An efficient load balancing using Bee foraging technique with Random stealing

IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 2, Ver. V (Mar – Apr. 2015), PP 97-104
www.iosrjournals.org
DOI: 10.9790/0661-172597104 www.iosrjournals.org 97 | Page
An efficient load balancing using Bee foraging technique with
Random stealing
Ms. Anna Baby 1
, Dr. Joshua Samuel Raj2
1
(Department of computer Science and Engineering, Karunya university, India)
2
(Department of computer Science and Engineering, Karunya university, India)
Abstract: Load balancing is an integrated aspect of cloud computing. Inorder to achieve optimal machine
utilization, tasks from overloaded virtual machines have to be transferred to underloaded virtual machines. In
this paper, the honey bee foraging mechanism for load balancing is improved by random stealing technique.
For finding the state of a virtual machine, the deviation of virtual machine load is calculated and checked for
confinement within a threshold condition set. With random stealing method, tasks are stolen from a random
Virtual machine when a VM is idle. It thus saves the idle time of the processing elements in the Virtual machine.
The performance evaluation is done by using cloudSim. Simulation results show that the modified honey bee
foraging technique with random stealing reduces the makespan of algorithm execution together with balancing
system load and reduces the idle time of Virtual machine.
Keywords: Bee foraging, Load balancing, Random stealing, Task scheduling, Thresholds
I. Introduction
Cloud computing is one of the distributed computing paradigm which mainly focuses on providing
everything as a service to the customer and it provides computational as well as storage resources to users. The
processing units in cloud environments are called as virtual machines (VMs). Scheduling of the customer tasks
within the available resources is a challenging task. More than one task is assigned to one or more VMs that run
the tasks simultaneously [1]. This kind of environments should make sure that the loads are well balanced in all
virtual machines.
Load balancing is the task of distribution of application tasks to different processors in an efficient
manner to minimize program execution time. Effective implementation of load balancing can make cloud
computing more effective and it also improves user satisfaction. Load balancing distributes workloads across
multiple computing resources such as computers, a computer cluster, network links, central processing units or
disk drives. Load balancing aims to optimize the resource use, maximize the makespan as well as to minimize
the response time. A load balancing algorithm attempts to improve the response time of user‟s submitted
applications by ensuring maximal utilization of available resources. Load balancing ensures that all the
processors in the system as well as in the network do approximately the equal amount of work at any instant of
time [2]. To achieve optimal machine utilization, tasks from overloaded virtual machines are transferred to the
neighbour Virtual machine whose load value is below threshold. The proposed algorithm improves the honey
bee foraging technique by ensuring that no virtual machines remain idle by employing random stealing
mechanism. When the virtual machine is idle, [3] the algorithm will make multiple attempts to steal jobs from a
random Virtual machine.
Cloud computing provides much more effective computing by centralized memory, processing, storage
and bandwidth. It should make sure that the tasks are not loaded heavily on one VM and also ensure that some
VMs do not remains idle and/or under loaded [4]. The main objective of load balancing methods is to speed up
the execution of applications on resources whose workload varies at run time in an unpredictable manner.
This paper is divided into the following sections: section 2 describes the related works, Section 3 gives
the description of enhanced algorithm, Section 4 provides experimental results and finally Section 5 gives the
conclusion and future enhancement possibilities.
II. Related works
Load balancing mechanism distributes the workload across multiple computing resources to utilize
them effectively and to reduce the response time of the job, simultaneously eliminating a condition in which
certain nodes are over loaded while others are under loaded.
Dhinesh Babu and P.Venkata Krishna [1] proposed an algorithm for Honey Bee inspired load
balancing(HBB-LB) of tasks in cloud computing environments which aims to achieve well balanced load across
Virtual machines for maximizing the throughput. In particle Swarm Optimization (PSO) proposed by Ayed
salman, Imtiaz Ahmed [5] combines local search methods with global search methods (through neighborhood
experience), attempting to balance exploration and exploitation. The algorithm is proposed for the task

An efficient load balancing using Bee foraging technique with Random stealing
assignment problem for homogeneous distributed computing systems. The result shows that this runs faster with
less complexity. Belabbas Yabougi and Meriem Meddeber [6] proposed a distributed load balancing model for
grid computing which can represent any grid topology into forest structure. Distributed approach gains are
always better than those achieved by the hierarchical approach.
Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros, Humberto Sossa, Valentín Osuna [7] introduces a
Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC). Here the numbers of
search locations are drastically reduced by considering a fitness calculation. In this algorithm, the computation
of search locations is drastically reduced by considering a fitness calculation strategy which indicates when it is
feasible to calculate or only estimate new search locations. It reduces the computational complexity.
Guopu.Zha, Sam Kwong [8] proposed Gbest guided artificial bee Colony algorithm for numerical
function optimization incorporating the information of global best (gbest) solution into the solution search
equation to improve the exploitation. GABC algorithm consists of the three different stages that are the
employed bee stage, onlooker stage and the scout stage. The onlooker stage tends to select the good solution to
further update, while both the employed bee stage and update every individual in the population.
Pei-Wei Tsai, Jeng-Shyang Pan, Bin-Yih Liao, and Shu-Chuan Chu [9] introduce an Enhanced
Artificial Bee Colony Optimization. The onlooker bee is designed to move straightly to the picked co- ordinate
indicated by the employed bees and evaluate the fitness value near it in the original ABC algorithm in order to
reduce computational complexity. M.V Panduranga Rao, S.Basavaraj Patil [10] proposed a dynamic tree based
model in load balancing strategies for Grid computing. It defines a hierarchical load balancing strategy that
estimate the current workload of a grid based on the workload information received from its elements.
Ant Colony Optimization (ACO) for effective load balancing in Cloud Computing proposed by
Shagufta khan, Niresh Sharma [11] aims at the load balancing of nodes. ACO is inspired from the ant colonies
that work together in foraging behavior. It works efficiently and achieves better utilization of resources, but give
more overhead during runtime. Yun-Han Lee, Seiven Leu, Ruay-Shiung Chang proposed an improved
hierarchical load balancing algorithm, iHLBA [12] to schedule jobs in the Grid environment wherein it assigns
the fittest resource to the job and compares the clusters‟ load with the adaptive balance threshold to balance the
system load. The iHLBA is capable of balancing the overall load on the system, and offers an improvement to
the makespan due to its selection of the most suitable resource for a given job based on the most current system
status.
In the existing load balancing algorithms the idle condition of virtual machine is ignored and thus leads
to the wastage of processing time. Hence the global optimal solution with a balanced load cannot be found out
within a short span of time.
III. Honey bee behaviour inspired load balancing of tasks
Scheduling of the customer tasks within the available resources is a challenging task. The scheduler
should do the scheduling process efficiently in order to utilize the available resources completely. Load
Balancing is a technique to divide the amount of work that a computer has to do between many computers,
processors or any other resources to utilize them effectively and to minimize the response time, simultaneously
eliminating a condition in which some of the nodes are over loaded while others are under loaded [13]. Effective
implementation of load balancing can make cloud computing more effective and it also improves user
satisfaction. In the proposed method, a honey bee foraging technique with random stealing is used for task
allocation and load balancing. When tasks are allocated to the VMs, current load is calculated. If the VM
becomes overloaded the task is transferred to the neighborhood VM whose load value is below threshold [14].
Honey Bee foraging technique employs decentralized load balancing methodology and task transfer will be
carried out on the fly. The algorithm ensures performance of the system and avoid system imbalance.
3.1 Bee foraging behaviour
The artificial bee colony algorithm (ABC) is an optimization algorithm based on the intelligent
foraging behavior of honey bee swarm and was proposed by Karaboga in 2005 [15]. The algorithm is
completely inspired by natural foraging behavior of honey bees.
3.1.1 Initialization process
During initialization process, Artificial Bee Colony (ABC) algorithm starts by correlating all the bees
with arbitrarily produced food sources. Certain food sources are randomly selected by bees and their nectar
amount is determined. These bees come onto the hive and share the information with bees waiting in dance area
[16]. Each food source is generated as follows:
  jjjij XXrandXX minmaxmin 1,0  (1)

Here jXmin
and jXmax
are bounds of xi in jth direction and  1,0rand is a uniformly distributed random number in
the range [0, 1].
3.1.2 Employed bee phase
Employed bees stay on the food source and provide the neighborhood of the source in its memory.
After sharing the information in the dance area, employed bees go to food source visited by its previous cycle
and choose new food source by using the information in the neighborhood. Then onlooker prefers a food source
depending on nectar information provided by employed bees.
3.1.3 Onlooker bee phase
Onlooker bees get the information about food sources from employed bees in hive and select one of the
sources. Onlooker bee is waiting for a dance to choose a food source. Waggle/tremble/Vibration dances are
performed by the bees to give an idea about quality and quantity of food and its distance from bee hive.
3.1.4 Scout bee phase
Scout bee carried out random search. When the nectar source is abandoned by the bees, a new food
source is randomly determined by a scout bee. Probability of selecting food source [17] is defined by the
equation (2) as follows:
 
 nS
n iii FitFitP 1
(2)
Where iFit will be the Fitness value of food source i and n
S will be the total number of food sources.
If the solution is represented by a food source does not improve for a particular number of iterations, then the
employed bee becomes scout.
3.2 Random stealing
With Random stealing [3], tasks are stolen from a random Virtual machine when a VM is idle. It thus
saves the idle time of the processing elements in the Virtual machine. The proposed algorithm with random
stealing makes multiple attempts to steal job from other Virtual machines. If there are N Virtual machines, the
algorithm will make N-1/N attempts on an average to steal a job. Job stealing is done only when a VM is idle.
3.3 Enhanced honey bee inspired load balancing algorithm
In Honey bee behavior inspired load balancing (HBB_LB), a method of task allocation and load
balancing is put forth. It ignores the idle condition of virtual machine and thus leads to the wastage of
processing time. Hence the global optimal solution cannot be found out within a short span of time. In the
proposed method, an enhanced honey bee foraging technique with random stealing is used for task allocation
and load balancing. When tasks are allocated to the VMs, current load is calculated. If the VM becomes
overloaded the task is transferred to the neighborhood Virtual machine whose load value is below threshold
[14]. Honey Bee foraging technique employs decentralized load balancing methodology and task transfer will be
carried out on the fly. The algorithm thus ensures performance of the system and avoid system imbalance.
Random stealing (RS) is an efficient load balancing technique which steals jobs from a random Virtual
machine when it is idle. It thus saves the idle time of the processing elements in the VM. The RS algorithm
makes multiple attempts to steal job from other Virtual machines. If there are N Virtual machines, the algorithm
will make N-1/N attempts on an average to steal a job. Job stealing is done only when a VM is idle.
Fig.1 illustrates the flow diagram of honey bee inspired load balancing of tasks with random stealing.
After allocating tasks to fittest virtual machine, load balancing is done among the available virtual machines
using random stealing technique. When a job request comes, the scheduler initializes job parameters and finds
the deviation of load. The probability value of deviation is checked for confinement within the range of 0 to 1.
All Virtual machines with probability of deviation within the given range are considered as underloaded and
outside the given range is marked as overloaded VM. After grouping the VMs, ACAP value of underloaded
virtual machines are compared with the ECAP value of tasks. Virtual machines with ACAP less than or equal to
ECAP are marked as fittest and jobs are allocated to it [18].
After job allocation to clusters, some clusters may remain underutilized. This leads to the wastage of
processor time. To avoid this, the proposed honey bee algorithm with random stealing sets two thresholds.
When a VM‟s load is less than or equal to threshold value TRS_LOW, then it steal tasks from any overloaded VMs.
Jobs are stolen from VMs with system load > TRS_HIGH and are allocated to free Virtual machines. This type of
load balancing techniques with random stealing can reduce the idle time of virtual machines.

Fig.1 Flow Diagram of Honey bee inspired load balancing of tasks with random stealing
3.4 Algorithm
Step 1 Get the available virtual resources from data center.ie, VM1, VM2… VMm
Step 2 Submit the list of tasks T=T1, T2…Tn by the user.
Step 3 When a request comes, the scheduler finds the Expected computing capacity for tasks


n
i
nmipsECAP 1
Where mips is million instructions per second and n is the total number of tasks.
Step 4 Compute the average computing capacity for each task using the equation,
ECAPmACAP
m
i
 1
/1 m: Number of VMs
Step 5 Find the load on a VM
iii eServiceratTasklengthLVM 
Step 6 Compute the average system load


m
i iLVMmASL 1
1
Step 7 The deviation of Load,  is found out as,

iLVMASL 
Step 7.1 The probability value is checked for confinement within the range 0 to 1 as,
If (0< P(  )<1)
Underloaded_list[]= VMi
else
Overloaded_list[]= VMi
Step 8 Select Underloaded VMs and compare its Average computing capacity with Expected computing power of tasks.
Step 8.1 Check if (ACAP< =ECAP), then
VMs are marked as Fittest and tasks are allocated to it
Step 9 After task allocation to VMs, some VMs remains underutilized. This leads to wastage of processor time
Step 9.1 Check if (system load < = TRS_LOW)
Perform Random Stealing
Step 9.1.1 Select VMs with (system load > TRS_HIGH)
Step 9.1.2 Randomly steal jobs from those VMs and allocate to VMs with system load < =
TRS_LOW
Step 9.2 If there are „N‟ VMs, the algorithm will make N-1 / N attempts on an average to steal a job.
Step 10 Stop
IV. Experimental results
In cloud computing, experimenting in real time is practically impossible as it needs excessive cost,
security etc. Hence a good simulator can be used for experimenting purpose. CloudSim is a generalized
simulation framework that allows modelling, simulation and experimenting the cloud computing infrastructure
and application services [19]. The main advantages of CloudSim are time effectiveness, it requires very less
time and effort to implement Cloud based application provisioning test environment [20].
Performance of our algorithm for load balancing with random stealing is evaluated by comparing it
with the performance of existing load balancing algorithm.
Fig.2 Comparison between algorithms based on Makespan
The comparison of Makespan for load balancing using honey bee inspired load balancing algorithm
(HBB_LB) and enhanced honey bee algorithm with random stealing (HBB_RS) is illustrated in Fig.2 Makespan
can be defined as the overall task completion time. We denote completion time of task Ti on VMj as CTij.
ijCTMakespan max{ | niTi ,.....2,1,  and },...2,1 mj  (3)
The X-axis represents the number of tasks and Y-axis represents Makespan in milliseconds. With load
balancing using honey bee inspired load balancing (HBB_LB), the makespan is reduced considerably. When
number of tasks increases, the difference in makespan is more and enhanced honey bee algorithm with random
stealing is more efficient.
The comparison is also made in terms of response time. Fig.3 shows the response time in milliseconds
for HBB_LB and HBB_RS. The X-axis represents number of tasks and Y-axis represents the response time in
milliseconds. It is the amount of time taken between submission of a request and the first response that is
produced. The reduction in waiting time is helpful in improving the responsiveness of the VMs. From this
graph, it is clear that enhanced honey bee foraging technique with random stealing is more effective and gives
better performance.

Fig.3 Comparison on Response time
Fig.4 illustrates the degree of imbalance in load in terms of number of tasks. The x-axis represents the
number of tasks and Y-axis represents the imbalance degree. Imbalance degree is defined in equation (4)
Degree of imbalance AVGLOWHIGH TTT  (4)
Where HIGHT is the highest task, LOWT is the lowest task among all the virtual machines and AVGT is
the average task of virtual machines. From Fig. 4, it is clear that imbalance degree is less for enhanced honey
bee inspired load balancing of tasks with random stealing and hence its performance is more when compared to
normal honey bee foraging load balancing technique.
Fig.4 Comparison on Imbalancing degree
An important parameter used in this work to analyze the load balancing strategy of the proposed
algorithm is the average resource utilization and is expressed in percentage.
sourceRe nutilizatio VM Numberdemand / of tasks (5)
Fig.5 Comparison based on Resource utilization

Fig.5 shows the resource utilization rate of proposed method with random stealing which is relatively
high when compared to existing honey bee load balancing method. With random stealing method, tasks are
stolen from a random Virtual machine when a VM is idle. It thus saves the idle time of the processing elements
in the Virtual machine.
Fig.6 Comparison of algorithms based on idle time
Idle time is the time between the times at which task is arrived on a virtual machine and time of task to
be assigned to one certain virtual machine. The comparison is made in terms of Number of tasks and the idle
time and the results are shown in Fig. 6.
All these results verify that the proposed honey bee foraging technique with random stealing performs
better than the existing honey bee inspired load balancing algorithm.
V. Conclusion
Load balancing of tasks in virtual machine plays an important role in cloud environments. In this paper,
an enhanced honey bee behavior inspired load balancing with random stealing is proposed. The existing load
balancing algorithms with bee foraging technique ignores the idle condition of a Virtual machine even after the
completion of tasks assigned to it. The modified bee foraging technique with random stealing method ensures
that load in the overall system is balanced and reduces the idle time of a processor. When tasks are allocated to a
VM, current load in that VM is calculated. If the VM becomes overloaded, the task is transferred to the
neighborhood Virtual machine whose load value is below threshold. With Random stealing, Jobs are stolen from
a random VM when a Virtual machine is idle. It thus saves the idle time of the processing element in a Virtual
machine. In the future we plan to extend this research for energy aware scheduling.
References
[1]. Dinesh Babu L.D, P.Venkata Krishna , “Honey Bee inspired Load Balancing of tasks in cloud computing environments”, Applied
soft computing 13,2292-2303,2013
[2]. Tejinder Sharma, Vijay Kumar Banga, “Efficient and Enhanced Algorithm in Cloud Computing”,IJSCE,ISSN:2231-2301,2013
[3]. Joshua Samuel Raj, Hridya K.S,V. Vasudevan, “Augmenting Hierarchical Load Balancing with Intelligence in Grid Environment”,
International Journal of Grid and Distributed Computing Vol. 5,No. 2, pp. 9- 8, 2012
[4]. Guopu.Zha, Sam Kwong , “Gbest-guided Artificial bee Colony algorithm for numerical function optimization”, Elsevier 3166-
3173,2010
[5]. Ayed salman, Imtiaz Ahmad, “Particle swarm optimization for task assignment problem”, Microprocessors and Microsystems
26,363-371,2002
[6]. Belabbas Yabougi, Meriem Meddeber, “Distributed Load balancing model”, ARIMA journal, Vol 12,pp 43-60,2010
[7]. Erik Cuevas, Daniel Zaldívar, Marco Pérez-Cisneros, Humberto Sossa, Valentín Osuna, “Block matching algorithm for motion
estimation based on ABC”, Applied soft computing,2012
[8]. Guopu.Zha, Sam Kwong ,“Gbest-guided Artificial bee Colony algorithm for numerical function optimization”, Elsevier 3166-
3173,2010
[9]. Pei-Wei Tsai, Jeng-Shyang Pan, Bin-Yih Liao, and Shu-Chuan Chu, “Enhanced Artificial Bee Colony Optimization”, International
journal of innovative computing,vol 5, no 12,2009
[10]. Kavya S.A, M.V Panduranga Rao,S.Basavaraj Patil, “Grid computing for load balancing strategies”,International journal of science
and research,ISSN:2319-7064,2012
[11]. Shagufta khan, Niresh Sharma, “Ant Colony Optimization for Effective Load Balancing In Cloud Computing”,IJARCSSE,Vol.4,
Issue 2,2014
[12]. Yun-Han Lee, Seiven Leu, Ruay-Shiung Chang, “Improving job scheduling algorithms in a grid environment”, Elsevier, 991–
998,2011
[13]. Arash Ghorbannia Delavar, yalda Aryan , “ A synthetic heuristic algorithm for independent task scheduling (SHIS) in cloud
systems”, IJCSI vol 8, Issue 6, No 2,2011

[14]. Thilo Kielmann, Henri E. Bal, Jason ,“Programming environments for high-performance Grid computing: the Albatross project”,
Elsevier 1113–1125,2010
[15]. D. Karaboga, “ An idea based on honey bee swarm for numerical optimization”, Technical Report TR06, Computer Engineering
Department, Erciyes University, Turkey,2005
[16]. A.M. Bernardino, E.M. Bernardino, J.M. Sánchez-Pérez, M.A. Vega-Rodríguez, J.A. Gómez-Pulido, “Efficient Load Balancing
Using the Bees Algorithm”, Springer-Verlag Berlin, Heidelberg, ISBN: 978-3-642-21826-2,2011
[17]. Vivek Kumar Sharma, Rajani Kumari,“ Improved Onlooker Bee Phase in Artificial Bee Colony Algorithm”, International Journal
of Computer Applications (0975 – 8887),2014
[18]. Ruay-Shiung Chang. Chun-Fu Lin, Jen-Jom Chen, “Selecting the most fitting resource for task execution”, Elsevier, 227–231,2011
[19]. R.N. Calheiros, R. Ranjan, A. Beloglazov, C.A.F. De Rose, R. Buyya , “CloudSim: a toolkit for modeling and simulation of cloud
computing environments and evaluation of resource provisioning algorithms, Software: Practice and Experience 41 ,23–50,2011
[20]. R.N. Calheiros, R. Ranjan, C.A.F.D. Rose, R. Buyya, “CloudSim: a novel framework for modeling and simulation of cloud
computing infrastructures and services”, Computing Research Repository, vol. abs/0903.2525, 2009
AUTHORS
Ms. Anna Baby received B.Tech degree in Computer science and Engineering from
Sree Narayana Gurukulam College of Engineering under Mahatma Gandhi
University, Kottayam in 2013. She is currently pursuing M.Tech in software
Engineering in Karunya University. Her research interests include Cloud computing,
Software Engineering and so forth.
Dr. Joshua Samuel Raj received his B.E degree in Computer Science and
Engineering from PETEC under Anna University and M.E degree in Computer
Science and Engineering from Jaya College of Engineering under Anna University
in 2005 and 2007, respectively. He received Ph.D degree in the area of Grid
scheduling under Kalasalingam University in 2015. He is currently an Assistant
Professor in Computer Science and Engineering at Karunya University, Coimbatore,
India. His research interests include Grid computing, Mobile Adhoc Networking,
Multicasting, Cloud Computing and so forth.

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