1. technology and computing

ISSN: 2393-994X
KARPAGAM JOURNAL OF ENGINEERING RESEARCH (KJER)
Volume No.: II, Special Issue on IEEE Sponsored International Conference on Intelligent Systems and Control (ISCO’15)
Power Management in Cloud Computing Using Artificial Bee
Colony
S.Saravanan1, Dr.V.Venkatachalam2 , S.Then Malliga3
1
Research Scholar, Computer science and Engineering, jeyasaraa@gmail.com,
M.Kumarasamy College of Engg, Karur, India
Principal, vv01062007@hotmail.com
The Kavery Engineering College Mecheri, India
2
3
ME Scholar, Computer science and Engineering, anbuthen.89@gmail.com,
M.Kumarasamy College of Engg, Karur, India
Abstract
Power consumption has become a major challenge in cloud computing. Thus to reduce energy
consumption and improving utilization of hosts ,a load balancing is approached using Artificial Bee
colony(ABC).In this algorithm it detect utilized and underutilized host, in utilized host it detect and
migrates one or more VMs thus it reduces its utilization and in underutilized it migrates all VMs and
switch them to sleep mode. Thus the tradeoff between power consumption provides the high quality of
service to the customer. Thus the power consumption and operational cost is reduced.
Keywords—Cloud computing; Artificial Bee Colony Algorithm (ABC); load balancing; Virtual Machine (VM).
1. INTRODUCTION
Cloud computing architecture allows access to information as long as an electronic device has access to the internet.
Cloud computing is getting popular day by day because the information and data being accessed is found in the
clouds i.e. internet, and hence does not require a user to access the data from exact place.
In Cloud computing energy consumption is a source of much discuss. On one side, some see a huge new form of
industrialization gobbling up resources; with large cloud and social networking sites consuming megawatts of power
to feed insatiable computing needs. Thus we propose a load balance aware using the artificial bee colony which
migrates minimal migration time (MMT) which detects the over utilized and underutilized host. The proposed
algorithm which shows the good result than other algorithm like local regression-MMT, Dynamic voltage frequency
scaling. We evaluate the simulation using the CloudSim toolkit [6].
2. RELATED WORK
Load balancing is an approach to reducing energy consumption and improving utilization of hosts. Babu L.D et al.
[1] have proposed a load balancing technique to balance the load and priorities of tasks that removed from heavily
loaded VMs. This technique is based on behavior of honey bee foraging strategy and improves the overall
throughput of processing and reduces the response of time of VMs. However authors have not investigated the
power consumption.
Dalapati et al. [2] have proposed a Green scheduling algorithm that optimizing power consumption in cloud
computing. It uses bee colony algorithm for service rescheduling and ant colony algorithm for power consumption
management. In contrast, in this paper we use bee colony algorithm for detection of over utilized hosts and for the
VM selection we use MMT.
40
ISSN: 2393-994X
KARPAGAM JOURNAL OF ENGINEERING RESEARCH (KJER)
Volume No.: II, Special Issue on IEEE Sponsored International Conference on Intelligent Systems and Control (ISCO’15)
Beloglazov et al. [3] have presented an architectural principles for energy aware management of clouds. Moreover,
they proposed energy-efficient resource allocation policies and scheduling algorithms. However, because of the fact
that they used fixed utilization thresholds, this approach is not be efficient for the cloud computing environments. In
adaptive heuristics for dynamic consolidation of VMs based on analysis of historical data from the resource usage
by VMs. This algorithm reduces the energy consumption. Authors propose algorithms like Median Absolute
Deviation, Interquartile Range, Local Regression (LR) and Robust Local Regression for host overloading detection.
Moreover, for the VM selection they use The Minimum Migration Time policy, The Random Choice Policy and
Maximum correlation policy. Based on the algorithm after the detection of overloaded hosts and select VMs to
migrate from these hosts, system finds the host with the minimum utilization and if it is possible tries to place the
VMs from this host on the other hosts while keep them not overloaded and when all the migration have been
complete switch host to the sleep mode. If this cannot be consummate, the host kept active. This process is
iteratively recurring for all host except the overloaded hosts. Whereas we have a same approach for underutilized
hosts and VM selection policy (MMT), our host overloading detection methods are different and we are using
artificial bee colony algorithm (ABC) to detect over utilized hosts.
Yao et al. [4] have presented a load balancing mechanism based on artificial bee colony algorithm (ABC). Authors
propose an improved artificial bee colony algorithm to increase the system throughput. However, they did not
investigate the energy consumption or SLA violation.
3. ARTIFICIAL BEE COLONY
Artificial Bee Colony algorithm (ABC) is an optimization algorithm based on the intelligent foraging behavior of
honey bee swarm, proposed by Karaboga in 2005 [5]. It is the swarm-based algorithms which simulates the
intelligent foraging behavior of a honeybee swarm. It is use for problem solving in distribution. The artificial bee
colony consists of two group of workers are employed bee and unemployed bee. The unemployed bee is the
onlooker bee and scout. In the colony there exists of two parts one is employed and rest is onlooker bee. Figure 1.
shows the behavior of bees. A “waggle dance” is done when a suitable food source is found from hive [9].
Figure 1. Artificial Bee Colony algorithm (ABC) Architecture
41
ISSN: 2393-994X
KARPAGAM JOURNAL OF ENGINEERING RESEARCH (KJER)
Volume No.: II, Special Issue on IEEE Sponsored International Conference on Intelligent Systems and Control (ISCO’15)
Employed bee: To search for food source within the neighborhood of food source in their memory and shares the
information to the onlooker bee.
Onlooker bee: It selects according to information produced which follows the employed bee
Scout: If the total search time exceeds then it will abandon the source and its start to search new solution.
The main step of algorithm is
Initialize
REPEAT
 Exploring employed bees to search food sources and determine their nectar amounts.
 Calculating the probability preferred by onlooker bee.
 Exploring onlooker bees onto the food sources and determines their nectar amounts.
 Discovering new food source by scout.
 Storing the best food source found so far.
UNTIL (requirements met)
To calculate the probability values Pi by means of their fitness values using the equation.
𝑓𝑖𝑡𝑖
𝑃𝑖 = ∑𝑛
𝑖=1 𝑓𝑖𝑡𝑖
………….……………Eq.1
Where n is number of food source fit is the fitness value of food source. Pi is the probability of the solution.
Consequently onlooker bee choose their food source based on the value calculated by the Eq.1.For the best solution
for delivering the need according to user with accurate, ensure the quality of result and economic result [10].
4. PROPOSED ALGORITHM
In this section to present load balancing method for power consumption management in three parts.
4.1. Detecting over utilized host
To detect over utilized host detection is done based on artificial bee colony algorithm (ABC) to determine which
hosts are over utilized and then the next migrate some VMs from one host to other hosts to improve its utilization.
After migrating various VMs from Over utilized hosts, because of the fact that their utilization reduced, they become
a lesser amount of suitable and less charming food sources for the bees. Hence other hosts with the higher load can
be moved for the best food sources. Thus to find it extends the PowerVmAllocationPolicyMigrationAbstract class in
CloudSim simulator [6].Initially the algorithm initialize variable with the utilization of and calculate the fitness
value and the onlooker bee calculate the probability for the best food source.
4.2. Detecting underutilized host
To manage the underutilized hosts. We utilize a method to deal with underutilized hosts which presented in [4].
Having detected the over utilized hosts and migrate some of their VMs, then require to discover hosts with the
minimum utilization and migrating all possible VMs which allocated to these hosts to the other hosts while keep
them not overloaded and when all the migration is completed, changing host to the sleep mode. If this cannot be
achieved, the host is kept active. This procedure is iteratively repeated for all hosts except the overloaded ones.
4.3. SLA violation
Service Level Agreements (SLAs) establish the Quality of Service (QoS) agreed between service-based systems
consumers and providers. They defined SLAs are delivered when 100% of performance requested by applications
inside a VM is provided at any time bounded only by the parameters of the VM. To evaluate SLA violation
presented in [3] there are three metric to measuring the SLA violation. The first metric is SLA Violation Time per
Active Host (SLATAH) which is percentage of time that active hosts experienced CPU utilization of 100% in Eq.2;
and the another metric is Performance Degradation due to Migrations (PDM) in Eq.3. The reasoning that they
42
ISSN: 2393-994X
KARPAGAM JOURNAL OF ENGINEERING RESEARCH (KJER)
Volume No.: II, Special Issue on IEEE Sponsored International Conference on Intelligent Systems and Control (ISCO’15)
considered SLATAH is that if host utilization is 100%, the performance of applications is bounded by host capacity
and VMs are not provided with the required performance level. The main metrics to measure SLA violation is
SLAV and is calculated using Eq.4.
1
𝑇𝑠𝑖
𝑁
𝑇𝑎
SLATAH= ∑𝑁
𝑖=1
1
𝐶𝑠
𝑀
𝐶𝑎𝑖
PDM= ∑𝑀
𝑗=1
𝑖
….… ………
Eq.2
𝑖
…………
SLAV=SLATAH.PDM
Eq.3
…….
Eq.4
where N is the number of hosts and M is the number of VMs; Ts i is the total time during that host i has experienced
the utilization of 100%. Tai is the time during which host i being in active state; Csi is the estimate of performance
degradation of VM j caused by migrations; Cai is the total CPU capacity requested by VM j during its lifetime.
Figure 2. Show the comparison between the policies.
Figure 2. Comparison between policy
5. SIMULATION RESULT
Energy consumption, SLA Violation and the migration are compared with the other methods.SLA Violation has
some metrics they are SLAV, SLATAH, PDM .Therefore SLAV is the important metrics to measure SLA Violation.
Thus comparing to other methods Bee-MMT consumes less energy consumption in cloud. Table 1. Show that BeeMMT has the minimum energy consumption compared to other policy.
Our proposed system has nearly 29 times less energy consumption 27.47% less than LR-MMT and 25.86% less than
MAD-MMT.
Table 1. Simulation Result
POLICY
ENERGY CONSUMPTION
SLAV
SLATAH
PDM
VM MIGRARION
LR-MMT
116.71
117.02
114.20
85.84
2.52%
5.14%
5.18%
6.45%
4.04%
5.08%
5.24%
80.85%
0.06%
0.10%
0.10%
0.01%
17.90
26.44
25.91
2.00
IQR-MMT
MAD-MMT
BEE-MMT
But the result in Bee-MMT has more SLA Violation than other method. Thus the Bee-MMT has high SLATAH
compared to other the percentage of time of active host experienced CPU utilization of 100%.Therefore the
utilization is more than other method.
6. CONCLUSION AND FUTURE WORK
43
ISSN: 2393-994X
KARPAGAM JOURNAL OF ENGINEERING RESEARCH (KJER)
Volume No.: II, Special Issue on IEEE Sponsored International Conference on Intelligent Systems and Control (ISCO’15)
Thus the power consumption in the present investigation still the problem exists. Thus
to reduce the power
consumption in cloud computing is reduced using the artificial bee colony algorithm. Thus tradeoff between power
consumption and providing high quality of service to the customer. Due to this it has more SLA violation, the
percentage of increasing in SLA violation in this algorithm is less than reducing entering energy consumption.
Future work requires reducing SLA violation as a requirement to satisfy high QOS to the customer.
References
[1] D. B. L.D and P. V. Krishna, "Honey bee behavior inspired load balancing of tasks in cloud computing
environments," Applied Soft Computing, May (2013) , vol. 13, no. 5, pp. 2292–2303.
[2] P. Dalapati and G. sahoo, "Green Solution for Cloud Computing with Load Balancing and Power
Consumption Management," International Journal of Emerging Technology and Advanced Engineering,
March (2013), vol. 3, no.3, pp. 353–359.
[3] .Beloglazov and R. Buyya, "Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy
and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers,"
Concurrency and Compution:practice and experience, September (2012), vol. 24, no. 13, pp. 1397-1420.
[4] J. Yao and J.-h. He, "Load balancing strategy of cloud computing based on artificial bee algorithm," 8th
International Conference on Computing Technology and Information Management (ICCM), Seoul, pp. 185
- 189, 24-26 April (2012).
[5] d. Karaboga, "An Idea Based on Honey Bee Swarm for Numerical Optimization," TECHNICAL REPORT-
TR06, Turkey, (2005).
[6] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose and R. Buyya, "CloudSim: a toolkit for
modeling and simulation of cloud computing Environments and Evaluation of Resource Provisioning
Algorithms,"Software:Practice and Exprience (SPE), (2011) , vol. 41, no. 1, pp.23-50.
[7] D. Karaboga and B. Basturk, "On the performance of artificial bee colony (ABC) Algorithm," Applied soft
computing, (2008), vol. 8, no. 1, pp.687-697.
[8] Belolazov, J. Abawajy and R. Buyya, "Energy-aware resource allocation heuristics for efficient
management of data centers for cloud computing," Future Generation Computer Systems, (2012), vol. 28,
no. 5, pp.755-768.
[9] M. Randles, D. Lamb and A. Taleb-Bendiab, "A comparative Study into Distributed Load Balancing
Algorithms for Cloud Computing," IEEE 24th International Advanced Information Networking and
Application Workshops, Liverpool, April (2010), pp. 551 – 556, 20-23.
[10] H. Deng-xu and J. Rui-min, "Cloud model-based Artificial Bee Colony Algorithm's Application in the
logistics Location Problem," International Conference on Information Management, Innovation
Management and International Engineering (ICIII), Sanya, Oct.(2012), pp. 256 – 259, 20-21.
[11] L. Maghali S.de Oliveira, R. Schirru and J. A.C.C. de Medeirose, "On The Performance of An Artificial
BEE Colony Optimization Algorithm Applied to The Accident Diagnosis In a PWR Nuclear Power Plant,"
in International Nuclear Atlantic conference-INAC, vol. 1, Rio de Janeiro Brazil, September 27 to October
2, (2009).
[12] K. Park and V. S. Pai, "CoMon: a mostly-scalable monitoring system for PlanetLab," ACM SIGOPS
Operating Systems Review, (2006), vol. 40, no. 1, pp.65-74.
44