Cloud computing is considered a pattern for distributed and heterogeneous computing derived from many resources, and requests aim to share resources. Recently, cloud computing is graded among the top best technologies globally, which must be scheduled favorably to maximize providers’ profit and improve service quality for their customers. Scheduling specifies how users’ requests are assigned to virtual machines, and it plays a vital role in the efficiency and capability of the system. Its objective is to have a throughput or complete jobs in minimum time and the highest standard. Scheduling jobs in heterogeneous distributed systems is an NP-hard polynomial indecisive problem that is not solvable in polynomial time for real-time scheduling. The time complexity of jobs is growing exponentially, and this problem has a considerable effect on the quality of cloud services and providers’ efficiencies. The optimization of scheduling-related parameters using heuristic and meta-heuristic algorithms can reduce the search space complexity and execution time. This study intends to represent a fitness function to minimize time and cost parameters. The proposed method uses a multi-purposed weighted genetic algorithm that provides six basic parameters: utility, task execution cost, response time, wait time, Makespan, and throughput to provide comprehensive optimization. The proposed approach improved response and wait times, throughput, Makespan, and utility 16, 9, 7, 8 percentages, respectively, by only a one cost unit reduction, which is dispensable. As a result, both providers and users will experience better services. The statistical tests show that the achieved improvement is valid for 94% of experiments.

Cloud computing became so important due to virtualization and IT systems in this decade. It has introduced as a distributed and heterogeneous computing pattern to sharing resources. Task Scheduling is necessary to make high performance heterogeneous computing. The optimization of related parameters, and using heuristic and meta-heuristic algorithms can lead to a reduction of the search space complexity and execution time. So, several studies have tried using a variety of algorithms to solve this issue and improve relative efficiency in their environments. This paper considered examines existing heuristic task scheduling algorithms. First, the concepts of scheduling, the layer of cloud computing, especially scheduling concept in the SaaS and PaaS layer, the main limits for improving the quality of service, evaluation methods of algorithms and applied tools for evaluating these ideas and practical experimental used methods were discussed and compared. Finally, future works in this area were also concluded and a summary of this article is presented in the form of a mind map.