Dynamic Load Balancing in Grid Computing with
Multi -Agent System Integration by Using Tree
A Report submitted for seminar assignment
VISHNU KUMAR PRAJAPATI - (2012AN20)
ABV INDIAN INSTITUTE OF INFORMATION
TECHNOLOGY AND MANAGEMENT
The Grids can be deﬁned as services that shares computer power and data storage
capacity over the Internet and Intranet. It is not just simple communication between
computers but it aims ﬁnally to turn the global network of computer into a huge computational resource. It can coordinate those resources which are not subject to centralized
control. The grid is to use standard, open, general-purpose protocols and interfaces.
The grid is to deliver nontrivial Quality of Service. A computational grid environment
behaves like a virtual organization consisting of distributed resources. A Virtual Organization is a set of individuals and institutions deﬁned by a deﬁnite set of sharing rules
like what is shared, who is allowed to share, and the conditions under which the sharing
takes place. A number of Virtual Organizations exist such as the application service
providers, storage service providers, but they do not completely satisfy the requirements
of the grid. Grid computing focuses on dynamic and cross-Organizational sharing, it
enhances the existing distributed computing technologies.
Historical Background of the Grid Computing
Networked operating systems
Distributed operating systems
Parallel and distributed computing
Table 1: history of grid computing
Load Balancing in Grid Environment
n a Grid environment , there are several Load Balancing techniques such as Randomized load balancing, round robin load balancing, dynamic load balancing, hybrid load
balancing, agent based load balancing and multi-agent load balancing . Round robin
and randomized load balancing are simple and easy to implement. Dynamic, hybrid,
agent base and multi-agent based load balancing are going to improvement or new ones
introduced in grid load balancing solution.
Goal of Load Balancing
The Goal of load balancing is that the workload is fairly distributed among the nodes
and that none of the nodes are overloaded or under loaded. So that the computing power
fully utilize from the multiple hosts without disturbing the user
Type of load balancing
there are two types of load balancing strategies called static load balancing and dynamic
load balancing - Static load balancing makes the balancing decision at compile time and
it will remain constant. In dynamic load balancing makes more informative decisions in
sharing the system load based on runtime. the dynamic load balancing provide better
performance compare to static load balancing. Dynamic load balancing classiﬁed into
centralized approach and decentralized approach. In Centralized approach is managed
by central controller that has a global view of load information in the system which is
used to decide how to allocate jobs to each other. Another one decentralized approach all
joints nodes are involved in making the load balancing decision. In the grid computing
is the method based on collecting the power of many computers, in order to solve the
large-scale problems; On the other hand, it oﬀers to share hardware and software grid
resources. So that maximizes the overall grid performance. Tree base infrastructure is
focusing on the load balancing algorithm for the grid computing services (GCS). The
main goal of the design to submit their computing task simply by having access to our
grid computing service web site(GCSWS)and another objective of GCS to access the
powerful computers or expensive software with very low cost to the our grid users.
The distributed computing technology are use to share the resources between the institutional, by using grid computing it will give more better performance them existing
distributed computing technology. Currently, Grid computing technology can be used to
connect heterogeneous computing resources to each other in a way that user can regard
all of this structure as a single machine on which we can run very highly complex and
massive application programs that require a high processing power and huge volume of
input data. The grid computing systems have improved the throughput and increase the
performance to the individual nodes and whole grid system by using the load balancing.
So the load balancing in the grid system has a big role for utilization of the resource and
reduced the response time.
Decentralize load balancing approach are based on redistribution of tasks among the
available processors. The processors which is overloaded are transfer the tasks to the
under load processors, by using High Level Architecture (HLA) environment. This
process work at the run time, so generally there is none of the nodes are heavily loaded.
Dynamic Load Balancing Policies
here are four type of load balancing .which consists of Transfer policy, Selection policy,
Location policy and Information policy.
Figure 1: Grid Structure Environment
• Transfer Policy: Transfer Policy should be transfer the load or not and it is based
on various criteria such as workload value and computing Power. If the load
balancing is needed it will sent to the selection policy. if not, the job will process
• Selection Policy: The tasks deﬁne that it should be Transference or migrated from
overloaded resources (source) to most idle resources (receiver). The decisions made
by selection policy are then directed to the location policy for further process.
• Location Policy: Location Policy are Uses the results of the Selection policy to
ﬁnd a suitable partner for a Sender or receiver.
• Information Policy: In the information policy, the worked as what workload information to be collected, when it is to be collected and from where it is collected.
An Agent is a computer system that has a capability of taking independent action on
behalf of its user or owner. The Multi-agent system hold several characteristics such as
autonomy, local views, cooperation, social ability, reactivity, proactive, goal oriented and
decentralized. Multi-agent system consists of communication layer, coordination layer
and local management layer. The communication layer provides an agent with interfaces
to heterogeneous networks and operating systems. It will receive the request and then
explain and submit to the coordination layer to decide the suitable action according to
its own knowledge. The local management layer performs functions of an agent for local
grid load balancing. A Multi-agent system is composed of multiple intelligent agents that
have the ability to interact or communicate, collaborate and negotiate among them.
Grid Computing Service Architecture
Grid computing service (GCS) is allows to submit their computing tasks along with
required hardware or software resources. It allocates tasks to the available resources
and then executes the tasks. After execution, grid computing service will reply to the
user and send back the results.
As the following ﬁgure, GCS have four layers Web Service Task Submission layer,
Grid Resource Monitoring layer, Task Allocation and Load balancing layer and Grid
Task Execution layer. In the Web Service Task submission layer, work with user tasks
submission and their requirements (resources and quality of service information). In the
Grid Resource Monitoring layer, need to monitor those resources which are underutilized
or overloaded. Each grid entry point is called a Grid Agent Manager (GAM).in the load
balancing layer, there are two level of load balancing which are worker layer and GAM
level load balancing. And the last Layer is Grid Task Execution layer, it is mainly
responsible to perform tasks executions and also update the status of the hardware and
software resources at a given computing unit.
Figure 2: Grid Computing Service Layered Architecture
To reduced the communication between worker nodes and Leader nodes and also between
the Leader nodes. So that reduced the overhead compare to pool based approach and do
the eﬃciently load balancing process. The main objective is to increase the performance
of the Grid system, maximize the overall system throughput, minimize the response time
and allow the good grid resources utilization.
Figure 3: Comparison between Existing policy and proposed policy
The information policy has making a decision and lot of contributions. We can say
that information policy has a big implication on performance in grid computing through
accurate, eﬃcient and suitable for taking a decision. The transfer policy and selection
policy are combined which is known as migration policy. By combining these policies,
reduced the internal communication between policies in the agent as showing the above
ﬁgure. Agent have a multifunction capabilities due to the role of embedded them. It will
be two statuses which are leader of the computing element and worker of the computing
element. The agent is automatic determined statuses or role themselves. If the agent
is leader, it wills auto-notify the workload system manager. It also has the capability
to communicate among the agent and exchange the information. The main work of the
migration policy is receiving the data or if already holding the data, it will analyze the
load and decide where process is locally or remotely. The decision made by the migration
policy will submit to the location policy for further processing. Here the load balancing
function work globally or locally. The load balancing decision making by the workload
system manager which sits at top of the grid as described in the following ﬁgure.
The workload system manager makes the decision based on computing element power
or index and also to allocate the correct load value the correct computing elements which
are the leaders in the local grid. Then, the computing element leader will decide how
Figure 4: Grid Structure Environment
to distribute the load according to the worker node available computing power. Each
worker node has the capability to auto-notify to the leader on itself computing power
information, so that reduce the communication overhead compare to polling method.
Load Balancing Algorithms:
• APC=PC*L GPC =is the maximum processing capacity (tasks/seconds) at grid
threshold utilization. So, AVGPC=GPC-APC All the above parameter are dynamic nature. APC=Actual Processing Capacity, GPC-Grid Processing Capacity,
AVGPC=Available Grid Processing capacity.
• Worker Level Load Balancing: If N is the number of received tasks at a given
GAM(General Agent Manager), we deﬁne the following parameters-
Where TPC= Total Processing Capacity, TAPC =Total Actual Processing Capacity.
• GAM level Load Balancing: The GAM have to managed by tree structure in grid,
the tree structure is selected to ensure the scalability (add/remove GAMS) and
minimize the communication between the GAMS. it also ensure that only one load
balancing operation work at a time, so that ignore the inconsistency or wrong load
balancing operations. by circulating the token message between GAM in the whole
tree for exchange the information. The token message contains the global view of
the grid system. it contain the following information about each GAM. Manager
ID ,Total Available Processing Capacity(TAPC) of the GAM, status, Neutral(N)
,Receiver(R) and Sender(S) .
Figure 5: Combining Architecture for Grid Load Balancing with service model
The user can be submitting a task to the grid web service. The user may choose
the deferent web browser though web server to submit the task and also responsible
to forward the request to the Grid Resource Monitoring Layer. The Grid Resource
Monitoring Layer do the monitoring in heterogeneous resources like diﬀerent Processing
Power, diﬀerent Internet speed, and the systems are in distributed manner, after that this
layer work the central Workload system Manager for doing the accurate load balancing
and task allocation (as by Bakri Yahaya ijincaa,2011) and forward the process to the next
layer for execution of task. Workload system Manager have a Multi- Agent, An agent
will determined what they are and automatically turn themselves into the determined
status or role. If the agent is a leader, it will auto-notify the workload system manager.
The agent itself has the capabilities to communicate among the agent and performs the
information exchange. The global load balancing decision will be made by Workload
system manager and the local load balancing will be made by leaders. The Grid Task
Execution layer work as existing architecture (as by Abderezak Touzene IJCSI 2011).
In the Tree base architecture for grid computing services and Multi-agent system will
reduced the internal communication. We also apply the load balancing policy to reduce
the communication between policies. The workload system maintains the consistency
and removed the wrong load balancing. By combining the policy method and grid
computing Service Architecture we can achieve the maximum throughput, minimize the
overall tasks response time and ﬁnding fault. In future we can apply the fault tolerance
in the grid load balancing strategy.
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