Cloud computing is solely network dependent
technology which completely rely on internet. The load
balancing is one of the vital parameter for judging the
efficiency of cloud computing. It continuously perform check
on the VMs and cloudlets, so that none of the VMs get
overloaded, while some machines still have space to allocate
the cloudlet. In such cases load balancing is done to use
resources in efficient manner. Here, we proposed a new
approach called modified honey bee behavior approach
which is based on HBB-LB. In this modified honey bee
algorithm, we keep account of cloudlet size, VM capacity and
priority. Knowledge of these entities of cloud environment is
very important to allocate cloudlets to VM. The utilization of
VM resources should be maximum and cloudlet will be
allocated to that VM which is best fit by principle of
optimality (on basis of memory allocation) with minimum
number of high priority task. Modified HBB-LB keeps track
of priorities of cloudlets, so high priority cloudlet should not
wait in queue as compared to other cloudlets. We have
compared modified honey bee approach with some previous
load balancing approaches and got minimum average
response time, makespan time and degree of imbalance.
Mohd Hamza : Dept. of Computer Science and Engineering, Samrat Ashok Technological Institute
Vidisha, (MP) , INDIA
Satish Pawar : Dept. of Computer Science and Engineering, Samrat Ashok Technological Institute
Vidisha, (MP) , INDIA
Yogendra Kumar Jain : Dept. of Computer Science and Engineering, Samrat Ashok Technological Institute
Vidisha, (MP) , INDIA
Cloud Computing
HBB-LB
Load Balancing
In this paper, we designed modified honey bee load
balancing technique inspired by honey bee behavior and
HBB-LB. In modified honey bee approach, it keep track
of load of all VMs, its capacity and cloudlet size, to
ensure that the assignment of jobs to best feasible VMs.
Allocation process of jobs should be in such a way that
VM resources should be utilized in optimized way to
reduce the wastage of resources. Proposed technique also
consider the priority of cloudlets while assigning.
Modified Honey Bee load balancing technique diminish
the wastage of resource as it was main drawback of HBBLB,
because of prior information of cloudlet size , VM
capacity and available free processing capacity left with
VM. Priority based allocation is there to give particular
importance to particular cloudlets.
[1] D.L. Eager, E.D. Lazowska, J. Zahorjan Adaptive load
sharing in homogeneous distributed systems, The
IEEE Transactions on Software Engineering, vol. 12,
1986, pp. 662–675.
[2] N. Malarvizhi, V. Rhymend Uthariaraj Hierarchical
load balancing scheme for computational intensive
jobs in Grid computing environment, in: Advanced
Computing, 2009 ICAC 2009. First International
Conference on, 13– 15, , 2009, pp.97-204.
[3] J. Rex Fiona, Roshni Thanka, Parallel Genetic Load
Balancing with Competency Rank in Computational
Grid Environment, International Journal of
Engineering Research and Applications, Vol. 3, Issue
1, 2013, pp.1814-1817.
[4] R.F. de Mello, L.J. Senger, L.T. Yang, A routing load
balancing policy for grid computing environments, in:
20th International Conference on, Advanced
Information Networking and Applications, vol. 1,
2006.
[5] Jasmin James efficient VM load balancing algorithm
for cloud computing environment, in International
Journal on Computer Science and Engineering
(IJCSE), Vol. 4 No, 2012, pp. 1658-1663.
[6] M. Houle, A. Symnovis, D. Wood, Dimension
exchange algorithms for load balancing on trees, in:
Proc. of 9th Int. Colloquium on Structural Information
and Communication Complexity, Andros, Greece,
2002, pp. 181–196.
[7] Y. Hu, R. Blake, D. Emerson, An optimal migration
algorithm for dynamic load balancing, Concurrency:
Practice and Experience 10, 1998, pp. 467–483.
[8] S. Genaud, A. Giersch, F. Vivien, Load balancing
scatter operations for grid computing, in: Proceedings
of the 12th Heterogeneous Computing Workshop
(HCW’2003), Nice, France, 2003, pp. 101–110.
[9] M. Moradi, M.A. Dezfuli, M.H. Safavi, Department
of Computer and IT, Engineering,Amirkabir
University of Technology, Tehran, Iran, A New Time
OptimizingProbabilistic “Load Balancing Algorithm in
Grid Computing” IEEE Vol.1 ,2010, pp. 232-237.
[10] Elina Pacini a, Cristian Mateos b,c,?, Carlos García
Garino a,dBalancing throughput and response time in
online scientific Clouds via Ant Colony Optimization
(SP2013/2013/00006) Vol. 84, 2015, pp. 31–47.
[11] Dhinesh Babu L.D. , P. Venkata Krishnab “Honey bee
behavior inspired load balancing of tasks in cloud
computing environments”, ELSEVIER Applied Soft
Computing 13, 2013, pp.292– 303 .