Variable threshold-based hierarchical load balancing technique in Grid

ORIGINAL ARTICLE

Variable threshold-based hierarchical load balancingtechnique in Grid

Neeraj Rathore • Inderveer Chana

Received: 8 January 2014 / Accepted: 17 April 2014

� Springer-Verlag London 2014

Abstract Load balancing is an important aspect of Grid

resource scheduling. This paper attempts to address the issue

of load balancing in a Grid, while maintaining the resource

utilization and response time for dynamic and decentralized

Grid environment. Here, to its optimum value, a hierarchical

load balancing technique has been analysed based on variable

threshold value. The load is divided into different categories,

such as lightly loaded, under-lightly loaded, overloaded, and

normally loaded. A threshold value, which can be found out

using load deviation, is responsible for transferring the task

and flow of workload information. In order to improve

response time and to increase throughput of the Grid, a random

policy has been introduced to reduce the resource allocation

capacity. The proposed model has been rigorously examined

over the GridSim simulator using various parameters, such as

response time, resource allocation efficiency, etc. Experi-

mental results prove the superiority of the proposed technique

over existing techniques, such as without load balancing, load

balancing in enhanced GridSim.

Keywords Grid computing � GridSim � Architecture �Load balancing � Workload � Job migration � Random

1 Introduction

The outburst of the Internet, along with the increase in the

computational power of the servers and emergence of high-

speed computer networks has given the power to the Grid

to share computational resources [1]. Grid is a vastly dis-

tributed enormous system that builds a global network of

computational resources and may span many continents.

The use of each small resource is flexible that combines

computer resources and information systems for creating a

ubiquitous source of computational power [2, 3]. Sharing

resources between organizational and institutional bound-

aries needs an infrastructure to coordinate the resources

across the boundary, so-called virtual organizations [3].

The Grid application performance is often degraded due to

numerous challenges, such as fault tolerance, security, het-

erogeneity of resource, load balancing, etc. Load balancing is

one of the critical features that ensures the load is balanced on

each node in the network. Load balancing is performed into

four basic steps [4, 6], namely as load monitoring, synchro-

nization, re-balancing criteria, job migration, as shown in

Fig. 1. In load balancing problem, resources on one Grid site

may be heavily loaded while on another Grid site may be

lightly loaded or idle. This may be the reason for unequal

computing and patchy template of task landing. Therefore, it is

desirable to transfer some jobs from the heavily loaded

resources to the lightly loaded, which will to reduce mean job

response time and increase the performance of each host while

ensuring that small jobs do not suffer from starvation [5].

1.1 Motivation

In this paper, the design and development of an optimal load

balancing algorithm have been proposed for the Grid. Due

to the materialization of Grid computing over the Internet,

N. Rathore (&)

Department of Computer Science and Engineering,

Jaypee University of Engineering and Technology, Guna,

Madhya Pradesh, India

e-mail: [email protected]

I. Chana

Department of Computer Science and Engineering, Thapar

University, Patiala, Punjab, India

e-mail: [email protected]

123

Engineering with Computers

DOI 10.1007/s00366-014-0364-z

the need for the hierarchical load balancing algorithm is in

demand [6]. Response time and resource allocation have

been recorded as a fair contribution of this research.

In the existing scenario, dynamic, decentralized load

balancing considers all the factors pertaining to the char-

acteristics of the Grid computing environment. To over-

come these problems, a dynamic threshold value is used at

each level in proposed technique. The value of the

threshold is dynamically changing according to the Grid

size in the network. Furthermore, it extends the existing

technique of dividing lightly loaded node into lightly and

under-lightly loaded categories in the context of variable

threshold and standard deviation. This approach minimizes

the searching time to find out the receiver machine in order

to transfer the Gridlets. A well-designed information

exchange scheduling scheme is adopted is the proposed

technique to enhance the efficiency of the load balancing

model. A comparative analysis shows difference between

the techniques and exhibits why the proposed algorithm is

better than any other existing algorithms presented in

Fig. 2 and Table 1. The proposed technique is a new ver-

sion of the load balancing mechanism using random policy

[7]. The random load balancing policy chooses the target

endpoint randomly from the specified list. The proposed

load balancing technique has not only increased resource

allocation efficiency of Grid resources, but also cut down

the response time of the entire Grid. This algorithm is

rigorously examined on the GridSim simulator to judge the

effectiveness of the algorithm, especially on Grid platform.

The simulation results shows significant advantages of

proposed algorithm over other existing approaches, such as

centralized load balancing, no load balancing, etc.

1.2 Contribution

Due to the appearance of load balancing challenge in Grid

computing, there is currently a need for a hierarchical load

balancing algorithm which takes into the account the Grid

architecture, heterogeneity, response time, and resource allo-

cation. Considering all the necessary factors, the main contri-

butions and novelties of the current research work are as follows:

1. This is a novel technique to tackle hierarchical load

balancing that takes into account all the factors

pertaining to the characteristics of the Grid computing

environment. This technique is provides a good backup

and support, in case of server failure.

2. The proposed approach eliminates the scalability

complexity of a site. Most of the existing load

balancing techniques uses a static threshold value that

is good for only restricted network size, but cause

problems in a large-scale Grid environment. It may

lead to scalability, response time issues, etc. To

overcome these problems, a dynamic threshold value

Synchronization Rebalancing Criteria Job MigrationLoad MonitoringStart End

Fig. 1 Basic load balancing steps

Under LightlyLoaded Pool

HeavilyLoaded Pool

NormallyLoaded Pool

Lightly LoadedPool

Lightly Loaded Pool Normally Loaded Pool Heavily Loaded Pool

Existing Technique

Proposed Technique

Choosing Node for Load Balance / Number of Comparisons

Choosing Node for Load Balance / Number of ComparisonsFig. 2 Basic difference

between existing and proposed

technique


123

is used here. Every time when the application or node has

been increased or decreased, the threshold value has to

be re-calculated. This is easy to use and more supportive

with the hierarchical load balancing technique.

3. An efficient load balancing technique is adopted to

enhance the efficiency of the load balancing model. An

extended version of the random policy [8] has been

proposed for selecting the node during load balancing

and initiated by under-loaded or overloaded node in

the pool of shared nodes.

4. Qureshi and Rehman [20] divided the load balancing

category into three parts. In the current research, the

lightly loaded node category is divided into under-

lightly loaded and lightly loaded node in order to

increase the response time.

The remainder of this paper is organized as follows:

related work is discussed in the Sect. 2. Section 3 describes

the structure of the load balancing model along with the

design of the system environment and its application to the

network environment. Section 4 focuses on the system

model and problem definitions in which implementation of

algorithms and analysis of mathematical calculations are

introduced. Performance evaluations and simulation results

are presented in Sect. 5. Finally, the last section is dedi-

cated to conclusion and future work.

2 Related work

The Grid is a distributed network in which the resources

are continuously added and deleted according to the

requirement [3]. Due to the above reason, a hierarchical

load balancing technique is used for dynamic and distrib-

uted Grid environment. Several authors have proposed

various load balancing techniques in Grid environments.

Yagoubi and Slimani [9] proposed a layered algorithm

based on a tree model, which achieved dynamic load bal-

ancing in Grid computing. It supports heterogeneity, sca-

lability, and total independence of any physical

architecture of a Grid, but lacks in response time. Erdil and

Lewis [10] have described an information dissemination

protocol that can distribute the load in a way without using

load re-balancing through job migration. But it is more

complex and costly in large-scale heterogeneous Grids.

The model proposed by Erdil and Lewis [10] mainly deals

with nodes adjustment, advertising rates and aggressive-

ness to influence scheduling, but the major problem with

this model is the scalability, resource failure and robust-

ness. Azin and Simone et al. [8] use an Ant colony Opti-

mization approach using state broadcast and random

methods. It invokes a response to submit a job to the Grid,

and surfs the network to find the best resource to deliver the

job. It has issues, such as scalability and resource failure.

Qingqi et al. [11] integrates the distributed mode, approx-

imate optimization and Agent Set Scheduling approach to

achieve short execution time in large-scale agent schedul-

ing. It describes the multi-level framework, agent distri-

bution, communication, architecture, event-driven

conservative time synchronization mechanism to avoid

potential failure problem caused by the centralized mode as

well as improve execution efficiency through parallel

computing, but lacks in migration cost, security and

scheduling. Zheng et al. [12] overcome the scalability

challenges and considerably reduce the memory require-

ments, the running time and provide interconnect topology-

aware strategies that map the communication graph on the

processor topology to minimize network contention. Nan-

dagopal et al. [13] address the problem of load balancing

using Min-Load and Min-Cost policies while scheduling

jobs to the resources in the multi-cluster environment. They

heuristically estimate the completion time of executing

Table 1 Comparison between existing and proposed technique

Techniques Existing approach Our approach (PLBA)

Categories Divided into three pools,

such as: lightly,

normally, heavily

Divided into four pools,

such as: under-lightly,

lightly, normally,

heavily

Techniques Each heavily loaded

node has to check all

the nodes in the lightly

loaded pool

According to load

percentage, we have to

check overloaded node

into one pool either

lightly or under-lightly

loaded pool because of

sorting

Comparisons 1 2

Scheduling No Yes

Sorting Not used Yes, it is used in the

algorithm

Ascending—under-lightly

and lightly loaded pool

Descending—heavily

loaded pool

Policy Not applicable Random policy—it means

we can do our process

vice versa also. (lightly

loaded node can also

search heavily loaded

node)

Response

time

Low High

Throughput Low High

Resource

allocation

capacity

Low High (due to idea of

threshold and load

deviation)

Cost Low Medium

Comparison

time

High (each node has

explored in the pool)

Low (due to sorting

technique)


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jobs in the remote clusters, improved system performance

in terms of mean response time and average slowdown, but

lacked precedence constraint among different tasks of a

job. Robson et al. [14] addressed the problem of monitor

resources, load reallocation and load migration. It also

minimized balancing overhead while load redistribution.

Balancing involves normalizing load distribution of shared

resources and maximizing overall resource utilization.

Migration technique provided reliably and low latency load

transfers, but need better detection and reactivity in the

model. Deepti et al. [15], able to trace dead machines,

framework works well with heterogeneous web servers for

high incoming load and low response is time, but the

bottleneck of the framework is centralized main server

controller. Nandagopal et al. [16] have reported a highly

decentralized, distributed and scalable algorithm for

scheduling jobs and balance the load across the resources

in heterogeneous computational Grid environment to

minimize response time and to reduce the communication

overhead. Zheng et al. [17] considerably reduce the

memory requirements, the running time and provide

interconnect topology-aware strategies that map the com-

munication graph on the processor topology to minimize

network contention, but faced the challenges in the scala-

bility of hierarchical LBA. Comparison of different load

balancing technique in [18] clearly shows that Tree

(Hierarchical)-based load balancing technique is better

than others.

In traditional distributed systems, Grid architecture has

the following properties that characterize, such as hetero-

geneity, scalability, adaptability, hierarchical, independent,

etc. These properties make the load balancing problem

more traditional parallel and distributed systems, which

offer homogeneity and stability of their resources. Also,

interconnected networks on grids have very disparate per-

formances and tasks submitted to the system that can be

very diversified and irregular. These various observations

show that it is very difficult to define a load balancing

system which can integrate all these factors (Table 1).

On the basis of existing load balancing techniques, all the

fundamental problems, such as response time, scalability,

etc., are improved by using the proposed efficient hierar-

chical load balancing technique because of its layered

approach and dynamic load calculation criteria, shown in

Table 2. The load balancing technique reported by literature

[19] can be categorized into lightly loaded, overloaded,

normally loaded. If load on one node exceeds or reduces or

equals to the threshold value then it may be treated as

overloaded, lightly loaded and normally loaded at each

level, respectively. Load parameters shown in Table 3 use

previously tested simulated work [13, 20], which was based

on the same hierarchical technique. Existing techniques use

the static threshold value at each level which requires

network scalability. Further, if the numbers of nodes are

increased in the network, then response time and resource

allocation efficiency are proportionally increased.

2.1 Existing referenced algorithms compared

with proposed algorithm

In the literature, many load balancing algorithms have been

proposed. Most of them can be applied to the Grid envi-

ronment with suitable modifications. In Without Load

Balancing (WLB), no load balancing has been done, while

Load Balancing on GridSim, load balancing is performed,

but only at the broker level. Load Balancing on Enhanced

GridSim (LBGES) [20], the load balancing is performed at

broker, resource, and machine levels. The Min–min algo-

rithm [21, 22] sets the jobs that can be completed earliest

with the highest priority. Each job will always be assigned

to the resource that can complete it earliest. Similar to Min–

min algorithm, Max–min algorithm [23, 24] sets the highest

priority to the job with the maximum earliest completion

time. The main idea of Max–min algorithm is to overlap

long running tasks with short-running tasks. Max–min can

be used in cases where there are many shorter tasks than

there are longer tasks. For example, if there is only one long

task, Min–min will first execute many short jobs concur-

rently, and then execute the long task. The Max–min will

execute short jobs concurrently with the longest job.

3 Preliminaries

Qureshi and Rehman [20] had documented Grid architec-

ture for load balancing, as illustrated in Fig. 3. Poisson

process has been used for random job arrival with a random

computation length [25]. Considering that the jobs are

sequenced, mutually independent with the arrival rate and

can be executed on any site. Furthermore, the site should

meet the job demand for computing resource and the

amount of data transmitted. Each processor can only exe-

cute one job at a time and execution of a job cannot be

interrupted or moved to another process during current

execution. This model has been divided into three levels:

Level-0 Broker, Level-1 Resource, and Level-2 Machine

Level. These entities can have customized characteristics.

When a new Gridlet arrives on a machine, it may go to

under-lightly loaded, lightly loaded, overloaded and normal

loaded resources by load calculation being computed at

each node. In order to compute the mean job response time

analysis one Grid Broker (GB) section as a simplified Grid

model has been considered. Grid Broker is the top manager

of a Grid environment. It is liable for sustaining the overall

Grid activities of scheduling and rescheduling. It acquires

the information of the work load from Grid resources and


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sends the tasks to resources for optimization of load.

Resource that comes next to Grid Broker in the hierarchy,

are connected through internet. The resource manager is

responsible for maintaining the scheduling and load bal-

ancing of its machines and it also sends an event to the Grid

broker during overload [26]. The machine is a Processing

Entity (PE) manager, responsible for task scheduling and

load balancing of its PEs connected with various resources

via LAN. Processing Entity (PE) manager also sends an

event to resource during overload. PEs next to machines are

mainly responsible for calculating workload and threshold

values for load balancing, job migration and passes the load

information upwards to machines via buses. Gridlet is

considered as a load and assigned to any of the PEs

according to their capability (i.e. computational speed,

queue length, etc.). The proposed hierarchical-based Load

Balancing technique in a heterogeneous Grid environment,

assumed the heterogeneity in terms of processing capability

and the same is applicable to the PEs.

3.1 Analytical view of local Grid manager

To get inside the model analytically, a Local Grid Manager

(LGM) section is considered as a simplified grid model, in

which the external tasks arrive sequentially at the LGM

according to a time-invariant Poisson process, with inter-

arrival times which are independent, identically, and expo-

nentially distributed. Simultaneous arrivals are excluded

[27]. Considering the following system parameters:

Table 2 Comparisons of load balancing techniques [8–18]

LB

technique/

parameters

Prediction based

approach

Agent-based approach Supportive node

approach

Recitation (policy

and strategy)

based

Hierarchical approach

Environment Heterogeneous

(centralized load

manager)

Distributed

heterogeneous P2P

Distributed

heterogeneous

Centralized and

sender initiate

Distributed heterogeneous

Aim Effective resources

utilization and

reduce average

job response time

Self-adaptive, avoiding

large amount of data

transfer and find under

load nodes more

quickly

Reduces

communication

delay using

supporting

nodes

Reduce response

time

Nodes at different levels of hierarchy

communicate with the nodes below

them to get information about the

network performance

Strength Better resource

utilization

Migrate load on the

basis of state

information exchange

and location policy

Minimum traffic

due to attached

central node at

each cluster

Based on load

balancing policy

Load index as a decisive factor for

scheduling tasks and balanced load

with the neighbour cluster

Criteria/

issues to

be

addressed

The selected task

basis of I/O-

intensive, CPU-

intensive and

memory intensive

Load estimates based on

the job queue length

and job arrival rates

Primary and

centralized

approach

Information,

triggering and

Selection

Policies

Threshold policies, information

exchange criteria, selection of

nodes at different levels of

network, failure intensity,

performance, migration time

Compose in

module/

matrices

1. Predictor 1. Job scheduler Provide support

while migrating

load in the

individual node

pool

Information policy Grid level

2. Selector 2. Communication layer Triggering policy Cluster level

3. Scheduler Resource managers Selection Policy Leaf nodes

Behaviour Cluster-based

queuing model

Analyzed by varying the

number of jobs in a

nest

Performance

criteria are load

index and queue

length

Based on CPU

consumption and

queue length

Load index based on CPU

utilization, queue length, scalable

Gap Need to evaluate

large-scale cluster

Number of nodes

increases in a nest will

cause to increase the

complexity of the

program

Complexity in

implementation

Resources submit

and withdrawn

in any

movement

(affect

performance)

Less fault tolerance

Table 3 Load parameter [13,

20]Level Load

parameter

PE level threshold q = 0.6

Machine-level

threshold

g = 0.75

Resource-level

threshold

q = 0.8


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k is external (remote) task arrival rate from grid clients and

other LGMs to an LGM out of which, the rateka is accepted for

processing by the LGM, and the rate kr is rejected up arrival

and forwarded upon arrival through the communication

means to another LGM. Hence, k = ka ? kr.

kai is the external task flow rate from the LGM to the ith

SM which is managed by that LGM. kLi and is the local task

arrival rate to the ith Site Manager (SM).

kL is the total local tasks arrival rate to the SMs man-

aged by a LGM, hence

kL ¼Pm

1¼1 kLi and i = 1,2,3…,m is the number of sites

managed by that LGM.

ki is the total arrival rate (external and local) to the ith

SM that is ki ¼ kai þ kL

i :

kij is the job flow rate from the ith SM to the jth PE

managed by that SM.

l is the LGM service rate.

li is service rate of the ith SM.

lij is the service rate of the jth PE which is managed by

ith SM.

Hence, the LGM traffic intensity is given by

q ¼ kþ kL

l¼ kþ

Pmi¼1 kL

iPmi¼1 li

¼ kþPm

i¼1 kLiPm

i¼1

Pnj¼1 lij

;

where i = 1��m is the SMs number, and j = 1��n is the

number of PEs in every SM.

For the LGM to be stable q must be \1. The ith SM

traffic intensity is given by,

q ¼ ki

li

¼ kai þ XL

iPnj¼1 lij

: ð1Þ

where j = 1��n is the number of PEs at the ith SM. For the

SM to be stable qi must be \1.

Resource Manager N

Node 11

Grid Broker

Resource Manager MResource Manager 1

Node N1 Node NNNode MNNode M1Node 1N

Gridlets 1…..n

Clusters of PE`s

Gridlets 1…..n Gridlets 1…..nGridlets 1…..n

Bus Topology

Level-1 Resource Managers

Level-2 PE Managers

Level-0 Grid Broker

Internet

LANLANLAN

Fig. 3 Hierarchical Load Balancing Model [28]


123

The traffic intensity of the jth PE which is managed by

ith SM qij = kij/lij, similarly qij must be\1 for that PE to

be stable.

During the analysis process, the following facts are used

[27]:

• Merging of k Poisson streams with mean rate ki results

in a Poisson stream with mean rate k given by

k ¼Xk

i¼1ki

• If a Poisson stream is split into k substreams such that

the probability of a task going to the ith substream is pi,

each substream is also Poisson with a mean rate of pi k.

Since the communication links between the LGM and its

SMs are heterogeneous, i.e. every communication link has

an individual bandwidth and latency, the G (ka) is used as a

general function for the overall mean expected communi-

cation (including queuing) time of forwarding external

accepted task flow rate ka from the LGM to its SMs. Refer

to the length of time between the instant when a task

arrives at the LGM and the instant when it leaves, after all

processing and communication, if any, are over as task

response time. Hence, the overall mean task response time

T can be computed as follows:

T ¼ G kað Þ þ TS for external tasks

T ¼ TS for local tasks,ð2Þ

where TS is the overall mean task response time all over

the sites managed by the LGM.

As mentioned earlier, the system is studied in the steady

state that is the traffic intensity is \1, i.e. q\ 1. To com-

pute, as in [27], the SM is modelled as a variant M/M/n

queue where the service rates of any two PE are not

identical which is similar to the case of heterogeneous

multiprocessor system. Figure 4 shows an LGM Queuing

Model, the queuing structure of a site is a part of this figure.

Define the state of the SM by (k1, k2,…,kn), where ki is the

number of tasks at the ith PE queue, i = 1, 2,…,n. Reorder

the PEs in a list such that the occupation ratio k1/li1 B k2/

li2 B ,…, B kn/lin. The LS at the SM services the arriving

tasks (local or remote) based on their arrival time (FCFS).

It distributes tasks on the fastest PE (i.e. the PE has the

lowest occupation ratio). The traffic intensity of ith SM is

given by:

q ¼ ki

li¼ ka

i þ kLiPn

j¼1 lij

; n is the number of PEs in that site:

E½N�iSM ¼1

Xð1� qiÞ2:

The mean number of tasks at the ith site is then given by

[27]:

Here,

X ¼ð1þ 2qiÞ

Qn

j¼1

lij

ki

Pnj¼1 ðki þ lijÞ

þ 1

1� qi

E½N�iSM

¼ 1

Xð1� qiÞ2: ð3Þ

To compute the expected mean task response time,

Little’s formula will be used. Let E T½ �iSM denote the mean

time spent by a task at the ith SM to the arrival rate ki and

denote the mean number of tasks at that site. Hence by

Little’s formula, E T½ �iSM can be computed as follows:

E½T �iSM ¼1

kðE½N�iSMÞ: ð4Þ

So, the overall mean task response time all over the sites

at an LGM is then given by:

TS ¼1

m

Xm

i¼1

E½T �iSM; ð5Þ

where m is the number of sites managed by that LGM. By

substituting the value of TS in Eq. (1), the overall mean

task (external and local) response time can be computed.

3.2 System framework

Grid topology and Grid mapping into a tree-based model has

been shown by Fig. 5 with some applications and sequence

diagram design. The interaction of each site by computing

nodes in a network is given through the following steps: (i) in

the network each site may contain multiple machines and

each machine may have a single or multiple processing

elements (PEs). Grid scheduler is a software component that

runs on each site. It manages the system-related information

(i.e. CPU utilization, remaining CPU capability, remaining

memory, etc.) computing nodes and sites in order to join the

Grid, provide resources, receives jobs from Grid clients,

assigns them to the processors in the Grid system. (ii) It

provides backup to the jobs and discovers the candidate set of

nearest site based on the job requirements, resource char-

acteristics and sends the list to load balancing decision-

maker. (iii) Decision-maker decides on the basis of the

minimum Grid site, whether the job required an execution at

the local site or remote site and transfers the job accordingly.

(iv) Job dispatcher ships the jobs, checks site availability of

the fault detector and monitors the state of sites. (v) If site

failures or system degradation occurs, the fault detects or

sends a failure message to fault manager. If the fault manager

receives a failure message, it reschedules the job using a

primary backup approach [27].

In this system, sites play the role of assisting the execution

of jobs. When any site is in idle state and can provide its


123

resource, a joint message and related hardware information

will be transmitted to the Grid scheduler of the nearest

neighbour site. When it can no longer provide its resource, it

will transmit an exit message to the Grid scheduler of the

nearest neighbour site. The Grid scheduler uses a derived

threshold to effectively select suitable sites. This threshold

value is based on the load and demands for resource needed

to execute the job and will evaluate the sites in the system.

This paper proposes a hierarchical load balancing technique

strong point of dynamic load balancing method over static

load balancing. In static load balancing state, when a job has

to be executed, it will be assigned to an appropriate site. The

minimum requirement of job’s needs for sites is considered

as the threshold for the table of effective sites. Sites passing

this threshold are called effective sites. As a grid is composed

of sites with different performances, the execution time of a

job on each site is also different.

In dynamic load balancing state, the system will

adjust dynamically until it reaches a balance. In a Grid

environment, effectiveness of sites varies with time.

Thus, the assignment of jobs must be adjusted dynami-

cally in accordance with the variation of the site status.

4 System model and problem description

The work, proposed here, has been derived from the

work of [20], with some modification. It suggests the

necessity of quantification of the load in order to achieve

load balancing in computational grid. Quantification of

the load is done and the objective function is derived

based on the load distribution of the computational

nodes.

4.1 Notations used in algorithms

Notations that are used in PE, Machine and Resource level

are summarized and shown in Table 4:

Global Scheduler

External Arrival (λ)

Load Grid Manager Rejected Rate (λr)

Local Arrivals (λL1)

PE2 PEnPE1

U11 U12 U1n

λ11 λ12 λ1n

c

λ1Site 1 Manager

Acceptance Rate (λn)

λ1n λ2

n λnn

Local Scheduler Local Scheduler Local Scheduler

PEkPE2PE1PEmPE2PE1

c c

UnkUn2Un1U2mU22U21

λ22λ21 λ2m λn1

λn2 λnk

Site 2 Manager Site n Managerλ2λn

Local Arrivals (λL2)

Local Arrivals (λLn)

Fig. 4 Queuing Model of Grid Manager [27]


123

4.2 The fitness function

All the mathematical steps use in the network model is

described below.

4.2.1 Machine-level calculation

When the scheduler receives a job submitted by a user, it

will transfer a request to the information service in order to

obtain the necessary information such as the idle PE per-

centage of each resource, average load of each cluster and

average load of the system. Then the scheduler chooses a

cluster which is the fastest average computing power

(ACP).

Firstly, the ACP of the cluster (ACP (PEi)) is defined as:

ACPðPEiÞ ¼Pn

k¼1 PE Speedk � ð1� PEkÞn

; ð6Þ

where PE_Speedk = processing speed of processing

entity k in cluster i (MIPS).

PEk = current CPU utilization of the PEs in cluster i,

expressed as a percentage, PE = Processing Entity (Sin-

gle CPU-Processing capability of each PE is same for a

single machine) and n = number of processing entity in

cluster i.

After that machine scheduler selects the PE which has

the fastest ACP. The average load of the cluster is

defined by the average load of each processing entity in

cluster i.

The current load of PE (Curr_Load) PE is defined as:

ðCurr LoadÞPE ¼Xg

1

FileSize; ð7Þ

where g = number of Gridlets (Application/Task) on any

PE.

GridClient

FaultManager

FaultDetector

JobDispatcher

SiteSelector

LB DecisionMaker

Local GridScheduler

1 SubmitJob

3 Selectedminimum

Loaded Site

4 Local

Extension

5 Dispatchjob & check

siteavailability

6 FailureMessage

Remote GridScheduler

Grid InformationServer

Remote SiteInterface

7 Back upActivation

8 or 6 Job Result

2.1 Backup Job

2.2 Node Searchat Nearest Site

8 Jobresult

Fig. 5 Sequence diagram of components and their interaction


123

The average Load (Avg_load) of each PE is defined as:

Avg load ¼Xp

1

ðCurr LoadÞpP

� �

; ð8Þ

where p = number of PE in cluster (total load of available

PE in any Machine).

To measure the load of each PE (PELoadk,i), as

PELoadk;i ¼ðCurr LoadPE � Avg LoadÞ

Ratingð9Þ

where PELoadk,i = load of each processing entity (PE)k in

the cluster i.

Rating = actual capacity of PE, i.e. CPU speed in terms

of MIPS rating.

Calculate average load of each cluster i (ALCi) is

defined as:

ALCi ¼1

p

Xp

k¼1

PELoadk;i; ð10Þ

where PELoadk,i = load of all PEs in cluster i.

p = number of PE in cluster (total load of available PE

in any machine).

The average load of the machine system (ALMi) is

defined as:

ALMi ¼1

m

Xm

i¼1

ALCi; ð11Þ

where m = number of clusters in the machine system.

We set the average load of each cluster, ALCi to be less

than the balance threshold of the machine system. Hence,

this threshold called balance threshold.

The threshold value of the machine level denoted as qand defined as:

o ¼ ALMi þ r; ð12Þ

where r = standard deviation of the load of the system

and defined as:

o ¼ 1

N

XN

i¼1

ðXi � XÞ2; for all i; ð13Þ

where x = average loads of the machine system which

equals ALM, xi is the average load of each cluster i which

equals ALC and n is the number of clusters in the machine

system.

Table 4 List of notations used in the algorithm

S. no. Notations Description

1 PE Processing element (single CPU); processing capability of each PE is same for a single machine.

2 G Number of Gridlets (applications/task) on any PE

3 P Number of PEs on any machine

4 PELoad Load of any PE = ((Curr_Load) PE - Avg_load)/Rating)

5 PEsLoad Total load of available PE in any machine =

6 OPEList List of PE with overloaded Gridlets

7 OP Size of OPEList (O: Overloaded and P: PE)

8 LPEList List of PE with lightly loaded Gridlets

9 LP Size of LPEList (L: lightly loaded and P: PE)

10 ULPEList List of PE with under-lightly loaded Gridlets

11 UP Size of ULPEList (U: under-lightly loaded and P: PE)

12 NPEList List of PE with normal task

13 NP Size of NPEList (N: normal loaded and P: PE)

14 (PELoad)D Load of processing element for lightly loaded PE list

15 (TaskLoad)L Load of Gridlets

16 (PELoad)E Load of processing element for under-lightly loaded PE list

17 Rating Actual capacity of PE, i.e. CPU speed in terms of MIPS rating

18 (Curr_Load)PE Current load of PE =//where g is the number of Gridlets

19 (Task Load)L Load of Gridlets

20 File_size Is a workload in bytes

21 Gridlets An arrival task as a load

22 ALCi Average load on each cluster (collection of PE’s)

23 ALMi Average machine load

24 r Standard deviation


123

4.2.2 Resource-level calculation

Firstly, the ACP of the machine is defined as:

ACMi ¼Pn

k¼1 Mac Speedk � ð1�MackÞn

; ð14Þ

where Mac_Speedk = speed of machine k in resource

i (MIPS), Mack = current CPU utilization of the

k machines in resource i (in percentage), n = number of

machine in resource i.

The average load of the resource system (ALRi) is

defined as:

ALRi ¼1

r

Xr

k¼1

ALMi; ð15Þ

where r is the number of machines in the resource system.

The average load of each ALMi, is set as less than the

balance threshold of the resource system. Hence, a

threshold is set called balance threshold.

The threshold value of the resource level denoted as gdefined as:

g ¼ ALRi þ r1; ð16Þ

where r1 is the standard deviation of the load of the system

and defined as below:

r1 ¼1

N

XN

i¼1

ðXi � XÞ2 ; for all I, ð17Þ

where x = average loads of the resource system which

equals ALR, xi = average loads of each machine i which

equals ALM, N = number of machines in the resource

system.

4.2.3 Broker-level calculation

Firstly, the ACP of the resource is defined as:

ACRi ¼Pn

k¼1 Rec Speedk � ð1� ReckÞn

; ð18Þ

where Rec_Speedk = speed of resource k in the broker

(MIPS).

Reck = current CPU utilization of resource i (in

percentage).

n = number of resources in broker.

The average load of the Broker system (ALBi) is defined

as:

ALBi ¼1

b

Xb

k¼1

ALRi; ð19Þ

where b = number of resources in the broker system, the

average load of each resource, ALRi, is set as less than the

balance threshold of the system. Hence, a threshold is set

called balance threshold.

The threshold value of the machine level denoted as qdefined as:

q ¼ ALBi þ r2; ð20Þ

where r2 = standard deviation of the load of the system

and defined as:

r2 ¼1

N

XN

i¼1

ðXi � XÞ2 ; for all I, ð21Þ

where x = average load of the broker system which equals

ALB.

xi = average load of each resource i which equals ALR.

N = number of resources in the broker system.

The complexity of the hierarchical network depends

on max number of nodes at any level multiply with the

level of the tree. On our network, generally no. of PEs

is always more than the no. of machine and resources

under same broker. So, total complexity of the network

is O(3P) where P = no. of PEs, or we can say

O(P) because we generally eliminate constant from

complexity calculation.

4.3 The model and algorithms

This section deals with a load balancing model that can be

classified into three levels, such as resource, machine and

PE’s shown in Fig. 3. The proposed algorithm has been

executed individually, according to the threshold value at

each level and only PE level algorithms at level-2 con-

sidered. Initially, the load has been calculated according to

Gridlet (task) arrive on each node and it is connected to the

corresponding PEs. The entire tasks are compute intensive

that maximizes system resources for processing large

computations for simulation. Furthermore, performed load

balancing operation, according to the availability of lightly

loaded or under-lightly loaded node list and at the same

time it passes information to associated machines. To

perform load balancing, a random strategy is used for

selecting tasks randomly either from overloaded or under-

loaded node in the pool. It can reduce the time to manage

the load and improve execution time as well. A random

strategy is implemented at the machine-level algorithm. At

last, the job migration operation has been performed till the

overload queue is not empty and same procedure has been

followed at machine level and resource level. PE level load

threshold, machine-level load threshold and the resource-

level load threshold denoted by q, g, and q, respectively, in

algorithms 1–4, has been found out according to the task

arrival.


123


123

The above-mentioned algorithms propose an efficient

load balancing algorithm using random policy with vari-

able threshold value. Here, machine-level algorithms have

been discussed. The same algorithm procedure has been

followed at resource and PE level.

5 Experimental result and discussion

Through this part of paper, firstly the configuration of the

simulation setup is introduced and then the experimental

results are given. Each algorithm was run five consecutive

times and the average of the results was taken as the final

result of that algorithm.

5.1 Experimental setup

Windows 7 on an Intel i5 with 3 GB of RAM and 360 GB

of hard disk has been used during simulation experiments

in GridSim version 5.0 [26] with JDK 1.5 updates 6 and

JRE 1.3. The goal of this experiment is to compare the

performance of a decentralized proposed load balancing

algorithms (PLBA) with conventional WLB and load bal-

ancing in enhanced GridSim (LBEGS) algorithm.

Grid systems are randomized in various sizes: 100, 500,

1,000, and 2,000 nodes. In the experiments we change some of

test parameters, such as the size of the Grid that is denoted by

S on figures, resource requests frequency with 50, 100, 200

and 400 ms are denoted by Ri. The experiment is to randomly

submit 1,000 Grid requests at the starting and schedule them to

the specific Grid resource based on WLB, LBGES and PLBA.

The size of data carried by the Grid task agent that is denoted

by D, all tasks have the same computation size D = 25 KB.

There are 25 Grid resource agents in the system. The arrival

time of each resource request is determined randomly by an

exponential distribution with mean of 200 ms, but we will

change the values of arrival time when testing the effect of

requests frequency on response time and resource allocation

efficiency. All parameters are summarized in Table 5.

Initially, all nodes have a zero load. Throughout the

experiment, only one Grid user agent is used for the gen-

eration of Grid resource requests. For the simulated


123

scenarios, it does not matter how many Grid user agents

there are in the system. These experimental configurations

are to bring up the performance of the load balancing

algorithm as many as possible.

5.2 Simulation results

The first experiment is to compare the performance of

WLB, an LBGES algorithm with PLBA in terms of

response time and resource allocation efficiency.

Figure 6 shows the response times that are influenced by

Grid size, available connections and bandwidth using

request intensity (Ri = 200 ms). For comparing different

size Grid, a lower average response time is considered to

be better depicted through Fig. 6. All algorithms present

the best results in the small size Grid but, when the size of

the Grid is larger, PLBA is decreasing quickly. The

response time using PLBA can be 50 and 44 % shorter than

that using the WLB and LBGES, respectively.

As shown in Fig. 7, the PLBA outperforms the con-

ventional WLB and LBEGS for the different size grid.

Secondly, we measured the resource allocation efficiency

of PLBA and WLB and LBEGS using Ri = 200 ms. The

WLB and LBEGS achieves to match nearly 98 % of all

requests in the small size Grid scenario, with PLBA closely

behind. However, as Grid size increases, the WLB and

LBEGS degraded performance than the PLBA. Under the

large size Grid, decrease of results for WLB and LBEGS is

lower than in the small size. Resource allocation efficiency

using PLBA is 27 and 15 % larger than that using the WLB

and LBEGS. Varying Grid size, result decreases for both

methods in a similar manner.

The basic experiments described above have been con-

ducted to show that the fundamental differences between

the PLBA and the WLB and LBEGS. Next, a sensitivity

experiment is devised in order to change the request fre-

quency variables. The effects of request frequency to

response time and resource allocation efficiency under

different size of the Grid through request intensity shown

in below mentioned experiments. The following figure

shows the behaviour of two allocation methods when

changing the resource requests frequency from 50 to 100

and 200 to 400 ms.

For the response time, a lower request frequency leads to

faster access times; whereas, X-axis shows a change in

request frequency. Presented through Figs. 8, 9, 10, the

response time using PLBA can be 21 and 9 % shorter than

that using the WLB and LBEGS, respectively, under small

size Grid (S = 100) and low request frequency

(Ri = 200 ms). When changing the size of Grid by S = 500

and remain request frequency as Ri = 200 ms, the response

time of WLB and LBEGS is 19 and 11 % and longer than

that using the PLBA. When increasing the size of Grid by

S = 2000 and request frequency by Ri = 50 ms, the per-

formance of WLB and LBEGS deteriorates quickly, the

response time is 79 and 29 % longer than that using the

PLBA. Under big size Grid (S = 2000) and high request

frequency (Ri = 50, 100 ms), WLB and LBEGS take more

time to allocate appropriate resources, the response time is

70 and 43 % longer than that using the PLBA. This effect

could be caused by an increasing synchronization of the

frequency request generation with the negotiations that lead

to successful resource provisioning. In other words, the

large number of open requests leads to longer queues for

processing, whereas the queues get shorter with decreasing

request frequency, the overall processing gets faster.

Considering the resource allocation efficiency, from the

results in Figs. 11, 12, 13, under small size Grid and low

request frequency (Ri = 200 ms), the resource allocation

efficiency using PLBA is nearly the same as that using the

WLB and LBEGS. When increasing the size of Grid by

S = 100 and remain request frequency as Ri = 200 ms, the

resource allocation efficiency of PLBA as much as 23 and

12 % larger that using the WLB and LBEGS, respectively.

When increasing the size of Grid by S = 500 and request

frequency by Ri = 50 ms, the resource allocation effi-

ciency of WLB and LBEGS is as much as 43 and 11 % less

than that using the PLBA. Under big size Grid (S = 2000)

and high request frequency (Ri = 50), the resource all

Table 5 Simulation parameters

Parameter Value

Number of nodes in a cluster 25 nodes in a cluster

Number of nodes (S) 100, 500, 1,000, 2,000

Reschedule interval (s) 600

Number of jobs 1,000

Requests intensity/frequency (Ri) ms 50, 100, 200, 400

Arrival time (ms) 200

Fig. 6 Comparison of response time


123

location efficiency of WLB and LBEGS is as much as

43 % less than that using the PLBA. Resource allocation

efficiency of 93 % can be achieved for the PLBA with a

200-ms pulse even in high request density. Complete sat-

uration of request in WLB and LBEGS can be attained in a

small size Grid/high-request density scenario. Increasing

Fig. 7 Resource allocation

efficiency

Fig. 8 Effects of request

frequency on response time

under S = 100



under S = 500


123



under S = 2000


frequency resources on

allocation S = 100

Fig. 12 Frequency effect on

resource allocation efficiency

under S = 500


123

the request frequency implies a high charge to the system

and in case of 50-ms pulses reduce the resource allocation

efficiency to nearly 50 % in all scenarios. In most of the

test cases, the PLBA is more efficient than the WLB and

LBEGS to allocate Grid resources in the test application.

When Grid size is creasing, it has more merits to use the

PLBA to schedule Grid resource; the PLBA has better

performance than usual WLB and LBEGS.

5.3 Overall comparison of PLBA with existing

algorithms

In Sect. 5, an algorithm is introduced for resource

selection including min–min, max–min, WLB, LBEGS,

PLBA, etc. In this section, the performance of WLB,

min–min, max–min, LBEGS and PLBA in a Grid will be

investigated by simulation. Considering that there are ten

Fig. 13 Frequency effect on

resource allocation efficiency

under S = 2,000

Fig. 14 Communication

overhead times vs. Gridlets on

PEs = 200

200 400 600 800 1000 1200 1400 1600 1800 20000

5

10

15

No. of Gridlets

Mak

espa

n

Makespan VS Gridlets on PEs= 200

WLBMin-MaxMax-MinLBGESPLBA

Fig. 15 Makespan vs. Gridlets

on PEs = 200


123

resources that every resource has five machines, and

every machine has four PEs. That is leading to 200 PEs in

total. The numbers of Gridlets are kept constant at 500

and every Gridlet at the beginning time and without a

deadline request. Every PE is between grade 1 and 10 and

every Gridlet is between grade 1 and 10 too. For ease of

study, we set the local load factor of resources to be zero;

this does not affect the performance of the algorithms.

These settings are convenient for us to analyse the

effectiveness and the behaviour of the algorithms. The

resource load threshold parameters and Gridlet parameters

are listed in Table 3. The comparison of communication

overhead times and makespan between above algorithms

are depicted through Figs. 14 and 15.

Figure 14 shows the communication overhead times of

the different algorithms keeping the number of Gridlets up

to 2,000. It shows that the times of WLB and min–min

increase quickly by increasing Gridlets; keeping max–min

is in the middle. It can be observed that LBEGS and PLBA

always have the minimum of five algorithms and increase

very slowly. Figure 14 illustrates that PLBA gives better

performance by reducing communication overhead times

the makespan of the different algorithms is included in

Fig. 15 which demonstrates that the makespan of all

algorithms increases with the number of Gridlets increas-

ing; PLBA is the minimum of all algorithms that gives

good performance with respect to makespan. In conclusion,

PLBA has better performance in reducing communication

overhead times and makespan.

6 Conclusions

This paper proposed a variable threshold-based dynamic

load balancing algorithm with random policy for better

response time, communication overhead and resource

allocation efficiency, etc., wherein resources, machines and

the Grid broker participates in the load balancing opera-

tions. Dynamic threshold value has been used at each level

according to the Grid size in the network. Furthermore, it is

extended by dividing lightly loaded node into lightly and

under-lightly loaded categories, in the context of variable

threshold and standard deviation. This approach actually

minimizes the searching time to find out the receiver

machine in order to transfer the Gridlets. A rigorous

examination of the proposed algorithm on the GridSim

simulator has been carried out in order to judge the

effectiveness of the algorithm. The simulation results are

establishing the effectiveness of the PLBA over WLB and

LBEGS. PLBA enhances resource allocation capacity and

better response time and communication overhead. Future

work can be extended towards adjusting the balance

threshold function and making it more adaptable to dif-

fering environments.

Acknowledgments This work has been carried out at Distributed

and Grid Computing Lab, Department of Computer Science and

Engineering at Jaypee University of Engineering and Technology,

Guna, M.P. The authors acknowledge the support provided. We

would like to thank all anonymous reviewers for their comments and

suggestions for improving the paper. We would like to thank Mr.

Punit Pandey and Dr. Ashutosh Singh for helping in improving the

language and expression of a preliminary version of this paper.

References

1. Rehman A, Qureshi K, Manuel P, Rashid H (2008) Resource

topology aware GridSim: a step ahead. J Comput 19(2):3–22

(special issue on Grid and Cluster Computing)

2. Shah M, Qureshi K, Rasheed H (2010) Optimal job packing, a

backfill scheduling optimization for a cluster of workstations.

J Supercomput 54(3):381–399 (ISSN 0920-8542)

3. Qureshi K, Hussain SS (2008) A comparative study of parallel-

ization strategies for fractal image compression on a cluster of

workstations. Int J Comput Methods 5(3):463–482

4. Lin W, Shen W (2010) Tree-based task scheduling model and

dynamic load-balancing algorithm for P2P computing. CIT

2903–2907

5. Rathore NK, Chana I (2013) A sender initiate based hierarchical

load balancing technique for grid using variable threshold value.

In: International conference IEEE-ISPC JUIT, Waknaghat, H.P.,

978-1-4673-6190

6. Wang B, Shen Q (2011) ID management and allocation algorithm

for P2P load balancing. Wuhan Univ J Nat Sci 16(4):293–300

7. Peng L, Xiao W (2011) A binary-tree based hierarchical load

balancing algorithm in structured peer-to-peer systems. JCIT 6(4)

8. Ludwig SA, Moallem A (2011) Swarm intelligence approaches

for grid load balancing. J Grid Computing 9(3):279–301

9. Yagoubi B, Slimani Y (2007) Task load balancing strategy for

grid computing. J Comput Sci 3(3):186–194 (ISSN 1546-9239,

Science Publications-2007)

10. Erdil D, Lewis M (2012) Dynamic Grid load sharing with adaptive

dissemination protocols. J Supercomput 59(3):1139–1166

11. Long Q, Lin J, Sun Z (2011) Agent scheduling model for adaptive

dynamic load balancing in agent-based distributed simulations.

Tongji University, Shanghai, China simulation modelling prac-

tice and theory. Science Direct Elsevier, pp 1021–1034

12. Zheng G, Bhatele A, Meneses E, Kale LV (2011) Periodic

hierarchical load balancing for large supercomputers. IJHPCA

25(4):371–385

13. Nandagopal M, Uthariaraj RV (2010) Hierarchical status infor-

mation exchange scheduling and load balancing for computa-

tional grid environments. Int J Comput Sci Netw Secur

10(2):177–185

14. De Grande RE, Boukerche A (2011) Dynamic balancing of

communication and computation load for HLA-based simulations

on large-scale distributed systems. J Parallel Distrib Comput

71(1):40–52

15. Sharma D, Saxena AB (2011) Framework to solve load bal-

ancing problem in heterogeneous web servers. IJCSES 2(1)

16. Nandagopal M, Gokulnath K, Rhymend Uthariaraj V (2010)

Sender initiated decentralized dynamic load balancing for multi

cluster computational grid environment. In: Proceedings of

A2CWiC ’10. Art No. 63, ISBN: 978-1-4503-0194-7


123

17. Zheng G, Bhatele A, Meneses E, Kale LV (2011) Periodic

hierarchical load balancing for large supercomputers. IJHPCA

25(4):371–385

18. Rathore N, Chana I (2011) A cognitive analysis of load balancing

and job migration technique in grid. In: WICT-IEEE Conference,

University of Mumbai, pp 77–82

19. El-Zoghdy SF (2012) A Hierarchical load balancing policy for

grid computing environment. IJCNIS 4(5):1–12

20. Qureshi K, Rehman A (2011) Enhanced GridSim architecture

with load balancing. J Supercomput 57:265–275

21. Rings T, Caryer G, Gallop J, Grabowski J, Kovacikova T, Schulz

S, Stokes-Rees I (2009) Grid and cloud computing: opportunities

for integration with the next generation network. J Grid Com-

puting 7(3):375–393

22. Hao Y, Liu G, Wen N (2012) An enhanced load balancing

mechanism based on deadline control on GridSim. Future Gener

Comp Syst 28(4):657–665

23. Suresh P, Balasubramanie P (2013) User demand aware grid

scheduling model with hierarchical load balancing. Math Prob

Engg 2013, Art No. 439362. doi:10.1155/2013/439362

24. Chen H, Wang F, Helian N, Akanmu G (2013) User-priority

guided min-min scheduling algorithm for load balancing in cloud

computing. National Conference on Parallel Computing Tech-

nologies (PARCOMPTECH)

25. Abdi S, Mohamadi S (2010) The impact of data replication on job

scheduling performance in hierarchical data grid. Int J Appl

Graph Theory Wirel Ad hoc Netw Sens Netw 2(3):15–21

26. Kamarunisha M, Ranichandra S, Rajagopal TKP (2011) Recita-

tion of load balancing algorithms in grid computing environment

using policies and strategies - an approach. IJSER 2(3)

27. Balasangameshwara J, Raju N (2012) A hybrid policy for fault

tolerant load balancing in Grid computing environments. J Netw

Comput Appl 35:412–422

28. Mandalapu S (2005) Dynamic load balancing design and mod-

eling in MPIAB: general architecture. PDPTA, pp 710–716


123

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Variable threshold-based hierarchical load balancing technique in Grid