Performance analysis of Load balancing algorithms using ...

© 2018 IJRAR September 2018, Volume 5, Issue 3 www.ijrar.org (E-ISSN 2348-1269, P- ISSN 2349-5138)

IJRAR190B026 International Journal of Research and Analytical Reviews (IJRAR) www.ijrar.org 140

Performance analysis of Load balancing algorithms

using LBaaS

1V.M.Sivagami,2.K.S.EaswaraKumar

1. Associate Professor,Sri Venkateswara College of Engineering, Chennai-602117,India.

2.Professor,DCSE,CEG,Anna University,Chennai-25,India.

Abstract:

Cloud computing is a framework for enabling a suitable, on-demand network access to a shared pool of computing resources (e.g.

networks, servers, storage, applications, and services). These resources can be provisioned and deprovisioned quickly with

minimal management effort or service provider interaction. This further helps in promoting availability. The excess dynamic local

workload is distributed evenly across all the nodes by the load balancer . Load balancing focuses on maximum throughput, avoid

overloading and reducing energy consumption by evenly distributing the load, minimizes response time, reduces network latency.

In addition, load estimation, load comparison, system stability, system performance, interaction between the nodes, nature of work

to be transferred, selection of nodes etc..are the functionalities of Load balancer.

Keywords: LBaaS,Curl,Load balancer,openstack

I.INTRODUCTION

Due to the exponential growth of cloud computing, it has been widely adopted by the industry and there is a rapid

expansion in data-centers. This expansion has caused the dramatic increase in energy use and its impact on the environment in

terms of carbon footprints. The link between energy consumption and carbon emission has given rise to an energy management

issue which is to improve energy-efficiency in cloud computing to achieve Green computing. Besides this, there are many other

existing issues like Load Balancing, Virtual Machine Migration, Server Consolidation etc. that have not been fully addressed.

Virtual Machine Migration enabled by virtualization can help in balancing load, enabling highly responsive provisioning

and avoiding hot-spots in data centers thereby reducing power consumption. Server Consolidation helps in improving resource

utilization by consolidating various VMs residing on multiple under-utilized servers onto a single server, thereby turning off

unused servers, hence reducing energy consumption in a cloud computing environment. Load balancing can help in reducing

energy consumption by evenly distributing the load and minimizing the resource consumption.

LOAD BALANCING

. The excess dynamic local workload is distributed evenly across all the nodes by the load balancer . Load balancing focuses on

maximum throughput, avoid overloading and reducing energy consumption by evenly distributing the load, minimizes response

time, reduces network latency. In addition, load estimation, load comparison, system stability, system performance, interaction

between the nodes, nature of work to be transferred, selection of nodes etc..are the functionalities of Load balancer.

As more and more computing is done online, load balancing has taken on a broader meaning. Global Server Load

Balancing is the same in principle but its implementation is not confined to one local network. The workload is still distributed,

but it’s distributed planet-wide instead of just across a data center. As a result, modern-day solutions face new challenges, as they

are required to take into account not just an individual cluster of servers, but also communications parameters (e.g. link quality)

and geographical location of remote requesters. Today, as more and more online businesses seek to leverage Content Delivery

Networks (CDNs), load balancing has become a key component in most content distribution tasks.

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Load balancing algorithm is classified into two categories such as static and dynamic load balancing.

Static load balancing algorithm: In static load balancing algorithm load balancer uses priori knowledge of the

applications and statistical information about the system and distributes the load equivalently between servers. These

algorithms do not need the information regarding current state of the system. This type of algorithms have serious

drawbacks in case of sudden failure of system resource, tasks and also task can’t be shifted during its execution for load

balancing.

Dynamic load balancing algorithm: Dynamic load balancing algorithms are those algorithms which search for the lightest

server in the network and then designates appropriate load on it. In this, work load is distributed among the processors at

runtime.

Metrics of Load Balancing

OverheadAssociated, Throughput, Performance, ResourceUtilization, Scalability, Response time, Fault Tolerance and

Migration time are the factors that affect Load balancer performance.

EXISTING SYSTEM

Virtual Machine (VM) allocation for multiple tenants is an important and challenging problem to provide efficient

infrastructure services in cloud data centres. VMs collaborating on a job are split in geographically distributed clouds, the low

bandwidth and high latency inter-cloud communication via a wide area network (WAN) will dramatically degrade the system

performance. To facilitate live migration of VM, many approaches has been proposed and tested. Virtual machines migration is

improved when the clusters of virtual machine coordinate together and when one of virtual machine or multiple of virtual

machine faces failures during the coordination, it should be able to recover. Fault tolerance enables a system to continue operating

properly in the event of the failure or one or more faults within some of its components. Hybrid fault tolerance is proposed to

overcome the issues of failures between single node and multimode virtual machines.

II.RELATED WORK

In [3],Collaborative Agents for Distributed Load Management in Cloud Data Centers Using Live Migration of Virtual

Machines discusses distributed problem solving techniques for load management in data centers supported by VM live

migration. Collaborative agents are end owed with a load balancing protocol and an energy-aware consolidation protocol

(EProtocol) to balance and consolidate heterogeneous loads (e.g., migrating memory-intensive loads to memory intensive hosts)

in a distributed manner while reducing energy consumption costs.

VMbuddies: Coordinating Live Migration of Multi-Tier Applications in Cloud Environments [2] to investigate the

effectiveness and efficiency of VMbuddies by using a public multi-tier application benchmark. The evaluation compares a

number of metrics such as migration completion time, network traffic, migration downtime and application performance

degradation with several alternative approaches. The results show VMbuddies can significantly reduce the performance

degradation of multi-tier applications during migrations, but experiences overhead of synchronization cost in terms of migration

completion time and network traffic.

In paper [1], proposed ACO algorithm to find the optimal resource allocation for tasks in the dynamic cloud system to

minimize the make span of tasks on the entire system. Experimental results compared to First Come First Serve (FCFS) and

Round Robin (RR) shows that ACO algorithm satisfies expectation.




Paper [5] provides details about Desktop Clouds which are built on top of computing resources that are prone to failure

at any time without prior knowledge. Such volatile infrastructure can have negative impact upon the running and output of

Desktop Clouds. The contribution of this paper is three fold. Firstly, the paper introduces Desktop Cloud systems as being a new

type of Cloud computing, and compares it with related systems, Traditional Clouds and Desktop Grids, in order to clarify Desktop

Clouds further. Secondly, the work proposes three metrics that can be used to evaluate VM mechanisms employed by Cloud

middleware platform. Finally, the paper simulates a Desktop Cloud using empirical data of node failures collected online from

SETI@home system.

In [6], OpenStack is a cloud operating system that controls large pools of compute, storage, and networking resources

throughout a datacenter, all managed through a dashboard that gives administrators control while empowering their users to

provision resources through a web interface.It also provides details about OpenStack's compute, storage, networking, take a tour

of the dashboard, or dive in and learn how to get started with OpenStack today. The Project Navigator is aimed at helping users

make informed decisions about how to consume the software. Data used to power the Project Navigator website is provided by

the OpenStack Technical and User Committees.

Autonomous Agent Based Load balancing algorithm in Cloud Computing is presented in [11] and it proposed a novel

autonomous agent based load-balancing algorithm called A2LB for cloud environments. The algorithm tries to balance the load

among VMs through three agents: load agent, channel agent, and migration agent. Load and channel agents are static agents

whereas migration agent is an ant, which is a special category of mobile agents. Load agent controls the information policy and

calculates a load of VMs after allocating a job. Channel agent controls the transfer policy, selection policy, and location policy.

Finally, the channel agent initiates the migration agents. They move to other data centers and communicate with the load agent of

that data center to acquire the status of VMs present there, looking for the desired configuration.

In [7], virtual graph which is constructed with the connectivity of each node representing the load on server is proposed.

Each node is represented as a vertex in a directed graph and each indegree represents free resources of that node. Whenever a

client sends a request to the load balancer, the load balancer allocates the job to the node which has atleast one indegree. The

addition and deletion of processes is done by the process of random sampling. Each process is characterized by a parameter know

as threshold value, which indicates the maximum walk length. A walk is defined as the traversal from one node to another until

the destination is found. At each step on the walk, the neighbour node of current node is selected as the next node. In this

algorithm, upon receiving the request by the load balancer, it would select a node randomly and compares the current walk length

with the threshold value. If the current walk length is equal to or greater than the threshold value, the job is executed at that node.

Else, the walk length of the job is incremented and another neighbour node is selected randomly. The performance is degraded as

the number of servers increase due to additional overhead for computing the walk length.

OPENSTACK SCHEDULER

OpenStack compute and volume for containing the scheduler, through a series of rules set by the administrator

parameters and filters, OpenStack scheduler virtual machines deployed to the appropriate host. In terms of the filter, the scheduler

is very flexible, you can do your own filters JSON format and filter also contains a number of predefined filters. While OpenStack

scheduler is very flexible, but still can not completely replace the DRS, for the following reasons:




III.PROPOSED SYSTEM

LOAD BALANCING AS A SERVICE (LBaaSv2)

LBaaS enables Networking to distribute incoming requests evenly among designated instances. This distribution ensures

that the workload is shared predictably among instances and enables more effective use of system resources.

LBaaS v2 has several new concepts to understand:

Load balancer - The load balancer occupies a neutron network port and has an IP address assigned from a subnet.

Listener - Load balancers can listen for requests on multiple ports. Each one of those ports is specified by a listener.

Pool - A pool holds a list of members that serve content through the load balancer.

Member - Members are servers that serve traffic behind a load balancer. Each member is specified by the IP address and port that

it uses to serve traffic.

Health monitor - Members may go offline from time to time and health monitors divert traffic away from members that are not

responding properly. Health monitors are associated with pools.

FIGURE 3.1 LOAD BALANCING As A SERVICE ARCHITECTURE

Use one of these load balancing methods to distribute incoming requests:

Round robin

Rotates requests evenly between multiple instances.

Source IP

Requests from a unique source IP address are consistently directed to the same instance.

Least connections

Allocates requests to the instance with the least number of active connections.

LBaaS have several features such as,

Monitors-LBaaS provides availability monitoring with the ping, TCP, HTTP and HTTPS GET methods. Monitors are

implemented to determine whether pool members are available to handle requests.

Management-LBaaS is managed using a variety of tool sets. The REST API is available for programmatic administration and

scripting. Users perform administrative management of load balancers through either the CLI (neutron) or the OpenStack

Dashboard.




Connection Limits-Ingress traffic can be shaped with connection limits. This feature allows workload control, and can also assist

with mitigating DoS (Denial of Service) attacks.

Session Persistence-LBaaS supports session persistence by ensuring incoming requests are routed to the same instance within a

pool of multiple instances. LBaaS supports routing decisions based on cookies and source IP address.

AVOS CISCO SYSTEM

AVOS (Analytics & Visualisation on OpenStack) Developed by a team at Cisco to give operators a clearer picture of

their clouds, it was shown at the monitoring session at the Mid-Cycle Ops Meetup, along with VMTP. It solves the problem of

presenting operational insights of an OpenStack cloud in a crisp fashion, interactively conveying a lot of data with a very intuitive

user experience.

We believe that a combination of smart insights, data science and interactive visualization could lead to better tools for both the

operator as well as the tenants. This is a gap in OpenStack today.

This paper deals with the research field of load balancing in cloud. In a cloud environment there are many unknown

nodes called Virtual machines (VM).In other words, VM is a processing machine in the server. In cloud computing, the user data

is replicated in many VM’s. The user request is passed onto all the available VM’s and balanced it.

IV.Experimental Setup and Discussion

The proposed work is simulated using Oracle VM virtualbox software and openstack tool.Haproxy will be used as the

load balancer. Be sure that you either have haproxy installed or have access to a yum repository that provides the haproxy

package.Neutron LBaas can be enabled and configured on the time of installation.

CentOS is installed Oracle VM virtualbox in separate virtual machines. one of the ways for installation is

1. Select the CentOS to install. Choose installation image for the CentOS Desktop or Server version.

2. Insert the CentOS 7 CD/DVD into the CD-ROM drive.

3. Power on the virtual machine.

FIGURE 4.1 SUCCESSFULL INSTALLATION OF OPENSTACK


https://github.com/CiscoSystems/avos

http://superuser.openstack.org/search/results?utf8=%E2%9C%93&query=mid-cycle+ops+meetup

https://github.com/stackforge/vmtp



OPEN THE OPENSTACK DASHBOARD

Set the root and database password and open the openstack horizon web interface http://$HOSTIP/dashboard.

FIGURE 4.2 OPENSTACK LOGIN PAGE

CHECK THE LIST OF IMAGES

FIGURE 4.3 LIST OF IMAGES IN OPENSTACK

CREAT ION OF INSTANCES

1. Login to Dashboard as user admin and your password where located in keystonerc_admin file.

2. Expand “Project” and click “Instances” within the “Compute” option.

FIGURE 4.4 ADDING LAUNCH INSTANCE DETAILS


http://$hostip/dashboard



FIGURE 4.5 RUNNING INSTANCES IN OPENSTACK DASHBOARD

CREATING NETWORK WITH FLOATING IP

FIGURE 4.6 DISPLAYS THE ACTIVE NETWORKS IN OPENSTACK

V.RESULTS AND DISCUSSIONS

FIGURE 5.1 CREATE AND SHOW THE LOAD BALANCER DETAIL

FIGURE 5.2 LIST OF NEUTRON LOAD BALANCER IN CLOUD




The figure 5.1 shows the creation of load balancer in load balancing as a service(LBAAS) on openstack

environment.And figure 5.2 also list out the set of load balancer in detail.

FIGURE 5.3 CREATING A POOL IN LOAD BALANCER

FIGURE 5.4 CREATING LISTENER FOR PORT 80

In figure 5.3 shows the creation of pool for adding members in a load balancing environment and figure 5.4 also

displays the creation of listener with the protocol HTTP with port 80.A listener is useful to reduces the network traffic.

FIGURE 5.5 CREATE AND ADDING A FIRST MEMBER INTO THE POOL

FIGURE 5.6 CREATE AND ADDING A SECOND MEMBER INTO THE POOL

In figure 5.5and 5.6 displays the two members are create and adding into the pool respectively. The members are identify by the

allocated floating IPs in the openstack environment.




FIGURE 5.7 USING CURL TO PERFORM ROUND ROBIN ALGORITHM

In figure 5.7 explains the dynamic round robin algorithm in a load balancing environment. A curl command is useful to

transfer data in a network.

FIGURE 5.8 DISPLAYS THE OVERALL DASHBOARD OPERATION

In figure 5.8 shows the overall running instances in a openstack and its visualize in a dashboard.Using an AVOS system

to visualize this in a tree structure.

5.4 PERFORMANCE EVALUATION

Performance evaluation is made by comparison between a round robin and least connection IP algorithm with the load

balancing parameters.

FIGURE 5.9 COMPARATIVE EVALUATION OF VARIOUS ALGORITHMS USING TIME PARAMETER

Figure 5.9 depicts the comparison between round robin and least connection algorithm response during the load

balancing operation for the various time parameters such as average, minimum and maximum time.




FIGURE 5.10 COMPARATIVE EVALUATION OF VARIOUS ALGORITHMS BASED ON AVERAGE AND

RESPONSE TIME

Figure 5.10 exhibits the performance of round robin and least connection algorithm during the load balancing operation

for average and response time.

VI.CONCLUSION AND FUTURE WORK

Thus in cloud environment, proper load balancing avoids fail-over and bottlenecks which in turn improves flexibility,

scalability of resources and it also reduces over-provisioning of VM allocation and minimizes resource utilization. And also

visualise the dashboard operation in a tree structure.

In future, research work will be carried out in conservation of energy on cloud and also workload optimization by

providing efficient computation and effective processing of resources.

VII. REFERENCES

1. Fahimeh Farahnakian, Member, IEEE, Adnan Ashraf, Tapio Pahikkala, Member, IEEE, Pasi Liljeberg, Juha Plosila,

Ivan Porres, and Hannu Tenhunen, Member, IEEE,”Using Ant Colony System to Consolidate VMs for Green Cloud

Computing” in IEEE transactions on services computing, vol. 8, no. 2, March/April 2015

2. Haikun Liu and Bingsheng He,“VMbuddies:Coordinating Live Migration of Multi-Tier Applications in Cloud

Environments” in IEEE transactions on parallel and distributed systems, vol. 26, no. 4, April 2015

3. J.Octavio Gutierrez-Garcia and Adrian Ramirez-Nafarrate,”Collaborative Agents for Distributed Load Management in

Cloud Data Centers Using Live Migration of Virtual Machines,” in IEEE transactions on services computing, vol. 8, no.

6, November/December 2015

4. Kejiang Ye, Student Member, IEEE, Zhaohui Wu, Senior Member, IEEE, Chen Wang, Bing Bing Zhou, Weisheng Si,

Member, IEEE, Xiaohong Jiang, and Albert Y. Zomaya, Fellow, IEEE,”Profiling-Based Workload Consolidation and

Migration in Virtualized Data Centers,”in IEEE transactions on parallel and distributed systems, vol. 26, no. 3, March

2015

5. Abdulelah Alwabel, Robert Walters, Gary Wills,”Evaluation of Node Failures in Cloud Computing Using Empirical

Data” in open journal of Cloud computing(OGC),Vol 1,issue 2,ISSN 2199-1987.

6. http://openstack.org

7. Nayandeep Sran, Navdeep kaur ,”Comparative Analysis of Existing Load balancing techniques in cloud

computing”,International Journal of Engineering Science Invention,ISSN (Online): 2319 –6734, ISSN (Print): 2319 –

6726


http://openstack.org/



8. Santanu Dam, Gopa Mandal, Kousik Dasgupta and Paramartha Dutta,Genetic Algorithm and Gravitational Emulation

Based Hybrid Load Balancing Strategy in Cloud Computer, Communication, Control and Information Technology

(C3IT), 2015 Third International Conference 2015, IEEE 2015

9. Stuti Dave, Prashant Mehta “Utilizing Round Robin Concept for Load Balancing Algorithm at Virtual Machine Level in

Cloud Computing” IJAC (0975-8887) Volume 94-No.4, May 2014

10. Benifa, JVB. and Dejey (2017). Performance Improvement of MapReduce for Heterogeneous Clusters Based on

Efficient Locality and Replica Aware Scheduling (ELRAS) Strategy. Wireless Personal Communications, 1-25.

11. S. G. Domanal and G. R. M. Reddy, "Load Balancing in Cloud Environment Using a Novel Hybrid Scheduling

Algorithm," 2015 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), Bangalore, 2015,

pp. 37-42

12. Singha, A. and Juneja, D., and Malhotra, M. (2015). Autonomous Agent Based Load balancing algorithm in Cloud

Computing. International Conference on Advanced Computing Technologies and Applications (ICACTA), 45, 832–841


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