## 3 Related Work  
Load balancing has been extensively studied in the literature, and many algorithms have been proposed. Authors in [16] proposed a two-stage strategy for enhancing the performance of task scheduling and decreasing unnecessary task allocation in clouds. The Bayes classifier principle is first adopted for classifying tasks based on historical scheduling data, and then tasks are dynamically coordinated with corresponding concrete VMs. However, the Bayes classifier requires considerable historical data to obtain accurate results, and consequently, this strategy leads to accuracy loss. The research reported in [17] introduced a priority-based task scheduler that ranks users on the basis of task length and memory. A hybrid optimization algorithm that integrates genetic and PSO algorithms for efficient task scheduling was also proposed. A queue manager is utilized to store and prioritize user tasks. Rejected tasks from the prioritized queue are given to the on-demand queue, and tasks from both queues are given to the hybrid algorithm. Nevertheless, slow convergence rate and poor local search capability are observed from the PSO algorithm. In [18], a hybrid optimization algorithm was suggested that combines cuckoo and harmony search algorithms to develop an intelligent method for enhancing the scheduling process. Tasks are initially assigned to VMs, and fitness functions, which consider metric energy consumption, memory usage, credit, and penalty, are calculated. The cuckoo and harmony search algorithms are used for updating, and the best fitness function is selected through hybridization. However, slow local search convergence, due to the harmony search algorithm, limits the method. Authors in [19] proposed two hybrid algorithms, namely a combination of fuzzy logic and PSO algorithm and a combination of simulated annealing and PSO algorithm, for improving task scheduling performance in cloud computing. A waiting time optimization algorithm based on the PSO algorithm is used to minimize task waiting time. The objectives of the two hybrid algorithms are to utilize resources, optimize performance metrics, minimize makespan, and achieve good load balancing. Nevertheless, the two hybrid algorithms require considerable computational time. The researchers in [20; 21] introduced dynamic voltage and frequency scaling that describes the quality-of-service requirements of tasks with minimum frequency. The energy consumption ratio is used and evaluated to determine different frequencies. Upcoming tasks are distributed to active servers. After a task is completed, the remaining tasks are rescheduled among processors via a processor-level migration algorithm. Runtime optimization is also analyzed, and a task migration and frequency readjustment scheme are provided to decrease the energy consumption of the server. Reference [21] further proposed a dynamic heterogeneous SJF model for minimizing actual Central Processing Unit (CPU) time and overall system execution time or makespan. Energy consumption is decreased via dynamic provisioning, and the dynamic request is matched with dynamic heterogeneity, thereby reducing makespan and increasing resource utilization. The dynamic heterogeneities of workload and resources are considered. Dynamic requests for heterogeneous resources are defined to minimize execution time and increase resource utilization. Reference [22] aimed to solve the problem of dynamic load balancing by proposing a hybrid PSO algorithm. Workload is distributed across different resources via load balancing. The multiple kernel function support vector machine (SVM) algorithm is used to evaluate the disturbances in utilizing various resources. A centralized cloud-based multimedia system that comprises a resource manager, cluster heads, and server clusters is considered. Client requests are assigned by the resource manager to the servers within its cluster head. However, the training time of the SVM algorithm is slow. Reference [23] presented a load balancing method based on constraint measure.