LBPSGORA: Create Load Balancing with Particle Swarm Genetic Optimization Algorithm to Improve Resource Allocation and Energy Consumption in Clouds Networks

Due to the purpose of this study that reducing power consumption in the cloud network is based on load balancing, the fitness function measures the load balance between cloud network and servers (the hosts). *is technique is appropriate for handling the resource optimization challenges, due to the ability to convert the load balancing problem into an optimization problem (reducing imbalance cost). In this research, combining the results of the particle swarm genetic optimization (PSGO) algorithm and using a combination of advantages of these two algorithms lead to the improvement of the results and introducing a suitable solution for load balancing operation, because in the proposed approach (LBPSGORA), instead of randomly assigning the initial population in the genetic algorithm, the best result is procured by putting the initial population.*e LBPSGORAmethod is compared with PSO, GA, and hybrid GA-PSO. *e execution cost, load balancing, and makespan have been evaluated and our method has performed better than similar methods.


Introduction
Recent advances in computer networks, devices, Internet speeds, and so forth have led many companies and users to use cloud services. Resource sharing, virtualization of various services, and scheduling techniques have increased the efficiency of data centers. All of this has led users to use cloud services to meet their needs. Users need strong processors and fast networks to set up cloud data centers in different parts of the world. One of the most important concerns in this area is related to energy [1,2]. For this purpose, in recent years, attention has been paid to enhanced techniques for providing power management for cloud servers. erefore, many researchers have worked on different algorithms to reduce costs by reducing energy consumption and also reduce greenhouse gas emissions. To improve the performance of several algorithms to affect the hardware or the software has been tested. With the help of virtualization techniques, several virtual machines can be created on a physical machine, so we need less hardware and can increase performance. Cloud computing uses virtualization technology based on user's needs to provide resources, which reduces energy consumption. One of the most popular ways to reduce energy consumption is to use metaheuristic techniques [3,4]. is technique could attract attention due to its simplicity of implementation. e cloud services providers, because of the benefits they gain from serving customers as well as cloud consumers, such as organizations and economic organizations, tend to cloud computing because of the benefits they receive from reducing or eliminating infrastructure maintenance costs for these services [1]. However, since applications for business operations of users running in the cloud may be highly sensitive, consumers need to have a guarantee by the cloud providers when the service is delivering. Usually, such a guarantee is supplied between the consumer and the provider through a service level agreement. Recently, the use of virtualization to improve the performance of cloud networks has flourished [2,5]. So virtual machines can be moved from one host to another to enhance the efficiency of users' applications without interrupting service. Also, it is possible that the number of resources allocated to a customer increases or decreases dynamically [6][7][8][9].
In cloud computing, the users pay the cost for the amount of use and services they receive, and the workspace is transmitted to the cloud (the Internet) from data and information on the personal computer [10] so users can access their data and evidence anytime, anywhere in the world. Users also do not need hardware systems with high processing capacity and storage capacity, as all cloud computing operations and storage are handled in the cloud service providers section by equipped and advanced servers [11]. Currently, cloud computing services are offered in three different forms: SaaS_Software as a Service, PaaS_-Platform as a Service, and IaaS_Infrastructure as a Service [1]. Organizations and companies can select and use one or more service strategies depending on their needs. Cloud accounting is one of the most famous words in today's economic organization. An intrinsic cloud feature that distinguishes it from traditional and old hosting services. e amount of unlimited resource capacity (such as CPUs, memory, input-output network, and disk) is provided at a competitive rate [12]. is subject provides opportunities for startup companies to host their applications in the cloud, so it eliminates the overflow of prepared old ultrastructural resources that lasted several months [1,13,14].
Cloud service providers need to reassure customers that their needs will be fulfilled completely [15][16][17].

Motivations and Goals.
Using virtualization-based networks and storing bits for cloud computing can be a way to balance the load on networks; just as in the phase map and radius created in the big data, it can upset the balance. Using virtualization methods and load balancing methods can help a lot. In this paper, we present an algorithm PSGO considering scheduling. e main challenge about earlier researches during past years was that although resource scheduling and request management were taken into place, there was not considerable attention for energy optimization. In this research, we considered energy optimization as the main objective and tried to present a solution based on this. e energy-performance tradeoff can be considered for a physical machine that has computing ability in its peak usage. For optimizing energy consumption, we used the PSO optimization algorithm [14]. In this approach, user applications are mapping to virtual machines through the autonomous domain layer. erefore, resource scheduling has been done with the assistance of an autonomous domain and virtual machine layer. e advantage of this heuristic algorithm is that it starts searching from some areas of space and extends the exploration domain and develops a stronger algorithm. In this work, we do the following to improve results: the final result of the PSO is to initiate population for GA. us, the first population of the GA is the optimal solution of the last step ( Figure 1). Bestowing to the goal of this study is reducing load power in the cloud network based on load balancing, and the fitness function measures the load balance between the cloud network (host) servers.
In this study, we have dealt with the issue of virtualized reserve controlling of cloud computing records middles and, in particular, the load balancing among the servers, and we have done this by considering and implementing particle swarm optimization algorithms and genetic procedures [3,18]. e sequel displays that the combination of these two can be effective in reducing the cost of resource allocation and load balancing. On the other hand, considering the combination of two optimization algorithms, the program execution time is increased compared to the implementation of an optimization algorithm. In this study, to improve the results, we will do the following: the final result of the PSO algorithm will be the initial value used in the genetic algorithm. erefore, the initial population used in the genetic algorithm is the optimal solution obtained from the previous step. is method is very suitable for answering the main question of the current research due to its ability to convert the load balancing problem to the optimization problem (reduction of the imbalance cost function) and has been used in studies in this field as well.
In the first part of this article, some of the definitions related to this field and the introduction are discussed. e second section discusses the research background. In the third part, LBPSGORA based on the heuristic algorithms is presented [19], and, in the fourth part, an implementation of the proposed method and the result of the power consumption reduction are presented. In Section 5, we present future work and conclusions.

Literature Review
e load balancing algorithms are in three categories: (1) sender initiated load balancing algorithms, (2) receiver initialed load balancing algorithms, and (3) symmetric load balancing algorithms, a combination of both modes. e Round Robin algorithm is a load balancing method with an optimization energy consumption methodology. is issues tasks to all secondary processors. All tasks are assigned to secondary processors based on Round Robin (RR-Order) ( Figure 2). Resources are selected with the machine to run in the alone direction. Selecting processors locally and independently of the allocation of processors is performed on each processor. e RR-Order algorithm generally is recycled in web servers where generally the WEB applications have the same nature and, as a result, they are evenly distributed.
is defined pattern has not been under the principle of SLA [20].
With increasing the number of intelligent communication monitoring system devices such as WSN, WBANs, Big Data, SDN, and mCloud (mobile-Clouds), these devices have become moveable, proficient, and personal accounting infrastructure for the end handler in various modes [2,8,17,[21][22][23][24][25]. ese devices come with cloud network intermediaries and IoT and SDNs, which cause them to enter the global Internet resources [12,17]. Many end-users tend to use these devices instead of desktops or clients. In this case, CloudIoT and fog computing have appeared as a practical explanation, and their tasks can be offloaded on the cloud substructure [1]. As another example, network bandwidth may change over time, and the performance levels of data rate cannot be guaranteed for task scheduling [18].
is is especially regarded in the area of real-time applications, where minimum latency is important. Resource management allocated to the mobile cloud must be considered in the cloud infrastructure. Because load-free applications and tasks run on the cloud substructure, fog resources such as storing are recycled to complement mobile device home-grown resources [9,26].
In this regard [2], power consumption in CDC (CloudDataCenters) has been used. In this paper, the goal is to achieve an efficient resource management system to reduce operating costs and ensure service quality. e model of reducing energy consumption in this paper is based on continuous control of virtual machines using efficient methods. e main tool is the use of live migration. In this case, they can transfer virtual machines to physical machines without any interruption and with the highest flexibility, so that they can use the highest capacity of physical machines.
In [16], genetic algorithms for allocating virtual machines to reduce energy consumption in data centers are proposed, which shows that evolutionary algorithms are useful for allocating virtual machines because they have been able to reduce energy consumption by up to 10%.
In [27], a method of load balancing using PSO was provided. e authors have used many mobile cloud solutions to conserve many properties resources such as power and energy in mobile-portable devices. ey optimized the grid energy by examining the SLA and placing the virtual machines using the PSO method.
In [28], in PSOSA, the PSO is used to minimize the energy. In experiments using cellular communications ad hoc mobile, the load balancer decision-maker used cellular convergence by PSO by load balancing on the network. e authors tried to evaluate their mechanism on a cloud-based cellular network.

Mathematical Problems in Engineering
Another method of load balancing the MOPSO multiobjective code transfer interval is proposed: the Energy Management and Service Quality Awareness (QOS) Framework [29]. e authors have used the Internet of ings for their inputs.
ey have considered the infrastructure for the network. eir approach focuses on portable mob-cloud service transfer created on QOS as well as power constrictions. If using the mobile device remote service is costly or the latency time is so high that QoS is not available, the fog service can be transmitted by communication and locally at a lower cost. It proposes algorithms for migration and transfers alongside the decision process for fog-grown performance against inaccessible performance.
In [30], a solution was presented based on the migration of virtual machines using PSO. e authors have used mobile devices to deliver cloud-based services. As part of their method, they supported fuzzy to apply multiple services; for example, a service output can be used as another service input. eir goal is to reduce energy consumption in the network, and resources and the potential of this platform in data are shared by online services. ey have analyzed this potential and mentioned the difficulty of this way.
In [31], a C-RAN application parsing solution was presented; it works by splitting Apps to different mechanisms; then these modules are activated. e authors also discussed how to avoid the middleware solution from allocating further mob-resources to the fog server by using the OFDMA and PSO facilities instead of allocating all virtual machines. In the results section, several experimentations were achieved with m-devices to reach a DSS model to place the load on the network and reduce energy consumption.
In [32], a method with PSO considers reducing the consumption of available fog resources without the need for additional overflow in the m-device. In the obtained method, the implementation of PSO algorithm has been used to find suitable places in the network. Cloudlets and their modifications to find the suitable virtual machine location are another method for mobile clouds which are used like VM by placing load on the cloud infrastructure that may be nearby.
In [33], a scheduling method called NP-Hard is presented for the cloud. e authors have used PSO to reduce costs. How the cloudlet approach to scaling is used according to their resources, how m-devices network efficiency will cooperate with multiple users, and just how fog cloud will manage the stimulus ability need to be explored. e authors have introduced the CSOS method and optimized network energy by reducing makespan. In their method, the calculated cost has a low value.
In [34], the analysis of energy consumption by PSO on mobile devices has been examined to some extent in articles and writings. As mentioned, most of power and costs connected with using an m-device come from "DPRA" energy, known as the network interface. When the connection is made, the net edge remains active for each outstanding packet from the fog server, and it is costly for net operation. e work done in this paper is to implement a system called VM-DPRA, which groups network needs and requests together to reduce the number of tail energy occurrences. eir tail-ender method similarly attempts to cluster network requirements to minimize the power. In this research, K-means clustering method has been used for mapping tasks and microgenetic algorithm for dynamic aggregation of virtual machines. It has also been able to reduce the execution time.
In [28], a dynamics algorithm is presented. is algorithm is proposed to create load balance in cloud computing environments using the ant colony algorithm.
is algorithm is one of the biological algorithms and almost simulates the behavior of ants. e authors in [29] provide a method for allocating virtual machines using a linear search to optimize resources. e architecture used in this paper is that data centers include several physical machines and physical machines have a fixed size of RAM, CPU, and memory. e broker is responsible for managing user's requests; then the requests are transferred to the data center via the cloud, where the center manager reserves a certain number of virtual machines to execute the users' request and provides different classes of virtual machines by cloud maker is suggested, and the user can choose from these classes that have special features such as RAM, CPU, and memory. In this way, it has been able to reduce energy consumption to some extent. e issue of proper placement of virtual machines in physical machines has a great impact on optimizing energy consumption. In [30], the ant colony system has been used to place virtual machines. In this research, the dynamic placement system of virtual machines has been considered. In this way, the request for several virtual machines changes in each time interval; at the beginning of the time interval, the list of virtual machines needs to be updated, and this happens by deleting the expired virtual machines before the previous interrupt.
en the resources of these expired machines are released, and new virtual machines are given to them. Updated virtual machines receive the right information and resources, which improves energy efficiency. e study in [7] develops new VM scheduling to reduce energy costs for cloud services providers. In this study, it is assumed that there is an optimal frequency for one PM for VM processing. e optimal rate is also defined for the performance-power ratio based on which the heterogeneous PMs are weighted in the cloud environment. is problem is defined for the time-bound networks and has achieved good results. e workflow process is divided into several periods, where in each one the virtual machines have been dedicated to the proper PM and each core is operating at its optimal frequency. After each period, the cloud reconfigures to consolidate computing resources, which further reduces energy consumption. Table 1 illustrates some related works with metaheuristics algorithms for solving time scheduling problems and improving energy consumptions in cloud networks.

The Proposed Method (LBPSGORA)
3.1. Problem Statement. Here, ADS � AD 1 , AD 2 , . . ., AD r represents the autonomous domains set and each AD represents an autonomous domain. Each autonomous domain has several virtual machines, as shown in Figure 3 [18,35]. e cloud is mapped user applications to the virtual machine by running user applications and through a layer of autonomous domains. erefore, the source environment scheduling of the cloud should be done by the layer of autonomous domains and the layer of virtual machines.
So here we have used two layers for scheduling; in this way, first, we can find autonomous domains that have more fitness and then search for the best virtual machines for assigning tasks among virtual machines at those domains. In addition, we considered some autonomous tasks as an application and attempted to assign them to the virtual machines by the proposed scheduling method. us, a hierarchical scheduling framework is used for the proposed algorithm. To illustrate this problem, we present a two-dimensional form in Figure 4, in which X axis shows speed of CPUs and Y axis shows the amount of RAM ( Table 2 and Table 3).

Proposed Method LBPSGORA.
In this study, a load balancing algorithm is presented for optimizing energy consumption by considering scheduling. e problem with research that has been done over the last years is that, despite addressing resource allocation and requests management, there has not been much attention paid to energy efficiency in it. But this research aims to provide a solution based on this vital problem, considering energy efficiency as the primary goal. e energy efficiency ratio for a physical machine (PM) that contains the computational power ratio at the peak of energy can be used for this purpose.
e algorithm used to optimize energy consumption is the particle swarm optimization algorithm. In this method, users' applications are mapped to virtual machines through a layer of autonomous domains. erefore, the scheduling of cloud environment resources is done by the layer of autonomous domains and the layer of virtual machines. e advantages of using metaheuristic algorithms are that one can start searching from several areas of the problem space and increase the level of exploration and have a stronger algorithm. In this study, to improve the results, the final achieved result of the PSO algorithm will be used as an initial value in the genetic algorithm. erefore, the initial population used in the genetic algorithm yields is the optimal response obtained from the previous step.

Particle Swarm Optimization Algorithm.
In this part, we describe the several specifics of the LBPSGORA. As virtual machines are the subset of physical machines that are processing tasks, the PSO algorithm can work in a complex problem to find suitable tasks. e formulas that are presented in this section are related to the particle swarm optimization method.
is method is based on using metaheuristic algorithms, in such a way that the metaheuristic methods have been used for assigning VM to PMs. e PSO algorithm is a global optimization method that can solve problems with solutions in a point or surface in ndimensional space.
erefore, in addition to the initial velocity of particles, the channels of communication between the particles are identified. By moving these particles into the solution space, the results are evaluated based on a merit criterion. A large number of swarm particles make the method flexible against the local optimal solution problem. Each particle has a location. x i (t) specifies the position of particle i at time t. Each particle also needs a swiftness to move through space. v i (t) specifies the velocity of particle (i) at time (t). e equations for updating the particle positions are given below. e particles are talented at memorizing the best position in their lives. e best individual experience of a particle or the best position met by a particle is called y i . e particles know the best position met in the whole group. is is called the y position. e two values of c 1 and c 2 are constant and w is the initial weight that is decreased linearly from w max to w min . t max is the maximum number of repetitions intended for algorithm (Table 4). erefore, it can be expected that, by utilizing metaheuristics in the initial allocation of VM to PMs, it would be closer to the optimal solution and reduce the number of migrations ( Figure 5, part A).

Genetic
Algorithm. e steps used in the genetic algorithm are as follows: at first, building the initial population is based on the best solution obtained from the PSO algorithm. Selecting and combining initial samples (arrays) to generate a new sample or a crossover are done in this algorithm. In the crossover phase, a random combination of two fathers was used to generate a new child. If the generated children contain repeated genes, they will be replaced by the number of unused genes. e whole crossover phase is performed in ten iterations with a combination of 10 randomly selected pairs. Modifying existent samples and building a new sample prevent local optimization because similar samples may be used for the combination in the crossover phase. Selecting a sample with the best fitness among the produced samples in this generation continues to produce the next generation of samples until the end condition is met ( Figure 5, part B).
On the path to the aim and on the way to achieving the optimal solution for the problem, it is necessary at each step to measure the proximity of the current solution to the aim using a criterion. is is done by the fitness function. In this paper, the fitness function is carried out by using the study in [17]. To achieve an optimal solution where there is load balancing between cloud (hosts) network servers, we need to minimize the mean deviation between the loads allocated to Mathematical Problems in Engineering 5 the servers. Suppose that T i denotes all computing powers of a server and L i is the computational power left by the server after assigning it to requests. e computing power of a server is calculated by the speed of CPU and the number of storage resources and its memory.
If the number of servers or hosts is m (the m physical hosts are heterogeneous), erefore, according to the standard deviation formula to optimize the load balancing in the cloud network, we use the following fitness function, where E(X) is a mathematical expectation ( Figure 6): e remaining load ratio in a server is defined as (residual load rate) erefore, the fitness function is defined as the standard deviation function:

Simulation and Results
In the cloud computing environment, some slaves are the central interfaces between users and the cloud infrastructure. is slave is responsible for resources management policy and allocating requests for machines. In addition to the slave that manages the cloud with its policies, due to the distributed nature of the cloud computing network, distributed resource management algorithms can also be provided. e purpose of this research is to manage resources in the cloud environment and to present a new method for examining the current status and history of semicentralized cloud-level machines. e cloud environment includes the physical infrastructure and specific control functions that the cloud, with their collaboration, will be able to provide and manage services. ese infrastructures and resources are integrated and configured by the infrastructure provider in the data centers. e cloud provider  (Figure 7). We used MATLAB and CloudSim to evaluate the algorithm. e simulations have been run in an environment with an Intel processor and 6 GB of RAM. e operating system used is 64bit Windows 7 Ultimate (Tables 5 and 6).
MATLAB's programming language is used to simulate the peer-to-peer system, connect between records, and construct the data distribution. Figure 8 illustrates the implementation steps of this research. After performing this initialization, Figure 9 shows the number of resources and registered requests in the program when the LBPSGORA is evaluating.

Applying PSO to Data Resources Matrix.
In the process of implementing LBPSGORA after identifying the resource matrix and also the submissions matrix, the PSO_Algorithm has been applied to data. So we can allocate sent requests appropriately so that the load balancing between the servers is maintained and the cost function defined for this research will be minimized. Table 7 shows the parameters used in the particle swarm optimization algorithm.
After the implementation of the PSO algorithm according to the initialization, the manner of initial and final allocation to some of the requests is given in the following table as an example. Comparing Table 8 and Table 9, it can be seen that the final cost considered to the resource allocation is much lower than the initial cost. Figure 10 shows a comparison of the initial cost and the final cost in resource allocation to the requests for a better understanding of how the algorithm works. Also, Figure 11 shows the amount of resource allocation cost per replicate. As can be seen, this algorithm can find the optimal path correctly and after 100 iterations can propose the desired results at the lowest cost. Table 7 and Table 8 represent the details of how to allocate a resource to received requests. e number of resources is considered to be 100, where, for each of these resources, the parameters related to CPU capacity, memory, and so forth are given values according to the initial values in Table 9.

Applying Genetic Algorithm.
After applying the PSO to data related to the defined requests and resources in the system, it is time to implement the genetic algorithm. us, after performing the PSO and achieving the best cost in allocating resources for the received requests to balance genetic servers, the GA_algorithm is applied to the best results of the particle swarm optimization algorithm. us, we believe that performing this method can reduce the cost of resource allocation for received requests.
In this research, we also intend to minimize the cost defined. By comparing Table 10 with the final output of the

Mathematical Problems in Engineering
PSO algorithm and Table 11 showing the parameters of the genetic algorithm, it can be concluded that the final cost considered to allocate resources and load balancing has been reduced desirably in comparison to the cost obtained in the PSO algorithm. erefore, the LBPSGORA can propose appropriate allocations that maintain the load balancing between the servers. Figure 12 shows a comparison of the initial cost and the final cost in allocating resources for the requests for a better understanding of how the algorithm works. Also, this method reduces the cost of resources to requests, and Figure 12 shows the amount of allocation costs of resources in each iteration. is algorithm can find an optimal path and can propose the desired results with the minimum cost after 100 iterations.

Runtime Calculation.
In this sector, we investigate the runtime calculation for the LBPSGORA, PSO algorithm, and genetic algorithm. To be fair to the obtained results, we assume that the numbers of rings are equal, as depicted in Figure 13. e numerical value of the LBPSGORA runtime in different repetitions is shown in Table 12.
e variable of the cost of consumption is taken as a threshold to load balancing in the cloud network and optimizing energy consumption. By spending more time and developing a hybrid algorithm, we have improved energy consumption and cost. Without load balancing, when clients send requests to the cloud network, data transmission operations are complicated, and retransmission occurs. As a result, it needs to spend money and extra energy (Table 12).   In this section, the details of LB in exchange for increasing tasks in the physical machine are discussed. In the proposed method, the LBPSGORA combination is intended to handle tasks. e proposed method is compared with the method in [16], hybrid GA-PSO, and GA algorithms. In hybrid GA-PSO method, due to having higher complexity compared to our proposed method, it has a weaker load balancing compared to our proposed method. e difference between our work and the article is that the authors therein used a combination of PSO + GA for their method and the two methods depend on internal steps to create the initial population. In LBPSGORA method, two separate algorithms work, and the optimal PSO response for the initial population of GA is considered.
As can be seen in Figure 14, the proposed method has better conditions for increasing tasks.
is makes it less Table 4: Resource parameters and their initialization range.

Components Application
Description v e velocity of each particle Each particle has a velocity vector such that v i � (v i1 , v i2 , . . . , v in ) x e location of each particle Each particle has a position vector such that x i � (x i1 , x i2 , . . . , x in ) Two random coefficients c 1 , c 2 are acceleration coefficients. e acceleration of each particle is directed to P best and G best p best Best value e particle's best known position g best e best cost considered Moving to the best position that has been in the neighborhood of the swarm from the beginning until now Step 2 Best result from PSO

Initialization -get the best solution with PSO
Find optimal solution problem Each particle (task) has a location Xi (t) specifies the position of the particle i at time t. Particles are capable to remember the best position Distinguish unallocated amount tasks and responsibilities Distinguish tasks apportion to more than one virtual machine  Figure 5: e proposed algorithm.
Step 2 is related to PSO for distinguishing tasks apportion to more than one virtual machine and step 3 is related to GA for returning the best solution.
Where L_i is used to represent the remaining computing power of the host i; L_c is the remaining CPU resource amount of physical host i; α is the weight value of CPU and β is the weight value of memory. S = (s_1, s_2, ...s_n) is an n-dimensional solution vector, Its every elements s_i stands for the optimal physical host which deals with the corresponding task request i.
This algorithm will work out the final deployment strategy obtained in the form of solution vector S by utilizing its algorithm mechanism.
T_i denotes the total computing power of host i; T_c is the total CPU resource amount of physical host i; T_r is the total RAM resource amount of physical host i; The number of task requests received during time delta (t) by the cloud data center is considered as the workload.
L_r is the remaining RAM resource amount of physical host i; Figure 6: Each step to measure the proximity of the current solution to the aim using a criterion.
Each request parameter has a minimum value that increases to reach a maximum value. e requests are dynamically generated and continue to reach 1.5 times the total resource capacity at each time interval.
We generated 100 sources randomly using the parameter ranges listed in table 5 Same resources physical machine cannot be used by different virtual machines We consider four parameters for each source following

Resources (MAX, MIN)
Num_of_CPU numbersNum_of_RAM Total_Bandwidth Num_ofStorage Table 5: each parameter has a minimum value that can be increased in steps to reach a maximum value (max) Line box represents a physical machine and small boxes represent the host virtual machine in it.            energy-consuming. e PSO and GA methods are also unfavorable conditions for observing the LB of the other methods. e circles in Figure 15 show the effect of increasing tasks on LB. With the increase of tasks, we see that the load created on the network has increased. As shown in Figure 13, the proposed method is similar to the compared methods in different cases and the improvement rate of the proposed method compared to hybrid GA-PSO is 8.42%, and it is 10.61% compared to GA and 11.71% compared to PSO. Also, the simulation results for tasks 100 to 1000 are examined in detail in Figure 15. e improvement rate of LBPSGORA method compared to hybrid GA-PSO, respectively, is as follows: 5.12% for 100 tasks, 3.67% for 200 tasks, 4.18% for 400 tasks, 2.28% for 600 tasks, 5.12% for 800 tasks, and 3.23% for 1000 tasks.

Check Makespan against Task Execution.
is section compares the proposed LBPSGORA method with the hybrid GA-PSO method and the number of makespan changes against the number of tasks. As shown in Figure 16, as the number of tasks increases in different cases, the proposed method and other methods will be changed. Because the tasks in the proposed method are more efficient than the hybrid GA-PSO method, makespan changes in various tests are due to the use of two-stage PSO and GA and network updates. In some incremental modes, load balancing changes are also observed. e proposed method is similar to other compared methods in different cases and the improvement rate of the proposed method is 7.32%. Also, the simulation results for the 100 to 1000 tasks are discussed in detail in Figure 15. e improvement rate of LBPSGORA method compared to hybrid GA-PSO in makespan, respectively, is as follows: 2.1% for 100 tasks, 1.39% for 200 tasks, 1.36% for 400 tasks, 2.74% for 600 tasks, 1.06% for 800 tasks, and 2.60% for 1000 tasks. Figure 17 examines the costs of task execution changes in different implementation of tasks, and, as can be seen, the proposed method has a lower implementation cost task execution compared to other methods. In the hybrid GA-PSO method, the cost changes are similar to the performance of the proposed method and the LBPSGORA is 6.87% more efficient. As can be seen, with the increase in the number of tasks, the cost task execution for both methods of implementation costs is increasing. Also, the simulation results for 100 to 1000 tasks are reviewed in detail. e    improvement cost task execution rate of LBPSGORA compared to hybrid GA-PSO, respectively, is as follows: 0.4% for 100 tasks, 1% for 200 tasks, 1.6% for 400 tasks, 2.32% for 600 tasks, 2.48% for 800 tasks, and 1.53% for 1000 tasks.

Conclusions
e most significant challenges that cloud service providers face are managing physical resources and scheduling tasks by taking into consideration the reduction of energy consumption throughout the network. In the LBPSGORA approach, instead of randomly assigning the initial population to the genetic algorithm, the best result obtained from the PSO algorithm is considered as the initial population in GA. e hybrid GA-PSO method is similar in performance to the proposed method with tasks changes. e LBPSGORA method is 6.87% more efficient in execution cost and 7.32% more efficient in makespan compared to hybrid GA-PSO. In load balancing, LBPSGORA was 8.42% superior to the hybrid GA-PSO, 10.61% superior to GA, and 11.71% superior to PSO. For future work of this research, the use of other heuristic methods with load balancing and scheduling problems will be considered. Besides, reliability and response time considerations are other areas of future research.
Data Availability e data underlying this article will be shared on reasonable request to the corresponding author.

Conflicts of Interest
e authors declare that they have no conflicts of interest.