Load Balancing as an Search engine optimization Problem: GSO Solution



In this chapter, we presented a novel strategy which considers load managing as an search engine optimization problem. A stochastic way, Glowworm swarm marketing (GSO) is utilized to solve all these optimization problem. Inside the proposed method, excellent top features of various existing load controlling algorithms as reviewed chapter 2 are also integrated.


There are numerous cloud computing categories. This work mainly targets a public cloud. A public cloud is dependant on the normal cloud processing model, and its own services provided by provider [42]. A open public cloud will consists of several nodes and the nodes are in various physical locations. Cloud is partitioned to manage this large cloud. A cloud contains several cloud partition with each partition having its own insert balancer and there is a main controller which deal with each one of these partition.

3. 2. 1 Job Project Strategy

Algorithm for assigning the jobs to cloud partition as shown in Fig. 2

Step 1: jobs arrive at the primary controller

Step 2: choosing the cloud partition

Step 3: if cloud partition status is idle or normal state then

Step 4: jobs reach the cloud partition balancer.

Step 5: assigning the careers to particular nodes predicated on the strategy.

Figure 3. 1: Flowchart of Proposed Job Task Strategy.

  1. Load Balancing Strategy

In cloud, Insert Balancing is a technique to allocate workload over a number of machines, network boundary, hard drives, or other total resources. Representative datacenter implementations is determined by massive, significant computing hardware and network marketing communications, which are subject to the common hazards associated with any physical device, including hardware failure, power interruptions and tool limits in case of popular.

High-quality of load balance will improve the performance of the whole cloud. Though, there is absolutely no general method that can work in all possible different conditions. There are several method have been hired to solve existing problem.

Each specific method has its merit in a specific area however, not in all circumstances. Hence, proposed model combines various methods and interchanges between appropriate insert balance methods as per system status. Here, the idle position uses an Fuzzy Reasoning as the normal status uses a global swarm search engine optimization based insert balancing strategy.

  1. Load Balancing using Fuzzy Logic

When the position of cloud partition is idle, several processing resources are free and relatively few jobs are receiving. In these situations, this cloud partition gets the capability to process jobs as quickly as possible so an simple and easy fill balancing method can be utilized.

Zadeh [12] suggested a fuzzy set in place theory in which the set boundaries were not precisely defined, however in fact limitations were gradational. Such a set is characterized by continuum of marks of membership function which allocates to each thing a membership grade which range from zero to 1 [12]. A new load controlling algorithm based on Fuzzy Logic in Virtualized environment of cloud computing is implemented to attain better handling and response time. The load balancing algorithm is integrated before it outstretch the processing servers the work is programmed predicated on various input parameters like assigned weight of Virtual Machine (VM) and processor speed. It includes the information in each Online machine (VM) and numbers of request currently given to VM of the machine. Therefore, It acknowledge the least filled machine, when a user submission come to process its job then it identified the first least packed machine and process consumer request but in case greater than one least packed machine available, If so, we tried out to implement the new Fuzzy logic based load balancing technique, where in fact the fuzzy reasoning is very natural like real human language by which we can formulate the strain balancing problem.

The fuzzification process is completed by fuzzifier that transforms two types of type data like designated load and processor swiftness of Virtual Machine (VM) and one output as balanced fill that are required in the inference system shown in physique 3. 2, number 3. 3 and shape 3. 4 respectively. By evaluating the load and processor rate in digital machine inside our proposed work like two suggestions parameters to create the better value to equalize the load in cloud environment, fuzzy reasoning can be used. These guidelines are considered for inputs to the fuzzifier, that happen to be needed to estimate the balanced fill as output as shown in amount 3. 4.

Figure 3. 2: Account insight function of Processor chip Speed

Figure 3. 3: Account insight function of Assigned Load

Figure 3. 3: Account end result function of Balanced Load

To internet marketer the outputs of the inferential guidelines [13], low-high inference method is utilized. Several IF-THEN rules are determined by taking a rule-based fuzzy reasoning to obtain the outcome response with given type conditions, here the guideline is comprised from a set of semantic control guidelines and the supporting control targets in the machine.

  1. If (cpu_speed is low) and (given_load is least) then (balanced_load is medium)
  2. If (processor chip_speed is low) and (allocated_load is medium) then (well balanced_load is low)
  3. If (cpu_speed is low) and (given_load is high) then (balanced_load is low)
  4. If (processor_speed is Medium) and (allocated_load is least) then (well balanced_load is high)
  5. If (processor_speed is Medium) and (assigned_load is medium) then (balanced_fill is medium)
  6. If (processor chip_speed is Medium) and (given_load is high) then (balanced_load is low)
  7. If (processor_speed is high) and (designated_load is least) then (well balanced_load is high)
  8. If (processor chip_speed is high) and (assigned_load is medium) then (balanced_weight is medium)
  9. If (cpu_speed is high) and (designated_load is high) then (balanced_load is medium)
  10. If (processor_speed is very_high) and (assigned_load is least) then (well balanced_load is high)
  11. If (processor chip_speed is very_high) and (given_load is medium) then (balanced_load is high)
  12. If (cpu_speed is very_high) and (given_load is high) then (balanced_fill is medium)

As shown above, there are 12 potential reasonable result response conclusions in our proposed work. The Defuzzification is the technique of changing fuzzy output set into an individual value and the tiniest of minimum (SOM) procedure is utilized for the defuzzification.

The total total of an fuzzy set comprises a variety of output prices that are defuzzified to be able to decode a single outcome value. Defuzzifier embraces the accumulated semantic beliefs from the latent fuzzy control action and produces a non-fuzzy control end result, which enacts the well-balanced load associated to weight conditions.

The defuzzification process is utilized to judge the membership function for the gathered end result. The algorithm-1 is defined to manage the load in Virtual machine of cloud computing as follows


Request_to_learning resource()


If (resource free)


Estimate connection_string()

Select fuzzy_rulebase()

Return resource




If (Anymore source found)


Go to L1





The proposed algorithm starts off with question a connection to resource. It checks for option of resource. It Calculate the connection strength if the resource found. Then select the connection, which is employed to gain access to the resource as per processor speed and load in electronic machine using fuzzy reasoning.

  1. Load Balancing using GSO (Glowworm Swarm Optimization)

When the status of cloud partition is normal, jobs happens with faster rate compare to idle express and the problem becomes more technical, thus a novel strategy is deployed for insert balancing. Each individual desired his job in the shortest time; because of this the general public cloud requires a strategy that can conclude the job of most users with sufficient response.

In this optimization algorithm, each glowworm i is sent out in the target function meaning space [14]. These glowworms transfer own luciferin worth and have the respective opportunity called local-decision range. As the shine searches in the local-decision range for the neighbor place, in the neighbor arranged, glow drawn to the neighbor with brightest glow. That is glow selects neighbor whose luciferin value higher than its own, and the journey direction changes every time different will change with change in specific neighbor.

Each glowworm encodes the object function value at its current location into luciferin value and advertises the same within its neighborhood. The neighbor's collection of glowworm consists of those glowworms that have comparatively an increased luciferin value which are situated in just a active decision range and their motions are up to date by equation (8) at each iteration.

Local-decision range update


and is the glowworm local-decision range at the iteration, is the sensor range, is the neighbourhood threshold, the parameter creates the pace of change of the neighborhood range. Local-decision range contain the following variety of glow


and, is the glowworm position at the t iteration, is the glowworm luciferin at the iteration. ; the group of neighbours of glowworm comprises of those glowworms that contain a comparatively higher luciferin value which are situated inside a dynamic decision range whose range is described above with a circular sensor range Each glowworm as given in equation (10), i elects a neighbor j with a probability and process toward it as
Probability distribution used to choose a neighbor


Movement upgrade




and is a luciferin value of glowworm at each iteration, contributes to the reflection of the accumulative goodness of the road. This avenue is followed by the glowworms in their ongoing luciferin beliefs, the parameter only ascends the function fitness values, is the worthiness of test function.

In this optimization algorithm, each glowworm is allocated in the objective function meaning space [43]. These glowworms copy own luciferin values and have the respective scope called local-decision range. As the shine searches in the local-decision range for the neighbor set, in the neighbor arranged, glow drawn to the neighbor with brightest shine. That is shine selects neighbor whose luciferin value higher than its own, and the airfare direction changes every time different will change with change in particular neighbor. Figure 3. 4 shows the flowchart of GSO algorithm.

In the framework of load controlling for cloud computing GSO algorithm check the position of the server together if it's free. For example a user wants to down load a file size of 50 MB. It checks by iteration if user gets got into in server, it has got the message as achieve goal.

Figure 3. 4: Flowchart of GSO

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