IACSIT International Journal of Engineering and Technology Vol. 2, No.1, February, 2010
ISSN: 1793-8236
57
Abstract—Load balancing system is commonly used for
highly efficient utilization of the physical or logical resources
and enhancing the performance of the distributed systems
and scalability of the Internet. Numerous proposals exist for
load balancing system in peer-to-peer networks, but it does not
mainly address the security issues. Load balancing among the
peers is critical to provide a solution for distribution of
resources with security. These paper propose the three
methodology for addressing an architecture for load balancing
system with security. First, it address an architecture for mobile
agent to roam all the nodes in an distributed network and.
second, it address an architecture to rearrange the loads among
the peers for better performance of the distributed system.
Third, and perhaps more significantly, address the mobile
agent to provide the security in a network.
Index Terms—Structured peer-to-peer system, Load
balancing system, Mobile Agent, Intrusion detection system,
Virtual Server Reassignment problem.
I. INTRODUCTION
Distributed computing deals with hardware and software
systems containing more than one processing element or
storage element, concurrent processes, or multiple programs,
running under a loosely or tightly controlled regime [2]. In
distributed computing a program is split up into parts that run
simultaneously on multiple computers communicating over a
network. Distributed computing is a form of parallel
computing, but parallel computing is most commonly used to
describe program parts running simultaneously on multiple
processors in the same computer [9]. Both types of
processing require dividing a program into parts that can run
simultaneously, but distributed programs often must deal with
heterogeneous environments, network links of varying
latencies, and unpredictable failures in the network or the
computers. Load balancing is a method to distribute the work
between two or more computers, network links, CPUs, hard
drives, or other resources, in order to get efficient resource
utilization, maximize throughput, and minimize response time.
With the multiple components, instead of a single component,
may increase reliability through redundancy for load
balancing. The balancing service is usually provided by a
dedicated program or hardware device (such as a multilayer
switch). Most common applications of load balancing is to
provide a Internet service from multiple servers [4]. Commonly
Department of Computer Science, Pondicherry University,Pondicherry,
India. (e-mail: Ezumalai1984@gmail.com, aghilaa@yahoo.com,
lakshmi22006@yahoo.co.in).
load- balanced systems include popular web sites, large
Internet Relay Chat networks, high-bandwidth File Transfer
Protocol sites, NNTP servers and DNS servers.For Internet
services, the load balancer is usually a software program which
is listening on the port where external clients connect to
access services.
PEER-TO-PEER (P2P) systems make it possible to
hardness resources such as the storage, bandwidth, and
computing power of large populations of networked
computers in a cost-effective manner. In structured P2P
systems, data items are spread across distributed computers
(nodes), and the location of each item is determined in a
decentralized manner using a distributed hash lookup table
(DHT) [1]. Structured P2P systems based on the DHT
mechanism have proven to be an effective design for resource
sharing on a global scale and on top of which many
applications have been designed such as file sharing,
distributed file systems [2], real-time streaming, and
distributed processing [6] and In computer science, a software
agent is a piece of software that acts for a user or other program
in a relationship of agency.Such "action on behalf of" implies
the authority to decide which (and if) action is appropriate.
The idea is that agents are not strictly invoked for a task, but
activate themselves. A Multi Agent system is a system
composed of multiple interacting intelligent agents.
Multi-agent systems can be used to solve problems which are
difficult or impossible for an individual agent or monolithic
system to solve. Examples of problems which are
appropriate to multi-agent systems research include online
trading, disaster response, and modelling social structures.
The agents in a multi-agent system have several important
characteristics:
1) Autonomy: Agents are at least partially autonomous.
2) Local views: There is no agent has a full global view of
the system, or the system is too complex for an agent to
make practical use of such knowledge
3) Decentralization: In Decentralization environment,
agents are to share the knowledge and information and
no agent will control another agent. Multi-agent systems
research refers to software agents. However, the agents in
a multi-agent system could equally well be robots,
humans or human teams [4]. A multi-agent system may
contain combined human-agent teams. Multi-agent
systems can manifest self-organization and complex
behaviors even when the individual strategies of all their
agents are simple. The remainder of the paper is
organized as follows: Section 2 contains related work;
Design and Architecture for Efficient Load
Balancing with Security Using Mobile Agents
R. Ezumalai, G. Aghila and R. Rajalakshmi
IACSIT International Journal of Engineering and Technology Vol. 2, No.1, February, 2010
ISSN: 1793-8236
58
Section 3 describes our proposed approach, Section 4
describes the conclusion and future work.
.
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II. RELATED WORK
There are many techniques is available to provide efficient
load balancing system, but it consumes a lot of resources for
distributing the work. This load balancing system does not
have centralized co- coordinator for allocating the resources
and security. But this architecture provides the way of
rearranging the loads efficiently with security. Many
techniques have been discussed below for load balancing
system with merits and demerits.
CFS [7] accounts for node heterogeneity by allocating to
each node some number of virtual servers proportional to the
node capacity. In addition, CFS proposes a simple solution to
shed the load from an overloaded node by having the
overloaded node remove some of its virtual servers.
However,this scheme may result in thrashing as removing
some virtual servers from an overloaded node may result in
another node becoming overloaded. Adler et al [9] present a
DHT which provably ensures that, as nodes join the system,
the ratio of loads of any two nodes is O(1) with high
probability. The system is organized as a tree, with additional
links for routing in a hypercube topology. A joining node
considers a small set of leaf nodes of the tree and joins the
system by splitting an appropriately chosen leaf. However, no
analysis of node departure was given and the system does not
deal with varying node capacity or object distribution.
Karger and Ruhl [10] propose algorithms which
dynamically balance load among peers without using multiple
virtual servers by reassigning lightly loaded nodes to be
neighbors of heavily loaded nodes. However, they do not fully
handle the case of heterogeneous node capacities, and while
they prove bounds on maximum node utilization and load
movement, it is unclear whether their techniques would be
efficient in practice. Douceur and Wattenhofer [11] have
proposed algorithms for replica placement in a distributed
filesystem which are similarin spirit with our algorithms.
However, their primary goal is to place object replicas to
maximize the availability in an untrusted P2P system, while
we consider the load balancing problem in a cooperative
system. Triantafillou et al. [12] have recently studied the
problem of load balancing in the context of content and
resource management in P2P systems. However, their work
considers an unstructured P2P system, in which meta-data is
aggregated over a two-level hierarchy.
Grosu, D [22] propose to designing protocols for resource
allocation involving selfish agents and design a truthful
mechanism for solving the static load balancing problem in
heterogeneous distributed systems. This paper prove that
using the optimal allocation algorithm the output function
admits a truthful payment scheme satisfying voluntary
participation. Miron Livny [2] propose an load balancing
algorithms for distributed systems that consist of a number of
identical processors and a CSMA communication system are
presented in this paper. Some of the properties of a
multi-resource system and the balancing process are
demonstrated by an analytic model. Simulation is used as a
mean for studying the interdependency between the
parameters of the distributed system and the behaviour of the
balancing algorithm.
Yu-Kwong [20] propose an approach works by using a
fuzzy logic controller which informs a client object to use the
most appropriate service such that load balancing among
servers is achieved. Authors chosen Jini to build our
experimental middleware platform, other related techniques
are implemented and compared. Extensive experiments are
conducted to investigate the effectiveness of our
fuzzy-decision- based algorithm, which is found to be
consistently better than other approaches. Yamaguchi et al
[19], propose an autonomous load balancing system for
distributed servers using the active object model. This system
consists of distributed servers. Each server has a load balancer
whichmonitors the server load, and controls the load,
communicating with other load balancers. Distributed load
balancers communicate in a peer-to-peer way, and a
client/server model is not suitable, the active object model is
very suitable. Cardellini [18] propose Distributed Web server
architectures that transparently schedule client requests offer a
way to meet dynamic scalability and availability requirements.
The authors review the state of the art in load balancing
techniques on distributed Web-server systems, and analyze
the efficiencies and limitations of the various approaches.
Frank Lingen [23] propose propose a system, in which mobile
agents will transport, schedule, execute and return results for
heavy computational jobs submitted by handheld devices.
Moreover, in this way, our system provides high throughput
computing environment for hand-held devices.
Jian Feng Cui [17] propose an approach to load balancing
for RFID middlewares based on Mobile Agent System. Two
agents, LGA and RLBA, are designed. LGA is used to gather
global workload of RFID middlewares, and RLBA executes
load balancing process in case of overloading.
Many authors proposed different techniques to efficiently
distribute the loads among the system. But all these methods
reported to have a lot of pros and cons of its own proposal.
This architecture provides a technique to rearrange the loads
efficiently and security with the help of agents.
Yu-Kwong [20] propose an approach works by using a
fuzzy logic controller which informs a client object to use the
most appropriate service such that load balancing among
servers is achieved. Authors chosen Jini to build our
experimental middleware platform, other related techniques
are implemented and compared. Extensive experiments are
conducted to investigate the effectiveness of our
IACSIT International Journal of Engineering and Technology Vol. 2, No.1, February, 2010
ISSN: 1793-8236
59
fuzzy-decision- based algorithm, which is found to be
consistently better than other approaches. Yamaguchi et al
[19], propose an autonomous load balancing system for
distributed servers using the active object model. This system
consists of distributed servers. Each server has a load balancer
whichmonitors the server load, and controls the load,
communicating with other load balancers. Distributed load
balancers communicate in a peer-to-peer way, and a
client/server model is not suitable, the active object model is
very suitable. Cardellini [18] propose Distributed Web server
architectures that transparently schedule client requests offer a
way to meet dynamic scalability and availability requirements.
The authors review the state of the art in load balancing
techniques on distributed Web-server systems, and analyze
the efficiencies and limitations of the various approaches.
Frank Lingen [23] propose propose a system, in which mobile
agents will transport, schedule, execute and return results for
heavy computational jobs submitted by handheld devices.
Moreover, in this way, our system provides high throughput
computing environment for hand-held devices.
Jian Feng Cui [17] propose an approach to load balancing
for RFID middlewares based on Mobile Agent System. Two
agents, LGA and RLBA, are designed. LGA is used to gather
global workload of RFID middlewares, and RLBA executes
load balancing process in case of overloading.
Many authors proposed different techniques to efficiently
distribute the loads among the system. But all these methods
reported to have a lot of pros and cons of its own proposal.
This architecture provides a technique to rearrange the loads
efficiently and security with the help of agents.
III. OUR APPROACH
An agent is a small active object, which is able to carry out
activities continuously and autonomously in a particular
environment. Agent is autonomous, lightweight, adaptive, and
mobile. Multi agent can communicate and cooperate with each
other. These qualities make agent a choice for efficient
rearranged of loads in a distributed network and security
architecture in sensitive distributed system.
Figure 1 shows the three essential components of the
architecture: Supervisor Agent, Monitoring Agent and Locate
Agent are present in this system. Monitor agent is a mobile
agent which gathers information from each system and sends it
to the Supervisor Agent. Supervisor agent is present in virtual
server analyze the information and make a decision using
many different types of algorithm [1] [2] [5]. If the supervisor
agent can judge that any node have more load than the current
load, it will use locate agent. Locate agent use different types
of existing algorithm [3] [2] to find out neighboring node that
has more load than current load. It shifted a load from this
present node with all information to that respective node and it
act as a virtual server.
Figure1 System Architecture
a. Monitoring Agent
Monitoring agent or mobile agent can roam on each node in
distributed system networks. It gathers each and every system
information dynamically and sends this information to
supervisor agent. In this process it gathers security side
information and it monitors the environment dynamically.
There are four components in this agents, which are
information collections, filtration, coder and communication.
Fig2 shows its components of Monitor agents.
The collection component is responsible for collecting
information of all the nodes and its hand in filter component.
Coder component is useful to code the individual system
information by number for identification of the system. In
order to avoid malicious access, to number all the nodes in the
network.
Filter information is useful to filter the unwanted
information of the collections. Communication is useful to
communicate with the supervisor agent for further analysis of
the load target system.
Figure 2 Monitoring Agent
b. Supervisor Agent
Core of the architecture is the supervisor agent. It collects
the information from monitoring agents. Then, it finds which
system has a more load rather than its current load. To save
network resources and its bandwidth, Supervisor agent could
not reside on all the system in the network. It present only in
one system and move to the network. There are three
components in this supervisor agent, which are
communication, analysis, and response components. It has set
of rules and regulation and it analyse whether any suspicious
activity has been made in the network or not and gathers an
system target information
Communication agent gets communicated with the
monitoring agents for gathers an information for find out the
load target of the system. Analysis agents finds out the
Monitoring agent
Supervising agent
Distributed System
Locate Agent
collection
coder
Filter
Communication
IACSIT International Journal of Engineering and Technology Vol. 2, No.1, February, 2010
ISSN: 1793-8236
60
neighboring node which have more target rather than its
present load system using existing algorithm . The approach to
select a node may be competitive or negotiated. In addition to
that, analysis agent uses set of rules and regulation for
comparing the predefined data with collected data for security.
Then, it communicates with the locate agent to move with all
information from present node to the target node and it act as a
server and to distribute the work effectively against the load
and also if any suspicious activity found, it locate this agent
and it disconnect the respective node from the network.
Figure 2 Supervisor Agent
c. Locate Agent
Locate agent is an moving agent to move all the information
along with the virtual server to the heavy load system and it act
as an virtual server to distribute the work very effectively.
There are three components are move, Locate and set up agent
which is responsible for locate the virtual server to the heavy
load system. Locate components are to find out the system
where to locate the virtual server for distribution of the work.
Move components are move the virtual server from the present
system to heavy load system. Set up agent is to set up the
virtual server in that heavy load system for analysis the load
balancing factors.
Figure 2 Monitoring Agent
IV. CONCLUSION
This paper proposed a system to efficiently distribute the
loads among the nodes in the network efficiently. Our
approach using Mobile agent as a core to collect the
information from all the nodes and rearrange the loads
efficiently with security. Our mechanism also provides
advantages over the other existing techniques whose
application requires load balancing system efficiently with
security. Traditional mechanism have not been sufficient to
provide the distribution of the work efficiently with security,
however, this mechanism is able to distribute the work
efficiently and to detect known and unknown attacks which is
present in this system.
REFERENCES
[1] S. Ratnasamy, P. Francis, M. Handley, R. Karp, and S. Shenker,“A
Scalable Content-Addressable Network,” in Proc. ACM SIGCOMM,
San Diego, 2001.
[2] Ion Stoica, Robert Morris, David Karger, M. Frans Kaashoek,and Hari
Balakrishnan, “Chord: A Scalable Peer-to-peer Lookup Service for
Internet Applications,” in Proc. ACM SIGCOMM, San Diego, 2001, pp.
149–160.
[3] Kris Hildrum, John D. Kubatowicz, Satish Rao, and Ben Y. Zhao,
“Distributed Object Location in a Dynamic Network,” in Proc.ACM
SPAA, Aug.2002.
[4] Antony Rowstron and Peter Druschel, “Pastry: Scalable, Distributed
Object Location and Routing for Large-scale Peer-to-Peer Systems,” in
Proc. Middleware, 2001.
[5] Ananth Rao, Karthik Lakshminarayanan, Sonesh Surana, Richard Karp,
and Ion Stoica, “Load Balancing in Structured P2PSystems,” in Proc.
IPTPS, Feb. 2003.
[6] John Byers, Jeffrey Considine, and Michael Mitzenmacher,
[7] “Simple Load Balancing for Distributed Hash Tables,” in Proc.IPTPS,
Feb. 2003. Frank Dabek, Frans Kaashoek, David Karger, Robert Morris,
[8] and Ion Stoica, “Wide-area Cooperative Storage with CFS,” in
Proc.ACM SOSP, Banff, Canada, 2001. David Karger, Eric Lehman,
Tom Leighton, Matthew Levine,Daniel Lewin, and Rina Panigrahy,
“Consistent Hashing and Random Trees: Distributed Caching Protocols
for Relieving Hot Spots on the World Wide Web,” in Proc. ACM STOC,
May 1997.
[9] M. Adler, Eran Halperin, R. M. Karp, and V. Vazirani, “A stochastic
process on the hypercube with applications to peerto-peer networks,” in
Proc. STOC, 2003.
[10] David Karger and Matthias Ruhl, “New Algorithms for LoadBalancing in
Peer-to-Peer Systems,”Tech. Rep. MIT-LCS-TR-911, MIT LCS, July
2003.
[11] J. R. Douceur and R. P. Wattenhofer, “Competitive Hill-Climbing
Strategies for Replica Placement in a Distributed File System,”in Proc.
DISC, 2001.
Communication
Analysis
Response
Locate
Move
Set up