Load Balancing in Cloud Computing using Round Robin Algorithm

Load Balancing in Cloud Computing using Round Robin Algorithm

Anusha S.K. Information science & Engg.	Bindu Madhuri N.R Information science & Engg.	Nagamani N.P. (Asst. Prof.) Information science & Engg.
JSSATE	JSSATE	JSSATE
Bangalore, India	Bangalore, India	Bangalore, India
anusha.karandikar@gmail.com	bindumadhuri93@gmail.com	nagamaninp@redifffmail.com

Abstract Cloud computing is known as digital service delivery over the Internet by several applications which are carried out by computer systems in distributed datacenters. It supplies a high performance computing facilities which allow shared computation and storage over long distances. To properly manage the resources of the service provider we require balancing the load of the jobs that are submitted to the service provider. Load balancing is required as we dont want one centralized servers performance to be degraded. In this paper, we present Round Robin Algorithm for efficient load balancing in cloud Environment.

Keywords Cloud Computing, Load Balancing, Virtual Machine, Round Robin Algorithm.

1. INTRODUCTION

   Cloud computing is an attracting technology in the computer science. In Gartners report, it says that the cloud will bring changes to the IT industry [1]. The cloud is changing our life by providing users with new types of Services. Users get service from a cloud without paying attention to the details [2].NIST gave a definition of cloud computing as a model for enabling ubiquitous, convenient, on- demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction [3]. More and more people pay attention to cloud computing [4], [5]. Cloud computing is efficient and scalable but maintaining the stability of processing so many jobs in the cloud computing environment is a very complex problem with load balancing receiving much attention for researchers. Since the job arrival pattern is not predictable and the capacities of each node in the cloud differ, for load balancing problem, workload control is crucial to improve system performance and maintain stability. Load balancing schemes depending on whether the system dynamics are important can be either static or dynamic [6].

2. RELATED WORK

   Static schemes do not use the system information and are less complex while dynamic schemes will bring additional costs for the system but can change as the system status changes. A dynamic scheme is used here for its flexibility. The model has a main controller and balancers to gather and analyze the information. Thus, the dynamic control has little influence on the other working nodes. The system status then provides a basis for choosing the right load balancing strategy. The load balancing model given in this article is aimed at the public cloud which has numerous nodes with distributed computing resources in many different geographic locations. Thus, this model divides the public cloud into several cloud partitions. When the environment is very large and complex, these divisions simplify the load balancing. The cloud has a main controller that chooses the suitable partitions for arriving jobs while the balancer for each cloud partition chooses the best load balancing strategy.

   Fig1. Load balancing in Cloud Environment

   There have been many studies of load balancing for the cloud environment. Load balancing in cloud computing was described in a white paper written by Adler who introduced the tools and techniques commonly used for load balancing in the cloud [7]. However, load balancing in the cloud is still a new problem that needs new architectures to adapt too many changes. Chaczko et

   al. described the role that load balancing plays in improving the performance and maintaining stability [8]. There are many load balancing algorithms, such as Equally Spread Current Execution Algorithm, and Ant Colony algorithm. Nishant et al.[9] used the ant colony optimization method in nodes load balancing. Randles et al. gave a compared analysis of some algorithms in cloud computing by checking the performance time and cost[10]

   .

   Fig 2. Assigning jobs to the cloud partition

   When a job arrives at the public cloud, the rst step is to choose the right partition. The cloud partition status can be divided into three types:

   1. Idle

   2. Normal

   3. Overload.

   The job allocation strategy is described in figure 3.

   Fig 3. Job Assignment Strategy

3. PROPOSED ALGORITHM

   Our proposed algorithm is Round Robin to reschedule the CPUs. Here we use this because it is simple and it is a dynamic algorithm i.e. it can be adapted according to the changing system environment. Here at first consumers request submitted into the SA and SA search for free VM s. When it finds one it starts to serve the services to those VMs using RR (Round Robin) Algorithm .In Round Robin algorithm the time is divided into multiple slices and each node is given a particular time slice or time interval.

   Function Boolean TVM

   (Request_for Checking_of Virtual_Machine) Procedure GUIC (service)[11]

   Begin Boolean flag

   flag <- SA(services) if (flag = 0)

   then

   write (Request Service cannot be carried out)

   else

   write (Request Service is accepted) End

   Function Boolean SA (service)

   Begin Boolean flag.

   flag <- TVM(Request for checking of availability of If (flag =0)

   Return (false) else

   Return (true) End

   The decision of a service acceptation or rejection is taken by the service accepter. So it can be solved by the following flowchart.

   Fig 4. Flow Chart of Service Accepter.

   Begin Boolean flag

   If (available VM)

   then RR scheduling Algorithm else

   Return (False) End

   In the following flowcharts we will explain the whole work of this thesis. The first one is describing the process of scheduling and rescheduling

   In the following flowcharts we will explain the whole work of this thesis. The first one is describing the process of scheduling and rescheduling

   Fig 5. Flow Chart of Load Balancing

   Begin Boolean flag

   flag <- SA(services) if (flag = 0)

   then

   write (Request Service cannot be carried out)

   else

   write (Request Service is accepted) End

   Function Boolean SA (service) Begin

   Boolean flag.

   flag <- TVM(Request for checking of availability of Virtual Machines)

   If (flag =0) Return (false) else

   Return (true) End

   The decision of a service acceptation or rejection is taken by the service accepter. So it can be solved by the following flowchart.

   Begin Boolean flag

   If (available VM)

   then RR scheduling Algorithm else

   Return (False) End

   In the following flowcharts the TVM is explained.

   Fig 6. Flow chart of TVM

4. CONCLUSION

   The purpose is to focus on one major concerns of cloud computing i.e.; Load Balancing. The goal of Load Balancing is to increase client satisfaction and maximize resource utilization and substantially increase the performance of the cloud system. Also the purpose of load balancing is to make every processor or machine perform the same amoun of work throughout which helps to increase in throughput, minimizing the response time and reducing the number of job rejection. Here, to achieve Load Balancing, we have used Round Robin algorithm technique.

5. ACKNOWLEDGMENT

   The authors thank Dr. D V Ashoka, Professor and Head, Department of Information Science and Engineering, JSS Academy of Technical Education, Bangalore, for his constant review and support in writing this paper.

6. REFERENCES

[1]. R.Hunter, The why of cloud, http://www.gartner.com/DisplayDocument?doc cd=226469&ref= g noreg, 2012.

[2]. M. D. Dikaiakos, D. Katsaros, P. Mehra, G. Pallis,and A. Vakali, Cloud

computing: Distributed internetcomputing for IT and scientific research, InternetComputing, vol.13, no.5, pp.10-13, Sept.-Oct. 2009.

[3]. P. Mell and T. Grance, The NIST definition of cloudcomputing,

http://csrc.nist.gov/ publications/nistpubs/800-145/SP800-145.pdf, 2012.

[4]. Microsoft Academic Research, Cloud

computing,http://libra.msra.cn/Keyword/6051/cloud-computing?query= cloud%20computing, 2012.

[5]. Google Trends, Cloud computing,

http://www.google.com/trends/explore#q=cloud%20computing, 2012.

[6]. N. G. Shivaratri, P. Krueger, and M. Singhal, Loaddistributing for locally distributed systems, Computer,vol. 25, no. 12, pp. 33-44, Dec.

1992

[7]. B. Adler, Load balancing in the cloud: Tools, tips andtechniques, http://www.rightscale. com/info center/whitepapers/Load-Balancing-in-

the-Cloud.pdf, 2012

[8]. Z. Chaczko, V. Mahadevan, S. Aslanzadeh, andC. Mcdermid, Availability and load balancing in cloudcomputing, presented at the

2011 International Conferenceon Computer and Software Modeling, Singapore, 2011.

[9]. K. Nishant, P. Sharma, V. Krishna, C. Gupta, K. P. Singh,N. Nitin, and

R. Rastogi, Load balancing of nodesin cloud using ant colony optimization, in Proc. 14thInternational Conference on Computer Modelling andSimulation (UKSim), Cambridgeshire, United Kingdom,Mar. 2012, pp. 28-30.

[10]. M. Randles, D. Lamb, and A. Taleb-Bendiab, Acomparative study

into distributed load balancingalgorithms for cloud computing, in Proc. IEEE 24thInternational Conference on Advanced InformationNetworking and Applications, Perth, Australia, 2010,pp. 551-556.

[11]. Syed Tauhid Zuhori, Tamanna Sharmin, Runia Tanbin, Firoz Mahmud, An effective loadbalancing approach in cloud environment using round robin

algorithm. International journal of artificial intelligence and mechatronics