## 2 Related Works  
All the resource allocation for the clients was done by the cloud service providers who first obtained the resources required for computing. In order to obtain efficient utilization of the resources, ideal task schedulers play an important role. In this work, RADL (Resource and Deadline Aware Dynamic Load balancer) for task scheduling in the cloud was introduced. The introduced task scheduler method evenly distributes the arriving work, which is considered to be computer-intensive and has a run time independent task. Moreover, the RADL algorithm has the ability to adjust the new incoming task effectively, and the task rejection is reduced. Experimental results obtained display that the introduced method attained up to 259.2%, 303.57%, 67.74%, 259.14%, 405.06% and 146.13% improvement for meeting task execution cost, tasks deadlines, meeting tasks deadlines, average resource utilization, penalty cost, and task response time, respectively, in comparison to the state-of-the-art heuristic scheduling of tasks using the benchmark datasets [6]. A huge volume of data means a large amount of data processing is required, and, due to this fact, the execution of the algorithm in the cloud environment becomes costly and consumes considerable energy. Many different algorithms have been introduced that try to search for the optimal result for scheduling workflow on the cloud, but there are some drawbacks in most of them. In this work, an EViMA (Energy Efficient Virtual Machine Mapping Algorithm) was introduced in order to enhance the management of the resources in the environment and to accomplish efficient task scheduling, which would reduce the data consuming energy, execution time, and cost. This algorithm is designed in such a way that it will meet all the necessities of the cloud user, and, at the same time, the quality of service is increased. In the introduced algorithm, the heterogenous properties of the scheduler from both workflow application user and cloud user were considered. By conducting experimental analysis on the real dataset, the introduced EViMA can give improved results for cloud providers and users by decreasing the makespan, cost, and consumption of energy [7]. As cloud computing offers various advantageous benefits, it attracts many entrepreneurs and IT leaders of various levels to use the cloud environment. Cloud computing makes use of many different technologies, such as mobile computing, the Internet of Things, and many more, out of which the task scheduler is one of the most important parts and is a challenging issue itself due to its NP-hard nature. Various methods have been introduced for ideal task scheduling that aim to improve the quality of service as well. This work tries to introduce a new Sailfish Optimization-based task Scheduling Algorithm (SOSA). SOSA was applied on two datasets, which showed a result of 7.81% and 13.71% which is an average improvement of performance in terms of makespan in comparison to Particle Swarm Optimization (PSO) and Genetic Algorithm(GA), respectively, and 30.78% and 11.30% of average improvement in terms of cost of execution in comparison to PSO and GA, respectively [8]. Due to the versatile nature of the cloud environment, which includes features such as fast and high accessibility, cheaper cost of operation, availability, and elasticity, the cloud computing environment has been utilized by researchers for the implementation of huge-scale task processing. Moreover, a pay-per-use charge is applied to each end user and, due to this factor, the efficiency is improved. The process of scheduling the work is difficult, as the processor in charge of executing this procedure needs to consider the dependence of the work in the environment along with the QoS (quality of service) demands. When there are many quality of service demands, the issue becomes more challenging. A lot of algorithms have been proposed in order to reduce the cost of execution and makespan under budget and deadline constraints.