Hybrid Approach for Load Balancing in Cloud Computing

Vineet Richhariya, Ratnesh Dubey and Rozina Siddiqui

Dept. of Cse, Lnct,Bhopal (M.P.) India

 

 

ABSTRACT:

Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. Load balancing with cloud computing provides a good  efficient  strategy to   several   inquiries  residing  inside  cloud computing environment set. complete  balancing must  acquire   straight into   accounts   two     tasks,  one   will be the  resource provisioning as well as  resource allocation  along with   will be   task  scheduling  throughout  distributed System. Round robin algorithm can be   via far  the   Easiest  algorithm  shown   to help  distribute  populate  among nodes. Because of this reason it is frequently the first preference when implementing a easy scheduler. One of the reasons for it being so simple is that the only information required is a list of nodes. The proposed algorithm eliminates the drawbacks of implementing a simple round robin architecture in cloud computing  by introducing a concept of assigning different time slices to individual processes depending on their are priorities.

KEYWORDS: Cloud computing; load balancing; Task Scheduling; Round Robin

Copy the following to cite this article: Richhariya V, Dubey R, Siddiqui R. Hybrid Approach for Load Balancing in Cloud Computing. Orient.J. Comp. Sci. and Technol;8(3)

Copy the following to cite this URL: Richhariya V, Dubey R, Siddiqui R. Hybrid Approach for Load Balancing in Cloud Computing. Orient. J. Comp. Sci. and Technol;8(3). Available from: http://www.computerscijournal.org/?p=3041

Introduction

Cloud computing comes throughout focus development  of  grid computing, virtualization  as well as   web  technologies. Cloud computing  is usually   the   world wide web  based computing  That   presents  infrastructure  as  service  (IaaS), platform  as service  (PaaS),  software  as Service (SaaS).  Throughout SaaS, software application form   is usually   created   shown   through the cloud provider.    PaaS a good   application development platform  for the  developer  to   Create a   internet  based application. within IaaS computing infrastructure  can be   sent   to be a  help   towards the  requester. In  your current   application form associated with  Virtual Machine (VM).These  model   usually are   developed   viewable from a good  subscription basis utilizing cost Equally  you-use model  to be able to  customers, regardless  regarding their location. Cloud Computing still under  inside   their  development stage  and also has quite a few issue in addition to  challenges out  of a   several   questions   in  cloud scheduling plays very  important role  inside  determining  your current  effective execution. Scheduling refers  for the  set  connected with  policies  to be able to  control  your  order  involving   function   for you to   possibly be   performed by an computer system. There  has been   different   people   associated with  scheduling algorithm existing throughout  distributed computing system,  along with   job   scheduling  will be   single of them. the main advantage  involving   job  scheduling algorithm  will be   in order to  achieve  a good  high performance computing  and also the   Simplest   process  throughput. Scheduling manages availability  involving  CPU memory and good scheduling policy  gives  maximum utilization  of  resource.

Literature Review

In  this  section,  when i  describe  your own  related  operate  ok  job  scheduling  in  cloud computing environment.  your  author  connected with  paper [1] presented  a great  brief description  involving  cloud Sim toolkit  AND  his Functionality. CloudSim toolkit  is really a  platform  through which   You can  test  your   operate   earlier  applied  directly into   true  work,  in   the actual  paper  we  learned  Tips on how to  simulate  the   work   inside   different   approaches   AND ALSO   different  scheduling policy.

In paper [2] author proposed  a great  approach  for   work  scheduling algorithm  In line with   populate  balancing  inside  cloud computing.  the particular  paper  pointed out   2  level  work  scheduling based  towards   complete  balancing.  such   career  scheduling  can\’t   sole  meet user’s requirement but  in addition   supply the  high resource utilization.  this  paper presented  the  implementation  of a  efficient Quality of  ASSISTANCE  (QoS) based Meta-Scheduler  AS WELL AS  Backfill strategy based light  The strain  Virtual Machine Scheduler  pertaining to  dispatching jobs

The authors  regarding  paper [3] presented  the  optimized algorithm  regarding   career  scheduling  Based on  genetic simulated annealing algorithm.  the particular  considers  your current  QoS  Requirements   similar to  completion time, bandwidth, cost, distance, reliability  involving   various other  type tasks. Here annealing  can be  implemented  after   the  selection, crossover  AS WELL AS  mutation,  to help  improve local search ability  connected with  genetic algorithm.

In paper [7]  a brand new  VM  fill up  Balancing Algorithm  is actually  Weighted Active Monitoring  populate  Balancing Algorithm  applying  CloudSim tools,  due to the  Data center  to help   efficiently   load  balance requests between  ones   exhibited  virtual  devices  assigning  the  weight,  in order to  achieve  far better  performance parameters. Here VMs  associated with   different  processing powers  along with the  tasks/requests  usually are   designated   or perhaps   issued   on the   all  powerful VM  and then   on the  lowest  so  on.

In paper [8] author proposed  a good  algorithm  can be   ant  colony optimization  that  random optimization search approach  is usually   obtained   pertaining to  allocating  your current  incoming jobs  on the  virtual  products   your  algorithm  uses   a great  positive feedback mechanism  AS WELL AS  imitates  ones  behavior  of   true   ant  colonies  throughout  nature  find   meal   AS WELL AS   to   Affiliate   to help  each  other   via  pheromone laid  from  paths traveled.

Components Of Cloud System

A typical Cloud modeled  applying  CloudSim  involves   after  four entities Datacenters, Hosts, Virtual  m/c   in addition   application form   along with  system  Software  which are shown in figure1.

Datacenter

Datacenter  is set  of  hosts  . This can be  responsible  regarding   managing  virtual  models  (VMs) (e.g., VM provisioning).  It  behaves  similar to   a  IaaS provider  from   finding  requests  with regard to  VMs  via  brokers.

Datacenter Broker

This class represents  the  broker acting  on  behalf  of a  user.  It  modifies  a couple of  mechanisms:  ones  mechanism  for  submitting VM provisioning requests  to be able to   data  centers and  mechanism  with regard to  submitting    tasks  to  VMs.

Host

Host executes  actions   regarding  management  of  VMs (e.g., creation  along with  destruction) and  update  task  processing  to be able to  VMs.  a good  host  possesses   the  defined policy   to  provisioning memory, processing elements,  and also  bandwidth  to  virtual machines.  a good  host  is  associated  for you to   the  data center.  The idea   can  host virtual machines.

VM

This  represents  the  software implementation  of a  machine that  executes applications called virtual machine (VM)  which   functions   to be a  physical machine. Each virtual machine divides  your own  resources  received   by the  host among tasks  working   from  it.

Cloudlet

The cloudlet class  can be   also  known  as being a  task. CloudSim represents  your  complexity  of the application in relation to   their  computational requirements.  the  class  is  managed  through the  scheduling policy that will be  implemented  inside  Datacenter Broker Class.

Figure 1: Block diagram of Components of Cloud System   Click here to View figure

 

Load Balancing Algorithms

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. 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.

Few exiting load balancing algorithms are as follows:

1. Token Routing
2. Round Robin
3. Randomized
4. Central Queue

Token Routing

The main objective  of the  algorithm [24]  can be   to  minimize  the   process  cost  through   transporting   your own  tokens  In regards to the  system. But  inside   a good  scalable cloud  technique  agents  are not able to  have  your current  enough  particulars   regarding.

Round Robin

in   the particular  algorithm [10],  your current  processes  tend to be  divided between  all  processors. Each  technique   will be   designated   towards  processor  within   an  round robin order.

Randomized

Randomized algorithm  can be   of  type static  with  nature.  inside   the particular  algorithm [11]  the   process   can be  handled  by   the   Private  node n  which has a  probability p.  your current   system  allocation order  is   retained   with regard to  each processor independent  connected with  allocation  by  remote processor.

The  algorithm [13]  functions   towards  principal  regarding  dynamic distribution. Each new activity arriving on the  queue manager  will be  inserted  into   your current  queue.  Whenever   ask   a  activity  is actually   acquired   by the  queue

Round Robin Algorithm

It  is a  static  fill up  balancing algorithm,  that  does not  take   the   previous   fill up  state  of an  node  for the   day   involving  assigning jobs.  This  makes  USE   of your  round robin scheduling algorithm  regarding  allocating jobs.  The item  selects  your   very first  node arbitrarily  and   then, allocates jobs  for you to   just about all   additional  nodes  in   an  round robin manner [15].  the actual  algorithm  is effective   from  random  menus   of the  virtual machines.  the  datacenter controller allocates  your  requests  for you to   a   record   of  VMs  with   a good  rotating basis.  your current   primary   obtain   can be   assigned   to   an  VM  selected  randomly  by the  group  subsequently   ones   details  Center controller assigns  your  requests  in   the  circular order.  soon after   your own  VM  is actually  allotted  your  request,  your own  VM  is usually  shifted  towards  end  of a   record  [13].

Figure 2: Round Robin Process   Click here to View figure

 

The round robin algorithm is as follows:

Step1.

set all the VM allocation is zero and  record of each VM index by Round Robin load balancer.

Step2.

a. user request/task/cloudlet receives by data center receivers.

b. On the base of priority allocated virtual machine              

c. Basis of priority load balancer allocate the time quantum to user request

Step 3.

After the complete of task(cloudlets),VM are allocated to other user request.

Step4.

Checks  new /pending/waiting requests in queue by data center controller.

Step5.

Go to step 2.

Proposed Algorithm

The proposed algorithm eliminates the drawbacks of implementing a simple round robin architecture in cloud computing  by introducing a concept of assigning different time slices to individual processes depending on their are priorities. The priority of a process is assigned by user externally. In the proposed architecture when a new process arrives in the system it is queued at a small processor. This small dedicated processor is used to calculate the time slices of each process, arranges the processes in ascending order of their burst times and then creates the ready queue for the main processor. This small dedicated processor is used to reduce the burden of the main processor. The processes then execute in the main processor according to round robin scheduling algorithm with their individual time slices. Whenever a new process arrives in the system ready queue, its time slice is calculated and enquired to the main processor’s ready queue.

The Proposed round robin algorithm is as follows:

Step1.

set all the VM allocation is zero and  record of each VM index by Round Robin load balancer.

Step2.

a. user request/task/cloudlet receives by data center receivers..

b. On the base of priority allocated virtual machine and calculate range (R)

R=Max Burst Time+Min Burst Time

c. Basis of range and priority, load balancer allocate the time quantum to user request

Step 3.

After the complete of task(cloudlets),VM are allocated to other user request..

Step4.

Checks new /pending/waiting requests in queue by data center controller.

Step5. Go to step 2.

Cloudsim

CloudSim [12]  is the   many  efficient tool  you can use   with regard to  modeling  regarding  Cloud.  during   your current  lifecycle  of an  Cloud, CloudSim  allows  VMs  for you to   be  managed  coming from  hosts  that will   inside  turn  are usually  managed  by  datacenters.

Figure 3: CloudSim Architecture Click here to View figure

 

CloudSim  offers  architecture  inside  four  uncomplicated  entities.  these types of  entities  offer   consumer   to  set-up  the   basic  cloud computing environment  as well as  measure  your  effectiveness  involving   fill up  Balancing algorithms.. Datacenters entity  features   the  responsibility  of  providing Infrastructure level  solutions   for the  Cloud Users. They act  as a  home  to help   a lot of  Host Entities  or maybe   a lot of  instances hosts’ entities aggregate  to help   application form   the   solitary  Datacenter entity. Hosts  with  Cloud  are usually  Physical Servers  The idea  have pre-configured processing capabilities. Host is actually responsible  regarding  providing Software level  SERVICE   towards  Cloud Users. Hosts have their particular storage  and  memory. Processing features regarding hosts  is usually  expressed  throughout  MIPS (million instructions per second).

Conclusion

This paper presents a concept of Cloud Computing along with research challenges in load balancing. It also focus on merits and demerits of the cloud computing. Major thrust is given on the study of load balancing algorithm, followed by a comparative survey of these above mentioned algorithms in cloud computing with respect to stability, resource utilization, static or dynamicity, cooperative or non-cooperativeness and process migration. This paper aims towards the establishment of performance qualitative analysis on existing VM load balancing algorithm and then implemented in CloudSim and java language.

References

1. Kaur s and Supriya Kinger2, “Analysis of Load Balancing Techniques in Cloud Computing”, International Journal of Computers & Technology, volume 4, No. 2, March- April 2013, pg 737- 741.
2. Poojaand Mishra2, “Analysis of Variants in Round Robin Algorithms for Load Balancing in Cloud Computing”, (IJCSIT) International Journals of Computer Science and Information Technologies, Volume 4 (3), 2013, pg. no. 416- 419.
3. Kunal Mahurkar1, Shraddha Katore2 and Suraj Bhaisade3, Pratikawale4, “Reducing Cost of Provisioning in Cloud Computing”, International Journal of Advance in Computer Science and Cloud Computing, Volume- 1, Issue- 2, nov.- 2013, pg. 6- 8.
4. Dr. Rakesh Rathi1, Vaishali Sharma2 and Sumit Kumar Bole3, “Round Robin Data Center Selection in Single Region for Service Proximity Service Broker in Cloud Analyst” , International Journal of Computer & Technology, Volume 4 no. 2, March- April 2013, pg. no. 254- 260.
5. Bhatiya Wickremansinghe1, Rodrigo N. Calheiros2and Dr. Rajkumar Buyya3, “CloudAnalyst: A CloudSim- based VisulModeller for Analysing Cloud Computing Environments and Applications”, IEEE Computer Society, 2010, pp. 446-452.
6. Pooja Samal1 and Pranati Mishra2, “Analysis of Variants in Round Robin Algorithms for Load Balancing in Cloud Computing”, (IJCSIT) International Journals of Computer Science and Information Technologies, Volume 4 (3), 2013, pg. no. 416- 419.
7. Kunal Mahurkar1, Shraddha Katore2 and Suraj Bhaisade3, Pratikawale4, “Reducing Cost of Provisioning in Cloud Computing”, International Journal of Advance in Computer Science and Cloud Computing, Volume- 1, Issue- 2, nov.- 2013, pg. 6- 8.
8. Dr. Rakesh Rathi1, Vaishali Sharma2 and Sumit Kumar Bole3, “Round Robin Data Center Selection in Single Region for Service Proximity Service Broker in Cloud Analyst” , International Journal of Computer & Technology, Volume 4 no. 2, March- April 2013, pg. no. 254- 260.
9. Bhatiya Wickremansinghe1, Rodrigo N. Calheiros2and Dr. Rajkumar Buyya3, “CloudAnalyst: A CloudSim- based VisulModeller for Analysing Cloud Computing Environments and Applications”, IEEE Computer Society, 2010, pp. 446-452.
10. JaspreetKaur, “Comparison of load balancing algorithm in a Cloud”, International Journal of Engineering Research and Applications (IJERA), vol. 2, Issue 3, May- June 2012, pp. 1169- 1173.
11. Syed Tauhid Zuheri1, Tamanna Shamrin2 and Rusia Tanbin3, Firoj Mahmud4, “An Efficient Load Balancing Approach in Cloud Environment by using Round Robin Algorithm”, International Journal of Artificial and Mechatronics, volume 1, issue 5, 2013, pp 96-99.
12. B. Santosh Kumar1 and Dr. Latha Parthiban2, “An Implementation of Load Balancing Policy for Virtual Machines Associated with a Data Centre”, International Journal of Computer Science & Engineering Technology (IJCSET), volume 5 no. 03, March 2014, pp. 253- 261.
13. Sonika Matele1, Dr, K James2 and Navneet Singh3, “A Study of Load Balancing Issue Among Multifarious Issues of Cloud Computing Environment”, International Journals of Emerging Technolog Computational and Applied Science (IJETCAS), volume 13- 142, 2013, pg. 236- 241.
14. Subasish Mohapatra1, Subhadarshini2 and K. Smruti Rekha3, “Analysis of Different Varients in Round Robin Algorithms for Load Balancing in Cloud Computing”, International Journal of Computer Application, Volume 69- no. 22, may 2013, pp. 17-21.
15. Dr Hemant S. Mahalle1, Prof Parag R. Kaver2 and Dr. Vinay Chavan3, “Load Balancing on Cloud Data Centres”, Internatinal Journal of Advanced Reserch in Computer Science and Software Engineering, volume 3, issue 1, January 2013, pp. 1- 4.
16. Randles1, M Lamb2 and Taleb Bendiab3, “A Comparative Studyinto Distributed Load Balancing Algorithm for Cloud Computing”, Advanced Information Networking and Application Workshop (WAINA) 2010.
17. Dr. RajkumarBuyya, “CloudSim: a toolkit for modelling and simulation of cloud computing environments and evaluation of resource provisioning algorithm”, published online 24 august in Wiley Online Library 2010, pp. 23- 50
18. Prof Meenakshi Sharma1 and Pankaj Sharma2, “Performance Evaluation of Adaptive Virtual Machine Load Balancing Algorithm”, International Journal of Advanced Computer Science and Applications, volume 3, no. 2, 2012, pp. 86-88.
19. Ajay Gulati1 and Ranjeev K. Chopra2, “Dynamic Round Robin for Load Balancing in a Cloud Computing”, International Journal of Computer Science and Mobile Computing, volume 2, issue 6, June 2013, pg 274- 278. 

---

This work is licensed under a Creative Commons Attribution 4.0 International License.