An enhanced exponential distribution optimizer for solving task scheduling in cloud computing systems

Cloud computing has become integral to information technology, offering a flexible and scalable way to access and utilize computing resources. In cloud computing, task scheduling policies can affect the resource usage efficiency of the underlying system. Hence, allocating user-input tasks to appropriate computing resources is an essential issue in cloud task scheduling. For this, many meta-heuristic algorithms have been introduced. This paper addresses the problem of cloud task scheduling using the exponential distribution optimizer (EDO) for the first time. Meanwhile, an enhanced EDO variant, called EEDO, is proposed to enhance the optimal solution search ability further. Specifically, a chaotic oppositional learning strategy is proposed to improve population diversity. Then, a guided solution weight adaptation technique is developed to augment the accuracy. Experimental findings indicate that the proposed EEDO makes better use of system resources for different tasks. Hence, EEDO can be considered as a promising scheduling method.