## 2 Motivation  
This work is motivated by the following aspects:  
* Most of the existing task schedulers doesn't consider the task's requirements, e.g., number of tasks and the task's priority, and some of them consider only the waiting time and response time reduction. [3] In this paper, we introduce a hybrid task scheduling algorithm that utilizes the shortest job first (SJF) and round-robin (RR) algorithms. The hybrid task scheduling algorithm comprises two major stages. First, the waiting time between short and long tasks is balanced. Waiting time and starvation are decreased via two sub-queues, Q1 and Q2. The combination in both versions of SJF and RR is evaluated with dynamic and static quanta, and optimality in the task scheduling and load balancing methods is achieved via evaluation. Second, the ready queue is divided into two sub-queues, in which Q1 denotes short tasks, whereas Q2 denotes long tasks [4]. * A good task scheduler considers environmental changes in its scheduling strategy [5]. This research proposes to use the ant colony optimization (ACO) algorithm for effectively allocating tasks to virtual machines (VMs). Slave ants adapt to diversification and reinforcement. The ACO algorithm avoids long paths with pheromones incorrectly accumulated by leading ants, thereby rectifying the ubiquitous optimization problem with slave ants [6]. * An NP-hard problem is a critical issue in cloud task scheduling. A metaheuristic algorithm can be used to solve this problem. A cloud task scheduling algorithm based on the ACO algorithm is proposed in this research to achieve efficient load balancing. * The primary contribution of this research is minimizing the makespan time of a given set of tasks. ACO or a modified ACO algorithm is used to optimize makespan time [7]. The key aspects of cloud computing are task scheduling and resource allocation. A heuristic approach that combines the modified analytic hierarchy process (MAHP), bandwidth-aware divisible scheduling (BATS) + BAR optimization, longest expected process time (LEPT) preemption, and divide-and-conquer methods is adopted to schedule tasks and allocate resources. MAHP is used to rank incoming tasks. The BATS + BAR methodology is utilized to allocate resources to each ranked task. The loads of VMs are continuously checked with the LEPT method. If a VM has a large load, then other VMs are assigned tasks via the divide-and-conquer methodology [8]. The additional load is distributed across multiple servers using the load balancing strategy to optimize the performance of cloud computing. Load balancing issues are addressed via a hybrid bacterial swarm optimization algorithm. The particle swarm optimization (PSO) and bacteria foraging optimization (BFO) algorithms are combined--the former is for global search and the latter is for local search [9; 10]. A critical factor in conflicting bottlenecks is to improve the response time for user requests on cloud computing. Accordingly, the throttled modified algorithm (TMA) is proposed in this research to improve the response time of VMs and enhance the performance of end to end users. Load balancing is performed via the TMA load balancer by updating and maintaining two index variables: Busy and available indices [10]. This research introduces a new and extensible VM migration scheduler to minimize completion time in scheduling. Live migration, which consumes network bandwidth and energy, is a high-cost scheduler. A migration scheduler computes the best moment for each migration and the amount of bandwidth to allocate by relying on realistic migration and network models. The migrations that will be executed in parallel for fast migrations and short completion times are also decided by the scheduler [11]. This research proposes a hybrid load balancing algorithm that combines teaching-learning-based and gray wolf optimization algorithms to optimize load balancing for cloud computing. The scheduler allocates jobs to VMs in a distributed system and creates scheduling for resource allocation.