systems, 24(6):585–
Elmroth, E. and Tordsson, J. (2009). A standards-based grid
resource brokering service supporting advance reser-
vations, coallocation, and cross-grid interoperability.
Concurrency Computation Practice and Experience,
Foster, I. and Kesselman, C. (2004). The grid: blueprint for
a new computing infrastructure. Morgan Kaufmann.
Habibi, B. N. and Jafar (2009). Bidding strategically for
scheduling in grid systems. Journal of Information
Processing Systems, 5(2):87–96.
Haji, M., Djemame, K., and Dew, P. (2006). Deployment
and performance evaluation of a snap-based resource
broker on the white rose grid. volume 2, pages 3365–
3370. IEEE.
Haji, M. H., Gourlay, I., Djemame, K., and Dew, P. M.
(2005). A snap-based community resource broker us-
ing a three-phase commit protocol: A performance
study. Computer Journal, 48(3):333–346.
Harold, R. and Loukides, M. (2000). Java network pro-
gramming. O’Reilly & Associates, Inc. Sebastopol,
Hongbo, Z., Hai, J., Zongfen, H., Jing, T., and Xuanhua,
S. (2005). A virtual-service-domain based bidding al-
gorithm for resource discovery in computational grid.
In Web Intelligence, 2005. Proceedings. The 2005
IEEE/WIC/ACM International Conference on, pages
Iamnitchi, A. and Foster, I. (2001). On fully decentralized
resource discovery in grid environments. Grid Com-
putingGRID 2001, pages 51–62.
Krauter, K., Buyya, R., and Maheswaran, M. (2002). A tax-
onomy and survey of grid resource management sys-
tems for distributed computing. Software: Practice
and Experience, 32(2):135–164.
Park, J. and Kang, J. (2007). Optimization of xquery queries
including for clauses. page 37. IEEE.
Schnizler, B. (2007). Resource Allocation in the Grid: A
Market Engineering Approach. Univ.-Verl. Karlsruhe.
Vilajosana, X. (2008). Bidding support for computational
resources. pages 309–314. IEEE.
Wang, C.-M., Chen, H.-M., Hsu, C.-C., and Lee, J.
(2010). Dynamic resource selection heuristics for a
non-reserved bidding-based grid environment. Future
Generation Computer Systems, 26(2):183–197.
Yousif, A., Abdullah, A. H., and Ahmed, A. A. (2011). Ar-
ticle: A bidding-based grid resource selection algo-
rithm using single reservation mechanism. Interna-
tional Journal of Computer Applications, 16(4):39–
43. Published by Foundation of Computer Science.
Yuesheng, T., Lu, H., and Jingyu, W. (2009). Research
of the xml-based personal resource description in grid
computing. volume 2, pages 639–641. IEEE.