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Quasi Oppositional Dragonfly Algorithm for Load Balancing in Cloud Computing Environment

In Cloud Computing (CC), load balancing tasks remain an essential problem of spreading resources from a data center to ensure that each Virtual Machine (VM) has a balanced load to achieve maximum utilization of its capabilities. In the CC world, load balancing is a NonPolynomial (NP) problem solved with metaheuristic algorithms. A new Quasi Oppositional Dragonfly Algorithm for Load Balancing (QODA-LB) was developed to achieve the optimal resource scheduling in a CC setting. The proposed QODA-LB algorithm uses three variables to compute an objective function: run time, running cost, and load. The QODA-LB algorithm assigns tasks to VM based on its potential and the derivative objective function. Also, the QODA-LB algorithm uses the principle of Quasi-Oppositional Based Learning (QOBL) to increase the standard Dragonfly Algorithm's (DA) convergence rate. A comprehensive series of experiments were conducted, and the findings were analyzed in a variety of ways to ensure the efficient performance increased by the QODA-LB algorithm. The simulation's results demonstrated optimum load balancing efficiency and outperformed the leading approaches.


Introduction
Load balancing in CC is thought to be a complicated study for separating virtual machine operations in data centers. CC is an essential method for distributing on-demand services over the Internet in this situation. The cloud is a large, interconnected system that uses all files and fields in a variety of ways. To deliver the distribution of resources such as software, hardware, data, and files according to the requirements of alternative machines on the cloud, CC is combined with the concept of distributed and parallel computing. On the shared framework, it has a "Pay as You Want" module. The user does not need a computing environment to measure an operation but requires an Internet connection to allocate resources when spending money for a certain period. As a result, it restricts the amount of software purchased that is not needed fulltime, and CC offers dynamic resource features. VMs are CC processing units that measure and allocate resources when needed dynamically during the execution of a transaction.
Huge VMs are connected in CC by allocating resources in a pre-emptive and non-preemptive manner; however, resources are not allocated equally, and only a few VMs are capable of completing the tasks. If a task is delivered in the cloud, VMs must complete the process quickly and with minimal complexity, and all VMs must operate in parallel. Determines the need for task planning and implements it with the resources available. When several tasks are assigned to huge VMs, they are all executed at the same time to complete the tasks. If the trade is allocated to VMs, the scheduler must ensure that all trades are not invoked in the same VM and that alternate VMs are usable. As a result, all user tasks on all VMs in CC must be handled by the scheduler role. To resolve the issue of load balancing in all VMs, a smart load balancing model is needed to improve the response time of allocated operations by maximizing the use of available resources, as shown in Fig. 1.
The input task is spread uniformly through VMs during load balancing. The main goal of load balancing is to relieve all VCs of their pressure and report it to other weighted minimum VMs. It boasts a system's performance and throughput. Developers also used heuristic and meta-heuristic templates to address load balancing issues. On a distributed network, protected load balancing is recorded [1]. Furthermore, three models have been used to address load balancing and security issues in a distributed network. For instance, it provides a mechanism for a mobile agent to broadcast to all nodes in a distributed network. Following that, it provides a framework for reforming peer-to-peer load to provide maximum efficiency, and, as a result, it provides network security. To solve the load distribution in the Grid computer platform, a hierarchical load balancing strategy is proposed. When dynamically sharing the input task from the VMs and the merits of this application, which restricts the overall response time for the Grid application, the specifics of the actual node are used. By comparing a method's speed to the Minimum Completion Time and Perfect Details on Arrival, the speed of the method has been validated.

Fig. 1. Load balancing in CC platform
Load balancing based on honey bee nature is resolved in non-empty autonomous tasks on VMs [2]. The newly developed method helps resolve load balancing over VM to enhance the throughput, limiting the waiting time of tasks by assuming the preferences consequently. The associations performed with alternate models like weighted Round Robin (RR), FIFO, and Dynamic Load Balancing provide the effectiveness of a system for throughput and limit the response time of VMs. Dynamic load balancing solved on a virtual heat distribution platform [3].
It projects two dynamic load balancing models initially it applies local and global load balancing in the distributed virtual platforms by using heat diffusion, and second, it examined two functional aspects like load balancing factor as well as convergence threshold.
An expanded Particle Swarm Optimization (PSO) model is being developed to address the load balancing issue in the CC system [4]. It has made implementation faster and more effective. A Tabu Search (TS) approach is developed for resource management in the CC model. Optimized dynamic asset scheduling management is deployed based on factors including time constraints, cost constraints, and optimal solutions. The TS method is used to resolve resource allocation through prioritization as well as task grouping. The TS method is used to optimize the positions of cloud data centers software units such as data routing and network linkage capabilities [5].
The makespan and higher application of resources are limited by a simple scheduling approach in a grid computing platform [6]. It presents a load balancing model where the load is shared uniformly and reduces the response time. Fuzzy Logic (FL) is implied for effective load balancing and minimizes the cost and power in Geo-Distributed several data Centers. It showcases the best offline geographical load balancing by using the FL inference system. The input data mapping is non-linear such as the current application of renewable power, accessibility of electric cost as well as power utilization to produce the requests by the data center.
Load rebalancing is performed for distributed file systems in cloud systems where it optimizes the network traffic by improving the bandwidth of a system [7]. The complete distribution of load balancing rebalancing model is developed to solve the load imbalance and consequently, the newly developed approach is related to the previous centralized approach. An online algorithm with Lyapunov optimization strategy is proposed for load scheduling eco-aware power management for cloud data centers [8]. The main theme of this approach is to reduce the time average eco-aware power cost of CC data centers at the time of assuring Quality-of-Experience (QoE) constraints. The honey bee method is executed for limiting the makespan and allocates the resource for extending the throughput in the CC platform [9]. It is considered as a dynamic approach that has been applied for identifying the variations in nature among dependence and independence operations limit the makespan for all tasks by using task preferences. A load of a server is managed by applying self-adaptive Randomized Optimization. Power consumption has been lowered at the time of allocating resources to each task in the CC platform. Load prediction and requirement of resources are defined by Enhanced Exponentially Weighted Moving Average. Soft computing methods are projected to resolve the dynamic load balancing in the CC environment [10].
The load balancing method for CC is performed by a Genetic Algorithm (GA). Resource allocation is done in homogeneous as well as heterogeneous CC platforms [11]. Researchers have depicted the makespan and power application at the time of resource allocation. A static load balancing approach is developed for resolving the load balancing [12]. It employs static data for balancing the load with no influence of a load of cluster node which contains inferior adaptive capability. Bayes scheme is executed for prolonged load balancing. An improved weighted round-robin approach is deployed for resolving the load balancing in case of non-preemptive dependent tasks [13].
The function of load balancing is realized by various load balancing modules and results are related to throughput and speed [14]. Various scheduling approaches are deployed for the Map-Reduce environment that involves load balancing for the CC network [15]. Adaptive task allocation scheduler is projected for maximizing the function of Map Reduce in a dissimilar cloud network [16]. The deadline reduction scheduler is developed to reduce the deadline of a task in which it is collapsed while computing the massive data like video and image in Map Reduce frameworks [17]. An independent agent which depends upon the load balancing approach is presented for balancing a load by VMs with the help of 3 agents like Channel agent, load agent, and migration agent [18]. This paper introduces an effective QODA-LB in the CC environment for optimal resource scheduling. The proposed QODA-LB algorithm derives an objective function utilizing three variables namely execution time, execution cost, and load for allocating tasks to VM for its capacity. Besides, the QODA-LB algorithm incorporates the Quasi Oppositional Based Learning (QOBL) concept to improve the convergence rate of classical DA. A series of simulations took place to ensure the effective performance of the QODA-LB algorithm and the results are examined under several aspects.

Problem definition with solution framework
Assume cloud , that have " " number of PM is comprised of " number of VM.
where indicates the cloud, shows the initial and PM while implies the nth PM which is represented in the following: where is a first VM and refers the final . Likewise, count of users is loaded in cloud and user is composed of count of task. A user is represented in the following; The main theme of these models is to limit the execution time and expense of the task and to accomplish a balanced load entire VMs in CC system. Hence, it is operated using 3 objectives namely, limitation of task Execution Time, reduction of Execution Cost and finally, to scatter the burden for all VM in CC.
The overall Execution Time (ET) is defined with the help of Eq. (4).
In order to accomplish the appropriate scheduling, load balancing should be performed accurately. In absence of balanced load, a system consumes higher time and cost to implement the operation. In order to resolve the issue, an effective multiobjective based load balancing scheme has been deployed [19]. The projected Multi-Objective Function (MOF) is described in Eq. (7).

Proposed Load Scheduling Algorithm
The key objective of the presented model is for allocating a task to VM under the application of QODA-LB to reduce the overall ET and EC at the time of balancing the load. The load is a vital attribute of scheduling, where the method of scattering the load over diverse nodes of an allocated model for enhancing resource application task response time. To overcome these issues, multiobjective-based load balancing has been deployed with the help of QODA-LB. DA is defined as a meta-heuristic approach that is evolved by the static and dynamic swarming nature of dragonflies. The dragonflies swarm for 2 objectives like Hunting (static swarm) and migration (dynamic swarm). First, massive dragonflies swarm at the time of roaming in longer distances and various territories those results in the exploration phase. The previous function is showcased as 1 which is allocated to , task 2 is declared to and task 10 is declared to . According to the solution encoding, population matrix (SM) has been implemented with or 1. The framework depends upon the association between task and .
The task is related through VM denotes , or . However, the column contains a single component for 1, otherwise .
Firstly, the initial population is depending upon dragonflies.

Step 2: Fitness calculation
Once the solution is generated, the fitness of all solutions is calculated. The FF is provided in Eq. (9). where is the present iteration.

Step 4: Select best solution
When the DA and QODA are compared, DA has optimal fitness value which is lower than DA fitness value , the best place of DA is interchanged by QODA. Otherwise, AODA fitness value is minimum than DA fitness value, the position is changed by Step

5: Termination criteria
The iteration is terminated if a better solution is attained. The consequent solution is submitted to CC platform.

Quasi-oppositional based learning (Q-OBL)
It accomplishes maximum attention from developers in Computation Intelligence (CI). The main aim of OBL in evolutionary processing is to maximize the solution accuracy and simulate the convergence rate to reach global solution. It contains higher probability in optimization method for generating suboptimal solution. Here, recent populations as well as inverse values are produced at the same time and results in optimal candidate solution. Hence, inverse value is produced correctly at mirror position of recent population. From [20], the opposite population contains optimal chance to attain global optimal solution when compared to randomly provided population. The OBL is obtained by describing 2 of significant mathematical features:

Opposite number
It is referred as a mirror point of candidate solution from center of search space. When is a number in real plane with search interval , the parallel opposite number in 1D search space is described by (17).
where implies the randomly invoked candidate solution, and are lower and higher limits of search space. The previous definition is improved todimensional search space and formalized by (18).
where and

Quasiopposite number
Furthermore, the mechanism of OBL based learning is improved to quasi-oppositional relied learning that showcases that quasiopposite number is nearby global optimal solution when compared to opposite number. The quasiopposite number is meant to be among the center of search space while opposite number , is depicted by (19).
The pseudo code to accomplish quasiopposite value is given in the following: where denotes a randomly produced value from .

Jumping rate
It assists the DA to move from recent solution to fresh candidate solution where it has optimal fitness value than present one. According to the jumping rate, as said in (20), novel population is developed and then quasiopposite population has been determined. The selection of jumping value guides DA to eliminate suboptimal solution and stimulate the DA to attain globally optimized solution. Basically, the jumping rate is decided from where and are lower and higher value of jumping rate, refers higher value in generation and denotes the value of function call at present iteration.

Performance Validation
The

Conclusion
This paper has presented an effective QODA-LB algorithm in the CC environment to achieve optimal resource scheduling. The main theme of this model is to limit the execution time and expense of the task and to accomplish a balanced load over all VMs in CC system. The proposed QODA-LB algorithm derives an objective function using three variables, namely execution time, execution cost, and load. According to the derived objective function, the QODA-LB algorithm allocates tasks to VM with respect to its capacity. The simulation outcome has depicted optimal load balancing performance and demonstrated better results compared to state of art methods. A set of simulations were carried out to examine the execution cost and execution time analysis of the QODA-LB algorithm under a varying number of PMs, VMs and tasks. In future, the performance of the QODA-LB algorithm is further enhanced by the use of deep learning models.