Application Load Balancing is actually a rather simplistic technology at its core. It provides a unified connection point between the end users of an application and the application itself. Throughput processing and the amount of secure transactions capable of being processed simultaneously are two key metrics that contribute to measuring the value of any Load Balancing solution. An additional and very important item to consider is the security functions that are bundled in.
A stochastic approach, Glowworm swarm optimization GSO is employed to solve the above mentioned optimization problem. In the proposed method, excellent features of various existing load balancing algorithms as discussed chapter 2 are also integrated.
This work mainly focuses on a public cloud.
A public cloud is based on the typical cloud computing model, and its services provided by service provider . A public cloud will comprises of several nodes and the nodes are in different physical locations. Cloud is partitioned to manage this large cloud. A cloud consists of several cloud partition with each partition having its own load balancer and there is a main controller which manage all these partition.
Step 5: assigning the jobs to particular nodes based on the strategy. Figure 3. Load Balancing Strategy In cloud, Load Balancing is a technique to allocate workload over one or more servers, network boundary, hard drives, or other total resources.
Representative datacenter implementations depends on massive, significant computing hardware and network communications, which are subject to the common risks linked with any physical device, including hardware failure, power interruptions and resource limits in case of high demand.
High-quality of load balance will increase the performance of the entire cloud. Though, there is no general procedure that can work in all possible different conditions.
There are several method have been employed to solve existing problem.
Each specific method has its merit in a specific area but not in all circumstances. Hence, proposed model combines various methods and interchanges between appropriate load balance methods as per system status. Here, the idle status uses an Fuzzy Logic while the normal status uses a global swarm optimization based load balancing strategy.
Load Balancing using Fuzzy Logic When the status of cloud partition is idle, several computing resources are free and comparatively few jobs are receiving. In these circumstances, this cloud partition has the capability to process jobs as fast as possible so an effortless load balancing method can be used.
Zadeh  proposed a fuzzy set theory in which the set boundaries were not precisely defined, but in fact boundaries were gradational.
Such a set is characterized by continuum of grades of membership function which allocates to each object a membership grade ranging from zero to one . A new load balancing algorithm based on Fuzzy Logic in Virtualized environment of cloud computing is implemented to achieve better processing and response time.
The load balancing algorithm is implemented before it outstretch the processing servers the job is programmed based on various input parameters like assigned load of Virtual Machine VM and processor speed.
It contains the information in each Virtual machine VM and numbers of request currently assigned to VM of the system. Therefore, It recognize the least loaded machine, when a user request come to process its job then it identified the first least loaded machine and process user request but in case of more than one least loaded machine available, In that case, we tried to implement the new Fuzzy logic based load balancing technique, where the fuzzy logic is very natural like human language by which we can formulate the load balancing problem.
The fuzzification process is carried out by fuzzifier that transforms two types of input data like assigned load and processor speed of Virtual Machine VM and one output as balanced load which are required in the inference system shown in figure 3.
By evaluating the load and processor speed in virtual machine in our proposed work like two input parameters to produce the better value to equalize the load in cloud environment, fuzzy logic is used.
These parameters are taken for inputs to the fuzzifier, which are needed to estimate the balanced load as output as shown in figure 3.• Computer load balancing. Perhaps a rigorous solution to the optimization problem, most often reduced to the problem of integer programming.
However, when solving it, there are serious difficulties (a large amount of data of the problem and the need for a significant free resource, a shortage of solution time) and a gain in comparison. Load Balancing as an Optimization Problem: GSO Solution. METHODOLOGY. INTRODUCTION; In this chapter, we presented a novel methodology which considers load balancing as an optimization problem.
A stochastic approach, Glowworm swarm optimization (GSO) is employed to solve the above mentioned optimization problem. Need to prove a solution’s value is close to optimum, ‣ generalized load balancing ‣ knapsack problem SECTION Center selection problem Input.
Set of n sites s 1, , s n and an integer k > 0. Center selection problem.
Select set of k centers C so that maximum. Load Balancing using GSO (Glowworm Swarm Optimization) When the status of cloud partition is normal, tasks arrives with faster rate compare to idle state and the condition becomes more complex, thus a novel strategy is deployed for load balancing.
Adaptive neuro-fuzzy inference system (ANFIS) based load balancing algorithm and Glowworm swarm optimization (GSO) based load balancing algorithm are proposed to the load balancing strategy to.
GSO is observed to have provided significant optimal solution in lesser iterations. In this paper The Fuzzy logic and GSO based load balancing algorithm applied to the load balancing strategy to enhance the utilization and efficiency in the public cloud environment.
Keywords: Cloud partition, load balancing, Fuzzy logic, GSO 1.