Grid resource discovery a survey and comparative analysis 2

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 3, May – June (2013), © IAEME
550
GRID RESOURCE DISCOVERY: A SURVEY AND COMPARATIVE
ANALYSIS
Arjun Singh
Computer Science & Engineering Research Scholar at SGVU Jaipur,
Assistant Professor at SPSU Udaipur
Dr. Prasun Chakrabarti
Computer Science & Engineering
Associate Professor, SPSU Udaipur
Surbhi Chauhan
Computer Science & Engineering
Research Scholar
ABSTRACT
Grid computing is evolving as a sustainable choice for high-performance computing,
as the sharing of nodes or resources provides enhanced performance at a low cost compare to
individual machines computation dedicated for some task. Resource discovery is very
important phase of grid computing deployment. It is very complex and difficult to discover a
node because of the geographical resource dispersion and dynamic nature of the grid
topology. Discovery process is very critical to manage and allocate the resources. This paper
provides a depth insight of the different resource discovery approaches in grid computing.
This survey is based on ongoing research on node or resource discovery from 1997 to 2013.
At the end paper also present a comparative analysis of various approaches.
I. INTRODUCTION
A. Computational Grid
Many people terms the ‘Grid’ offers a potential means of surmounting these obstacles
to progress [1]. The computational grid is a new class of infrastructure which built on the
Internet and the World Wide Web and provides high performance, secure and scalable
mechanisms for discovering and negotiating access to remote resources, the Grid assurances
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ISSN 0976 – 6367(Print)
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Volume 4, Issue 3, May-June (2013), pp. 550-559
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551
to make it promising for scientiﬁc collaborations to share resources on an unparalleled scale,
and for purely distributed groups to work together in ways that were previously impossible.
In 1965, Fernando Corbato and other developers of the Multics Operating System had
anticipated a computer structure, which is same as of power grid and a water grid. J. C. R.
Licklider and Robert W. Taylor in 1968 thought a Grid-like infrastructure in their research
paper The Computer as a Communications Device [2]. In 1998, Ian Foster explained details
purpose, shape, and architecture of a computational grid. In Ian Foster discussed six main
important factors such as why grid computing is essential, for what kind of work it is
required, what type of applications it is needed, How the Grid infrastructure will be used,
who will the stake holders in grid, how they will be benefited, how to build the grid and what
kind of difficulties will be confronted to design a grid. Ian Foster proposed various aspects to
design and maintain a heterogeneous large distributed computer network called grid.
In Computational environment it is essential to describe the resources. Resources can be
computers, online instruments, data, information, storage, routing devices and resource
discovery mechanism which returns desired resources after matching based on certain
parameters.
The main objective of this paper is to get in-depth information of various approaches
used in resource discovery.
B. Need of Resource Discovery
Resource discovery is a challenging task in large heterogeneous and distributed
network compare to the traditional computer network [3]. There are several factors such as
large number of users with heterogeneous hardware and software platform, large pool of
resources and heterogeneity in the user’s request, makes resource discovery difficult. There
are more complicated factor which contribute in the difficult node discovery. For example,
some organization leaving and joining the grid network and resources (dynamic nature),
different administrative domains, availability of resource and CPU load changing. So, an
appropriate and effective resource discovery mechanism is very crucial aspect of Grid
computing. Success of a grid computer infrastructure lies in finding desired resource for a
specific task.
C. Components of Resource Discovery
Iamnitchi, I. Foster, and Daniel C. Nurmi identified four components of general
resource discovery [4]-
a. Membership Protocol: Membership Protocol defines that how new nodes join the
network and how do they learn about each other.
b. Overlay Construction function: It selects the group of active collaborators from
local membership list.
c. Preprocessing: It refers to the offline preparations for better search performance,
independent of request.
d. Request processing: Request processing can have local and remote processing
components.
Apart from this author also identified few environmental parameter factors, which control the
performance and design strategies for a resource discovery solution. These factors are
a. Resource information distribution and density: It states that different load of
information sharing on different nodes.

552
b. Resource information dynamism: It indicates the different attributes of resources,
which are dynamic and fixed in nature.
c. Request popularity distribution: It denotes to possible closer uniform distribution of
popular requests.
d. Peer participation: The peer membership depends on different type of networks.
D. Grid Resource Managemtn Model and Algorithms
Rajkumar Buyya discussed three different models for grid resource management
architecture inspired by three different philosophies such as hierarchical model, abstract
owner model and (computational) market model [5]. The hierarchical model captures the
approach followed in many contemporary grid systems. Authors shows the potential of an
order and delivery approach in job submission and result gathering. The (computational)
market model captures the essentials of both hierarchical and abstract owner models and uses
the concept of computational economy. Klaus Krauter ,Ra jkumar Buyya, and Muthucumaru
Maheswaran acknowledged some important resource management approaches to project a
comprehensive resource management system [6]. The authors explained the resource
management system as the core component of a network computing system. The authors
discussed several dimensions of resource management such as quality of service (QoS) issue,
different scheduling approaches, different heterogeneity issues, different resource distribution
approaches and resource discovery technique. The authors propose that a resource
management system can uphold a replicated network directory, which contain resource
information and then resource discovery function queries the “resource dissemination
function” for a specific resource. There are many algorithms for resource discovery algorithm
used in node discovery.
In 1999, Harchol and Balter [7], discussed the flooding algorithm, used in internet
routers to find the routes and routers. In this algorithm, a machine is configured to have a
fixed set of neighboring machines, and only direct communication is allowed with machines
in this set. In terms of the graph, a node only communicates over the edges that were initially
in the graph; new edges that are added to the graph are not used for communication. Those
edges that constitute the initial neighbors are not necessarily the links in the underlying
physical network, but somewhat they are virtual links, every possibly corresponding to a path
in the underlying network. The number of rounds of this algorithm is equivalent to the
diameter of the graph. Author claimed that this algorithm can be very slow if it is not started
with a small diameter graph.
Harchol and Blater [7] also discussed the swamping algorithm. This algorithm is same
as of flooding algorithm except that t that machines may now open connections with al l their
current neighbors, not just their initial neighbors. Also since the neighbor sets change, al l of
the current neighbor set is transferred, not just the updates. The advantage of the Swamping
algorithm is that the graph always converges to a complete graph in O (log n) steps,
irrespective of the initial configuration.
In 2012, Mehajabeen Fatima and Roomap Gupta present a new approach for node
discovery called Route Discovery by Cross Layer Approach for MANET [8]. In new
generation communication, mobile or other network is expected that Mobile adhoc Network
(MANET) can be used cautiously anywhere any time. But pervasive computing MANET
face lots of methodical challenges. One of them is traditional architecture. The Traditional
architecture is hierarchical model of TCPIP which is used for networking. Traditional
hierarchical layered design of network protocols is uncompromising to handle the dynamics

553
of Mobile adhoc Network. Cross-layer design breakdowns from traditional network design.
In traditional network design, each layer of the protocol stack operates independently and
information is exchanged only between adjacent layers of the protocol stack. Because of the
direct dependencies between the physical layer and the upper layers, the traditional protocol
stack is not sufficient for mobile adhoc network. The cautious exploitation of protocol
interactions that cross the normal layer boundaries can improve the performance of the
communication and hence better application-layer performance. The dependent design
between the layers blur the boundary between two adjacent layers.
Link breakage is of high probability in mobile wireless adhoc networks because of
highly dynamic nature of MANET. This happens because of limited battery power, mobility
and limited transmission range. It becomes difficult to maintain continuous links in the
networks. In AODV, node sends a RERR message for the specified destination when it
noticed that a link rupture has taken place. It hampers the stability of the link and results in
link rupture, in loss of few packets and escalation in delay. Link breakage, packet loss and
delay is not acceptable in real time communication and loss of packets, excessive overhead is
not acceptable in non-real time communication. So AODV is improved to manage with these
problems. An algorithm with cross layer approach is suggested which helps in maintaining
the continuity of the network resulting in less delay, more throughput, less overhead and less
battery power consumption which can be used for both real and non-real time
communication.
Shay Kutten and David Peleg proposed an algorithm for resource discovery [9]. With
reference to to the randomized algorithm of Harchol Balter, time complexity is scale down to
O (log2
n) to O (log n). This algorithm takes O(n log n) time compare to O (n log2
n) for
message complexity.
Congfeng Jiang , Xianghua Xu, and Jian Wan proposed replication based job scheduling in
Grids with Security Assurance [10] . Security assurance is a perilous prerequisite for QoS or
Service level agreement satisfactions in dynamic grid environments. Jobs may be scheduled
to numerous machines through dissimilar distributed administrative domains. Instead of
traditional methods using fixed-number job replications, author proposed a security-aware
parallel and independent job scheduling algorithm based on adaptive job replications. It make
sure the job scheduling decision, reliable, secure and fault tolerant. In dynamic and error-
prone grids, the replication number is changed as per the current security conditions and the
end-user settings.
Tung-Shih Su, Chih-Hung Lin and Wen-ShyongHsieh proposed a QoS-aware routing
protocol [11] that includes an admission control system into route discovery and route setup.
When a node need to find optimal route to a destination, it uses the information that collected
from a card with another signal for QUART-DD. For the route discovery and route setup an
admission control system is used. Before selecting a path system waits for some time to
measure the best possible path. After selecting best path it sends out the packets.
II. RESOURCE DISCOVERY APPROACHES
There are various approaches specified and proposed by various scientist and
organization for resource discovery. Below section discuss these approaches in detail.
Resource Discovery is methodical process of defining which grid resource is the best
resource to complete a task with following parameters:
• It should take shortest amount of time to complete the job.

554
• It should make optimal and efficient use of resources
• Cost of computation should be minimum.
Main issue of grid deployment is resource discovery. Dynamic and heterogeneous
nature of nodes and network make it complicated task. Many approaches are proposed in
various research paper for resource discovery in grid environment. All proposed approaches
are based on query and software agent technology. In Query based system, to discover the
node a request (query) is forwarded to neighbor node for resource availability. Most of the
grid systems use this approach. Query based system are characterized based on whether the
query is executed for a centralized database or a distributed database. Resource Discovery
can be considered in two forms: Query Based Resource Discovery and Agent Based
Resource Discovery. Agent based approach send software code across machines in the Grid
that are inferred locally on each machine. Agents are software components with autonomy,
intelligence and response. Agents interact with neighbor nodes and on behalf of its users they
can accomplished the task.
The basic difference between a query based approach and an agent based approach is
that agent based systems allow the software code (agent) to control the query procedure and
based on its intelligence makes resource discovery decisions. Agent does not depend on fixed
function query engine. Agent based resource discovery is essentially distributed. In this
subsequent subsections we have discussed resource discovery approaches like decentralized,
agent based approach, routing transferring model-based approach, ontology description based
system, parameter-based approach, quality of service based approach and request forwarding
approach.
A. Decentralized Approach
Iamnitchi and Rana proposed decentralized approach in 2001[15]. This approach
described decentralized resource discovery and management architecture based on software
agents. These agents can be a service or an application. This approach provide dynamic
registration of nodes and task. According to this research paper, basically this approach is a
match making approach, which helps in dynamic resource discovery and their management in
grid environment. Proposed system use XML documents for resource capability and resource
availability.
B. Agent Based Approach
Kyungo Jun and L. Boloni proposed agent based resource discovery [16]. This paper
present a distributed discovery method called Distributed Awareness Algorithm. Distributed
Awareness is a learning system, in which a nodes gets aware about it neighbor in the
network. Each node maintain the awareness table and exchange this table with neighbor
node. This table have several information such as, node location (IP address), information
when the last time awareness table was sent to other nodes and when the last time heard from
other nodes. Author also claimed that by using agent‘s independent behavior this agent based
resource discovery provides better discovery compare to other system.
C. Routing Transffering Model Based Approach
Wei Li, Zhiwei Xu, Fangpeng Dong, Jun Zhang proposed a Routing-Transferring
resource discovery model [17]. This model have three basic components: resource requester,
the resource router and the resource provider. The provider sends its resource information to
a router, which store this information in routers’ tables. When requester demand a resource

555
from router, router checks it table and find appropriate resource. Once router finds the
resource, it forward it to the requester.
Author formalized this model and analyze the complexity of the SD-RT (Shortest Distance
Routing-Transferring) algorithm. Author claimed that the resource discovery time depends on
topology and distribution of resources. When topology and distribution are definite, the SD-
RT algorithm can find a resource in the shortest time. Experiments shows that when topology
is definite, the performance is determined by resource distribution, which includes two
important factors: resource frequency and resource location.
D. Ontology Discription Based Approach
Ontology refers to a description of a resource service, Ludwig proposed a semantic
service discovery framework in a grid environment [18]. Author proposed a service
matchmaking mechanism based on ontology knowledge and they claimed that this
matchmaking framework can provide a better service discovery and also can provide close
matches. The main idea behind this approach is the advertisement of the resource. In this
approach, service provider registers its service description into the service registry database.
When a Grid application sends a request to service directory, matchmaker returns the matches
to the service requester. Requester chooses the best resource based on the specific need.
E. Parameter Based Approach
M. Maheswaran and K. Krauter inspected different approaches for resource discovery
in a grid environment. Author proposed a new model, Grid potential, which has the
processing capabilities of resources in a large network. The authors also discussed an
algorithm called Data Dissemination Algorithm. Algorithm follows swamping approach for
message distribution. A message gets validated when it comes to the node. The validation
procedure depends on three types of distribution, universal awareness which permits all
incoming messages, neighborhood awareness that allows messages from a certain distance,
and distinctive awareness, and it drop the messages if it finds out that the less Grid
potentiality at the local node in remote node, is less than that of the requestor node. The
authors also measured the performance of neighborhood awareness, universal awareness, and
distinctive awareness dissemination schemes. The authors claimed that universal approach is
more expensive in terms of message complexity than that of neighborhood and distinctive
approach. The authors also claimed that this new class of dissemination could reduce the
communication overhead during the resource discovery.
F. Quality Of Service Based Approach
Huang proposed an algorithm to discover the occasionally available resources in a
multimedia environment [20]. For a given graph theoretical approach authors define
different policies for a QoS based resource discovery service. A generalized version of
Discovering Intermittently Available Resources (DIAR) algorithm based on occasionally
available resources is presented in the paper. The Author evaluated the performance of QoS
policies based on different time-map strategies in a centralized system.
There are various QoS parameters include storage capacity, processor runtime, network
bandwidth. On these basis, parameters QoS guarantees the best behavior of grid. By
experimental study author found that randomized placement strategies and increased server
storage can provide better performance for resource discover.

556
G. Request Forwarding Approach
Iamnitchi identified following four-request forwarding approaches [15].
i. Random Walk Approach: In this Approach, A node is chosen randomly to forward the
request.
ii. Learning-Based Approach: In this approach a request is send to a node who answered
same kind of request before. In case, if there is no similar answer is found, the request
is forwarded to a randomly selected node.
iii. Best-Neighbor Approach: The number of received answer is recorded without
recording the type of requests. n. This Approach is identical to learning-based
Approach except when no similar answer is found, request is forwarded to the best
neighbor. The resource discovery mechanism in an emulated grid, which is a large
grid network (for this case up to 5000 peers) based on the assumption that every peer
provides at least one resource is analyzed. The measured performance evaluation of a
simple resource discovery technique is based on request propagation.
iv. Learning-Based + Best-Neighbor Approach This Approach is identical to learning-
based Approach except when no similar answer is found. Request is forwarded to the
best neighbor.
H. Peer-to-Peer Approach
Iamnitchi proposed a Peer-to-Peer resource discovery architecture for a grid
environment [15]. This resource discovery architecture can reduce administrative overhead
and it can also provide effective search-performance result. Author point out various resource
discovery problems in a very large distributed resource-sharing environment [4]. This paper
identify four different architectural components namely Membership protocol, Overlay
construction, Preprocessing, and Request processing. Paper also described four environment
parameter factors, which govern the performance and design strategies are Resource
information distribution and density, Resource information dynamism, Request popularity
distribution and Peer participation. Author gives brief description of different resource
discovery Approaches in Peer-to-Peer networking is described [4]. The authors claimed that
using four axes framework; it is possible to design any resource discovery architecture in a
grid.
A general purpose query support enabled “Unified Peer-to-Peer Database Framework
(UPDF)” for a large distributed system has been proposed [21]. UPDF can be recognized as a
Peer-to-Peer database framework for a general purpose query support. It is unified because it
supports arbitrary random node topologies, query languages, different query response modes,
different data types, different neighbor selection policies for expressing specific applications.
III.COMPARISON
Based on different parameters such as reliability scalability, adaptability, and
manageability, a comparison between the Resource Discovery Approaches are done in table
1. To find an optimal and best resource on the grid, various parameters required to consider to
deal with complexity of resource discovery. Resource discovery becomes complex with the
increasing size of grid. Peer-to-Peer is the best approach to be used in the large global grids.
It uses the graph theoretic approach to realize scalability and manageability. Routing
Transferring based resource discovery approach can also be used for large grids. It is reliable
and scalable but create more overheads on the network. Ontology based approach
matchmaking algorithm, which limited its scalability.

557
Table 2.1
Comparison of different Resource Discovery Approaches
Approach Ontology
Description
Routing
Transferring
Parameter Quality of
Service
Request
Forwarding
Peer-to-
Peer
Type of
approach
Agent Query Agent Query Agent/Query Agent
/Query
Scalability Scalability
limited due to
centralized
broker
Scalable, use
routing
protocol
Scalable due
to grid
potential
concept used
Scalable,
Use time
Map
strategies
in
centralized
system
Due to
random
selection of
nodes,
it is scalable
More
scalable as
it uses the
four
axes
framework
Reliability Failures are
detected as
soon as they
occurs so
more reliable
Quite reliable
as it uses
the routing
concept
Reliable as we
can add
or delete a
node from
anywhere
Considers
parameters
like
network
bandwidth,
required
CPU,
storage
capacity,
that make it
less
reliable
Random
walk
Approach
make it
reliable in
case the
resources are
equally
distributed
Based on
graph
theory so
reliability
increases
Adaptability Can be made
adaptable
by providing
manager
information
about
different
platforms
Routing table
is used to
make records
of different
platforms.
Adaptable due
to
universal,
network and
distractive
awareness
parameters
Depends
upon the
Service
Level
Agreement
(SLA)
sign with
user for
providing
adaptability
Using Best
neighbor
Approach
adaptability
is easy
Multiple
platforms
environment
make it
more
adaptive
Manageability Quite easy to
manage as a
lot of its
working is
dependent on
single node.
Management
is easy
due to SDRT
algorithms as
it deals
with different
topologies
Manage the
consistency
by using
the data
dissemination
algorithms
Uses
algorithm
like
DIAR for
the
resource
discovery
Better
Management
can be
achieved by
combining
its two sub
approaches.
Complex
architecture
hence
difficult to
manage.
Complexity O(log log n) Θ(n)1/2 (n) (log2n) Θ(n) O(log n)
Development
Agent
OWL, RDF C Java, C Any
Description
Language
C DHT
Algorithm
Used
Matchmaking,
Gang
matchmaking
SDRT,
Routing
Dissemination DIAR Request
Forwarding
Algorithm
Swamping

558
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