## 2 Related Works
This paper introduces an algorithm for workflow scheduling which is based on java to reduce the makespan and execution cost to return a result in accordance with the weights provided by the end user. The introduced algorithm is compared with various other algorithms such as Round-Robin, MIN-MIN, Heterogenous Earliest Finish Time First, MAX-MIN, Dynamic Heterogenous Earliest Finish Time First, First-Come-First-Serve, and Minimum Completion Time in WorkFlowSim. The experimental results are compared based on both time and cost. The results obtained suggest that the introduced algorithm performs better when compared to other algorithms [9]. Edge computing is new method, which is closely related to the idea of the IoT (Internet of Things), as this method considers only those resources for computing that are essential in order to fulfil the user/consumer demand--that is, making use of the edge resourcing during computation, due to less bandwidth of the network being used and less time taken to respond, and, in order to achieve these goals, workflow scheduling must be taken into consideration. In this work, an improved version of the Firefly Algorithm, which is used to tackle the workflow scheduling challenge in the edge-based cloud environment, is introduced. The introduced algorithm overcame the deficiencies seen in the traditional meta-heuristic Firefly Algorithm by incorporating quasi-reflection-based learning methods and genetic operators. First, we checked the introduced method on 10 standard benchmark illustrations and made comparison of its performance with the traditional and other improved algorithms. Then, we performed experiments to generate a set of results for the scheduling of the workflow issue with two goals, which were the makespan and the cost. The algorithm introduced in this paper displayed vital improvement in comparison with the traditional Firefly Algorithm and meta-heuristic algorithms in terms of quality and speed of convergences [10]. The field of computing has seen a boost in its usage after the introduction of cloud computing, which has the capability of handling internet traffic and the various tasks requested by the end user. The cloud is made up of many different components, such as the resources manager, task manager, scheduler, and many more. In order to enhance the performance of the cloud, the performance of various components present in the cloud must be improved; as a result, many scholars have introduced various algorithms. Scheduler algorithms, to be more specific, have been introduced, as they provide a near optimal solution. This paper tries to introduce a new updated Sun Flower Optimization Algorithm to improve the task scheduler in terms of makespan and execution cost. The algorithm comprises two steps--movement and pollination--that suggest an ideal firmness among exploitation and exploration searches. The experimental stimulation obtained from the introduced algorithm was compared with various well-known algorithms, and it was seen that the introduced algorithm performed better in terms of makespan and cost of execution [11]. In this work, the proposed task scheduler algorithm--LPMSA (The Local Pollination-based Month Search Algorithm)--is based on the hybridization of the Flower Pollination Algorithm (FPA) along with the Moth Search Algorithm (MSA). The introduced algorithm selects an ideal result for the correct scheduling of tasks in the cloud environment. Inaddition, the capacity of exploitation of the MSA is enhanced by making use of the local search technique as seen in the FPA algorithm. In this paper, a two-fold experimental simulation technique was used. The introduced algorithm LPMSA for scheduling of the task performance was evaluated based on the high and low heterogeneous machine with both non-uniformity and uniformity parameters. The experimental analysis showed that the introduced algorithm LPMSA had better performance in terms of consumption of energy and makespan [12].