IJSMER201906 721
Comparative Performance analysis for Load balancing Mechanism in Cloud Computing Environment Using Optimization Methods
ABSTRACT
Cloud computing environment provides various service-oriented access to computing, storage, networking and sharing hardware resource, this entire internet based on the demand of user requirements. In the era of processing multiple jobs the resource sharing and load balancing is a very necessary operation to improve the user requirement and satisfaction. In this paper has been proposed a model for comparative various techniques for load balancing mechanism in cloud computing environments. It has been compared the techniques for the further improvement to satisfaction for the user requirement and needs.
INTRODUCTION
Cloud computing provides large number of beneficial services to share huge amount of information, great capacity for storage resources, numerous computing resources and detailed knowledge for the research. While applications need to retrieve its data from distributed storages, the bandwidth between Computing nodes and Storage nodes could influence the overall applications performance specially, when network status is unstable [1]. National Institute of Standards and Technology (NIST) definition says [3], “Cloud computing is 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.”
Load balancing mechanism is an area to broadcast requests out over large number of resources and it provides helps to a network for avoiding annoying downtime and delivers optimal performance to users [6]. It is the process of distributing the load among various nodes or devices of distributed
systems need to improve resource utilization and job response time, also avoiding a situation of filling up a certain node with heavy load. It is schemes ensure that all processors in the system or every device in the network execute approximately an equal amount of workload at any instant of time [5].
Figure 1: Workflow of network load balancing.
The above diagram depicts the workflow of load balancing mechanism in which integrate all the hardware and software resources with using internet from the help of some connecting internetworking devices like router and nay other device.
The first chapter introduced about the cloud computing services and model and the rest of this paper is organized as in following manner in section in section II we discuss about the proposed methodology and architecture for the load balancing in cloud computing. In section IV we discuss about the experimental result analysis and the comparative study about the time taken in a Rahul Sahu
Department of CSE, MIT, Bhopal (M.P) India Mrs. Jayshree Boaddh
Department of CSE, MIT, Bhopal (M.P) India
IJSMER201906 722 particular process, finally in section V we conclude
the about the conclusion and future scope.
II PROPOSED METHOD
The currently available algorithms do not save the state of the previous allocation of the VMs to a request from a given User base. As such, every time a request is received from the same User base, the algorithm needs be run again, which increases the total response time and processing time of the requests. This research work aims to reduce the total response time and processing time of User base requests by proposing Round Robin with server affinity VM load balancing algorithm.
Figure 2: Job allocation processes in public cloud computing model.
1. Generate random population of n solution (particles)
2. For each individual i€N: calculate fitness (i) 3. Initialize the value of the weight factor, w 4. For i=1 to n particles
Set pBest as the best position of particle i If fitness (i) is better than pBest then Set pBest (i) =fitness (i)
End for i
5. Set gBest as the best fitness of all particles 6. For i=1 to n particles
7. Update the value factor of the weight w 8. Check if termination is true.
9. End
COUNTING PRIMITIVE OPERATIONS The numbers of operations are performed in above algorithm as:
1. Step 1 contributes one operation for n times.
2. Step 2 contributes one operation for n times.
3. Step 3 requires one operation to count for initialization
4. Step 4 performs n iteration in outer loop and using bubble sort for inner loop
is n.log (n).
5. Step 5 finds best in n particles in log (n) operations.
6. Step 6 performs n loop with 14 operations.
7. Step 7 updates weight factor in one operation.
T (n) = n+n+1+n. log (n) + log (n) 14+1.
T (n) = 2n+16+ (n.log (n).t).
IV EXPERIMENTAL RESULT ANALYSIS A cloud segment is a subarea of the general population cloud with divisions in view of the geographic areas. The heap adjusting technique depends on the cloud dividing idea. In this paper we proposed a comparative performance analysis model for the load balancing in a cloud computing.
There are various types of techniques such as Round Robin, Equality space current execution load and particle of swarm optimization etc. we compare all the techniques and find that the particle of swarm optimization better results than other existing techniques as a proposed strategy.
For the further execution and examination for execution assessment we utilized java programming dialects with Net Beans IDE 8.0.1 instruments for finish usage/comes about process.
IJSMER201906 723 Figure 3:Implementation window for the
experimental process using in a load balaning.
Figure 4:Implementation window with method uisng in a load balancing.
Figure 5:Comparative graph with methods for the Data processing time.
Figure 6:Comparative graph with methods for the overall processing time.
V CONCLUSIONS AND FUTURE WORK Cloud computing play a very key role in the modern computer era it’s provide large number of services for the user level at on demand, the number of active user may be try to attempt services simultaneously. In this paper we emphasizes on the comparative study of various load balancing algorithm to find using user base and data centre for each algorithms. In future work we used some more meta heuristics algorithm to
IJSMER201906 724 improve the experimental results in the terms of
computation time.
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